Fifth Fundamental Catalogue (FK5). Part 1: Basic fundamental stars (Fricke, Schwan, and Lederle 1988): Documentation for the machine-readable version

Science.gov (United States)

Warren, Wayne H., Jr.

1990-01-01

The machine-readable version of the catalog, as it is currently being distributed from the Astronomical Data Center, is described. The Basic FK5 provides improved mean positions and proper motions for the 1535 classical fundamental stars that had been included in the FK3 and FK4 catalogs. The machine version of the catalog contains the positions and proper motions of the Basic FK5 stars for the epochs and equinoxes J2000.0 and B1950.0, the mean epochs of individual observed right ascensions and declinations used to determine the final positions, and the mean errors of the final positions and proper motions for the reported epochs. The cross identifications to other designations used for the FK5 stars that are given in the published catalog were not included in the original machine versions, but the Durchmusterung numbers have been added at the Astronomical Data Center.

Readability versus Leveling.

Science.gov (United States)

Fry, Edward

2002-01-01

Shows some similarities and differences between readability formulas and leveling procedures and reports some current large-scale uses of readability formulas. Presents a dictionary definition of readability and leveling, and a history and background of readability and leveling. Discusses what goes into determining readability and leveling scores.…

ClearTK 2.0: Design Patterns for Machine Learning in UIMA.

Science.gov (United States)

Bethard, Steven; Ogren, Philip; Becker, Lee

2014-05-01

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework.

Elementary epistemological features of machine intelligence

OpenAIRE

Horvat, Marko

2008-01-01

Theoretical analysis of machine intelligence (MI) is useful for defining a common platform in both theoretical and applied artificial intelligence (AI). The goal of this paper is to set canonical definitions that can assist pragmatic research in both strong and weak AI. Described epistemological features of machine intelligence include relationship between intelligent behavior, intelligent and unintelligent machine characteristics, observable and unobservable entities and classification of in...

Novel Automatic Filter-Class Feature Selection for Machine Learning Regression

DEFF Research Database (Denmark)

Wollsen, Morten Gill; Hallam, John; Jørgensen, Bo Nørregaard

2017-01-01

With the increased focus on application of Big Data in all sectors of society, the performance of machine learning becomes essential. Efficient machine learning depends on efficient feature selection algorithms. Filter feature selection algorithms are model-free and therefore very fast, but require...... model in the feature selection process. PCA is often used in machine learning litterature and can be considered the default feature selection method. RDESF outperformed PCA in both experiments in both prediction error and computational speed. RDESF is a new step into filter-based automatic feature...

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

Science.gov (United States)

Suykens, Johan A K

2017-08-01

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

The Machine / Job Features Mechanism

Energy Technology Data Exchange (ETDEWEB)

Alef, M. [KIT, Karlsruhe; Cass, T. [CERN; Keijser, J. J. [NIKHEF, Amsterdam; McNab, A. [Manchester U.; Roiser, S. [CERN; Schwickerath, U. [CERN; Sfiligoi, I. [Fermilab

2017-11-22

Within the HEPiX virtualization group and the Worldwide LHC Computing Grid’s Machine/Job Features Task Force, a mechanism has been developed which provides access to detailed information about the current host and the current job to the job itself. This allows user payloads to access meta information, independent of the current batch system or virtual machine model. The information can be accessed either locally via the filesystem on a worker node, or remotely via HTTP(S) from a webserver. This paper describes the final version of the specification from 2016 which was published as an HEP Software Foundation technical note, and the design of the implementations of this version for batch and virtual machine platforms. We discuss early experiences with these implementations and how they can be exploited by experiment frameworks.

The machine/job features mechanism

Science.gov (United States)

Alef, M.; Cass, T.; Keijser, J. J.; McNab, A.; Roiser, S.; Schwickerath, U.; Sfiligoi, I.

2017-10-01

Within the HEPiX virtualization group and the Worldwide LHC Computing Grid’s Machine/Job Features Task Force, a mechanism has been developed which provides access to detailed information about the current host and the current job to the job itself. This allows user payloads to access meta information, independent of the current batch system or virtual machine model. The information can be accessed either locally via the filesystem on a worker node, or remotely via HTTP(S) from a webserver. This paper describes the final version of the specification from 2016 which was published as an HEP Software Foundation technical note, and the design of the implementations of this version for batch and virtual machine platforms. We discuss early experiences with these implementations and how they can be exploited by experiment frameworks.

Clozing in on readability : How linguistic features affect and predict text comprehension and on-line processing

NARCIS (Netherlands)

Kleijn, S.

2018-01-01

Is my text comprehensible for my audience? It is a question publishers, organizations and governments struggle with and it is a question that readability formulae proclaim to solve. With a press of a button the readability of a text is assessed and users know whether texts are suited for their

Blue gum gaming machine: an evaluation of responsible gambling features.

Science.gov (United States)

Blaszczynski, Alexander; Gainsbury, Sally; Karlov, Lisa

2014-09-01

Structural characteristics of gaming machines contribute to persistence in play and excessive losses. The purpose of this study was to evaluate the effectiveness of five proposed responsible gaming features: responsible gaming messages; a bank meter quarantining winnings until termination of play; alarm clock facilitating setting time-reminders; demo mode allowing play without money; and a charity donation feature where residual amounts can be donated rather than played to zero credits. A series of ten modified gaming machines were located in five Australian gambling venues. The sample comprised 300 patrons attending the venue and who played the gaming machines. Participants completed a structured interview eliciting gambling and socio-demographic data and information on their perceptions and experience of play on the index machines. Results showed that one-quarter of participants considered that these features would contribute to preventing recreational gamblers from developing problems. Just under half of the participants rated these effects to be at least moderate or significant. The promising results suggest that further refinements to several of these features could represent a modest but effective approach to minimising excessive gambling on gaming machines.

Feature Import Vector Machine: A General Classifier with Flexible Feature Selection.

Science.gov (United States)

Ghosh, Samiran; Wang, Yazhen

2015-02-01

The support vector machine (SVM) and other reproducing kernel Hilbert space (RKHS) based classifier systems are drawing much attention recently due to its robustness and generalization capability. General theme here is to construct classifiers based on the training data in a high dimensional space by using all available dimensions. The SVM achieves huge data compression by selecting only few observations which lie close to the boundary of the classifier function. However when the number of observations are not very large (small n ) but the number of dimensions/features are large (large p ), then it is not necessary that all available features are of equal importance in the classification context. Possible selection of an useful fraction of the available features may result in huge data compression. In this paper we propose an algorithmic approach by means of which such an optimal set of features could be selected. In short, we reverse the traditional sequential observation selection strategy of SVM to that of sequential feature selection. To achieve this we have modified the solution proposed by Zhu and Hastie (2005) in the context of import vector machine (IVM), to select an optimal sub-dimensional model to build the final classifier with sufficient accuracy.

The Principles of Readability

Science.gov (United States)

DuBay, William H.

2004-01-01

The principles of readability are in every style manual. Readability formulas are in every writing aid. What is missing is the research and theory on which they stand. This short review of readability research spans 100 years. The first part covers the history of adult literacy studies in the U.S., establishing the stratified nature of the adult…

A Modular Framework for Transforming Structured Data into HTML with Machine-Readable Annotations

Science.gov (United States)

Patton, E. W.; West, P.; Rozell, E.; Zheng, J.

2010-12-01

There is a plethora of web-based Content Management Systems (CMS) available for maintaining projects and data, i.a. However, each system varies in its capabilities and often content is stored separately and accessed via non-uniform web interfaces. Moving from one CMS to another (e.g., MediaWiki to Drupal) can be cumbersome, especially if a large quantity of data must be adapted to the new system. To standardize the creation, display, management, and sharing of project information, we have assembled a framework that uses existing web technologies to transform data provided by any service that supports the SPARQL Protocol and RDF Query Language (SPARQL) queries into HTML fragments, allowing it to be embedded in any existing website. The framework utilizes a two-tier XML Stylesheet Transformation (XSLT) that uses existing ontologies (e.g., Friend-of-a-Friend, Dublin Core) to interpret query results and render them as HTML documents. These ontologies can be used in conjunction with custom ontologies suited to individual needs (e.g., domain-specific ontologies for describing data records). Furthermore, this transformation process encodes machine-readable annotations, namely, the Resource Description Framework in attributes (RDFa), into the resulting HTML, so that capable parsers and search engines can extract the relationships between entities (e.g, people, organizations, datasets). To facilitate editing of content, the framework provides a web-based form system, mapping each query to a dynamically generated form that can be used to modify and create entities, while keeping the native data store up-to-date. This open framework makes it easy to duplicate data across many different sites, allowing researchers to distribute their data in many different online forums. In this presentation we will outline the structure of queries and the stylesheets used to transform them, followed by a brief walkthrough that follows the data from storage to human- and machine-accessible web

Machine-readable files developed for the High Plains Regional Aquifer-System analysis in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming

Science.gov (United States)

Ferrigno, C.F.

1986-01-01

Machine-readable files were developed for the High Plains Regional Aquifer-System Analysis project are stored on two magnetic tapes available from the U.S. Geological Survey. The first tape contains computer programs that were used to prepare, store, retrieve, organize, and preserve the areal interpretive data collected by the project staff. The second tape contains 134 data files that can be divided into five general classes: (1) Aquifer geometry data, (2) aquifer and water characteristics , (3) water levels, (4) climatological data, and (5) land use and water use data. (Author 's abstract)

Methods to Measure Map Readability

OpenAIRE

Harrie, Lars

2009-01-01

Creation of maps in real-time web services introduces challenges concerning map readability. Therefore we must introduce analytical measures controlling the readability. The aim of this study is to develop and evaluate analytical readability measures with the help of user tests.

Universally Designed Text on the Web: Towards Readability Criteria Based on Anti-Patterns.

Science.gov (United States)

Eika, Evelyn

2016-01-01

The readability of web texts affects accessibility. The Web Content Accessibility guidelines (WCAG) state that the recommended reading level should match that of someone who has completed basic schooling. However, WCAG does not give advice on what constitutes an appropriate reading level. Web authors need tools to help composing WCAG compliant texts, and specific criteria are needed. Classic readability metrics are generally based on lengths of words and sentences and have been criticized for being over-simplistic. Automatic measures and classifications of texts' reading levels employing more advanced constructs remain an unresolved problem. If such measures were feasible, what should these be? This work examines three language constructs not captured by current readability indices but believed to significantly affect actual readability, namely, relative clauses, garden path sentences, and left-branching structures. The goal is to see whether quantifications of these stylistic features reflect readability and how they correspond to common readability measures. Manual assessments of a set of authentic web texts for such uses were conducted. The results reveal that texts related to narratives such as children's stories, which are given the highest readability value, do not contain these constructs. The structures in question occur more frequently in expository texts that aim at educating or disseminating information such as strategy and journal articles. The results suggest that language anti-patterns hold potential for establishing a set of deeper readability criteria.

The Readability of an Unreadable Text.

Science.gov (United States)

Gordon, Robert M.

1980-01-01

The Dale-Chall Readability Formula and the Fry Readability Graph were used to analyze passages of Plato's "Parmenides," a notoriously difficult literary piece. The readability levels of the text ranged from fourth to eighth grade (Dale-Chall) and from sixth to tenth grade (Fry), indicating the limitations of the readability tests. (DF)

Virtual Machine Language

Science.gov (United States)

Grasso, Christopher; Page, Dennis; O'Reilly, Taifun; Fteichert, Ralph; Lock, Patricia; Lin, Imin; Naviaux, Keith; Sisino, John

2005-01-01

Virtual Machine Language (VML) is a mission-independent, reusable software system for programming for spacecraft operations. Features of VML include a rich set of data types, named functions, parameters, IF and WHILE control structures, polymorphism, and on-the-fly creation of spacecraft commands from calculated values. Spacecraft functions can be abstracted into named blocks that reside in files aboard the spacecraft. These named blocks accept parameters and execute in a repeatable fashion. The sizes of uplink products are minimized by the ability to call blocks that implement most of the command steps. This block approach also enables some autonomous operations aboard the spacecraft, such as aerobraking, telemetry conditional monitoring, and anomaly response, without developing autonomous flight software. Operators on the ground write blocks and command sequences in a concise, high-level, human-readable programming language (also called VML ). A compiler translates the human-readable blocks and command sequences into binary files (the operations products). The flight portion of VML interprets the uplinked binary files. The ground subsystem of VML also includes an interactive sequence- execution tool hosted on workstations, which runs sequences at several thousand times real-time speed, affords debugging, and generates reports. This tool enables iterative development of blocks and sequences within times of the order of seconds.

Banknotes and unattended cash transactions

Science.gov (United States)

Bernardini, Ronald R.

2000-04-01

There is a 64 billion dollar annual unattended cash transaction business in the US with 10 to 20 million daily transactions. Even small problems with the machine readability of banknotes can quickly become a major problem to the machine manufacturer and consumer. Traditional note designs incorporate overt security features for visual validation by the public. Many of these features such as fine line engraving, microprinting and watermarks are unsuitable as machine readable features in low cost note acceptors. Current machine readable features, mostly covert, were designed and implemented with the central banks in mind. These features are only usable by the banks large, high speed currency sorting and validation equipment. New note designs should consider and provide for low cost not acceptors, implementing features developed for inexpensive sensing technologies. Machine readable features are only as good as their consistency. Quality of security features as well as that of the overall printing process must be maintained to ensure reliable and secure operation of note readers. Variations in printing and of the components used to make the note are one of the major causes of poor performance in low cost note acceptors. The involvement of machine manufacturers in new currency designs will aid note producers in the design of a note that is machine friendly, helping to secure the acceptance of the note by the public as well as acting asa deterrent to fraud.

Understanding and Writing G & M Code for CNC Machines

Science.gov (United States)

Loveland, Thomas

2012-01-01

In modern CAD and CAM manufacturing companies, engineers design parts for machines and consumable goods. Many of these parts are cut on CNC machines. Whether using a CNC lathe, milling machine, or router, the ideas and designs of engineers must be translated into a machine-readable form called G & M Code that can be used to cut parts to precise…

Reading apps for children: Readability from the design perspective

Science.gov (United States)

Mohammed, Wasan Abdulwahab; Husni, Husniza

2017-10-01

Electronic reading for young children opens new avenues especially with the advance of modern reading devices. The readability of mobile learning applications has received extensive attention by the designers and developers. The reason for such concern is due to its importance in determining usability related issues especially in the design context for children. In many cases, children find it difficult to interact with mobile reading apps. This is because apps for reading and for entertainment require different features. As such, this study sets out three objectives: 1) to evaluate five reading apps for young children from design perspectives; 2) to examine the readability for current existing mobile apps for reading and 3) to propose and evaluate mobile apps UI guideline for readability. Readability indices, observation and interview were conducted on 6 - 8 years old students. The obtained result showed that certain reading apps provide better reading experience for children than others. Some of the reasons are mostly related to the design characteristics embedded within the app. In addition, the use of animation was found to stimulate children reading experience as it is believed to offer the interactivity elements to gain their interest and willingness to read. These findings are believed to provide the recommendations and insights for designers of reading apps for children.

A possible extension to the RInChI as a means of providing machine readable process data.

Science.gov (United States)

Jacob, Philipp-Maximilian; Lan, Tian; Goodman, Jonathan M; Lapkin, Alexei A

2017-04-11

The algorithmic, large-scale use and analysis of reaction databases such as Reaxys is currently hindered by the absence of widely adopted standards for publishing reaction data in machine readable formats. Crucial data such as yields of all products or stoichiometry are frequently not explicitly stated in the published papers and, hence, not reported in the database entry for those reactions, limiting their usefulness for algorithmic analysis. This paper presents a possible extension to the IUPAC RInChI standard via an auxiliary layer, termed ProcAuxInfo, which is a standardised, extensible form in which to report certain key reaction parameters such as declaration of all products and reactants as well as auxiliaries known in the reaction, reaction stoichiometry, amounts of substances used, conversion, yield and operating conditions. The standard is demonstrated via creation of the RInChI including the ProcAuxInfo layer based on three published reactions and demonstrates accurate data recoverability via reverse translation of the created strings. Implementation of this or another method of reporting process data by the publishing community would ensure that databases, such as Reaxys, would be able to abstract crucial data for big data analysis of their contents.

Analyzing readability of medicines information material in Slovenia

Science.gov (United States)

Kasesnik, Karin; Kline, Mihael

2011-01-01

Objective: Readability has been claimed to be an important factor for understanding texts describing health symptoms and medications. Such texts may be a factor which indirectly affects the health of the population. Despite the expertise of physicians, the readability of information sources may be important for acquiring essential treatment information. The aim of this study was to measure the readability level of medicines promotion material in Slovenia. Methods: The Flesch readability formula was modified to comply with Slovene texts. On the basis of determining the Slovene readability algorithm, the readability ease related to the readability grade level of different Slovene texts was established. In order to estimate an adjustment of the texts to the recommended readability grade level of the targeted population, readability values of English texts were set. One sample t-test and standard deviations from the arithmetic mean values were used as statistical tests. Results: The results of the research showed low readability scores of the Slovene texts. Difficult readability values were seen in different types of examined texts: in patient information leaflets, in the summaries of product characteristics, in promotional materials, while describing over-the-counter medications and in the materials for creating disease awareness. Especially low readability values were found within the texts belonging to promotional materials intended for the physicians. None of researched items, not even for the general public, were close to primary school grade readability levels and therefore could not be described as easily readable. Conclusion: This study provides an understanding of the level of readability of selected Slovene medicines information material. It was concluded that health-related texts were not compliant with general public or with healthcare professional needs. PMID:23093886

Machine learning in geosciences and remote sensing

Directory of Open Access Journals (Sweden)

David J. Lary

2016-01-01

Full Text Available Learning incorporates a broad range of complex procedures. Machine learning (ML is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficult-to-program applications, and software applications. It is a collection of a variety of algorithms (e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc. that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore, nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.

Readability of Invasive Procedure Consent Forms.

Science.gov (United States)

Eltorai, Adam E M; Naqvi, Syed S; Ghanian, Soha; Eberson, Craig P; Weiss, Arnold-Peter C; Born, Christopher T; Daniels, Alan H

2015-12-01

Informed consent is a pillar of ethical medicine which requires patients to fully comprehend relevant issues including the risks, benefits, and alternatives of an intervention. Given the average reading skill of US adults is at the 8th grade level, the American Medical Association (AMA) and the National Institutes of Health (NIH) recommend patient information materials should not exceed a 6th grade reading level. We hypothesized that text provided in invasive procedure consent forms would exceed recommended readability guidelines for medical information. To test this hypothesis, we gathered procedure consent forms from all surgical inpatient hospitals in the state of Rhode Island. For each consent form, readability analysis was measured with the following measures: Flesch Reading Ease Formula, Flesch-Kincaid Grade Level, Fog Scale, SMOG Index, Coleman-Liau Index, Automated Readability Index, and Linsear Write Formula. These readability scores were used to calculate a composite Text Readability Consensus Grade Level. Invasive procedure consent forms were found to be written at an average of 15th grade level (i.e., third year of college), which is significantly higher than the average US adult reading level of 8th grade (p readability guidelines for patient materials of 6th grade (p readability levels which makes comprehension difficult or impossible for many patients. Efforts to improve the readability of procedural consent forms should improve patient understanding regarding their healthcare decisions. © 2015 Wiley Periodicals, Inc.

Emotioncy: A Potential Measure of Readability

Science.gov (United States)

Pishghadamn, Reza; Abbasnejad, Hannaheh

2016-01-01

Given the deficiencies of readability formulae as reliable tools for measuring text readability in educational settings, this study aims to offer a new measure to improve the current methods of testing the readability levels of texts through the incorporation of the newly-developed concept of emotioncy. To this end, a group of 221 students were…

Readability assessment of online ophthalmic patient information.

Science.gov (United States)

Edmunds, Matthew R; Barry, Robert J; Denniston, Alastair K

2013-12-01

Patients increasingly use the Internet to access information related to their disease, but poor health literacy is known to impact negatively on medical outcomes. Multiple agencies have recommended that patient-oriented literature be written at a fourth- to sixth-grade (9-12 years of age) reading level to assist understanding. The readability of online patient-oriented materials related to ophthalmic diagnoses is not yet known. To assess the readability of online literature specifically for a range of ophthalmic conditions. Body text of the top 10 patient-oriented websites for 16 different ophthalmic diagnoses, covering the full range of ophthalmic subspecialties, was analyzed for readability, source (United Kingdom vs non-United Kingdom, not for profit vs commercial), and appropriateness for sight-impaired readers. Four validated readability formulas were used: Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), and Gunning Fog Index (GFOG). Data were compared with the Mann-Whitney test (for 2 groups) and Kruskal-Wallis test (for more than 2 groups) and correlation was assessed by the Spearman r. None of the 160 webpages had readability scores within published guidelines, with 83% assessed as being of "difficult" readability. Not-for-profit webpages were of significantly greater length than commercial webpages (P = .02) and UK-based webpages had slightly superior readability scores compared with those of non-UK webpages (P = .004 to P readability formula used). Of all webpages evaluated, only 34% included facility to adjust text size to assist visually impaired readers. To our knowledge, this is the first study to assess readability of patient-focused webpages specifically for a range of ophthalmic diagnoses. In keeping with previous studies in other medical conditions, we determined that readability scores were inferior to those recommended, irrespective of the measure used. Although readability is only one

Grammatical Metaphor, Controlled Languageand Machine Translation

DEFF Research Database (Denmark)

Møller, Margrethe

2003-01-01

It is a general assumption that 1) the readability and clarity of LSP texts written in a controlled language are better than uncontrolled texts and 2) that controlled languages produce better results with machine translation than uncontrolled languages. Controlled languages impose lexical...

Simplifying the Reuse and Interoperability of Geoscience Data Sets and Models with Semantic Metadata that is Human-Readable and Machine-actionable

Science.gov (United States)

Peckham, S. D.

2017-12-01

Standardized, deep descriptions of digital resources (e.g. data sets, computational models, software tools and publications) make it possible to develop user-friendly software systems that assist scientists with the discovery and appropriate use of these resources. Semantic metadata makes it possible for machines to take actions on behalf of humans, such as automatically identifying the resources needed to solve a given problem, retrieving them and then automatically connecting them (despite their heterogeneity) into a functioning workflow. Standardized model metadata also helps model users to understand the important details that underpin computational models and to compare the capabilities of different models. These details include simplifying assumptions on the physics, governing equations and the numerical methods used to solve them, discretization of space (the grid) and time (the time-stepping scheme), state variables (input or output), model configuration parameters. This kind of metadata provides a "deep description" of a computational model that goes well beyond other types of metadata (e.g. author, purpose, scientific domain, programming language, digital rights, provenance, execution) and captures the science that underpins a model. A carefully constructed, unambiguous and rules-based schema to address this problem, called the Geoscience Standard Names ontology will be presented that utilizes Semantic Web best practices and technologies. It has also been designed to work across science domains and to be readable by both humans and machines.

The Readability of Principles of Macroeconomics Textbooks

Science.gov (United States)

Tinkler, Sarah; Woods, James

2013-01-01

The authors evaluated principles of macroeconomics textbooks for readability using Coh-Metrix, a computational linguistics tool. Additionally, they conducted an experiment on Amazon's Mechanical Turk Web site in which participants ranked the readability of text samples. There was a wide range of scores on readability indexes both among…

Readability Approaches: Implications for Turkey

Science.gov (United States)

Ulusoy, Mustafa

2006-01-01

Finding the right fit between students' reading ability and textbooks is very important for comprehension. Readability studies aim to analyse texts to find the right fit between students and texts. In this literature review, readability studies are classified under quantitative, qualitative and combined quantitative-qualitative readability…

[Readability of surgical informed consent in Spain].

Science.gov (United States)

San Norberto, Enrique María; Gómez-Alonso, Daniel; Trigueros, José M; Quiroga, Jorge; Gualis, Javier; Vaquero, Carlos

2014-03-01

To assess the readability of informed consent documents (IC) of the different national surgical societies. During January 2012 we collected 504 IC protocols of different specialties. To calculate readability parameters the following criteria were assessed: number of words, syllables and phrases, syllables/word and word/phrase averages, Word correlation index, Flesch-Szigriszt index, Huerta Fernández index, Inflesz scale degree and the Gunning-Fog index. The mean Flesch-Szigriszt index was 50.65 ± 6,72, so readability is considered normal. There are significant differences between specialties such as Urology (43.00 ± 4.17) and Angiology and Vascular Surgery (63.00 ± 3.26, P<.001). No IC would be appropriate for adult readability according to the Fernández-Huerta index (total mean 55.77 ± 6.57); the IC of Angiology and Vascular Surgery were the closest ones (67.85 ± 3.20). Considering the Inflesz scale degree (total mean of 2.84 ± 3,23), IC can be described as «somewhat difficult». There are significant differences between the IC of Angiology and Vascular Surgery (3.23 ± 0.47) that could be qualified as normal, or Cardiovascular Surgery (2.79 ± 0.43) as «nearly normal readability»; and others such as Urology (1, 70 ± 0.46, P<.001) and Thoracic Surgery (1.90 ± 0.30, P<.001), with a readability between «very» and «somewhat» difficult. The Gunning-Fog indexes are far from the readability for a general audience (total mean of 26.29 ± 10,89). IC developed by scientific societies of different surgical specialties do not have an adequate readability for patients. We recommend the use of readability indexes during the writing of these consent forms. Copyright © 2012 AEC. Published by Elsevier Espana. All rights reserved.

Readability assessment of online tracheostomy care resources.

Science.gov (United States)

Kong, Keonho Albert; Hu, Amanda

2015-02-01

To assess the readability of online tracheostomy care resources. Cross-sectional study. Academic center. A Google search was performed for "tracheostomy care" in January 2014. The top 50 results were categorized into major versus minor websites and patient-oriented versus professional-oriented resources. These websites were evaluated with the following readability tools: Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), and Gunning Frequency of Gobbledygook (GFOG). Readability scores for the websites were FRES 57.21 ± 16.71 (possible range = 0-100), FKGL 8.33 ± 2.84 (possible range = 3-12), SMOG 11.25 ± 2.49 (possible range = 3-19), and GFOG 11.43 ± 4.07 (possible range = 3-19). There was no significant difference in all 4 readability scores between major (n = 41) and minor (n = 9) websites. Professional-oriented websites (n = 19) had the following readability scores: FRES 40.77 ± 11.69, FKGL 10.93 ± 2.48, SMOG 13.29 ± 2.32, and GFOG 14.91 ± 3.98. Patient-oriented websites (n = 31) had the following readability scores: FRES 67.29 ± 9.91, FKGL 6.73 ± 1.61, SMOG 10.01 ± 1.64, and GFOG 9.30 ± 2.27. Professional-oriented websites had more difficult readability scores than patient-oriented websites for FRES (P readability between major and minor websites. Professional-oriented websites were more difficult to read than patient-oriented websites. © American Academy of Otolaryngology—Head and Neck Surgery Foundation 2014.

The Readability and Complexity of District-Provided School-Choice Information

Science.gov (United States)

Stein, Marc L.; Nagro, Sarah

2015-01-01

Public school choice has become a common feature in American school districts. Any potential benefits that could be derived from these policies depend heavily on the ability of parents and students to make informed and educated decisions about their school options. We examined the readability and complexity of school-choice guides across a sample…

The readability of American Academy of Pediatrics patient education brochures.

Science.gov (United States)

Freda, Margaret Comerford

2005-01-01

The purpose of this study was to evaluate the readability of American Academy of Pediatrics (AAP) patient education brochures. Seventy-four brochures were analyzed using two readability formulas. Mean readability for all 74 brochures was grade 7.94 using the Flesch-Kincaid formula, and grade 10.1 with SMOG formula (P = .001). Using the SMOG formula, no brochures were of acceptably low (education brochures have acceptably low levels of readability, but at least half are written at higher than acceptable readability levels for the general public. This study also demonstrated statistically significant variability between the two different readability formulas; had only the SMOG formula been used, all of the brochures would have had unacceptably high readability levels. Readability is an essential concept for patient education materials. Professional associations that develop and market patient education materials should test for readability and publish those readability levels on each piece of patient education so health care providers will know if the materials are appropriate for their patients.

Determining Readability: How to Select and Apply Easy-to-Use Readability Formulas to Assess the Difficulty of Adult Literacy Materials

Science.gov (United States)

Burke, Victoria; Greenberg, Daphne

2010-01-01

There are many readability tools that instructors can use to help adult learners select reading materials. We describe and compare different types of readability tools: formulas calculated by hand, tools found on the Web, tools embedded in a word processing program, and readability tools found in a commercial software program. Practitioners do not…

Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods.

Science.gov (United States)

Shan, Juan; Alam, S Kaisar; Garra, Brian; Zhang, Yingtao; Ahmed, Tahira

2016-04-01

This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification performance using a "bottom-up" approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine. Using 10-fold cross-validation on the database of 283 cases, the highest area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 from a support vector machine with 77.7% overall accuracy; the highest overall accuracy, 78.5%, was from a random forest with the AUC 0.83. Lesion margin and orientation were optimum features common to all of the different machine learning methods. These features can be used in CAD systems to help distinguish benign from worrisome lesions. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. All rights reserved.

Machine learning spatial geometry from entanglement features

Science.gov (United States)

You, Yi-Zhuang; Yang, Zhao; Qi, Xiao-Liang

2018-02-01

Motivated by the close relations of the renormalization group with both the holography duality and the deep learning, we propose that the holographic geometry can emerge from deep learning the entanglement feature of a quantum many-body state. We develop a concrete algorithm, call the entanglement feature learning (EFL), based on the random tensor network (RTN) model for the tensor network holography. We show that each RTN can be mapped to a Boltzmann machine, trained by the entanglement entropies over all subregions of a given quantum many-body state. The goal is to construct the optimal RTN that best reproduce the entanglement feature. The RTN geometry can then be interpreted as the emergent holographic geometry. We demonstrate the EFL algorithm on a 1D free fermion system and observe the emergence of the hyperbolic geometry (AdS3 spatial geometry) as we tune the fermion system towards the gapless critical point (CFT2 point).

Simultaneous feature selection and classification via Minimax Probability Machine

Directory of Open Access Journals (Sweden)

Liming Yang

2010-12-01

Full Text Available This paper presents a novel method for simultaneous feature selection and classification by incorporating a robust L1-norm into the objective function of Minimax Probability Machine (MPM. A fractional programming framework is derived by using a bound on the misclassification error involving the mean and covariance of the data. Furthermore, the problems are solved by the Quadratic Interpolation method. Experiments show that our methods can select fewer features to improve the generalization compared to MPM, which illustrates the effectiveness of the proposed algorithms.

Readability of Online Materials for Rhinoplasty.

Science.gov (United States)

Santos, Pauline Joy F; Daar, David A; Paydar, Keyianoosh Z; Wirth, Garrett A

2018-01-01

Rhinoplasty is a popular aesthetic and reconstructive surgical procedure. However, little is known about the content and readability of online materials for patient education. The recommended grade level for educational materials is 7th to 8th grade according to the National Institutes of Health (NIH). This study aims to assess the readability of online patient resources for rhinoplasty. The largest public search engine, Google, was queried using the term "rhinoplasty" on February 26, 2016. Location filters were disabled and sponsored results excluded to avoid any inadvertent search bias. The 10 most popular websites were identified and all relevant, patient-directed information within one click from the original site was downloaded and saved as plain text. Readability was analyzed using five established analyses (Readability-score.com, Added Bytes, Ltd., Sussex, UK). Analysis of ten websites demonstrates an average grade level of at least 12 th grade. No material was at the recommended 7 th to 8 th grade reading level (Flesch-Kincaid, 11.1; Gunning-Fog, 14.1; Coleman-Liau, 14.5; SMOG 10.4; Automated Readability, 10.7; Average Grade Level, 12.2). Overall Flesch-Kincaid Reading Ease Index was 43.5, which is rated as "difficult." Online materials available for rhinoplasty exceed NIH-recommended reading levels, which may prevent appropriate decision-making in patients considering these types of surgery. Outcomes of this study identify that Plastic Surgeons should be cognizant of available online patient materials and make efforts to develop and provide more appropriate materials. Readability results can also contribute to marketing strategy and attracting a more widespread interest in the procedure.

The Evaluation of High School Geography 9 and High School Geography 11 Text Books with Some Formulas of Readability

Science.gov (United States)

Gecit, Yilmaz

2010-01-01

The purpose of this study is to evaluate readability of 9th and 11th grade geography text-books currently used in schools. As known, one of the most fundamental features in a text-book is the readability of the text by students. In addition, it is also very important that the fluency and suitability of books match age level. In this study, the…

Evaluating four readability formulas for Afrikaans.

NARCIS (Netherlands)

Jansen, C. J. M.; Richards, Rose; Van Zyl, Liezl

2017-01-01

For almost a hundred years now, readability formulas have been used to measure how difficult it is to comprehend a given text. To date, four readability formulas have been developed for Afrikaans. Two such formulas were published by Van Rooyen (1986), one formula by McDermid Heyns (2007) and one

Readability of Online Health Information: A Meta-Narrative Systematic Review.

Science.gov (United States)

Daraz, Lubna; Morrow, Allison S; Ponce, Oscar J; Farah, Wigdan; Katabi, Abdulrahman; Majzoub, Abdul; Seisa, Mohamed O; Benkhadra, Raed; Alsawas, Mouaz; Larry, Prokop; Murad, M Hassan

2018-01-01

Online health information should meet the reading level for the general public (set at sixth-grade level). Readability is a key requirement for information to be helpful and improve quality of care. The authors conducted a systematic review to evaluate the readability of online health information in the United States and Canada. Out of 3743 references, the authors included 157 cross-sectional studies evaluating 7891 websites using 13 readability scales. The mean readability grade level across websites ranged from grade 10 to 15 based on the different scales. Stratification by specialty, health condition, and type of organization producing information revealed the same findings. In conclusion, online health information in the United States and Canada has a readability level that is inappropriate for general public use. Poor readability can lead to misinformation and may have a detrimental effect on health. Efforts are needed to improve readability and the content of online health information.

Readability and Understandability of Online Vocal Cord Paralysis Materials.

Science.gov (United States)

Balakrishnan, Vini; Chandy, Zachariah; Hseih, Amy; Bui, Thanh-Lan; Verma, Sunil P

2016-03-01

Patients use several online resources to learn about vocal cord paralysis (VCP). The objective of this study was to assess the readability and understandability of online VCP patient education materials (PEMs), with readability assessments and the Patient Education Materials Evaluation Tool (PEMAT), respectively. The relationship between readability and understandability was then analyzed. Descriptive and correlational design. Online PEMs were identified by performing a Google search with the term "vocal cord paralysis." After scientific webpages, news articles, and information for medical professionals were excluded, 29 articles from the first 50 search results were considered. Readability analysis was performed with 6 formulas. Four individuals with different educational backgrounds conducted understandability analysis with the PEMAT. Fleiss's Kappa interrater reliability analysis determined consistency among raters. Correlation between readability and understandability was determined with Pearson's correlation test. The reading level of the reviewed articles ranged from grades 9 to 17. Understandability ranged from 29% to 82%. Correlation analysis demonstrated a strong negative correlation between materials' readability and understandability (r = -0.462, P Online PEMs pertaining to VCP are written above the recommended reading levels. Overall, materials written at lower grade levels are more understandable. However, articles of identical grade levels had varying levels of understandability. The PEMAT may provide a more critical evaluation of the quality of a PEM when compared with readability formulas. Both readability and understandability should be used to evaluate PEMs. © American Academy of Otolaryngology—Head and Neck Surgery Foundation 2016.

Identifying predictive features in drug response using machine learning: opportunities and challenges.

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Vidyasagar, Mathukumalli

2015-01-01

This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

Readability of websites containing information on dental implants.

Science.gov (United States)

Jayaratne, Yasas S N; Anderson, Nina K; Zwahlen, Roger A

2014-12-01

It is recommended that health-related materials for patients be written at sixth grade level or below. Many websites oriented toward patient education about dental implants are available, but the readability of these sites has not been evaluated. To assess readability of patient-oriented online information on dental implants. Websites containing patient-oriented information on dental implants were retrieved using the Google search engine. Individual and mean readability/grade levels were calculated using standardized formulas. Readability of each website was classified as easy (≤ 6th-grade level) or difficult (≥ 10th grade level). Thirty nine websites with patient-oriented information on dental implant were found. The average readability grade level of these websites was 11.65 ± 1.36. No website scored at/below the recommended 6th grade level. Thirty four of 39 websites (87.18%) were difficult to read. The number of characters, words, and sentences on these sites varied widely. All patient-oriented websites on dental implants scored above the recommended grade level, and majority of these sites were "difficult" in their readability. There is a dire need to create patient information websites on implants, which the majority can read. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Forensic scientists' conclusions: how readable are they for non-scientist report-users?

Science.gov (United States)

Howes, Loene M; Kirkbride, K Paul; Kelty, Sally F; Julian, Roberta; Kemp, Nenagh

2013-09-10

Scientists have an ethical responsibility to assist non-scientists to understand their findings and expert opinions before they are used as decision-aids within the criminal justice system. The communication of scientific expert opinion to non-scientist audiences (e.g., police, lawyers, and judges) through expert reports is an important but under-researched issue. Readability statistics were used to assess 111 conclusions from a proficiency test in forensic glass analysis. The conclusions were written using an average of 23 words per sentence, and approximately half of the conclusions were expressed using the active voice. At an average Flesch-Kincaid Grade level of university undergraduate (Grade 13), and Flesch Reading Ease score of difficult (42), the conclusions were written at a level suitable for people with some tertiary education in science, suggesting that the intended non-scientist readers would find them difficult to read. To further analyse the readability of conclusions, descriptive features of text were used: text structure; sentence structure; vocabulary; elaboration; and coherence and unity. Descriptive analysis supported the finding that texts were written at a level difficult for non-scientists to read. Specific aspects of conclusions that may pose difficulties for non-scientists were located. Suggestions are included to assist scientists to write conclusions with increased readability for non-scientist readers, while retaining scientific integrity. In the next stage of research, the readability of expert reports in their entirety is to be explored. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Optical security features for plastic card documents

Science.gov (United States)

Hossick Schott, Joachim

1998-04-01

Print-on-demand is currently a major trend in the production of paper based documents. This fully digital production philosophy will likely have ramifications also for the secure identification document market. Here, plastic cards increasingly replace traditionally paper based security sensitive documents such as drivers licenses and passports. The information content of plastic cards can be made highly secure by using chip cards. However, printed and other optical security features will continue to play an important role, both for machine readable and visual inspection. Therefore, on-demand high resolution print technologies, laser engraving, luminescent pigments and laminated features such as holograms, kinegrams or phase gratings will have to be considered for the production of secure identification documents. Very important are also basic optical, surface and material durability properties of the laminates as well as the strength and nature of the adhesion between the layers. This presentation will address some of the specific problems encountered when optical security features such as high resolution printing and laser engraving are to be integrated in the on-demand production of secure plastic card identification documents.

Quality and readability of English-language internet information for aphasia.

Science.gov (United States)

Azios, Jamie H; Bellon-Harn, Monica; Dockens, Ashley L; Manchaiah, Vinaya

2017-08-14

Little is known about the quality and readability of treatment information in specific neurogenic disorders, such as aphasia. The purpose of this study was to assess quality and readability of English-language Internet information available for aphasia treatment. Forty-three aphasia treatment websites were aggregated using five different country-specific search engines. Websites were then analysed using quality and readability assessments. Statistical calculations were employed to examine website ratings, differences between website origin and quality and readability scores, and correlations between readability instruments. Websites exhibited low quality with few websites obtaining Health On the Net (HON) certification or clear, thorough information as measured by the DISCERN. Regardless of website origin, readability scores were also poor. Approximate educational levels required to comprehend information on aphasia treatment websites ranged from 13 to 16 years of education. Significant differences were found between website origin and readability measures with higher levels of education required to understand information on websites of non-profit organisations. Current aphasia treatment websites were found to exhibit low levels of quality and readability, creating potential accessibility problems for people with aphasia and significant others. Websites including treatment information for aphasia must be improved in order to increase greater information accessibility.

Learning features for tissue classification with the classification restricted Boltzmann machine

DEFF Research Database (Denmark)

van Tulder, Gijs; de Bruijne, Marleen

2014-01-01

Performance of automated tissue classification in medical imaging depends on the choice of descriptive features. In this paper, we show how restricted Boltzmann machines (RBMs) can be used to learn features that are especially suited for texture-based tissue classification. We introduce the convo...... outperform conventional RBM-based feature learning, which is unsupervised and uses only a generative learning objective, as well as often-used filter banks. We show that a mixture of generative and discriminative learning can produce filters that give a higher classification accuracy....

The readability of expert reports for non-scientist report-users: reports of forensic comparison of glass.

Science.gov (United States)

Howes, Loene M; Kirkbride, K Paul; Kelty, Sally F; Julian, Roberta; Kemp, Nenagh

2014-03-01

Scientific language contains features that may impede understanding for non-scientists. Forensic scientists' written reports are read by police, lawyers, and judges, and thus assessment of readability is warranted. Past studies of readability differed in background theory and approach, but analysed one or more of: content and sequence; language; and format. Using a holistic approach, we assessed the readability of expert reports (n=78) of forensic glass comparison from 7 Australian jurisdictions. Two main audiences for reports were relevant: police and the courts. Reports for police were presented either as a completed form or as a brief legal-style report. Reports for court were less brief and used either legal or scientific styles, with content and formatting features supporting these distinctions. Some jurisdictions prepared a single report to satisfy both the courts and police. In general, item list, analytical techniques, results, notes on interpretation, and conclusions were included in reports of all types. However, some reports omitted analytical techniques, and results and conclusions were sometimes combined. According to Flesch Reading Ease, language was difficult, with a Flesch-Kincaid grade level of university undergraduate. Sentences were long and contained undefined specialist terms. Information content per clause (lexical density), was typically high, as for other scientific texts. Uncertainty was expressed differently by jurisdiction. Reports from most jurisdictions were cluttered in appearance, with single-line spacing, narrow margins, and gridlines in tables. Simple suggestions, based on theory and past research, are provided to assist scientists to enhance the readability of expert reports for non-scientists. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Readability of medicinal package leaflets: a systematic review.

Science.gov (United States)

Pires, Carla; Vigário, Marina; Cavaco, Afonso

2015-01-01

OBJECTIVE To review studies on the readability of package leaflets of medicinal products for human use. METHODS We conducted a systematic literature review between 2008 and 2013 using the keywords "Readability and Package Leaflet" and "Readability and Package Insert" in the academic search engine Biblioteca do Conhecimento Online, comprising different bibliographic resources/databases. The preferred reporting items for systematic reviews and meta-analyses criteria were applied to prepare the draft of the report. Quantitative and qualitative original studies were included. Opinion or review studies not written in English, Portuguese, Italian, French, or Spanish were excluded. RESULTS We identified 202 studies, of which 180 were excluded and 22 were enrolled [two enrolling healthcare professionals, 10 enrolling other type of participants (including patients), three focused on adverse reactions, and 7 descriptive studies]. The package leaflets presented various readability problems, such as complex and difficult to understand texts, small font size, or few illustrations. The main methods to assess the readability of the package leaflet were usability tests or legibility formulae. Limitations with these methods included reduced number of participants; lack of readability formulas specifically validated for specific languages (e.g., Portuguese); and absence of an assessment on patients literacy, health knowledge, cognitive skills, levels of satisfaction, and opinions. CONCLUSIONS Overall, the package leaflets presented various readability problems. In this review, some methodological limitations were identified, including the participation of a limited number of patients and healthcare professionals, the absence of prior assessments of participant literacy, humor or sense of satisfaction, or the predominance of studies not based on role-plays about the use of medicines. These limitations should be avoided in future studies and be considered when interpreting the results.

A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

Science.gov (United States)

Peng, Bo; Wang, Suhong; Zhou, Zhiyong; Liu, Yan; Tong, Baotong; Zhang, Tao; Dai, Yakang

2017-06-09

Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to construct the multilevel ROI features. Filter- and wrapper- based feature selection method and multi-kernel support vector machine (SVM) are used in the classification algorithm. T1-weighted brain magnetic resonance (MR) images of 69 PD patients and 103 normal controls from the Parkinson's Progression Markers Initiative (PPMI) dataset are included in the study. The machine learning method performs well in classification between PD patients and normal controls with an accuracy of 85.78%, a specificity of 87.79%, and a sensitivity of 87.64%. The most sensitive biomarkers between PD patients and normal controls are mainly distributed in frontal lobe, parental lobe, limbic lobe, temporal lobe, and central region. The classification performance of our method with multilevel ROI features is significantly improved comparing with other classification methods using single-level features. The proposed method shows promising identification ability for detecting morphometric biomarkers in PD, thus confirming the potentiality of our method in assisting diagnosis of the disease. Copyright © 2017 Elsevier B.V. All rights reserved.

Machine Learning Method Applied in Readout System of Superheated Droplet Detector

Science.gov (United States)

Liu, Yi; Sullivan, Clair Julia; d'Errico, Francesco

2017-07-01

Direct readability is one advantage of superheated droplet detectors in neutron dosimetry. Utilizing such a distinct characteristic, an imaging readout system analyzes image of the detector for neutron dose readout. To improve the accuracy and precision of algorithms in the imaging readout system, machine learning algorithms were developed. Deep learning neural network and support vector machine algorithms are applied and compared with generally used Hough transform and curvature analysis methods. The machine learning methods showed a much higher accuracy and better precision in recognizing circular gas bubbles.

Readability of the written study information in pediatric research in France.

Directory of Open Access Journals (Sweden)

Véronique Ménoni

Full Text Available BACKGROUND: The aim was to evaluate the readability of research information leaflets (RIL for minors asked to participate in biomedical research studies and to assess the factors influencing this readability. METHODS AND FINDINGS: All the pediatric protocols from three French pediatric clinical research units were included (N = 104. Three criteria were used to evaluate readability: length of the text, Flesch's readability score and presence of illustrations. We compared the readability of RIL to texts specifically written for children (school textbooks, school exams or extracts from literary works. We assessed the effect of protocol characteristics on readability. The RIL had a median length of 608 words [350 words, 25(th percentile; 1005 words, 75(th percentile], corresponding to two pages. The readability of the RIL, with a median Flesch score of 40 [30; 47], was much poorer than that of pediatric reference texts, with a Flesch score of 67 [60; 73]. A small proportion of RIL (13/91; 14% were illustrated. The RIL were longer (p<0.001, more readable (p<0.001 and more likely to be illustrated (p<0.009 for industrial than for institutional sponsors. CONCLUSION: Researchers should routinely compute the reading ease of study information sheets and make greater efforts to improve the readability of written documents for potential participants.

Readability of Wikipedia

NARCIS (Netherlands)

Lucassen, T.; Dijkstra, Roald; Schraagen, Johannes Martinus Cornelis

2012-01-01

Wikipedia is becoming widely acknowledged as a reliable source of encyclopedic information. However, concerns have been expressed about its readability. Wikipedia articles might be written in a language too difficult to be understood by most of its visitors. In this study, we apply the Flesch

Text Readability and Intuitive Simplification: A Comparison of Readability Formulas

Science.gov (United States)

Crossley, Scott A.; Allen, David B.; McNamara, Danielle S.

2011-01-01

Texts are routinely simplified for language learners with authors relying on a variety of approaches and materials to assist them in making the texts more comprehensible. Readability measures are one such tool that authors can use when evaluating text comprehensibility. This study compares the Coh-Metrix Second Language (L2) Reading Index, a…

Readability of Online Patient Education Materials From the AAOS Web Site

Science.gov (United States)

Badarudeen, Sameer; Unes Kunju, Shebna

2008-01-01

One of the goals of the American Academy of Orthopaedic Surgeons (AAOS) is to disseminate patient education materials that suit the readability skills of the patient population. According to standard guidelines from healthcare organizations, the readability of patient education materials should be no higher than the sixth-grade level. We hypothesized the readability level of patient education materials available on the AAOS Web site would be higher than the recommended grade level, regardless when the material was available online. Readability scores of all articles from the AAOS Internet-based patient information Web site, “Your Orthopaedic Connection,” were determined using the Flesch-Kincaid grade formula. The mean Flesch-Kincaid grade level of the 426 unique articles was 10.43. Only 10 (2%) of the articles had the recommended readability level of sixth grade or lower. The readability of the articles did not change with time. Our findings suggest the majority of the patient education materials available on the AAOS Web site had readability scores that may be too difficult for comprehension by a substantial portion of the patient population. PMID:18324452

Readability, content, and quality of online patient education materials on preeclampsia.

Science.gov (United States)

Lange, Elizabeth M S; Shah, Anuj M; Braithwaite, Brian A; You, Whitney B; Wong, Cynthia A; Grobman, William A; Toledo, Paloma

2015-01-01

The objective of this study was to evaluate the readability, content, and quality of patient education materials addressing preeclampsia. Websites of U.S. obstetrics and gynecology residency programs were searched for patient education materials. Readability, content, and quality were assessed. A one-sample t-test was used to evaluate mean readability level compared with the recommended 6th grade reading level. Mean readability levels were higher using all indices (p education materials should be improved.

Feature recognition and detection for ancient architecture based on machine vision

Science.gov (United States)

Zou, Zheng; Wang, Niannian; Zhao, Peng; Zhao, Xuefeng

2018-03-01

Ancient architecture has a very high historical and artistic value. The ancient buildings have a wide variety of textures and decorative paintings, which contain a lot of historical meaning. Therefore, the research and statistics work of these different compositional and decorative features play an important role in the subsequent research. However, until recently, the statistics of those components are mainly by artificial method, which consumes a lot of labor and time, inefficiently. At present, as the strong support of big data and GPU accelerated training, machine vision with deep learning as the core has been rapidly developed and widely used in many fields. This paper proposes an idea to recognize and detect the textures, decorations and other features of ancient building based on machine vision. First, classify a large number of surface textures images of ancient building components manually as a set of samples. Then, using the convolution neural network to train the samples in order to get a classification detector. Finally verify its precision.

Readability of Malaria Medicine Information Leaflets in Nigeria ...

African Journals Online (AJOL)

Purpose: To assess the readability of malaria medicines information leaflets available in Nigeria. Methods: Fourty five leaflets were assessed using the Simplified Measure of Gobbledygook (SMOG) readability test and by examining them for paper type, font size type, use of symbols and pictograms, and bilingual information ...

Readability Assessment of Online Patient Abdominoplasty Resources.

Science.gov (United States)

Phillips, Nicole A; Vargas, Christina R; Chuang, Danielle J; Lee, Bernard T

2015-02-01

Limited functional health literacy is recognized as an important contributor to health disparities in the United States. As internet access becomes more universal, there is increasing concern about whether patients with poor or marginal literacy can access understandable healthcare information. As such, the National Institutes of Health and American Medical Association recommend that patient information be written at a sixth grade level. This study identifies the most popular online resources for patient information about abdominoplasty and evaluates their readability in the context of average American literacy. The two largest internet search engines were queried for "tummy tuck surgery" to simulate a patient search in lay terms. The ten most popular sites common to both search engines were identified, and all relevant articles from the main sites were downloaded. Sponsored results were excluded. Readability analysis of the articles was performed using ten established tests. Online information about abdominoplasty from the ten most popular publically available websites had an overall average readability of 12th grade. Mean reading grade level scores among tests were: Coleman-Liau 11.9, Flesch-Kincaid 11.4, FORCAST 11.1, Fry 13, Gunning Fog 13.5, New Dale-Chall 11.8, New Fog Count 9.9, Raygor Estimate 12, and SMOG 13.4; Flesch Reading Ease index score was 46. Online patient resources about abdominoplasty are uniformly above the recommended target readability level and are likely too difficult for many patients to understand. A range of readability identified among websites could allow surgeons to guide patients to more appropriate resources for their literacy skills.

Readability of pediatric health materials for preventive dental care

Directory of Open Access Journals (Sweden)

Riedy Christine A

2006-11-01

Full Text Available Abstract Background This study examined the content and general readability of pediatric oral health education materials for parents of young children. Methods Twenty-seven pediatric oral health pamphlets or brochures from commercial, government, industry, and private nonprofit sources were analyzed for general readability ("usability" according to several parameters: readability, (Flesch-Kincaid grade level, Flesch Reading Ease, and SMOG grade level; thoroughness, (inclusion of topics important to young childrens' oral health; textual framework (frequency of complex phrases, use of pictures, diagrams, and bulleted text within materials; and terminology (frequency of difficult words and dental jargon. Results Readability of the written texts ranged from 2nd to 9th grade. The average Flesch-Kincaid grade level for government publications was equivalent to a grade 4 reading level (4.73, range, 2.4 – 6.6; F-K grade levels for commercial publications averaged 8.1 (range, 6.9 – 8.9; and industry published materials read at an average Flesch-Kincaid grade level of 7.4 (range, 4.7 – 9.3. SMOG readability analysis, based on a count of polysyllabic words, consistently rated materials 2 to 3 grade levels higher than did the Flesch-Kincaid analysis. Government sources were significantly lower compared to commercial and industry sources for Flesch-Kincaid grade level and SMOG readability analysis. Content analysis found materials from commercial and industry sources more complex than government-sponsored publications, whereas commercial sources were more thorough in coverage of pediatric oral health topics. Different materials frequently contained conflicting information. Conclusion Pediatric oral health care materials are readily available, yet their quality and readability vary widely. In general, government publications are more readable than their commercial and industry counterparts. The criteria for usability and results of the analyses

Improving readability of informed consents for research at an academic medical institution.

Science.gov (United States)

Hadden, Kristie B; Prince, Latrina Y; Moore, Tina D; James, Laura P; Holland, Jennifer R; Trudeau, Christopher R

2017-12-01

The final rule for the protection of human subjects requires that informed consent be "in language understandable to the subject" and mandates that "the informed consent must be organized in such a way that facilitates comprehension." This study assessed the readability of Institutional Review Board-approved informed consent forms at our institution, implemented an intervention to improve the readability of consent forms, and measured the first year impact of the intervention. Readability assessment was conducted on a sample of 217 Institutional Review Board-approved informed consents from 2013 to 2015. A plain language informed consent template was developed and implemented and readability was assessed again after 1 year. The mean readability of the baseline sample was 10th grade. The mean readability of the post-intervention sample (n=82) was seventh grade. Providing investigators with a plain language informed consent template and training can promote improved readability of informed consents for research.

The readability of psychosocial wellness patient resources: improving surgical outcomes.

Science.gov (United States)

Kugar, Meredith A; Cohen, Adam C; Wooden, William; Tholpady, Sunil S; Chu, Michael W

2017-10-01

Patient education is increasingly accessed with online resources and is essential for patient satisfaction and clinical outcomes. The average American adult reads at a seventh grade level, and the National Institute of Health (NIH) and the American Medical Association (AMA) recommend that information be written at a sixth-grade reading level. Health literacy plays an important role in the disease course and outcomes of all patients, including those with depression and likely other psychiatric disorders, although this is an area in need of further study. The purpose of this study was to collect and analyze written, online mental health resources on the Veterans Health Administration (VA) website, and other websites, using readability assessment instruments. An internet search was performed to identify written patient education information regarding mental health from the VA (the VA Mental Health Website) and top-rated psychiatric hospitals. Seven mental health topics were included in the analysis: generalized anxiety disorder, bipolar, major depressive disorder, posttraumatic stress disorder, schizophrenia, substance abuse, and suicide. Readability analyses were performed using the Gunning Fog Index, the Flesch-Kincaid Grade Level, the Coleman-Liau Index, the SMOG Readability Formula, and the Automated Readability Index. These scores were then combined into a Readability Consensus score. A two-tailed t-test was used to compare the mean values, and statistical significance was set at P readability consensus than six of the top psychiatric hospitals (P readability consensus for mental health information on all websites analyzed was 9.52. Online resources for mental health disorders are more complex than recommended by the NIH and AMA. Efforts to improve readability of mental health and psychosocial wellness resources could benefit patient understanding and outcomes, especially in patients with lower literacy. Surgical outcomes are correlated with patient mental

Assessing readability formula differences with written health information materials: application, results, and recommendations.

Science.gov (United States)

Wang, Lih-Wern; Miller, Michael J; Schmitt, Michael R; Wen, Frances K

2013-01-01

Readability formulas are often used to guide the development and evaluation of literacy-sensitive written health information. However, readability formula results may vary considerably as a result of differences in software processing algorithms and how each formula is applied. These variations complicate interpretations of reading grade level estimates, particularly without a uniform guideline for applying and interpreting readability formulas. This research sought to (1) identify commonly used readability formulas reported in the health care literature, (2) demonstrate the use of the most commonly used readability formulas on written health information, (3) compare and contrast the differences when applying common readability formulas to identical selections of written health information, and (4) provide recommendations for choosing an appropriate readability formula for written health-related materials to optimize their use. A literature search was conducted to identify the most commonly used readability formulas in health care literature. Each of the identified formulas was subsequently applied to word samples from 15 unique examples of written health information about the topic of depression and its treatment. Readability estimates from common readability formulas were compared based on text sample size, selection, formatting, software type, and/or hand calculations. Recommendations for their use were provided. The Flesch-Kincaid formula was most commonly used (57.42%). Readability formulas demonstrated variability up to 5 reading grade levels on the same text. The Simple Measure of Gobbledygook (SMOG) readability formula performed most consistently. Depending on the text sample size, selection, formatting, software, and/or hand calculations, the individual readability formula estimated up to 6 reading grade levels of variability. The SMOG formula appears best suited for health care applications because of its consistency of results, higher level of expected

A Multiple Sensor Machine Vision System for Automatic Hardwood Feature Detection

Science.gov (United States)

D. Earl Kline; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman; Robert L. Brisbin

1993-01-01

A multiple sensor machine vision prototype is being developed to scan full size hardwood lumber at industrial speeds for automatically detecting features such as knots holes, wane, stain, splits, checks, and color. The prototype integrates a multiple sensor imaging system, a materials handling system, a computer system, and application software. The prototype provides...

Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

Science.gov (United States)

Jegadeeshwaran, R.; Sugumaran, V.

2015-02-01

Hydraulic brakes in automobiles are important components for the safety of passengers; therefore, the brakes are a good subject for condition monitoring. The condition of the brake components can be monitored by using the vibration characteristics. On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to such problems. The vibration signals for both good as well as faulty conditions of brakes were acquired from a hydraulic brake test setup with the help of a piezoelectric transducer and a data acquisition system. Descriptive statistical features were extracted from the acquired vibration signals and the feature selection was carried out using the C4.5 decision tree algorithm. There is no specific method to find the right number of features required for classification for a given problem. Hence an extensive study is needed to find the optimum number of features. The effect of the number of features was also studied, by using the decision tree as well as Support Vector Machines (SVM). The selected features were classified using the C-SVM and Nu-SVM with different kernel functions. The results are discussed and the conclusion of the study is presented.

Assessing Online Patient Education Readability for Spine Surgery Procedures.

Science.gov (United States)

Long, William W; Modi, Krishna D; Haws, Brittany E; Khechen, Benjamin; Massel, Dustin H; Mayo, Benjamin C; Singh, Kern

2018-03-01

Increased patient reliance on Internet-based health information has amplified the need for comprehensible online patient education articles. As suggested by the American Medical Association and National Institute of Health, spine fusion articles should be written for a 4th-6th-grade reading level to increase patient comprehension, which may improve postoperative outcomes. The purpose of this study is to determine the readability of online health care education information relating to anterior cervical discectomy and fusion (ACDF) and lumbar fusion procedures. Online health-education resource qualitative analysis. Three search engines were utilized to access patient education articles for common cervical and lumbar spine procedures. Relevant articles were analyzed for readability using Readability Studio Professional Edition software (Oleander Software Ltd). Articles were stratified by organization type as follows: General Medical Websites (GMW), Healthcare Network/Academic Institutions (HNAI), and Private Practices (PP). Thirteen common readability tests were performed with the mean readability of each compared between subgroups using analysis of variance. ACDF and lumbar fusion articles were determined to have a mean readability of 10.7±1.5 and 11.3±1.6, respectively. GMW, HNAI, and PP subgroups had a mean readability of 10.9±2.9, 10.7±2.8, and 10.7±2.5 for ACDF and 10.9±3.0, 10.8±2.9, and 11.6±2.7 for lumbar fusion articles. Of 310 total articles, only 6 (3 ACDF and 3 lumbar fusion) were written for comprehension below a 7th-grade reading level. Current online literature from medical websites containing information regarding ACDF and lumbar fusion procedures are written at a grade level higher than the suggested guidelines. Therefore, current patient education articles should be revised to accommodate the average reading level in the United States and may result in improved patient comprehension and postoperative outcomes.

Readability assessment of internet-based consumer health information.

Science.gov (United States)

Walsh, Tiffany M; Volsko, Teresa A

2008-10-01

A substantial amount of consumer health-related information is available on the Internet. Studies suggest that consumer comprehension may be compromised if content exceeds a 7th-grade reading level, which is the average American reading level identified by the United States Department of Health and Human Services (USDHHS). To determine the readability of Internet-based consumer health information offered by organizations that represent the top 5 medical-related causes of death in America. We hypothesized that the average readability (reading grade level) of Internet-based consumer health information on heart disease, cancer, stroke, chronic obstructive pulmonary disease, and diabetes would exceed the USDHHS recommended reading level. From the Web sites of the American Heart Association, American Cancer Society, American Lung Association, American Diabetes Association, and American Stroke Association we randomly gathered 100 consumer-health-information articles. We assessed each article with 3 readability-assessment tools: SMOG (Simple Measure of Gobbledygook), Gunning FOG (Frequency of Gobbledygook), and Flesch-Kincaid Grade Level. We also categorized the articles per the USDHHS readability categories: easy to read (below 6th-grade level), average difficulty (7th to 9th grade level), and difficult (above 9th-grade level). Most of the articles exceeded the 7th-grade reading level and were in the USDHHS "difficult" category. The mean +/- SD readability score ranges were: SMOG 11.80 +/- 2.44 to 14.40 +/- 1.47, Flesch-Kincaid 9.85 +/- 2.25 to 11.55 +/- 0.76, and Gunning FOG 13.10 +/- 3.42 to 16.05 +/- 2.31. The articles from the American Lung Association had the lowest reading-level scores with each of the readability-assessment tools. Our findings support that Web-based medical information intended for consumer use is written above USDHHS recommended reading levels. Compliance with these recommendations may increase the likelihood of consumer comprehension.

A readability assessment of online stroke information.

Science.gov (United States)

Sharma, Nikhil; Tridimas, Andreas; Fitzsimmons, Paul R

2014-07-01

Patients and carers increasingly access the Internet as a source of health information. Poor health literacy is extremely common and frequently limits patient's comprehension of health care information literature. We aimed to assess the readability of online consumer-orientated stroke information using 2 validated readability measures. The 100 highest Google ranked consumer-oriented stroke Web pages were assessed for reading difficulty using the Flesch-Kincaid and Simple Measure of Gobbledygook (SMOG) formulae. None of the included Web pages complied with the current readability guidelines when readability was measured using the gold standard SMOG formula. Mean Flesch-Kincaid grade level was 10.4 (95% confidence interval [CI] 9.97-10.9) and mean SMOG grade 12.1 (95% CI 11.7-12.4). Over half of the Web pages were produced at graduate reading levels or above. Not-for-profit Web pages were significantly easier to read (P=.0006). The Flesch-Kincaid formula significantly underestimated reading difficulty, with a mean underestimation of 1.65 grades (95% CI 1.49-1.81), Preadability guidelines and to be comprehensible to the average patient. The Flesch-Kincaid formula significantly underestimates reading difficulty, and SMOG should be used as the measure of choice. Copyright © 2014 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Readability of Brochures Produced by State of Florida.

Science.gov (United States)

Christ, William G.; Pharr, Paula

1980-01-01

A study of the readability of governmental pamphlets produced by the State of Florida, based on the use of the Flesch Reading Ease Formula and the Dale-Chall Formula, suggests that if a seventh or eighth grade readability level is considered an appropriate standard for public information brochures, the brochures tested may be too complex…

Gaze strategies can reveal the impact of source code features on the cognitive load of novice programmers

DEFF Research Database (Denmark)

Wulff-Jensen, Andreas; Ruder, Kevin Vignola; Triantafyllou, Evangelia

2018-01-01

As shown by several studies, programmers’ readability of source code is influenced by its structural and the textual features. In order to assess the importance of these features, we conducted an eye-tracking experiment with programming students. To assess the readability and comprehensibility of...

Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation.

Science.gov (United States)

Dominguez Veiga, Jose Juan; O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

2017-08-04

Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the

Readability of online patient education materials on adult reconstruction Web sites.

Science.gov (United States)

Polishchuk, Daniil L; Hashem, Jenifer; Sabharwal, Sanjeev

2012-05-01

Recommended readability of patient education materials is sixth-grade level or lower. Readability of 212 patient education materials pertaining to adult reconstruction topics available from the American Academy of Orthopaedic Surgeons, American Association of Hip and Knee Surgeons, and 3 other specialty and private practitioner Web sites was assessed using the Flesch-Kincaid grade formula. The mean Flesch-Kincaid score was 11.1 (range, 3-26.5). Only 5 (2%) articles had a readability level of sixth grade or lower. Readability of most of the articles for patient education on adult reconstruction Web sites evaluated may be too advanced for a substantial portion of patients. Further studies are needed to assess the optimal readability level of health information on the Internet. Copyright © 2012 Elsevier Inc. All rights reserved.

Clearly written, easily comprehended? The readability of websites providing information on epilepsy.

Science.gov (United States)

Brigo, Francesco; Otte, Willem M; Igwe, Stanley C; Tezzon, Frediano; Nardone, Raffaele

2015-03-01

There is a general need for high-quality, easily accessible, and comprehensive health-care information on epilepsy to better inform the general population about this highly stigmatized neurological disorder. The aim of this study was to evaluate the health literacy level of eight popular English-written websites that provide information on epilepsy in quantitative terms of readability. Educational epilepsy material on these websites, including 41 Wikipedia articles, were analyzed for their overall level of readability and the corresponding academic grade level needed to comprehend the published texts on the first reading. The Flesch Reading Ease (FRE) was used to assess ease of comprehension while the Gunning Fog Index, Coleman-Liau Index, Flesch-Kincaid Grade Level, Automated Readability Index, and Simple Measure of Gobbledygook scales estimated the corresponding academic grade level needed for comprehension. The average readability of websites yielded results indicative of a difficult-to-fairly-difficult readability level (FRE results: 44.0±8.2), with text readability corresponding to an 11th academic grade level (11.3±1.9). The average FRE score of the Wikipedia articles was indicative of a difficult readability level (25.6±9.5), with the other readability scales yielding results corresponding to a 14th grade level (14.3±1.7). Popular websites providing information on epilepsy, including Wikipedia, often demonstrate a low level of readability. This can be ameliorated by increasing access to clear and concise online information on epilepsy and health in general. Short "basic" summaries targeted to patients and nonmedical users should be added to articles published in specialist websites and Wikipedia to ease readability. Copyright © 2014 Elsevier Inc. All rights reserved.

Improving the readability of online foot and ankle patient education materials.

Science.gov (United States)

Sheppard, Evan D; Hyde, Zane; Florence, Mason N; McGwin, Gerald; Kirchner, John S; Ponce, Brent A

2014-12-01

Previous studies have shown the need for improving the readability of many patient education materials to increase patient comprehension. This study's purpose was to determine the readability of foot and ankle patient education materials and to determine the extent readability can be improved. We hypothesized that the reading levels would be above the recommended guidelines and that decreasing the sentence length would also decrease the reading level of these patient educational materials. Patient education materials from online public sources were collected. The readability of these articles was assessed by a readability software program. The detailed instructions provided by the National Institutes of Health (NIH) were then used as a guideline for performing edits to help improve the readability of selected articles. The most quantitative guideline, lowering all sentences to less than 15 words, was chosen to show the effect of following the NIH recommendations. The reading levels of the sampled articles were above the sixth to seventh grade recommendations of the NIH. The MedlinePlus website, which is a part of the NIH website, had the lowest reading level (8.1). The articles edited had an average reduction of 1.41 grade levels, with the lowest reduction in the Medline articles of 0.65. Providing detailed instructions to the authors writing these patient education articles and implementing editing techniques based on previous recommendations could lead to an improvement in the readability of patient education materials. This study provides authors of patient education materials with simple editing techniques that will allow for the improvement in the readability of online patient educational materials. The improvement in readability will provide patients with more comprehendible education materials that can strengthen patient awareness of medical problems and treatments. © The Author(s) 2014.

Application of higher order spectral features and support vector machines for bearing faults classification.

Science.gov (United States)

Saidi, Lotfi; Ben Ali, Jaouher; Fnaiech, Farhat

2015-01-01

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals. Copyright © 2014 ISA

Readability assessment of patient education materials on major otolaryngology association websites.

Science.gov (United States)

Eloy, Jean Anderson; Li, Shawn; Kasabwala, Khushabu; Agarwal, Nitin; Hansberry, David R; Baredes, Soly; Setzen, Michael

2012-11-01

Various otolaryngology associations provide Internet-based patient education material (IPEM) to the general public. However, this information may be written above the fourth- to sixth-grade reading level recommended by the American Medical Association (AMA) and National Institutes of Health (NIH). The purpose of this study was to assess the readability of otolaryngology-related IPEMs on various otolaryngology association websites and to determine whether they are above the recommended reading level for patient education materials. Analysis of patient education materials from 9 major otolaryngology association websites. The readability of 262 otolaryngology-related IPEMs was assessed with 8 numerical and 2 graphical readability tools. Averages were evaluated against national recommendations and between each source using analysis of variance (ANOVA) with post hoc Tukey's honestly significant difference (HSD) analysis. Mean readability scores for each otolaryngology association website were compared. Mean website readability scores using Flesch Reading Ease test, Flesch-Kincaid Grade Level, Coleman-Liau Index, SMOG grading, Gunning Fog Index, New Dale-Chall Readability Formula, FORCAST Formula, New Fog Count Test, Raygor Readability Estimate, and the Fry Readability Graph ranged from 20.0 to 57.8, 9.7 to 17.1, 10.7 to 15.9, 11.6 to 18.2, 10.9 to 15.0, 8.6 to 16.0, 10.4 to 12.1, 8.5 to 11.8, 10.5 to 17.0, and 10.0 to 17.0, respectively. ANOVA results indicate a significant difference (P < .05) between the websites for each individual assessment. The IPEMs found on all otolaryngology association websites exceed the recommended fourth- to sixth-grade reading level.

Assessing the Accuracy and Readability of Online Health Information for Patients With Pancreatic Cancer.

Science.gov (United States)

Storino, Alessandra; Castillo-Angeles, Manuel; Watkins, Ammara A; Vargas, Christina; Mancias, Joseph D; Bullock, Andrea; Demirjian, Aram; Moser, A James; Kent, Tara S

2016-09-01

The degree to which patients are empowered by written educational materials depends on the text's readability level and the accuracy of the information provided. The association of a website's affiliation or focus on treatment modality with its readability and accuracy has yet to be thoroughly elucidated. To compare the readability and accuracy of patient-oriented online resources for pancreatic cancer by treatment modality and website affiliation. An online search of 50 websites discussing 5 pancreatic cancer treatment modalities (alternative therapy, chemotherapy, clinical trials, radiation therapy, and surgery) was conducted. The website's affiliation was identified. Readability was measured by 9 standardized tests, and accuracy was assessed by an expert panel. Nine standardized tests were used to compute the median readability level of each website. The median readability scores were compared among treatment modality and affiliation categories. Accuracy was determined by an expert panel consisting of 2 medical specialists and 2 surgical specialists. The 4 raters independently evaluated all websites belonging to the 5 treatment modalities (a score of 1 indicates that readability and accuracy based on the focus of the treatment modality and the website's affiliation. Websites discussing surgery (with a median readability level of 13.7 and an interquartile range [IQR] of 11.9-15.6) were easier to read than those discussing radiotherapy (median readability level, 15.2 [IQR, 13.0-17.0]) (P = .003) and clinical trials (median readability level, 15.2 [IQR, 12.8-17.0]) (P = .002). Websites of nonprofit organizations (median readability level, 12.9 [IQR, 11.2-15.0]) were easier to read than media (median readability level, 16.0 [IQR, 13.4-17.0]) (P readability level, 14.8 [IQR, 12.9-17.0]) (P readability level, 14.0 [IQR, 12.1-16.1]) were easier to read than media websites (P = .001). Among treatment modalities, alternative therapy websites exhibited the

Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction

Directory of Open Access Journals (Sweden)

Jie Zhang

2017-04-01

Full Text Available Device-free localization (DFL is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF DFL system, radio transmitters (RTs and radio receivers (RXs are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN, support vector machine (SVM, back propagation neural network (BPNN, as well as the well-known radio tomographic imaging (RTI DFL approach.

Readability of patient information and consent documents in rheumatological studies.

Science.gov (United States)

Hamnes, Bente; van Eijk-Hustings, Yvonne; Primdahl, Jette

2016-07-16

Before participation in medical research an informed consent must be obtained. This study investigates whether the readability of patient information and consent documents (PICDs) corresponds to the average educational level of participants in rheumatological studies in the Netherlands, Denmark, and Norway. 24 PICDs from studies were collected and readability was assessed independently using the Gunning's Fog Index (FOG) and Simple Measure of Gobbledygook (SMOG) grading. The mean score for the FOG and SMOG grades were 14.2 (9.0-19.0) and 14.2 (12-17) respectively. The mean FOG and SMOG grades were 12.7 and 13.3 in the Dutch studies, 15.0 and 14.9 in the Danish studies, and 14.6 and 14.3 in the Norwegian studies, respectively. Out of the 2865 participants, more than 57 % had a lower educational level than the highest readability score calculated in the individual study. As the readability level of the PICDs did not match the participants' educational level, consent may not have been valid, as the participants may have had a limited understanding of what they agreed to participate in. There should be more focus on the readability of PICDs. National guidelines for how to write clear and unambiguous PICDs in simple and easily understandable language could increase the focus on the readability of PICD.

Readability of Internet Information on Hearing: Systematic Literature Review.

Science.gov (United States)

Laplante-Lévesque, Ariane; Thorén, Elisabet Sundewall

2015-09-01

This systematic literature review asks the following question: “ What is the readability of Internet information on hearing that people with hearing impairment and their significant others can access in the context of their hearing care?” Searches were completed in three databases: CINAHL, PubMed, and Scopus. Seventy-eight records were identified and systematically screened for eligibility: 8 records were included that contained data on the readability of Internet information on hearing that people with hear ing impairment and their significant others can access in the context of their hearing care. Records reported mean readability levels from 9 to over 14. In other words, people with hearing impairment and their significant others need 9 to 14 years of education to read and understand Internet information on hearing that they access in the context of their hearing care. The poor readability of Internet information on hearing has been well documented; it is time to focus on valid and sustainable initiatives that address this problem.

Readability Assessment of Online Patient Education Material on Congestive Heart Failure

Science.gov (United States)

2017-01-01

Background Online health information is being used more ubiquitously by the general population. However, this information typically favors only a small percentage of readers, which can result in suboptimal medical outcomes for patients. Objective The readability of online patient education materials regarding the topic of congestive heart failure was assessed through six readability assessment tools. Methods The search phrase “congestive heart failure” was employed into the search engine Google. Out of the first 100 websites, only 70 were included attending to compliance with selection and exclusion criteria. These were then assessed through six readability assessment tools. Results Only 5 out of 70 websites were within the limits of the recommended sixth-grade readability level. The mean readability scores were as follows: the Flesch-Kincaid Grade Level (9.79), Gunning-Fog Score (11.95), Coleman-Liau Index (15.17), Simple Measure of Gobbledygook (SMOG) index (11.39), and the Flesch Reading Ease (48.87). Conclusion Most of the analyzed websites were found to be above the sixth-grade readability level recommendations. Efforts need to be made to better tailor online patient education materials to the general population. PMID:28656111

Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.

Science.gov (United States)

Ranjith, G; Parvathy, R; Vikas, V; Chandrasekharan, Kesavadas; Nair, Suresh

2015-04-01

With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification. The aim of the study is to classify gliomas into benign and malignant types using MRI data. Retrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning. Three of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%). The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Fault diagnosis of rotating machine by isometric feature mapping

International Nuclear Information System (INIS)

Zhang, Yun; Li, Benwei; Wang, Lin; Wang, Wen; Wang, Zibin

2013-01-01

Principal component analysis (PCA) and linear discriminate analysis (LDA) are well-known linear dimensionality reductions for fault classification. However, since they are linear methods, they perform not well for high-dimensional data that has the nonlinear geometric structure. As kernel extension of PCA, Kernel PCA is used for nonlinear fault classification. However, the performance of Kernel PCA largely depends on its kernel function which can only be empirically selected from finite candidates. Thus, a novel rotating machine fault diagnosis approach based on geometrically motivated nonlinear dimensionality reduction named isometric feature mapping (Isomap) is proposed. The approach can effectively extract the intrinsic nonlinear manifold features embedded in high-dimensional fault data sets. Experimental results with rotor and rolling bearing data show that the proposed approach overcomes the flaw of conventional fault pattern recognition approaches and obviously improves the fault classification performance.

The Readability of Online Resources for Mastopexy Surgery.

Science.gov (United States)

Vargas, Christina R; Chuang, Danielle J; Lee, Bernard T

2016-01-01

As more patients use Internet resources for health information, there is increasing interest in evaluating the readability of available online materials. The National Institutes of Health and American Medical Association recommend that patient educational content be written at a sixth-grade reading level. This study evaluates the most popular online resources for information about mastopexy relative to average adult literacy in the United States. The 12 most popular sites returned by the largest Internet search engine were identified using the search term "breast lift surgery." Relevant articles from the main sites were downloaded and formatted into text documents. Pictures, captions, links, and references were excluded. The readability of these 100 articles was analyzed overall and subsequently by site using 10 established readability tests. Subgroup analysis was performed for articles discussing the benefits of surgery and those focusing on risks. The overall average readability of online patient information was 13.3 (range, 11.1-15). There was a range of average readability scores overall across the 12 sites from 8.9 to 16.1, suggesting that some may be more appropriate than others for patient demographics with different health literacy levels. Subgroup analysis revealed that articles discussing the risks of mastopexy were significantly harder to read (mean, 14.1) than articles about benefits (11.6). Patient-directed articles from the most popular online resources for mastopexy information are uniformly above the recommended reading level and likely too difficult to be understood by a large number of patients in the United States.

Quality and Readability of English-Language Internet Information for Voice Disorders.

Science.gov (United States)

Dueppen, Abigail J; Bellon-Harn, Monica L; Radhakrishnan, Nandhakumar; Manchaiah, Vinaya

2017-12-15

The purpose of this study is to evaluate the readability and quality of English-language Internet information related to vocal hygiene, vocal health, and prevention of voice disorders. This study extends recent work because it evaluates readability, content quality, and website origin across broader search criteria than previous studies evaluating online voice material. Eighty-five websites were aggregated using five different country-specific search engines. Websites were then analyzed using quality and readability assessments. The entire web page was evaluated; however, no information or links beyond the first page was reviewed. Statistical calculations were employed to examine website ratings, differences between website origin and quality and readability scores, and correlations between readability instruments. Websites exhibited acceptable quality as measured by the DISCERN. However, only one website obtained the Health On the Net certification. Significant differences in quality were found among website origin, with government websites receiving higher quality ratings. Approximate educational levels required to comprehend information on the websites ranged from 8 to 9 years of education. Significant differences were found between website origin and readability measures with higher levels of education required to understand information on websites of nonprofit organizations. Current vocal hygiene, vocal health, and prevention of voice disorders websites were found to exhibit acceptable levels of quality and readability. However, highly rated Internet information related to voice care should be made more accessible to voice clients through Health On the Net certification. Published by Elsevier Inc.

Readability as a Factor in Magazine Ad Copy Recall.

Science.gov (United States)

Wesson, David A.

1989-01-01

Examines the relationship between advertising copy readability and advertising effectiveness. Finds that recall is improved when the copy style is either fairly easy or fairly hard to read. Suggests the value of considering copy readability as a potential contributor, though a minor one, to the success of magazine advertising. (RS)

Consent information leaflets - readable or unreadable?

Science.gov (United States)

Graham, Caroline; Reynard, John M; Turney, Benjamin W

2015-05-01

The objective of this article is to assess the readability of leaflets about urological procedures provided by the British Association of Urological Surgeons (BAUS) to evaluate their suitability for providing information. Information leaflets were assessed using three measures of readability: Flesch Reading Ease, Flesch-Kincaid and Simple Measure of Gobbledygook (SMOG) grade formulae. The scores were compared with national literacy statistics. Relatively good readability was demonstrated using the Flesch Reading Ease (53.4-60.1) and Flesch-Kincaid Grade Level (6.5-7.6) methods. However, the average SMOG index (14.0-15.0) for each category suggests that the majority of the leaflets are written above the reading level of an 18-year-old. Using national literacy statistics, at least 43% of the population will have significant difficultly understanding the majority of these leaflets. The results suggest that comprehension of the leaflets provided by the BAUS is likely to be poor. These leaflets may be used as an adjunct to discussion but it is essential to ensure that all the information necessary to make an informed decision has been conveyed in a way that can be understood by the patient.

Readability Assessment of Patient Information about Lymphedema and Its Treatment.

Science.gov (United States)

Seth, Akhil K; Vargas, Christina R; Chuang, Danielle J; Lee, Bernard T

2016-02-01

Patient use of online resources for health information is increasing, and access to appropriately written information has been associated with improved patient satisfaction and overall outcomes. The American Medical Association and the National Institutes of Health recommend that patient materials be written at a sixth-grade reading level. In this study, the authors simulated a patient search of online educational content for lymphedema and evaluated readability. An online search for the term "lymphedema" was performed, and the first 12 hits were identified. User and location filters were disabled and sponsored results were excluded. Patient information from each site was downloaded and formatted into plain text. Readability was assessed using established tests: Coleman-Liau, Flesch-Kincaid, Flesch Reading Ease Index, FORCAST Readability Formula, Fry Graph, Gunning Fog Index, New Dale-Chall Formula, New Fog Count, Raygor Readability Estimate, and Simple Measure of Gobbledygook Readability Formula. There were 152 patient articles downloaded; the overall mean reading level was 12.6. Individual website reading levels ranged from 9.4 (cancer.org) to 16.7 (wikipedia.org). There were 36 articles dedicated to conservative treatments for lymphedema; surgical treatment was mentioned in nine articles across four sites. The average reading level for conservative management was 12.7, compared with 15.6 for surgery (p readability, and surgeons should direct patients to sites appropriate for their level. There is limited information about surgical treatment available on the most popular sites; this information is significantly harder to read than sections on conservative measures.

Readability Level of Spanish-Language Patient-Reported Outcome Measures in Audiology and Otolaryngology

Science.gov (United States)

Coco, Laura; Colina, Sonia; Atcherson, Samuel R.

2017-01-01

Purpose The purpose of this study was to examine the readability level of the Spanish versions of several audiology- and otolaryngology-related patient-reported outcome measures (PROMs) and include a readability analysis of 2 translation approaches when available—the published version and a “functionalist” version—using a team-based collaborative approach including community members. Method Readability levels were calculated using the Fry Graph adapted for Spanish, as well as the Fernandez-Huerta and the Spaulding formulae for several commonly used audiology- and otolaryngology-related PROMs. Results Readability calculations agreed with previous studies analyzing audiology-related PROMs in English and demonstrated many Spanish-language PROMs were beyond the 5th grade reading level suggested for health-related materials written for the average population. In addition, the functionalist versions of the PROMs yielded lower grade-level (improved) readability levels than the published versions. Conclusion Our results suggest many of the Spanish-language PROMs evaluated here are beyond the recommended readability levels and may be influenced by the approach to translation. Moreover, improved readability may be possible using a functionalist approach to translation. Future analysis of the suitability of outcome measures and the quality of their translations should move beyond readability and include an evaluation of the individual's comprehension of the written text. PMID:28892821

readability of comprehension passages in junior high school (jhs)

African Journals Online (AJOL)

CHARLES

... to enhance readability. Key Words: readability formulas, comprehension passages, Junior High School, .... Index has a manual version but in this study the electronic version was used. The ..... probably the majority of the people heard the news by word of mouth. A critical look ..... The Journal of Tourism Studies 9.2: 49-60.

Exploration on Automated Software Requirement Document Readability Approaches

OpenAIRE

Chen, Mingda; He, Yao

2017-01-01

Context. The requirements analysis phase, as the very beginning of software development process, has been identified as a quite important phase in the software development lifecycle. Software Requirement Specification (SRS) is the output of requirements analysis phase, whose quality factors play an important role in the evaluation work. Readability is a quite important SRS quality factor, but there are few available automated approaches for readability measurement, because of the tight depend...

Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

Directory of Open Access Journals (Sweden)

Jia Uddin

2014-01-01

Full Text Available This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D texture features and a multiclass support vector machine (MCSVM. The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns, and extracts these texture features by generating the dominant neighborhood structure (DNS map. The principal component analysis (PCA is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA multiclass support vector machines (MCSVMs to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.

Formalizing the Safety of Java, the Java Virtual Machine and Java Card

NARCIS (Netherlands)

Hartel, Pieter H.; Moreau, Luc

2001-01-01

We review the existing literature on Java safety, emphasizing formal approaches, and the impact of Java safety on small footprint devices such as smart cards. The conclusion is that while a lot of good work has been done, a more concerted effort is needed to build a coherent set of machine readable

Textbooks in Management, Marketing and Finance: An Analysis of Readability.

Science.gov (United States)

Gallagher, Daniel J.; Thompson, G. Rodney

1982-01-01

Examines the readability of texts in basic junior level college courses in the fields of management, marketing, and finance. The readability model is described, along with its application and results. Specific texts and how they fared are listed in accompanying tables. (CT)

Evaluation of the Readability of Dermatological Postoperative Patient Information Leaflets Across England.

Science.gov (United States)

Hunt, William T N; McGrath, Emily J

2016-06-01

Postoperative patient information leaflets (PILs) provide important guidance to patients after skin surgery. Readability is a method of evaluating information for text comprehension. The recommended level for PIL readability is US grade ≤6. To evaluate the readability of public English dermatological postoperative PILs. All dermatology departments in England were requested to provide their postoperative PILs. Patient information leaflets were evaluated using Readability Studio (Oleander Software, Vandalia, OH). Two preselected parameters were also noted: whether the PIL was doctor or nurse-written, and whether the PIL was Information Standard hallmarked. Eighty-five of one hundred thirty (65.4%) of PILs were evaluated. Only 29.4% of the PILs were grade level ≤6 with Flesch-Kincaid. The mean readability levels were 7.8 for Flesch-Kincaid, 67 for Flesch reading ease, 10.5 for Simple Measure of Gobbledygook (SMOG), 9.4 for Gunning-Fog, 8 for Fry, and 9.8 for FORCAST. No instruments demonstrated a significant difference between doctor (6) and nurse-written (7) PILs. Two instruments found that the 3 Information Standard hallmarked PILs had a higher (harder) readability than ordinary PILs (n = 82) (Gunning-Fog, p = .029*; SMOG p = .049*). Most English postoperative dermatological PILs' readability levels exceed recommendations (US grade ≤6). Departmental PILs should be reviewed to ensure that they are comprehensible to their patients.

Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Muhammad Naveed Iqbal Qureshi

2017-09-01

Full Text Available Multimodal features of structural and functional magnetic resonance imaging (MRI of the human brain can assist in the diagnosis of schizophrenia. We performed a classification study on age, sex, and handedness-matched subjects. The dataset we used is publicly available from the Center for Biomedical Research Excellence (COBRE and it consists of two groups: patients with schizophrenia and healthy controls. We performed an independent component analysis and calculated global averaged functional connectivity-based features from the resting-state functional MRI data for all the cortical and subcortical anatomical parcellation. Cortical thickness along with standard deviation, surface area, volume, curvature, white matter volume, and intensity measures from the cortical parcellation, as well as volume and intensity from sub-cortical parcellation and overall volume of cortex features were extracted from the structural MRI data. A novel hybrid weighted feature concatenation method was used to acquire maximal 99.29% (P < 0.0001 accuracy which preserves high discriminatory power through the weight of the individual feature type. The classification was performed by an extreme learning machine, and its efficiency was compared to linear and non-linear (radial basis function support vector machines, linear discriminant analysis, and random forest bagged tree ensemble algorithms. This article reports the predictive accuracy of both unimodal and multimodal features after 10-by-10-fold nested cross-validation. A permutation test followed the classification experiment to assess the statistical significance of the classification results. It was concluded that, from a clinical perspective, this feature concatenation approach may assist the clinicians in schizophrenia diagnosis.

Readability of Orthopedic Trauma Patient Education Materials on the Internet.

Science.gov (United States)

Mohan, Rohith; Yi, Paul H; Morshed, Saam

In this study, we used the Flesch-Kincaid Readability Scale to determine the readability levels of orthopedic trauma patient education materials on the American Academy of Orthopaedic Surgeons (AAOS) website and to examine how subspecialty coauthorship affects readability level. Included articles from the AAOS online patient education library and the AAOS OrthoPortal website were categorized as trauma or broken bones and injuries on the AAOS online library or were screened by study authors for relevance to orthopedic trauma. Subsequently, the Flesch-Kincaid scale was used to determine each article's readability level, which was reported as a grade level. Subspecialty coauthorship was noted for each article. A total of 115 articles from the AAOS website were included in the study and reviewed. Mean reading level was grade 9.1 for all articles reviewed. Nineteen articles (16.5%) were found to be at or below the eighth-grade level, and only 1 article was at or below the sixth-grade level. In addition, there was no statistically significant difference between articles coauthored by the various orthopedic subspecialties and those authored exclusively by AAOS. Orthopedic trauma readability materials on the AAOS website appear to be written at a reading comprehension level too high for the average patient to understand.

Measuring the Readability of Elementary Algebra Using the Cloze Technique.

Science.gov (United States)

Kulm, Gerald

The relationship to readability of ten variables characterizing structural properties of mathematical prose was investigated in elementary algebra textbooks. Readability was measured by algebra student's responses to two forms of cloze tests. Linear and currilinear correlations were calculated between each structural variable and the cloze test.…

Readability of "Dear Patient" device advisory notification letters created by a device manufacturer.

Science.gov (United States)

Mueller, Luke A; Sharma, Arjun; Ottenberg, Abigale L; Mueller, Paul S

2013-04-01

In 2006, the Heart Rhythm Society (HRS) recommended that cardiovascular implantable electronic device (CIED) manufacturers use advisory notification letters to communicate with affected patients. To evaluate the readability of the HRS sample "patient device advisory notification" letter and those created by 1 CIED manufacturer. The HRS sample letter and 25 Boston Scientific Corporation letters dated from 2005 through 2011 were evaluated by using 6 readability tests. Readability (Flesch-Kincaid score) of the HRS sample letter was grade level 12.5, and median readability of the device manufacturer letters was grade level 12.8 (range 10.8-18.9). Similar results were obtained by using other readability scales. No letters had readability scores at the National Work Group on Literacy and Health's recommended reading level-fifth grade; the letters' readability exceeded this recommended level by an average of 7.7 grades (95% confidence interval 6.9-8.5; Preadability scores at the average reading level of US adults-eighth grade; the letters' readability exceeded this level by an average of 4.7 grades (95% confidence interval 3.9-5.5; Preadability of the HRS sample letter and those created by a CIED manufacturer significantly exceeded the recommended and average US adults' reading skill levels. Such letters are unlikely to be informative to many patients. CIED manufacturers should ensure that advisory letters are comprehensible to most affected patients. Copyright © 2013 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

Readability Levels of the 1975 Third Grade Macmillan Basal Readers.

Science.gov (United States)

McKinney, Ernestine Williams

1983-01-01

Analysis of third-grade books in the New Macmillan Reading Program reveals that the books exceeded the publisher's designation of readability and did not progress in difficulty from easy to more difficult. Findings suggest the need for more complete and reliable information from publishers concerning textbook readability. (FL)

Readability and Reading Ability.

Science.gov (United States)

Wright, Benjamin D.; Stenner, A. Jackson

This document discusses the measurement of reading ability and the readability of books by application of the Lexile framework. It begins by stating the importance of uniform measures. It then discusses the history of reading ability testing, based on the assumption that no researcher has been able to measure more than one kind of reading ability.…

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

Directory of Open Access Journals (Sweden)

Alessandra Caggiano

2018-03-01

Full Text Available Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA is proposed. PCA allowed to identify a smaller number of features (k = 2 features, the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax was achieved, with predicted values very close to the measured tool wear values.

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

Science.gov (United States)

2018-01-01

Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features (k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax) was achieved, with predicted values very close to the measured tool wear values. PMID:29522443

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition.

Science.gov (United States)

Caggiano, Alessandra

2018-03-09

Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features ( k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear ( VB max ) was achieved, with predicted values very close to the measured tool wear values.

Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

Science.gov (United States)

Choi, Ickwon; Chung, Amy W; Suscovich, Todd J; Rerks-Ngarm, Supachai; Pitisuttithum, Punnee; Nitayaphan, Sorachai; Kaewkungwal, Jaranit; O'Connell, Robert J; Francis, Donald; Robb, Merlin L; Michael, Nelson L; Kim, Jerome H; Alter, Galit; Ackerman, Margaret E; Bailey-Kellogg, Chris

2015-04-01

The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine

International Nuclear Information System (INIS)

Jin Jing; Wei Biao; Feng Peng; Tang Yuelin; Zhou Mi

2010-01-01

Based on the interdependent relationship between fission neutrons ( 252 Cf) and fission chain ( 235 U system), the paper presents the time-frequency feature analysis and recognition in fission neutron signal based on support vector machine (SVM) through the analysis on signal characteristics and the measuring principle of the 252 Cf fission neutron signal. The time-frequency characteristics and energy features of the fission neutron signal are extracted by using wavelet decomposition and de-noising wavelet packet decomposition, and then applied to training and classification by means of support vector machine based on statistical learning theory. The results show that, it is effective to obtain features of nuclear signal via wavelet decomposition and de-noising wavelet packet decomposition, and the latter can reflect the internal characteristics of the fission neutron system better. With the training accomplished, the SVM classifier achieves an accuracy rate above 70%, overcoming the lack of training samples, and verifying the effectiveness of the algorithm. (authors)

Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

Directory of Open Access Journals (Sweden)

Ickwon Choi

2015-04-01

Full Text Available The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release. We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

Readability and suitability assessment of patient education materials in rheumatic diseases.

Science.gov (United States)

Rhee, Rennie L; Von Feldt, Joan M; Schumacher, H Ralph; Merkel, Peter A

2013-10-01

Web-based patient education materials and printed pamphlets are frequently used by providers to inform patients about their rheumatic disease. Little attention has been given to the readability and appropriateness of patient materials. The objective of this study was to examine the readability and suitability of commonly used patient education materials for osteoarthritis (OA), rheumatoid arthritis, systemic lupus erythematosus, and vasculitis. Five or 6 popular patient resources for each disease were chosen for evaluation. Readability was measured using the Flesch-Kincaid reading grade level and suitability was determined by the Suitability Assessment of Materials (SAM), a score that considers characteristics such as content, graphics, layout/topography, and cultural appropriateness. Three different reviewers rated the SAM score and means were used in the analysis. Twenty-three resources written on the 4 diseases were evaluated. The education material for all 4 diseases studied had readability above the eighth-grade level and readability did not differ among the diseases. Only 5 of the 23 resources received superior suitability scores, and 3 of these 5 resources were written for OA. All 4 diseases received adequate suitability scores, with OA having the highest mean suitability score. Most patient education materials for rheumatic diseases are written at readability levels above the recommended sixth-grade reading level and have only adequate suitability. Developing more appropriate educational resources for patients with rheumatic diseases may improve patient comprehension. Copyright © 2013 by the American College of Rheumatology.

Assessing readability of patient education materials: current role in orthopaedics.

Science.gov (United States)

Badarudeen, Sameer; Sabharwal, Sanjeev

2010-10-01

Health literacy is the single best predictor of an individual's health status. It is important to customize health-related education material to the individual patient's level of reading skills. Readability of a given text is the objective measurement of the reading skills one should possess to understand the written material. In this article, some of the commonly used readability assessment tools are discussed and guidelines to improve the comprehension of patient education handouts are provided. Where are we now? Several healthcare organizations have recommended the readability of patient education materials be no higher than sixth- to eighth-grade level. However, most of the patient education materials currently available on major orthopaedic Web sites are written at a reading level that may be too advanced for comprehension by a substantial proportion of the population. WHERE DO WE NEED TO GO?: There are several readily available and validated tools for assessing the readability of written materials. While use of audiovisual aids such as video clips, line drawings, models, and charts can enhance the comprehension of a health-related topic, standard readability tools cannot construe such enhancements. HOW DO WE GET THERE?: Given the variability in the capacity to comprehend health-related materials among individuals seeking orthopaedic care, stratifying the contents of patient education materials at different levels of complexity will likely improve health literacy and enhance patient-centered communication.

Initial Readability Assessment of Clinical Trial Eligibility Criteria

Science.gov (United States)

Kang, Tian; Elhadad, Noémie; Weng, Chunhua

2015-01-01

Various search engines are available to clinical trial seekers. However, it remains unknown how comprehensible clinical trial eligibility criteria used for recruitment are to a lay audience. This study initially investigated this problem. Readability of eligibility criteria was assessed according to (i) shallow and lexical characteristics through the use of an established, generic readability metric; (ii) syntactic characteristics through natural language processing techniques; and (iii) health terminological characteristics through an automated comparison to technical and lay health texts. We further stratified clinical trials according to various study characteristics (e.g., source country or study type) to understand potential factors influencing readability. Mainly caused by frequent use of technical jargons, a college reading level was found to be necessary to understand eligibility criteria text, a level much higher than the average literacy level of the general American population. The use of technical jargons should be minimized to simplify eligibility criteria text. PMID:26958204

Readability of patient education materials on the American Orthopaedic Society for Sports Medicine website.

Science.gov (United States)

Eltorai, Adam E M; Han, Alex; Truntzer, Jeremy; Daniels, Alan H

2014-11-01

The recommended readability of patient education materials by the American Medical Association (AMA) and National Institutes of Health (NIH) should be no greater than a sixth-grade reading level. However, online resources may be too complex for some patients to understand, and poor health literacy predicts inferior health-related quality of life outcomes. This study evaluated whether the American Orthopaedic Society for Sports Medicine (AOSSM) website's patient education materials meet recommended readability guidelines for medical information. We hypothesized that the readability of these online materials would have a Flesch-Kincaid formula grade above the sixth grade. All 65 patient education entries of the AOSSM website were analyzed for grade level readability using the Flesch-Kincaid formula, a widely used and validated tool to evaluate the text reading level. The average (standard deviation) readability of all 65 articles was grade level 10.03 (1.44); 64 articles had a readability score above the sixth-grade level, which is the maximum level recommended by the AMA and NIH. Mean readability of the articles exceeded this level by 4.03 grade levels (95% CI, 3.7-4.4; P reading level of US adults. Mean readability of the articles exceeded this level by 2.03 grade levels (95% CI, 1.7-2.4; P online AOSSM patient education materials exceeds the readability level recommended by the AMA and NIH, and is above the average reading level of the majority of US adults. This online information may be of limited utility to most patients due to a lack of comprehension. Our study provides a clear example of the need to improve the readability of specific education material in order to maximize the efficacy of multimedia sources.

Readability of Online Patient Education Materials Related to IR.

Science.gov (United States)

McEnteggart, Gregory E; Naeem, Muhammad; Skierkowski, Dorothy; Baird, Grayson L; Ahn, Sun H; Soares, Gregory

2015-08-01

To assess the readability of online patient education materials (OPEM) related to common diseases treated by and procedures performed by interventional radiology (IR). The following websites were chosen based on their average Google search return for each IR OPEM content area examined in this study: Society of Interventional Radiology (SIR), Cardiovascular and Interventional Radiological Society of Europe (CIRSE), National Library of Medicine, RadiologyInfo, Mayo Clinic, WebMD, and Wikipedia. IR OPEM content area was assessed for the following: peripheral arterial disease, central venous catheter, varicocele, uterine artery embolization, vertebroplasty, transjugular intrahepatic portosystemic shunt, and deep vein thrombosis. The following algorithms were used to estimate and compare readability levels: Flesch-Kincaid Grade Formula, Flesch Reading Ease Score, Gunning Frequency of Gobbledygook, Simple Measure of Gobbledygook, and Coleman-Liau Index. Data were analyzed using general mixed modeling. On average, online sources that required beyond high school grade-level readability were Wikipedia (15.0), SIR (14.2), and RadiologyInfo (12.4); sources that required high school grade-level readability were CIRSE (11.3), Mayo Clinic (11.0), WebMD (10.6), and National Library of Medicine (9.0). On average, OPEM on uterine artery embolization, vertebroplasty, varicocele, and peripheral arterial disease required the highest level of readability (12.5, 12.3, 12.3, and 12.2, respectively). The IR OPEM assessed in this study were written above the recommended sixth-grade reading level and the health literacy level of the average American adult. Many patients in the general public may not have the ability to read and understand health information in IR OPEM. Copyright © 2015 SIR. Published by Elsevier Inc. All rights reserved.

Readability and quality of wikipedia pages on neurosurgical topics.

Science.gov (United States)

Modiri, Omeed; Guha, Daipayan; Alotaibi, Naif M; Ibrahim, George M; Lipsman, Nir; Fallah, Aria

2018-03-01

Wikipedia is the largest online encyclopedia with over 40 million articles, and generating 500 million visits per month. The aim of this study is to assess the readability and quality of Wikipedia pages on neurosurgical related topics. We selected the neurosurgical related Wikipedia pages based on the series of online patient information articles that are published by the American Association of Neurological Surgeons (AANS). We assessed readability of Wikipedia pages using five different readability scales (Flesch Reading Ease, Flesch Kincaid Grade Level, Gunning Fog Index, SMOG) Grade level, and Coleman-Liau Index). We used the Center for Disease Control (CDC) Clear Communication Index as well as the DISCERN Instrument to evaluate the quality of each Wikipedia article. We identified a total of fifty-five Wikipedia articles that corresponded with patient information articles published by the AANS. This constitutes 77.46% of the AANS topics. The mean Flesch Kincaid reading ease score for all of the Wikipedia articles we analyzed is 31.10, which indicates that a college-level education is necessary to understand them. In comparison to the readability analysis for the AANS articles, the Wikipedia articles were more difficult to read across every scale. None of the Wikipedia articles meet the CDC criterion for clear communications. Our analyses demonstrated that Wikipedia articles related to neurosurgical topics are associated with higher grade levels for reading and also below the expected levels of clear communications for patients. Collaborative efforts from the neurosurgical community are needed to enhance the readability and quality of Wikipedia pages related to neurosurgery. Copyright © 2018 Elsevier B.V. All rights reserved.

Readability analysis of online resources related to lung cancer.

Science.gov (United States)

Weiss, Kathleen D; Vargas, Christina R; Ho, Olivia A; Chuang, Danielle J; Weiss, Jonathan; Lee, Bernard T

2016-11-01

Patients seeking health information commonly use the Internet as the first source for material. Studies show that well-informed patients have increased involvement, satisfaction, and healthcare outcomes. As one-third of Americans have only basic or below basic health literacy, the National Institutes of Health and American Medical Association recommend patient-directed health resources be written at a sixth-grade reading level. This study evaluates the readability of commonly accessed online resources on lung cancer. A search for "lung cancer" was performed using Google and Bing, and the top 10 websites were identified. Location services were disabled, and sponsored sites were excluded. Relevant articles (n = 109) with patient-directed content available directly from the main sites were downloaded. Readability was assessed using 10 established methods and analyzed with articles grouped by parent website. The average reading grade level across all sites was 11.2, with a range from 8.8 (New Fog Count) to 12.2 (Simple Measure of Gobbledygook). The average Flesch Reading Ease score was 52, corresponding with fairly difficult to read text. The readability varied when compared by individual website, ranging in grade level from 9.2 to 15.2. Only 10 articles (9%) were written below a sixth-grade level and these tended to discuss simpler topics. Patient-directed online information about lung cancer exceeds the recommended sixth-grade reading level. Readability varies between individual websites, allowing physicians to direct patients according to level of health literacy. Modifications to existing materials can significantly improve readability while maintaining content for patients with low health literacy. Copyright © 2016 Elsevier Inc. All rights reserved.

The quality and readability of online consumer information about gynecologic cancer.

Science.gov (United States)

Sobota, Aleksandra; Ozakinci, Gozde

2015-03-01

The Internet has become an important source of health-related information for consumers, among whom younger women constitute a notable group. The aims of this study were (1) to evaluate the quality and readability of online information about gynecologic cancer using validated instruments and (2) to relate the quality of information to its readability. Using the Alexa Rank, we obtained a list of 35 Web pages providing information about 7 gynecologic malignancies. These were assessed using the Health on the Net (HON) seal of approval, the Journal of the American Medical Association (JAMA) benchmarks, and the DISCERN instrument. Flesch readability score was calculated for sections related to symptoms and signs and treatment. Less than 30% of the Web pages displayed the HON seal or achieved all JAMA benchmarks. The majority of the treatment sections were of moderate to high quality according to the DISCERN. There was no significant relationship between the presence of the HON seal and readability. Web pages achieving all JAMA benchmarks were significantly more difficult to read and understand than Web pages that missed any of the JAMA benchmarks. Treatment-related content of moderate to high quality as assessed by the DISCERN had a significantly better readability score than the low-quality content. The online information about gynecologic cancer provided by the most frequently visited Web pages is of variable quality and in general difficult to read and understand. The relationship between the quality and readability remains unclear. Health care providers should direct their patients to reliable material online because patients consider the Internet as an important source of information.

Applying a machine learning model using a locally preserving projection based feature regeneration algorithm to predict breast cancer risk

Science.gov (United States)

Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qian, Wei; Zheng, Bin

2018-03-01

Both conventional and deep machine learning has been used to develop decision-support tools applied in medical imaging informatics. In order to take advantages of both conventional and deep learning approach, this study aims to investigate feasibility of applying a locally preserving projection (LPP) based feature regeneration algorithm to build a new machine learning classifier model to predict short-term breast cancer risk. First, a computer-aided image processing scheme was used to segment and quantify breast fibro-glandular tissue volume. Next, initially computed 44 image features related to the bilateral mammographic tissue density asymmetry were extracted. Then, an LLP-based feature combination method was applied to regenerate a new operational feature vector using a maximal variance approach. Last, a k-nearest neighborhood (KNN) algorithm based machine learning classifier using the LPP-generated new feature vectors was developed to predict breast cancer risk. A testing dataset involving negative mammograms acquired from 500 women was used. Among them, 250 were positive and 250 remained negative in the next subsequent mammography screening. Applying to this dataset, LLP-generated feature vector reduced the number of features from 44 to 4. Using a leave-onecase-out validation method, area under ROC curve produced by the KNN classifier significantly increased from 0.62 to 0.68 (p breast cancer detected in the next subsequent mammography screening.

Readability Comparison of Pro- and Anti-Cancer Screening Online Messages in Japan

Science.gov (United States)

Okuhara, Tsuyoshi; Ishikawa, Hirono; Okada, Masahumi; Kato, Mio; Kiuchi, Takahiro

2016-01-01

Background: Cancer screening rates are lower in Japan than those in western countries. Health professionals publish pro-cancer screening messages on the internet to encourage audiences to undergo cancer screening. However, the information provided is often difficult to read for lay persons. Further, anti-cancer screening activists warn against cancer screening with messages on the Internet. We aimed to assess and compare the readability of pro- and anti-cancer screening online messages in Japan using a measure of readability. Methods: We conducted web searches at the beginning of September 2016 using two major Japanese search engines (Google.jp and Yahoo!.jp). The included websites were classified as “anti”, “pro”, or “neutral” depending on the claims, and “health professional” or “non-health professional” depending on the writers. Readability was determined using a validated measure of Japanese readability. Statistical analysis was conducted using two-way ANOVA. Results: In the total 159 websites analyzed, anti-cancer screening online messages were generally easier to read than pro-cancer screening online messages, Messages written by health professionals were more difficult to read than those written by non-health professionals. Claim × writer interaction was not significant. Conclusion: When health professionals prepare pro-cancer screening materials for publication online, we recommend they check for readability using readability assessment tools and improve text for easy comprehension when necessary. PMID:28125867

The Readability of AAOS Patient Education Materials: Evaluating the Progress Since 2008.

Science.gov (United States)

Roberts, Heather; Zhang, Dafang; Dyer, George S M

2016-09-07

The Internet has become a major resource for patients; however, patient education materials are frequently written at relatively high levels of reading ability. The purpose of this study was to evaluate the readability of patient education materials on the American Academy of Orthopaedic Surgeons (AAOS) web site. Readability scores were calculated for all patient education articles on the AAOS web site using 5 algorithms: Flesch Reading Ease, Flesch-Kincaid Grade Level, SMOG (Simple Measure of Gobbledygook) Grade, Coleman-Liau Index, and Gunning-Fog Index. The mean readability scores were compared across the anatomic categories to which they pertained. Using a liberal measure of readability, the Flesch-Kincaid Grade Level, 3.9% of articles were written at or below the recommended sixth-grade reading level, and 84% of the articles were written above the eighth-grade reading level. Articles in the present study had a lower mean Flesch-Kincaid Grade Level than those available in 2008 (p readability levels of AAOS articles are higher than generally recommended. Although the mean Flesch-Kincaid Grade Level was lower in the present study than it was in 2008, a need remains to improve the readability of AAOS patient education articles. Ensuring that online patient education materials are written at an appropriate reading grade level would be expected to improve physician-patient communication. Copyright © 2016 by The Journal of Bone and Joint Surgery, Incorporated.

Quality and readability assessment of websites related to recurrent respiratory papillomatosis.

Science.gov (United States)

San Giorgi, Michel R M; de Groot, Olivier S D; Dikkers, Frederik G

2017-10-01

Recurrent respiratory papillomatosis (RRP) is a rare disease for which a limited number of information sources for patients exist. The role of the Internet in the patient-physician relationship is increasing. More and more patients search for online health information, which should be of good quality and easy readable. The study aim was to investigate the quality and readability of English online health information about RRP. Quality and readability assessment of online information. Relevant information was collected using three different search engines and seven different search terms. Quality was assessed with the DISCERN instrument. The Flesch Reading Ease Score (FRES) and average grade level (AGL) were determined to measure readability of the English websites. Fifty-one English websites were included. The mean DISCERN score of the websites is 28.1 ± 9.7 (poor quality); the mean FRES is 41.3 ± 14.9 (difficult to read); and the mean AGL is 12.6 ± 2.3. The quality and readability of English websites about RRP is alarmingly poor. NA. Laryngoscope, 127:2293-2297, 2017. © 2017 The Authors The Laryngoscope published by Wiley Periodicals, Inc. on behalf of American Laryngological, Rhinological and Otological Society Inc, “The Triological Society” and American Laryngological Association (ALA).

Readability of Spine-Related Patient Education Materials From Leading Orthopedic Academic Centers.

Science.gov (United States)

Ryu, Justine H; Yi, Paul H

2016-05-01

Cross-sectional analysis of online spine-related patient education materials from leading academic centers. To assess the readability levels of spine surgery-related patient education materials available on the websites of academic orthopedic surgery departments. The Internet is becoming an increasingly popular resource for patient education. Yet many previous studies have found that Internet-based orthopedic-related patient education materials from subspecialty societies are written at a level too difficult for the average American; however, no prior study has assessed the readability of spine surgery-related patient educational materials from leading academic centers. All spine surgery-related articles from the online patient education libraries of the top five US News & World Report-ranked orthopedic institutions were assessed for readability using the Flesch-Kincaid (FK) readability test. Mean readability levels of articles amongst the five academic institutions and articles were compared. We also determined the number of articles with readability levels at or below the recommended sixth- or eight-grade levels. Intraobserver and interobserver reliability of readability assessment were assessed. A total of 122 articles were reviewed. The mean overall FK grade level was 11.4; the difference in mean FK grade level between each department varied significantly (range, 9.3-13.4; P Online patient education materials related to spine from academic orthopedic centers are written at a level too high for the average patient, consistent with spine surgery-related patient education materials provided by the American Academy of Orthopaedic Surgeons and spine subspecialty societies. This study highlights the potential difficulties patients might have in reading and comprehending the information in publicly available education materials related to spine. N/A.

Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines.

Science.gov (United States)

Morisi, Rita; Manners, David Neil; Gnecco, Giorgio; Lanconelli, Nico; Testa, Claudia; Evangelisti, Stefania; Talozzi, Lia; Gramegna, Laura Ludovica; Bianchini, Claudio; Calandra-Buonaura, Giovanna; Sambati, Luisa; Giannini, Giulia; Cortelli, Pietro; Tonon, Caterina; Lodi, Raffaele

2018-02-01

In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classification), based on a set of binary classifiers that discriminate each disorder from all others. We combine diffusion tensor imaging, proton spectroscopy and morphometric-volumetric data to obtain MR quantitative markers, which are provided to support vector machines with the aim of recognizing the different parkinsonian disorders. Feature selection is used to find the most important features for classification. We also exploit a graph-based technique on the set of quantitative markers to extract additional features from the dataset, and increase classification accuracy. When graph-based features are not used, the MR markers that are most frequently automatically extracted by the feature selection procedure reflect alterations in brain regions that are also usually considered to discriminate parkinsonisms in routine clinical practice. Graph-derived features typically increase the diagnostic accuracy, and reduce the number of features required. The results obtained in the work demonstrate that support vector machines applied to multimodal brain MR imaging and using graph-based features represent a novel and highly accurate approach to discriminate parkinsonisms, and a useful tool to assist the diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Readability evaluation of Internet-based patient education materials related to the anesthesiology field.

Science.gov (United States)

De Oliveira, Gildasio S; Jung, Michael; Mccaffery, Kirsten J; McCarthy, Robert J; Wolf, Michael S

2015-08-01

The main objective of the current investigation was to assess the readability of Internet-based patient education materials related to the field of anesthesiology. We hypothesized that the majority of patient education materials would not be written according to current recommended readability grade level. Online patient education materials describing procedures, risks, and management of anesthesia-related topics were identified using the search engine Google (available at www.google.com) using the terms anesthesia, anesthesiology, anesthesia risks, and anesthesia care. Cross-sectional evaluation. None. Assessments of content readability were performed using validated instruments (Flesch-Kincaid Grade Formulae, the Gunning Frequency of Gobbledygook, the New Dale-Chall Test, the Fry graph, and the Flesch Reading Ease score). Ninety-six Web sites containing Internet patient education materials (IPEMs) were evaluated. The median (interquartile range) readability grade level for all evaluated IPEMs was 13.5 (12.0-14.6). All the evaluated documents were classified at a greater readability level than the current recommended readability grade, P Internet-based patient education materials related to the field of anesthesiology are currently written far above the recommended readability grade level. High complexity of written education materials likely limits access of information to millions of American patients. Redesign of online content of Web sites that provide patient education material regarding anesthesia could be an important step in improving access to information for patients with poor health literacy. Copyright © 2015 Elsevier Inc. All rights reserved.

Assessing the readability of ClinicalTrials.gov.

Science.gov (United States)

Wu, Danny T Y; Hanauer, David A; Mei, Qiaozhu; Clark, Patricia M; An, Lawrence C; Proulx, Joshua; Zeng, Qing T; Vydiswaran, V G Vinod; Collins-Thompson, Kevyn; Zheng, Kai

2016-03-01

ClinicalTrials.gov serves critical functions of disseminating trial information to the public and helping the trials recruit participants. This study assessed the readability of trial descriptions at ClinicalTrials.gov using multiple quantitative measures. The analysis included all 165,988 trials registered at ClinicalTrials.gov as of April 30, 2014. To obtain benchmarks, the authors also analyzed 2 other medical corpora: (1) all 955 Health Topics articles from MedlinePlus and (2) a random sample of 100,000 clinician notes retrieved from an electronic health records system intended for conveying internal communication among medical professionals. The authors characterized each of the corpora using 4 surface metrics, and then applied 5 different scoring algorithms to assess their readability. The authors hypothesized that clinician notes would be most difficult to read, followed by trial descriptions and MedlinePlus Health Topics articles. Trial descriptions have the longest average sentence length (26.1 words) across all corpora; 65% of their words used are not covered by a basic medical English dictionary. In comparison, average sentence length of MedlinePlus Health Topics articles is 61% shorter, vocabulary size is 95% smaller, and dictionary coverage is 46% higher. All 5 scoring algorithms consistently rated CliniclTrials.gov trial descriptions the most difficult corpus to read, even harder than clinician notes. On average, it requires 18 years of education to properly understand these trial descriptions according to the results generated by the readability assessment algorithms. Trial descriptions at CliniclTrials.gov are extremely difficult to read. Significant work is warranted to improve their readability in order to achieve CliniclTrials.gov's goal of facilitating information dissemination and subject recruitment. Published by Oxford University Press on behalf of the American Medical Informatics Association 2015. This work is written by US Government

Machine parameters and characteristic features

International Nuclear Information System (INIS)

Le Duff, J.

1979-01-01

The design and operating characteristics of LEP are presented. Its probable performance, possible improvements and cost are discussed and some comparisons are drawn with machines currently in operation. (W.D.L.)

How Readable Are Parenting Books?

Science.gov (United States)

Abram, Marie J.; Dowling, William D.

1979-01-01

The author's style of writing has implications for the ease with which the written material can be read. Using the Flesch Reading Ease Formula, the mean readability score, the standard deviation, and range are given for 50 parenting books. Discussion suggests how the list might be used by parent educators. (Author)

Readability assessment of online urology patient education materials.

Science.gov (United States)

Colaco, Marc; Svider, Peter F; Agarwal, Nitin; Eloy, Jean Anderson; Jackson, Imani M

2013-03-01

The National Institutes of Health, American Medical Association, and United States Department of Health and Human Services recommend that patient education materials be written at a fourth to sixth grade reading level to facilitate comprehension. We examined and compared the readability and difficulty of online patient education materials from the American Urological Association and academic urology departments in the Northeastern United States. We assessed the online patient education materials for difficulty level with 10 commonly used readability assessment tools, including the Flesch Reading Ease Score, Flesch-Kincaid Grade Level, Simple Measure of Gobbledygook, Gunning Frequency of Gobbledygook, New Dale-Chall Test, Coleman-Liau index, New Fog Count, Raygor Readability Estimate, FORCAST test and Fry score. Most patient education materials on the websites of these programs were written at or above the eleventh grade reading level. Urological online patient education materials are written above the recommended reading level. They may need to be simplified to facilitate better patient understanding of urological topics. Copyright © 2013 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Pylinguistics: an open source library for readability assessment of texts written in Portuguese

Directory of Open Access Journals (Sweden)

Castilhos, S.

2016-12-01

Full Text Available Readability assessment is an important task in automatic text simplification that aims identify the text complexity by computing a set of metrics. In this paper, we present the development and assessment of an open source library called Pylinguistics to readability assessment of texts written in Portuguese. Additionally, to illustrate the possibilities of our tool, this work also presents an empirical analysis of readability of Brazilian scientific news dissemination.

Readability of patient education materials in ophthalmology: a single-institution study and systematic review.

Science.gov (United States)

Williams, Andrew M; Muir, Kelly W; Rosdahl, Jullia A

2016-08-03

Patient education materials should be written at a level that is understandable for patients with low health literacy. The aims of this study are (1) to review the literature on readability of ophthalmic patient education materials and (2) to evaluate and revise our institution's patient education materials about glaucoma using evidence-based guidelines on writing for patients with low health literacy. A systematic search was conducted on the PubMed/MEDLINE database for studies that have evaluated readability level of ophthalmic patient education materials, and the reported readability scores were assessed. Additionally, we collected evidence-based guidelines for writing easy-to-read patient education materials, and these recommendations were applied to revise 12 patient education handouts on various glaucoma topics at our institution. Readability measures, including Flesch-Kincaid Grade Level (FKGL), and word count were calculated for the original and revised documents. The original and revised versions of the handouts were then scored in random order by two glaucoma specialists using the Suitability Assessment of Materials (SAM) instrument, a grading scale used to evaluate suitability of health information materials for patients. Paired t test was used to analyze changes in readability measures, word count, and SAM score between original and revised handouts. Finally, five glaucoma patients were interviewed to discuss the revised materials, and patient feedback was analyzed qualitatively. Our literature search included 13 studies that evaluated a total of 950 educational materials. Among the mean FKGL readability scores reported in these studies, the median was 11 (representing an eleventh-grade reading level). At our institution, handouts' readability averaged a tenth-grade reading level (FKGL = 10.0 ± 1.6), but revising the handouts improved their readability to a sixth-grade reading level (FKGL = 6.4 ± 1.2) (p readability and suitability of

Study of Machine-Learning Classifier and Feature Set Selection for Intent Classification of Korean Tweets about Food Safety

Directory of Open Access Journals (Sweden)

Yeom, Ha-Neul

2014-09-01

Full Text Available In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

Science.gov (United States)

Linn, Kristin A; Gaonkar, Bilwaj; Satterthwaite, Theodore D; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T

2016-05-15

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases. Copyright © 2016 Elsevier Inc. All rights reserved.

The readability and suitability of sexual health promotion leaflets.

Science.gov (United States)

Corcoran, Nova; Ahmad, Fatuma

2016-02-01

To investigate the readability and suitability of sexual health promotion leaflets. Application of SMOG, FRY and SAM tests to assess the readability and suitability of a selection of sexual health leaflets. SMOG and FRY scores illustrate an average reading level of grade 9. SAM scores indicate that 59% of leaflets are superior in design and 41% are average in design. Leaflets generally perform well in the categories of content, literacy demand, typography and layout. They perform poorly in use of graphics, learning stimulation/motivation and cultural appropriateness. Sexual health leaflets have a reading level that is too high. Leaflets perform well on the suitability scores indicating they are reasonably suitable. There are a number of areas where sexual health leaflets could improve their design. Numerous practical techniques are suggested for improving the readability and suitability of sexual health leaflets. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Consent information leaflets – readable or unreadable?

Science.gov (United States)

Graham, Caroline; Reynard, John M; Turney, Benjamin W

2016-01-01

Objective The objective of this article is to assess the readability of leaflets about urological procedures provided by the British Association of Urological Surgeons (BAUS) to evaluate their suitability for providing information. Methods Information leaflets were assessed using three measures of readability: Flesch Reading Ease, Flesch-Kincaid and Simple Measure of Gobbledygook (SMOG) grade formulae. The scores were compared with national literacy statistics. Results Relatively good readability was demonstrated using the Flesch Reading Ease (53.4–60.1) and Flesch-Kincaid Grade Level (6.5–7.6) methods. However, the average SMOG index (14.0–15.0) for each category suggests that the majority of the leaflets are written above the reading level of an 18-year-old. Using national literacy statistics, at least 43% of the population will have significant difficultly understanding the majority of these leaflets. Conclusions The results suggest that comprehension of the leaflets provided by the BAUS is likely to be poor. These leaflets may be used as an adjunct to discussion but it is essential to ensure that all the information necessary to make an informed decision has been conveyed in a way that can be understood by the patient. PMID:27867520

Readability of Online Patient Educational Resources Found on NCI-Designated Cancer Center Web Sites.

Science.gov (United States)

Rosenberg, Stephen A; Francis, David; Hullett, Craig R; Morris, Zachary S; Fisher, Michael M; Brower, Jeffrey V; Bradley, Kristin A; Anderson, Bethany M; Bassetti, Michael F; Kimple, Randall J

2016-06-01

The NIH and Department of Health & Human Services recommend online patient information (OPI) be written at a sixth grade level. We used a panel of readability analyses to assess OPI from NCI-Designated Cancer Center (NCIDCC) Web sites. Cancer.gov was used to identify 68 NCIDCC Web sites from which we collected both general OPI and OPI specific to breast, prostate, lung, and colon cancers. This text was analyzed by 10 commonly used readability tests: the New Dale-Chall Readability Formula, Flesch Reading Ease scale, Flesch-Kinaid Grade Level, FORCAST scale, Fry Readability Graph, Simple Measure of Gobbledygook test, Gunning Frequency of Gobbledygook index, New Fog Count, Raygor Readability Estimate Graph, and Coleman-Liau Index. We tested the hypothesis that the readability of NCIDCC OPI was written at the sixth grade level. Secondary analyses were performed to compare readability of OPI between comprehensive and noncomprehensive centers, by region, and to OPI produced by the American Cancer Society (ACS). A mean of 30,507 words from 40 comprehensive and 18 noncomprehensive NCIDCCs was analyzed (7 nonclinical and 3 without appropriate OPI were excluded). Using a composite grade level score, the mean readability score of 12.46 (ie, college level: 95% CI, 12.13-12.79) was significantly greater than the target grade level of 6 (middle-school: Preadability metrics (P<.05). ACS OPI provides easier language, at the seventh to ninth grade level, across all tests (P<.01). OPI from NCIDCC Web sites is more complex than recommended for the average patient. Copyright © 2016 by the National Comprehensive Cancer Network.

Readability assessment of package inserts of biological medicinal products from the European medicines agency website.

Science.gov (United States)

Piñero-López, Ma Ángeles; Modamio, Pilar; Lastra, Cecilia F; Mariño, Eduardo L

2014-07-01

Package inserts that accompany medicines are a common source of information aimed at patients and should match patient abilities in terms of readability. Our objective was to determine the degree of readability of the package inserts for biological medicinal products commercially available in 2007 and compare them with the readability of the same package inserts in 2010. A total of 33 package inserts were selected and classified into five groups according to the type of medicine: monoclonal antibody-based products, cytokines, therapeutic enzymes, recombinant blood factors and other blood-related products, and recombinant hormones. The package inserts were downloaded from the European Medicines Agency website in 2007 and 2010. Readability was evaluated for the entire text of five of the six sections of the package inserts and for the 'Annex' when there was one. Three readability formulas were used: SMOG (Simple Measure of Gobbledygook) grade, Flesh-Kincaid grade level, and Szigriszt's perspicuity index. No significant differences were found between the readability results for the 2007 package inserts and those from 2010 according to any of the three readability indices studied (p>0.05). However, there were significant differences (preadability scores of the sections of the package inserts in both 2007 and 2010. The readability of the package inserts was above the recommended sixth grade reading level (ages 11-12) and may lead to difficulties of understanding for people with limited literacy. All the sections should be easy to read and, therefore, the readability of the medicine package inserts studied should be improved.

The Readability of Information Literacy Content on Academic Library Web Sites

Science.gov (United States)

Lim, Adriene

2010-01-01

This article reports on a study addressing the readability of content on academic libraries' Web sites, specifically content intended to improve users' information literacy skills. Results call for recognition of readability as an evaluative component of text in order to better meet the needs of diverse user populations. (Contains 8 tables.)

Readability in reading materials selection and coursebook design for college English in China

OpenAIRE

Lu, Zhongshe

2002-01-01

This thesis studies the application of readability in reading materials selection and coursebook design for college English in an EFL context in China. Its aim is to develop rationales which coursebook writers can utilise in selecting materials as texts and as a basis for designing tasks. This study, through a combination of quantitative and qualitative research methods, argues that readability is applicable in the EFL Chinese context, and readability plays a important role in determining...

Using readability, comprehensibility and lexical coverage to ...

African Journals Online (AJOL)

experience academic difficulty in technical subjects such as Accounting. Davison and ...... The readability of managerial accounting and financial management textbooks. .... Principles and Practice in Second Language Acquisition. Available.

A readability comparison of anti- versus pro-influenza vaccination online messages in Japan

Directory of Open Access Journals (Sweden)

Tsuyoshi Okuhara

2017-06-01

When health professionals prepare pro-influenza vaccination materials for publication online, we recommend they check for readability using readability assessment tools and improve the text for easy reading if necessary.

A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

Science.gov (United States)

Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

Readability of the web: a study on 1 billion web pages

NARCIS (Netherlands)

de Heus, Marije; Hiemstra, Djoerd

We have performed a readability study on more than 1 billion web pages. The Automated Readability Index was used to determine the average grade level required to easily comprehend a website. Some of the results are that a 16-year-old can easily understand 50% of the web and an 18-year old can easily

Readability of HIV/AIDS educational materials: the role of the medium of communication, target audience, and producer characteristics.

Science.gov (United States)

Wells, J A

1994-12-01

The reading difficulty of many HIV/AIDS brochures and pamphlets limits their effectiveness. This analysis addresses correlates of readability in 136 HIV/AIDS educational items. Readability is measured using the SMOG Index. The medium of communication is significantly related to readability: comic books and brochures are, on average, more readable than books and pamphlets (10.9 versus 11.9). The target audience also differentiates readability. Materials for HIV antibody test seekers, the general community, and sexually active adults have a more difficult reading grade, averaging 12.1, whereas materials for ethnic minorities average a more readable 9.2. The producer organization's type and location are unrelated to readability, but an AIDS-specific organizational focus correlates with better readability (grade 10.8 vs. 11.8). These findings remain significant in multivariate analysis. The results indicate that brochures and comics are more likely to be comprehended by low-literacy populations, that an understanding of the literacy of target audiences is needed to produce materials with appropriate reading levels, and that policies to influence producer organizations may result in the creation of more readable materials.

Using Readability Tests to Improve the Accuracy of Evaluation Documents Intended for Low-Literate Participants

Science.gov (United States)

Kouame, Julien B.

2010-01-01

Background: Readability tests are indicators that measure how easy a document can be read and understood. Simple, but very often ignored, readability statistics cannot only provide information about the level of difficulty of the readability of particular documents but also can increase an evaluator's credibility. Purpose: The purpose of this…

Web-based information on the treatment of oral leukoplakia - quality and readability.

Science.gov (United States)

Wiriyakijja, Paswach; Fedele, Stefano; Porter, Stephen; Ni Riordain, Richeal

2016-09-01

To categorise the content and assess the quality and readability of the online information regarding the treatment for oral leukoplakia. An online search using the term 'leukoplakia treatment' was carried out on 8th June 2015 using the Google search engine. The content, quality and readability of the first 100 sites were explored. The quality of the web information was assessed using the following tools, the DISCERN instrument and the Journal of the American Medical Association (JAMA) benchmarks for website analysis and the HON seal. Readability was assessed via the Flesch Reading Ease Score. The search strategy generated 357 000 sites on the Google search engine. Due to duplicate links, non-operating links and irrelevant links, a total of 47 of the first 100 websites were included in this study. The mean overall rating achieved by included websites using the DISCERN instrument was 2.3. With regard to the JAMA benchmarks, the vast majority of examined websites (95.7%) completely fulfilled the disclosure benchmark and less than 50% of included websites met the three remaining criteria. A mean total readability score of 47.5 was recorded with almost 90% of websites having a readability level ranging from fairly difficult to very difficult. Based on this study, the online health information regarding oral leukoplakia has challenging readability with content of questionable accuracy. As patients often search for health information online, it would be prudent for clinicians to highlight the caution with which online information should be interpreted. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Assessment of readability, understandability, and completeness of pediatric hospital medicine discharge instructions.

Science.gov (United States)

Unaka, Ndidi I; Statile, Angela; Haney, Julianne; Beck, Andrew F; Brady, Patrick W; Jerardi, Karen E

2017-02-01

The average American adult reads at an 8th-grade level. Discharge instructions written above this level might increase the risk of adverse outcomes for children as they transition from hospital to home. We conducted a cross-sectional study at a large urban academic children's hospital to describe readability levels, understandability scores, and completeness of written instructions given to families at hospital discharge. Two hundred charts for patients discharged from the hospital medicine service were randomly selected for review. Written discharge instructions were extracted and scored for readability (Fry Readability Scale [FRS]), understandability (Patient Education Materials Assessment Tool [PEMAT]), and completeness (5 criteria determined by consensus). Descriptive statistics enumerated the distribution of readability, understandability, and completeness of written discharge instructions. Of the patients included in the study, 51% were publicly insured. Median age was 3.1 years, and median length of stay was 2.0 days. The median readability score corresponded to a 10th-grade reading level (interquartile range, 8-12; range, 1-13). Median PEMAT score was 73% (interquartile range, 64%-82%; range, 45%-100%); 36% of instructions scored below 70%, correlating with suboptimal understandability. The diagnosis was described in only 33% of the instructions. Although explicit warning signs were listed in most instructions, 38% of the instructions did not include information on the person to contact if warning signs developed. Overall, the readability, understandability, and completeness of discharge instructions were subpar. Efforts to improve the content of discharge instructions may promote safe and effective transitions home. Journal of Hospital Medicine 2017;12:98-101. © 2017 Society of Hospital Medicine.

A Software Application for Assessing Readability in the Japanese EFL Context

Science.gov (United States)

Ozasa, Toshiaki; Weir, George R. S.; Fukui, Masayasu

2010-01-01

We have been engaged in developing a readability index and its application software attuned for Japanese EFL learners. The index program, Ozasa-Fukui Year Level Program, Ver. 1.0, was used in developing the readability metric Ozasa-Fukui Year Level Index but tended to assume a high level of computer knowledge in its users. As a result, the…

Readability of Air Force Publications: A Criterion Referenced Evaluation. Final Report.

Science.gov (United States)

Hooke, Lydia R.; And Others

In a study of the readability of Air Force regulations, the writer-estimated reading grade level (RGL) for each regulation was rechecked by using the FORCAST readability formula. In four of the seven cases, the regulation writers underestimated the RGL of their regulation by more than one grade level. None of the writers produced a document with…

Readability of Questionnaires Assessing Listening Difficulties Associated with (Central) Auditory Processing Disorders

Science.gov (United States)

Atcherson, Samuel R.; Richburg, Cynthia M.; Zraick, Richard I.; George, Cassandra M.

2013-01-01

Purpose: Eight English-language, student- or parent proxy-administered questionnaires for (central) auditory processing disorders, or (C)APD, were analyzed for readability. For student questionnaires, readability levels were checked against the approximate reading grade levels by intended administration age per the questionnaires' developers. For…

Varying Readability of Science-Based Text in Elementary Readers: Challenges for Teachers

Science.gov (United States)

Gallagher, Tiffany L.; Fazio, Xavier; Gunning, Thomas G.

2012-01-01

This investigation compared readability formulae to publishers' identified reading levels in science-based elementary readers. Nine well-established readability indices were calculated and comparisons were made with the publishers' identified grade designations and between different genres of text. Results revealed considerable variance among the…

Readability of Trauma-Related Patient Education Materials From the American Academy of Orthopaedic Surgeons.

Science.gov (United States)

Eltorai, Adam E M; P Thomas, Nathan; Yang, Heejae; Daniels, Alan H; Born, Christopher T

2016-02-01

According to the american medical association (AMA) and the national institutes of health (NIH), the recommended readability of patient education materials should be no greater than a sixth-grade reading level. The online patient education information produced by the american academy of orthopaedic surgeons (AAOS) may be too complicated for some patients to understand. This study evaluated whether the AAOS's online trauma-related patient education materials meet recommended readability guidelines for medical information. Ninety-nine articles from the "Broken Bones and Injuries" section of the AAOS-produced patient education website, orthoinfo.org, were analyzed for grade level readability using the Flesch-Kincaid formula, a widely-used and validated tool to evaluate the text reading level. Results for each webpage were compared to the AMA/NIH recommended sixth-grade reading level and the average reading level of U.S. adults (eighth-grade). The mean (SD) grade level readability for all patient education articles was 8.8 (1.1). All but three of the articles had a readability score above the sixth-grade level. The readability of the articles exceeded this level by an average of 2.8 grade levels (95% confidence interval, 2.6 - 3.0; P reading skill level of U.S. adults (eighth grade) by nearly an entire grade level (95% confidence interval, 0.6-1.0; P education website have readability levels that may make comprehension difficult for a substantial portion of the patient population.

Sense and readability: participant information sheets for research studies.

Science.gov (United States)

Ennis, Liam; Wykes, Til

2016-02-01

Informed consent in research is partly achieved through the use of information sheets. There is a perception however that these information sheets are long and complex. The recommended reading level for patient information is grade 6, or 11-12 years old. To investigate whether the readability of participant information sheets has changed over time, whether particular study characteristics are related to poorer readability and whether readability and other study characteristics are related to successful study recruitment. Method: We obtained 522 information sheets from the UK National Institute for Health Research Clinical Research Network: Mental Health portfolio database and study principal investigators. Readability was assessed with the Flesch reading index and the Grade level test. Information sheets increased in length over the study period. The mean grade level across all information sheets was 9.8, or 15-16 years old. A high level of patient involvement was associated with more recruitment success and studies involving pharmaceutical or device interventions were the least successful. The complexity of information sheets had little bearing on successful recruitment. Information sheets are far more complex than the recommended reading level of grade 6 for patient information. The disparity may be exacerbated by an increasing focus on legal content. Researchers would benefit from clear guidance from ethics committees on writing succinctly and accessibly and how to balance the competing legal issues with the ability of participants to understand what a study entails. © The Royal College of Psychiatrists 2016.

[Systematic analysis of the readability of patient information on the websites of clinics for plastic surgery].

Science.gov (United States)

Esfahani, B Janghorban; Faron, A; Roth, K S; Schaller, H-E; Medved, F; Lüers, J-C

2014-12-01

The Internet is becoming increasing-ly important as a source of information for patients in medical issues. However, many patients have problems to adequately understand texts, especially with medical content. A basic requirement to understand a written text is the read-ability of a text. The aim of the present study was to examine texts on the websites of German -plastic-surgical hospitals with patient information regarding their readability. In this study, the read-ability of texts of 27 major departments of plastic and Hand surgery in Germany was systematically analysed using 5 recognised readability indices. First, texts were searched based on 20 representative key words and themes. Thereafter, texts were assigned to one of 3 major themes in order to enable statistical analysis. In addition to the 5 readability indices, further objective text parameters were also recorded. Overall, 288 texts were found for analyzation. Most articles were found on the topic of "handsurgery" (n=124), less were found for "facial plastic surgery" (n=80) and "flaps, breast and reconstructive surgery" (n=84). Consistently, all readability indices showed a poor readability for the vast majority of analysed texts with the text appearing readable only for readers with a higher educational level. No significant differences in readability were found between the 3 major themes. Especially in the communication of medical information, it is important to consider the knowledge and education of the addressee. The texts studied consistently showed a readability that is understandable only for academics. Thus, a large part of the intended target group is probably not reached. In order to adequately deliver online information material, a revision of the analysed internet texts appears to be recommendable. © Georg Thieme Verlag KG Stuttgart · New York.

The readability of scientific texts is decreasing over time

Science.gov (United States)

2017-01-01

Clarity and accuracy of reporting are fundamental to the scientific process. Readability formulas can estimate how difficult a text is to read. Here, in a corpus consisting of 709,577 abstracts published between 1881 and 2015 from 123 scientific journals, we show that the readability of science is steadily decreasing. Our analyses show that this trend is indicative of a growing use of general scientific jargon. These results are concerning for scientists and for the wider public, as they impact both the reproducibility and accessibility of research findings. PMID:28873054

EEG machine learning with Higuchi fractal dimension and Sample Entropy as features for successful detection of depression

OpenAIRE

Cukic, Milena; Pokrajac, David; Stokic, Miodrag; Simic, slobodan; Radivojevic, Vlada; Ljubisavljevic, Milos

2018-01-01

Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. In this study, we aimed to elucidate the effectiveness of two non-linear measures, Higuchi Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applied on EEG. HFD and SampEn of EEG signals were used as features for seven machine learning algorithms including Multilayer Perceptron, Logistic Regression, Support Vector Machines with the linea...

Readability of Patient Education Materials in Hand Surgery and Health Literacy Best Practices for Improvement.

Science.gov (United States)

Hadden, Kristie; Prince, Latrina Y; Schnaekel, Asa; Couch, Cory G; Stephenson, John M; Wyrick, Theresa O

2016-08-01

This study aimed to update a portion of a 2008 study of patient education materials from the American Society for Surgery of the Hand Web site with new readability results, to compare the results to health literacy best practices, and to make recommendations to the field for improvement. A sample of 77 patient education documents were downloaded from the American Society for Surgery of the Hand Web site, handcare.org, and assessed for readability using 4 readability tools. Mean readability grade-level scores were derived. Best practices for plain language for written health materials were compiled from 3 government agency sources. The mean readability of the 77 patient education documents in the study was 9.3 grade level. This reading level is reduced from the previous study in 2008 in which the overall mean was 10.6; however, the current sample grade level still exceeds recommended readability according to best practices. Despite a small body of literature on the readability of patient education materials related to hand surgery and other orthopedic issues over the last 7 years, readability was not dramatically improved in our current sample. Using health literacy as a framework, improvements in hand surgery patient education may result in better understanding and better outcomes for patients seeing hand surgeons. Improved understanding of patient education materials related to hand surgery may improve preventable negative outcomes that are clinically significant as well as contribute to improved quality of life for patients. Copyright © 2016 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.

Machine vision systems using machine learning for industrial product inspection

Science.gov (United States)

Lu, Yi; Chen, Tie Q.; Chen, Jie; Zhang, Jian; Tisler, Anthony

2002-02-01

Machine vision inspection requires efficient processing time and accurate results. In this paper, we present a machine vision inspection architecture, SMV (Smart Machine Vision). SMV decomposes a machine vision inspection problem into two stages, Learning Inspection Features (LIF), and On-Line Inspection (OLI). The LIF is designed to learn visual inspection features from design data and/or from inspection products. During the OLI stage, the inspection system uses the knowledge learnt by the LIF component to inspect the visual features of products. In this paper we will present two machine vision inspection systems developed under the SMV architecture for two different types of products, Printed Circuit Board (PCB) and Vacuum Florescent Displaying (VFD) boards. In the VFD board inspection system, the LIF component learns inspection features from a VFD board and its displaying patterns. In the PCB board inspection system, the LIF learns the inspection features from the CAD file of a PCB board. In both systems, the LIF component also incorporates interactive learning to make the inspection system more powerful and efficient. The VFD system has been deployed successfully in three different manufacturing companies and the PCB inspection system is the process of being deployed in a manufacturing plant.

Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer

Directory of Open Access Journals (Sweden)

Alim Samat

2016-03-01

Full Text Available In order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA in the context of hyperspectral image classification via a geodesic Gaussian flow kernel based support vector machine (GFKSVM. To show the superior performance of the proposed approach, conventional support vector machines (SVMs and state-of-the-art DA algorithms, including information-theoretical learning of discriminative cluster for domain adaptation (ITLDC, joint distribution adaptation (JDA, and joint transfer matching (JTM, are also considered. Additionally, unsupervised linear and nonlinear subspace feature transfer techniques including principal component analysis (PCA, randomized nonlinear principal component analysis (rPCA, factor analysis (FA and non-negative matrix factorization (NNMF are investigated and compared. Experiments on two real hyperspectral images show the cross-image classification performances of the GFKSVM, confirming its effectiveness and suitability when applied to hyperspectral images.

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

Science.gov (United States)

Hussain, Lal

2018-06-01

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

Can Readability Formulas Be Used to Successfully Gauge Difficulty of Reading Materials?

Science.gov (United States)

Begeny, John C.; Greene, Diana J.

2014-01-01

A grade level of reading material is commonly estimated using one or more readability formulas, which purport to measure text difficulty based on specified text characteristics. However, there is limited direction for teachers and publishers regarding which readability formulas (if any) are appropriate indicators of actual text difficulty. Because…

A Comparison of Readability in Science-Based Texts: Implications for Elementary Teachers

Science.gov (United States)

Gallagher, Tiffany; Fazio, Xavier; Ciampa, Katia

2017-01-01

Science curriculum standards were mapped onto various texts (literacy readers, trade books, online articles). Statistical analyses highlighted the inconsistencies among readability formulae for Grades 2-6 levels of the standards. There was a lack of correlation among the readability measures, and also when comparing different text sources. Online…

Readability levels of health pamphlets distributed in hospitals and health centres in Athens, Greece.

Science.gov (United States)

Kondilis, B K; Akrivos, P D; Sardi, T A; Soteriades, E S; Falagas, M E

2010-10-01

Health literacy is important in the medical and social sciences due to its impact on behavioural and health outcomes. Nevertheless, little is known about it in Greece, including patients' level of understanding health brochures and pamphlets distributed in Greek hospitals and clinics. Observational study in the greater metropolitan area of Athens, Greece. Pamphlets and brochures written in the Greek language were collected from 17 hospitals and healthcare centres between the spring and autumn of 2006. Readability of pamphlets using the Flesch-Kincaid, Simple Measure of Gobbledygook (SMOG) and Fog methods was calculated based on a Greek readability software. Out of 70 pamphlets collected from 17 hospitals, 37 pamphlets met the criteria for the study. The average readability level of all scanned pamphlets was ninth to 10th grade, corresponding to a readability level of 'average'. A highly significant difference (PPamphlets from private hospitals were one grade more difficult than those from public hospitals. Approximately 43.7% of the Greek population aged ≥20 years would not be able to comprehend the available pamphlets, which were found to have an average readability level of ninth to 10th grade. Further research examining readability levels in the context of health literacy in Greece is warranted. This effort paves the way for additional research in the field of readability levels of health pamphlets in the Greek language, the sources of health information, and the level of understanding of key health messages by the population. Copyright © 2010 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Readability of patient information and consent documents in rheumatological studies

DEFF Research Database (Denmark)

Hamnes, Bente; van Eijk-Hustings, Yvonne; Primdahl, Jette

2016-01-01

BACKGROUND: Before participation in medical research an informed consent must be obtained. This study investigates whether the readability of patient information and consent documents (PICDs) corresponds to the average educational level of participants in rheumatological studies in the Netherlands......, Denmark, and Norway. METHODS: 24 PICDs from studies were collected and readability was assessed independently using the Gunning's Fog Index (FOG) and Simple Measure of Gobbledygook (SMOG) grading. RESULTS: The mean score for the FOG and SMOG grades were 14.2 (9.0-19.0) and 14.2 (12-17) respectively....... The mean FOG and SMOG grades were 12.7 and 13.3 in the Dutch studies, 15.0 and 14.9 in the Danish studies, and 14.6 and 14.3 in the Norwegian studies, respectively. Out of the 2865 participants, more than 57 % had a lower educational level than the highest readability score calculated in the individual...

Evaluating the Readability of Radio Frequency Identification for Construction Materials

Directory of Open Access Journals (Sweden)

Younghan Jung

2017-01-01

Full Text Available Radio Frequency Identification (RFID, which was originally introduced to improve material handling and speed production as part of supply chain management, has become a globally accepted technology that is now applied on many construction sites to facilitate real-time information visibility and traceability. This paper describes a senior undergraduate project for a Construction Management (CM program that was specifically designed to give the students a greater insight into technical research in the CM area. The students were asked to determine whether it would be possible to utilize an RFID system capable of tracking tagged equipment, personnel and materials across an entire construction site. This project required them to set up an experimental program, execute a series of experiments, analyze the results and summarize them in a report. The readability test was performed using an active Ultra-High frequency (UHF, 433.92 MHz RFID system with various construction materials, including metal, concrete, wood, plastic, and aluminum. The readability distance distances are measured for each of the six scenarios. The distance at which a tag was readable with no obstructions was found to be an average of 133.9m based on three measurements, with a standard deviation of 3.9m. This result confirms the manufacturer’s claimed distance of 137.2m. The RFID tag embedded under 50.8mm of concrete was readable for an average distance of only 12.2m, the shortest readable distance of any of the scenarios tested. At the end of the semester, faculty advisors held an open discussion session to gather feedback and elicit the students’ reflections on their research experiences, revealing that the students’ overall impressions of their undergraduate research had positively affected their postgraduate education plans.

Readability assessment of online thyroid surgery patient education materials.

Science.gov (United States)

Patel, Chirag R; Cherla, Deepa V; Sanghvi, Saurin; Baredes, Soly; Eloy, Jean Anderson

2013-10-01

Published guidelines recommend written health information be written at or below the sixth-grade level. We evaluate the readability of online materials related to thyroid surgery. Thyroid surgery materials were evaluated using Flesch Reading Ease Score (FRES), Flesch Kincaid Grade Level (FKGL), Gunning Frequency of Gobbledygook (GFOG), and Simple Measure of Gobbledygook (SMOG). Thirty-one documents were evaluated. FRES scores ranged from 29.3 to 67.8 (possible range = 0 to 100), and averaged 50.5. FKGL ranged from 6.9 to 14.9 (possible range = 3 to 12), and averaged 10.4. SMOG scores ranged from 11.8 to 14.5 (possible range = 3 to 19), and averaged 13.0. GFOG scores ranged from 10.6 to 18.0 (possible range = 3 to 19), and averaged 13.5. Readability scores for online thyroid surgery materials are higher (i.e., more difficult) than the recommended levels. However, readability is only one aspect of comprehension. Written information should be designed with that fact in mind. Copyright © 2013 Wiley Periodicals, Inc.

Analysis of the readability of patient education materials from surgical subspecialties.

Science.gov (United States)

Hansberry, David R; Agarwal, Nitin; Shah, Ravi; Schmitt, Paul J; Baredes, Soly; Setzen, Michael; Carmel, Peter W; Prestigiacomo, Charles J; Liu, James K; Eloy, Jean Anderson

2014-02-01

Patients are increasingly using the Internet as a source of information on medical conditions. Because the average American adult reads at a 7th- to 8th-grade level, the National Institutes of Health recommend that patient education material be written between a 4th- and 6th-grade level. In this study, we assess and compare the readability of patient education materials on major surgical subspecialty Web sites relative to otolaryngology. Descriptive and correlational design. Patient education materials from 14 major surgical subspecialty Web sites (American Society of Colon and Rectal Surgeons, American Association of Endocrine Surgeons, American Society of General Surgeons, American Society for Metabolic and Bariatric Surgery, American Association of Neurological Surgeons, American Congress of Obstetricians and Gynecologists, American Academy of Ophthalmology, American Academy of Orthopedic Surgeons, American Academy of Otolaryngology-Head and Neck Surgery, American Pediatric Surgical Association, American Society of Plastic Surgeons, Society for Thoracic Surgeons, and American Urological Association) were downloaded and assessed for their level of readability using 10 widely accepted readability scales. The readability level of patient education material from all surgical subspecialties was uniformly too high. Average readability levels across all subspecialties ranged from the 10th- to 15th-grade level. Otolaryngology and other surgical subspecialties Web sites have patient education material written at an education level that the average American may not be able to understand. To reach a broader population of patients, it might be necessary to rewrite patient education material at a more appropriate level. N/A. © 2013 The American Laryngological, Rhinological and Otological Society, Inc.

Readability of pediatric otolaryngology information by children's hospitals and academic institutions.

Science.gov (United States)

Wong, Kevin; Levi, Jessica R

2017-04-01

Evaluate the readability of pediatric otolaryngology-related patient education materials from leading online sources. Cross-sectional analysis. All pediatric otolaryngology-related articles from the online patient health libraries of the top 10 US News & World Report-ranked children's hospitals, top 5 Doximity-ranked pediatric otolaryngology fellowships, and the American Academy of Otolaryngology-Head and Neck Surgery were collected. Each article was copied in plain text format into a blank document. Web page navigation, appointment information, references, author information, appointment information, acknowledgements, and disclaimers were removed. Follow-up editing was also performed to remove paragraph breaks, colons, semicolons, numbers, percentages, and bullets. Readability grade was calculated using the Flesch-Kincaid Grade Level, Flesch Reading Ease Score, Gunning-Fog Index, Coleman-Liau Index, Automated Readability Index, and Simple Measure of Gobbledygook. Intraobserver and interobserver reliability were assessed. A total of 502 articles were analyzed. Intraobserver and interobserver reliability were both excellent, with an intraclass correlation coefficient of 0.99 and 0.96, respectively. The average readability grade across all authorships and readability assessments exceeded the reading ability of the average American adult. Only 142 articles (28.3%) were written at or below the reading ability of the average American adult, whereas the remaining 360 articles (71.7%) were written above the reading level of the average adult. Current online health information related to pediatric otolaryngology may be too difficult for the average reader to understand. Revisions may be necessary for current materials to benefit a larger readership. NA Laryngoscope, 127:E138-E144, 2017. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

Readability of patient education materials available at the point of care.

Science.gov (United States)

Stossel, Lauren M; Segar, Nora; Gliatto, Peter; Fallar, Robert; Karani, Reena

2012-09-01

Many patient education materials (PEMs) available on the internet are written at high school or college reading levels, rendering them inaccessible to the average US resident, who reads at or below an 8(th) grade level. Currently, electronic health record (EHR) providers partner with companies that produce PEMs, allowing clinicians to access PEMs at the point of care. To assess the readability of PEMs provided by a popular EHR vendor as well as the National Library of Medicine (NLM). We included PEMs from Micromedex, EBSCO, and MedlinePlus. Micromedex and EBSCO supply PEMs to Meditech, a popular EHR supplier in the US. MedlinePlus supplies the NLM. These PEM databases have high market penetration and accessibility. Grade reading level of the PEMs was calculated using three validated indices: Simple Measure of Gobbledygook (SMOG), Gunning Fog (GFI), and Flesch-Kincaid (FKI). The percentage of documents above target readability and average readability scores from each database were calculated. We randomly sampled 100 disease-matched PEMs from three databases (n = 300 PEMs). Depending on the readability index used, 30-100% of PEMs were written above the 8(th) grade level. The average reading level for MedlinePlus, EBSCO, and Micromedex PEMs was 10.2 (1.9), 9.7 (1.3), and 8.6 (0.9), respectively (p ≤ 0.000) as estimated by the GFI. Estimates of readability using SMOG and FKI were similar. The majority of PEMS available through the NLM and a popular EHR were written at reading levels considerably higher than that of the average US adult.

The Readability of Malaysian English Children Books: A Multilevel Analysis

Directory of Open Access Journals (Sweden)

Adlina Ismail

2016-11-01

Full Text Available These days, there are more English books for children published by local publishers in Malaysia. It is a positive development because the books will be more accessible to the children. However, the books have never been studied and evaluated in depth yet. One important factor in assessing reading materials is readability. Readability determines whether a text is easy or difficult to understand and a balanced mix of both can promote learning and language development. Various researchers mentioned a multilevel framework of discourse that any language assessment on a text should take into account. The levels that were proposed were word, syntax, textbase, situation model and genre and rhetorical structures. Traditional readability measures such as Flesh Reading Ease Formula, Gunning Readability Index, Fog Count, and Fry Grade Level are not able to address the multilevel because they are based on shallow variables. In contrast, Coh-metrix TERA provided five indices that are correlated to grade level and aligned to the multilevel framework. This study analyzed ten Malaysian English chapter books for children using this Coh-metrix TERA. The result revealed that the Malaysian English children books were easy in shallow level but there was a possible difficulty in textbase and situation model level because of the lack of cohesion. In conclusion, more attention should be given on deeper level of text rather than just word and syntax level.

Readability of Healthcare Literature for Hepatitis B and C.

Science.gov (United States)

Meillier, Andrew; Patel, Shyam; Al-Osaimi, Abdullah M S

2015-12-01

Patients increasingly use the Internet for educational material concerning health and diseases. This information can be utilized to teach the population of hepatitis B and C if properly written at the necessary grade level of the intended patient population. We explored the readability of online resources concerning hepatitis B and C. Google searches were performed for "Hepatitis B" and "Hepatitis C." The Internet resources that were intended for patient education were used with specific exclusions. Articles were taken from 19 and 23 different websites focusing on the symptoms, diagnosis, and treatment of hepatitis B and C, respectively. The articles were analyzed using Readability Studio Professional Edition (Oleander Solutions, Vandalia, OH) using 10 different readability scales. The results were compared and averaged to identify the anticipated academic grade level required to understand the information. The average readability scores of the 10 scales had ranges of 9.7-16.4 for hepatitis B and 9.2-16.4 for hepatitis C. The average academic reading grade level for hepatitis B was 12.6 ± 2.1 and for hepatitis C was 12.7 ± 2.1. There was no significant discrepancy between the hepatitis B and C Internet resource averaged grade levels. The resources accessed by patients are higher than the previously determined necessary grade level for patients to properly understand the intended information. The American Medical Association recommends material should be simplified to grade levels below the sixth grade level to benefit the ideal proportion of the patient population.

Calisthenics with words: The effect of readability and investor sophistication on investors' performance judgment

OpenAIRE

Cui, Xiao Carol

2016-01-01

Since the 1990s, the SEC has advocated for financial disclosures to be in “plain English” so that they would be more readable and informative. Past research has shown that high readability is related to more extreme investor judgments of firm performance. Processing fluency is the prevalent theory to explain this: higher readability increases the investor’s subconscious reliance on the disclosure, so positive (negative) news leads to more positive (negative) judgments. The relationship may no...

Readability of Healthcare Literature for Gastroparesis and Evaluation of Medical Terminology in Reading Difficulty.

Science.gov (United States)

Meillier, Andrew; Patel, Shyam

2017-02-01

Gastroparesis is a chronic condition that can be further enhanced with patient understanding. Patients' education resources on the Internet have become increasingly important in improving healthcare literacy. We evaluated the readability of online resources for gastroparesis and the influence by medical terminology. Google searches were performed for "gastroparesis", "gastroparesis patient education material" and "gastroparesis patient information". Following, all medical terminology was determined if included on Taber's Medical Dictionary 22nd Edition. The medical terminology was replaced independently with "help" and "helping". Web resources were analyzed with the Readability Studio Professional Edition (Oleander Solutions, Vandalia, OH) using 10 different readability scales. The average of the 26 patient education resources was 12.7 ± 1.8 grade levels. The edited "help" group had 6.6 ± 1.0 and "helping" group had 10.4 ± 2.1 reading levels. In comparing the three groups, the "help" and "helping" groups had significantly lower readability levels (P Medical Association. Medical terminology was shown to be the cause for this elevated readability level with all, but four resources within the recommended grade levels following word replacement.

Computer-readable ''Nuclear Data Sheets''

International Nuclear Information System (INIS)

Ewbank, W.B.

1975-01-01

The evaluated nuclear structure data contained in ''Nuclear Data Sheets'' are available in computer-readable form. Experimentally established properties of nuclear levels are included as well as radiations from nuclear reactions and radioactive decay. Portions of the data can be selected for distribution in several formats on magnetic tape or computer cards. A variety of different listing and drawing formats are also available. 4 figures

Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

Directory of Open Access Journals (Sweden)

Ramesh Kumar Lama

2017-01-01

Full Text Available Alzheimer’s disease (AD is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR images to discriminate AD, mild cognitive impairment (MCI, and healthy control (HC subjects using a support vector machine (SVM, an import vector machine (IVM, and a regularized extreme learning machine (RELM. The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer’s disease neuroimaging initiative (ADNI datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

How Readable Is BPH Treatment Information on the Internet? Assessing Barriers to Literacy in Prostate Health.

Science.gov (United States)

Koo, Kevin; Yap, Ronald L

2017-03-01

Information about benign prostatic hyperplasia (BPH) has become increasingly accessible on the Internet. Though the ability to find such material is encouraging, its readability and impact on informing patient decision making are not known. To evaluate the readability of Internet-based information about BPH in the context of website ownership and Health on the Net certification, three search engines were queried daily for 1 month with BPH-related keywords. Website ownership data and Health on the Net certification status were verified. Three readability analyses were performed: SMOG test, Dale-Chall readability formula, and Fry readability graph. An adjusted SMOG calculation was performed to reduce overestimation from medical jargon. After a total of 270 searches, 52 websites met inclusion criteria. Mean SMOG grade was 10.6 ( SD = 1.4) and 10.2 after adjustment. Mean Dale-Chall score was 9.1 ( SD = 0.6), or Grades 13 to 15. Mean Fry graph coordinates (173 syllables, 5.1 sentences) corresponded to Grade 15. Seven sites (13%) were at or below the average adult reading level based on SMOG; none of the sites qualified based on the other tests. Readability was significantly poorer for academic versus commercial sites and for Health on the Net-certified versus noncertified sites. In conclusion, online information about BPH treatment markedly exceeds the reading comprehension of most U.S. adults. Websites maintained by academic institutions and certified by the Health on the Net standard have more difficult readability. Efforts to improve literacy with respect to urological health should target content readability independent of reliability.

Readability assessment of internet-based patient education materials related to facial fractures.

Science.gov (United States)

Sanghvi, Saurin; Cherla, Deepa V; Shukla, Pratik A; Eloy, Jean Anderson

2012-09-01

Various professional societies, clinical practices, hospitals, and health care-related Web sites provide Internet-based patient education material (IPEMs) to the general public. However, this information may be written above the 6th-grade reading level recommended by the US Department of Health and Human Services. The purpose of this study is to assess the readability of facial fracture (FF)-related IPEMs and compare readability levels of IPEMs provided by four sources: professional societies, clinical practices, hospitals, and miscellaneous sources. Analysis of IPEMs on FFs available on Google.com. The readability of 41 FF-related IPEMs was assessed with four readability indices: Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease Score (FRES), Simple Measure of Gobbledygook (SMOG), and Gunning Frequency of Gobbledygook (Gunning FOG). Averages were evaluated against national recommendations and between each source using analysis of variance and t tests. Only 4.9% of IPEMs were written at or below the 6th-grade reading level, based on FKGL. The mean readability scores were: FRES 54.10, FKGL 9.89, SMOG 12.73, and Gunning FOG 12.98, translating into FF-related IPEMs being written at a "difficult" writing level, which is above the level of reading understanding of the average American adult. IPEMs related to FFs are written above the recommended 6th-grade reading level. Consequently, this information would be difficult to understand by the average US patient. Copyright © 2012 The American Laryngological, Rhinological, and Otological Society, Inc.

Readability of internet-sourced patient education material related to "labour analgesia".

Science.gov (United States)

Boztas, Nilay; Omur, Dilek; Ozbılgın, Sule; Altuntas, Gözde; Piskin, Ersan; Ozkardesler, Sevda; Hanci, Volkan

2017-11-01

We evaluated the readability of Internet-sourced patient education materials (PEMs) related to "labour analgesia." In addition to assessing the readability of websites, we aimed to compare commercial, personal, and academic websites.We used the most popular search engine (http://www.google.com) in our study. The first 100 websites in English that resulted from a search for the key words "labour analgesia" were scanned. Websites that were not in English, graphs, pictures, videos, tables, figures and list formats in the text, all punctuation, the number of words in the text is less than 100 words, feedback forms not related to education, (Uniform Resource Locator) URL websites, author information, references, legal disclaimers, and addresses and telephone numbers were excluded.The texts included in the study were assessed using the Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), and Gunning Frequency of Gobbledygook (FOG) readability formulae. The number of Latin words within the text was determined.Analysis of 300-word sections of the texts revealed that the mean FRES was 47.54 ± 12.54 (quite difficult), mean FKGL and SMOG were 11.92 ± 2.59 and 10.57 ± 1.88 years of education, respectively, and mean Gunning FOG was 14.71 ± 2.76 (very difficult). Within 300-word sections, the mean number of Latin words was identified as 16.56 ± 6.37.In our study, the readability level of Internet-sourced PEM related to "labour analgesia" was identified to be quite high indicating poor readability.

The Effect of Technology-Based Altered Readability Levels on Struggling Readers' Science Comprehension

Science.gov (United States)

Marino, Matthew T.; Coyne, Michael; Dunn, Michael

2010-01-01

This article reports findings from a study examining how altered readability levels affected struggling readers' (N = 288) comprehension of scientific concepts and vocabulary. Specifically, the researchers were interested in learning what effect altered readability levels have when low ability readers participate in a technology-based science…

Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation.

Science.gov (United States)

Pereira, Sérgio; Meier, Raphael; McKinley, Richard; Wiest, Roland; Alves, Victor; Silva, Carlos A; Reyes, Mauricio

2018-02-01

Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially important as these systems are pervasively being introduced to critical domains, such as the medical field. Representation Learning techniques are general methods for automatic feature computation. Nevertheless, these techniques are regarded as uninterpretable "black boxes". In this paper, we propose a methodology to enhance the interpretability of automatically extracted machine learning features. The proposed system is composed of a Restricted Boltzmann Machine for unsupervised feature learning, and a Random Forest classifier, which are combined to jointly consider existing correlations between imaging data, features, and target variables. We define two levels of interpretation: global and local. The former is devoted to understanding if the system learned the relevant relations in the data correctly, while the later is focused on predictions performed on a voxel- and patient-level. In addition, we propose a novel feature importance strategy that considers both imaging data and target variables, and we demonstrate the ability of the approach to leverage the interpretability of the obtained representation for the task at hand. We evaluated the proposed methodology in brain tumor segmentation and penumbra estimation in ischemic stroke lesions. We show the ability of the proposed methodology to unveil information regarding relationships between imaging modalities and extracted features and their usefulness for the task at hand. In both clinical scenarios, we demonstrate that the proposed methodology enhances the interpretability of automatically learned features, highlighting specific learning patterns that resemble how an expert extracts relevant data from medical images. Copyright © 2017 Elsevier B.V. All rights reserved.

A machine vision system for automated non-invasive assessment of cell viability via dark field microscopy, wavelet feature selection and classification

Directory of Open Access Journals (Sweden)

Friehs Karl

2008-10-01

Full Text Available Abstract Background Cell viability is one of the basic properties indicating the physiological state of the cell, thus, it has long been one of the major considerations in biotechnological applications. Conventional methods for extracting information about cell viability usually need reagents to be applied on the targeted cells. These reagent-based techniques are reliable and versatile, however, some of them might be invasive and even toxic to the target cells. In support of automated noninvasive assessment of cell viability, a machine vision system has been developed. Results This system is based on supervised learning technique. It learns from images of certain kinds of cell populations and trains some classifiers. These trained classifiers are then employed to evaluate the images of given cell populations obtained via dark field microscopy. Wavelet decomposition is performed on the cell images. Energy and entropy are computed for each wavelet subimage as features. A feature selection algorithm is implemented to achieve better performance. Correlation between the results from the machine vision system and commonly accepted gold standards becomes stronger if wavelet features are utilized. The best performance is achieved with a selected subset of wavelet features. Conclusion The machine vision system based on dark field microscopy in conjugation with supervised machine learning and wavelet feature selection automates the cell viability assessment, and yields comparable results to commonly accepted methods. Wavelet features are found to be suitable to describe the discriminative properties of the live and dead cells in viability classification. According to the analysis, live cells exhibit morphologically more details and are intracellularly more organized than dead ones, which display more homogeneous and diffuse gray values throughout the cells. Feature selection increases the system's performance. The reason lies in the fact that feature

Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods

Science.gov (United States)

2013-01-01

Background Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers. Results In the first stage of this research, five feature selection methods have been proposed and experimented on the oral cancer prognosis dataset. In the second stage, the model with the features selected from each feature selection methods are tested on the proposed classifiers. Four types of classifiers are chosen; these are namely, ANFIS, artificial neural network, support vector machine and logistic regression. A k-fold cross-validation is implemented on all types of classifiers due to the small sample size. The hybrid model of ReliefF-GA-ANFIS with 3-input features of drink, invasion and p63 achieved the best accuracy (accuracy = 93.81%; AUC = 0.90) for the oral cancer prognosis. Conclusions The results revealed that the prognosis is superior with the presence of both clinicopathologic and genomic markers. The selected features can be investigated further to validate the potential of becoming as significant prognostic signature in the oral cancer studies. PMID:23725313

Quality and readability of online information resources on insomnia

Institute of Scientific and Technical Information of China (English)

Yan Ma; Albert C.Yang; Ying Duan; Ming Dong; Albert S.Yeung

2017-01-01

The internet is a major source for health information.An increasing number of people,including patients with insomnia,search for remedies online;however,little is known about the quality of such information.This study aimed to evaluate the quality and readability of insomnia-related online information.Google was used as the search engine,and the top websites on insomnia that met the inclusion criteria were evaluated for quality and readability.The analyzed websites belonged to nonprofit,commercial,or academic organizations and institutions such as hospitais and universities.Insomnia-related websites typically included definitions (85％),causes and risk factors (100％),symptoms (95％),and treatment options (90％).Cognitive behavioral therapy for insomnia (CBT-Ⅰ) was the most commonly recommended approach for insomnia treatment,and sleep drugs are frequently mentioned.The overall quality of the websites on insomnia is moderate,but all the content exceeded the recommended reading ease levels.Concerns that must be addressed to increase the quality and trustworthiness of online health information include sharing metadata,such as authorship,time of creation and last update,and conflicts of interest;providing evidence for reliability;and increasing the readability for a layman audience.

[Readability and internet accessibility of informative documents for spinal cord injury patients in Spanish].

Science.gov (United States)

Bea-Muñoz, M; Medina-Sánchez, M; Flórez-García, M T

2015-01-01

Patients with spinal cord injuries and their carers have access to leaflets on Internet that they can use as educational material to complement traditional forms of education. The aim of this study is to evaluate the readability of informative documents in Spanish, obtained from Internet and aimed at patients with spinal cord injuries. A search was made with the Google search engine using the following key words: recommendation, advice, guide, manual, self-care, education and information, adding spinal cord injury, paraplegia and tetraplegia to each of the terms. We analyzed the first 50 results of each search. The readability of the leaflets was studied with the Flesch-Szigriszt index and the INFLESZ scale, both available on the INFLESZ program. Also indicated were year of publication, country and number of authors of the documents obtained. We obtained 16 documents, developed between 2001 and 2011. Readability oscillated between 43.34 (some-what difficult) and 62 (normal), with an average value of 51.56 (somewhat difficult). Only 4 pamphlets (25%) showed a Flesch-Szigriszt index of ≥ 55 (normal). There was no difference in readability by year, authors or country of publication. The readability of 75% of the documents studied was "somewhat difficult" according to the INFLESZ scale. These results coincide with previous studies, in both Spanish and English. If the readability of this type of documents is improved, it will be easier to achieve their educational goal.

Calisthenics with Words: The Effect of Readability and Investor Sophistication on Investors’ Performance Judgment

Directory of Open Access Journals (Sweden)

Xiao Carol Cui

2016-01-01

Full Text Available Since the 1990s, the SEC has advocated for financial disclosures to be in “plain English” so that they would be more readable and informative. Past research has shown that high readability is related to more extreme investor judgments of firm performance. Processing fluency is the prevalent theory to explain this: higher readability increases the investor’s subconscious reliance on the disclosure, so positive (negative news leads to more positive (negative judgments. The relationship may not be so simple, though: drawing on research from cognitive psychology, I predict and find that investor financial literacy simultaneously influences investor decision-making, and that it has an interactive effect with readability. When presented with financial disclosure containing conflicting financial information, investors with higher financial literacy make more negative judgments than investors with low financial literacy when the disclosure is easy to read, but the effect becomes insignificant when the disclosure becomes difficult to read. This effect is moderated by a comprehension gap between the two investor groups. Financial literacy and readability interact to impact both how and how well the investor processes financial information.

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

Science.gov (United States)

Vidić, Igor; Egnell, Liv; Jerome, Neil P; Teruel, Jose R; Sjøbakk, Torill E; Østlie, Agnes; Fjøsne, Hans E; Bathen, Tone F; Goa, Pål Erik

2018-05-01

Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). Prospective. Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+). Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values. Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping. Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons. For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity. Our

Readability Levels of Dental Patient Education Brochures.

Science.gov (United States)

Boles, Catherine D; Liu, Ying; November-Rider, Debra

2016-02-01

The objective of this study was to evaluate dental patient education brochures produced since 2000 to determine if there is any change in the Flesch-Kincaid grade level readability. A convenience sample of 36 brochures was obtained for analysis of the readability of the patient education material on multiple dental topics. Readability was measured using the Flesch-Kincaid Grade Level through Microsoft Word. Pearson's correlation was used to describe the relationship among the factors of interest. Backward model selection of multiple linear regression model was used to investigate the relationship between Flesch-Kincaid Grade level and a set of predictors included in this study. A convenience sample (n=36) of dental education brochures produced from 2000 to 2014 showed a mean Flesch-Kincaid reading grade level of 9.15. Weak to moderate correlations existed between word count and grade level (r=0.40) and characters count and grade level (r=0.46); strong correlations were found between grade level and average words per sentence (r=0.70), average characters per word (r=0.85) and Flesch Reading Ease (r=-0.98). Only 1 brochure out of the sample met the recommended sixth grade reading level (Flesch-Kincaid Grade Level 5.7). Overall, the Flesch-Kincaid Grade Level of all brochures was significantly higher than the recommended sixth grade reading level (preadability of the brochures. However, the majority of the brochures analyzed are still testing above the recommended sixth grade reading level. Copyright © 2016 The American Dental Hygienists’ Association.

Computational Text Analysis: A More Comprehensive Approach to Determine Readability of Reading Materials

Science.gov (United States)

Aziz, Anealka; Fook, Chan Yuen; Alsree, Zubaida

2010-01-01

Reading materials are considered having high readability if readers are interested to read the materials, understand the content of the materials and able to read the materials fluently. In contrast, reading materials with low readability discourage readers from reading the materials, create difficulties for readers to understand the content of…

Readability of Early Intervention Program Literature

Science.gov (United States)

Pizur-Barnekow, Kris; Patrick, Timothy; Rhyner, Paula M.; Cashin, Susan; Rentmeester, Angela

2011-01-01

Accessibility of early intervention program literature was examined through readability analysis of documents given to families who have a child served by the Birth to 3 program. Nine agencies that serve families in Birth to 3 programs located in a county in the Midwest provided the (n = 94) documents. Documents were included in the analysis if…

Readability of American online patient education materials in urologic oncology: a need for simple communication.

Science.gov (United States)

Pruthi, Amanda; Nielsen, Matthew E; Raynor, Mathew C; Woods, Michael E; Wallen, Eric M; Smith, Angela B

2015-02-01

To determine the readability levels of reputable cancer and urologic Web sites addressing bladder, prostate, kidney, and testicular cancers. Online patient education materials (PEMs) for bladder, prostate, kidney, and testicular malignancies were evaluated from the American Cancer Society, American Society of Clinical Oncology, National Cancer Institute, Urology Care Foundation, Bladder Cancer Advocacy Network, Prostate Cancer Foundation, Kidney Cancer Association, and Testicular Cancer Resource Center. Grade level was determined using several readability indices, and analyses were performed on the basis of cancer type, Web site, and content area (general, causes, risk factors and prevention, diagnosis and staging, treatment, and post-treatment). Estimated grade level of online PEMs ranged from 9.2 to 14.2 with an overall mean of 11.7. Web sites for kidney cancer had the least difficult readability (11.3) and prostate cancer had the most difficult readability (12.1). Among specific Web sites, the most difficult readability levels were noted for the Urology Care Foundation Web site for bladder and prostate cancer and the Kidney Cancer Association and Testicular Cancer Resource Center for kidney and testes cancer. Readability levels within content areas varied on the basis of the disease and Web site. Online PEMs in urologic oncology are written at a level above the average American reader. Simplification of these resources is necessary to improve patient understanding of urologic malignancy. Copyright © 2015 Elsevier Inc. All rights reserved.

Readability of informed consent forms in vascular and interventional radiology

International Nuclear Information System (INIS)

Pinto, I.; Vigil, D.

1998-01-01

To evaluate the readability of the informed consent forms prepared for vascular and interventional radiology. The 18 informed consent forms were analyzed using the Gramatica tool employed in Microsoft Word 97 For Windows which combines the statistics on legibility in terms of three sections: scores, averages and legibility (Flech index, passive voice, sentence complexity and vocabulary complexity). For each, the integrated readability index was also manually calculated. All the documents present a Flesch index of over 10; the sentence complexity indexes are less than or equal to 20, demonstrating that the sentences are not long or complicated in structure. Finally, the integrated readability index of all of them is well over 70. The forms posses acceptable legibility indexes, but their evaluation should be completed by an opinion poll of the patients for whom they are written. Moreover, it must be kept in mind that these documents, like the procedures performed, are changing continually. Thus, it is necessary to update and modify the information to be provided to the patients. (Author) 11 refs

Visual Thing Recognition with Binary Scale-Invariant Feature Transform and Support Vector Machine Classifiers Using Color Information

OpenAIRE

Wei-Jong Yang; Wei-Hau Du; Pau-Choo Chang; Jar-Ferr Yang; Pi-Hsia Hung

2017-01-01

The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an importan...

Pipeline leakage recognition based on the projection singular value features and support vector machine

Energy Technology Data Exchange (ETDEWEB)

Liang, Wei; Zhang, Laibin; Mingda, Wang; Jinqiu, Hu [College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing, (China)

2010-07-01

The negative wave pressure method is one of the processes used to detect leaks on oil pipelines. The development of new leakage recognition processes is difficult because it is practically impossible to collect leakage pressure samples. The method of leakage feature extraction and the selection of the recognition model are also important in pipeline leakage detection. This study investigated a new feature extraction approach Singular Value Projection (SVP). It projects the singular value to a standard basis. A new pipeline recognition model based on the multi-class Support Vector Machines was also developed. It was found that SVP is a clear and concise recognition feature of the negative pressure wave. Field experiments proved that the model provided a high recognition accuracy rate. This approach to pipeline leakage detection based on the SVP and SVM has a high application value.

Method of generating a computer readable model

DEFF Research Database (Denmark)

2008-01-01

A method of generating a computer readable model of a geometrical object constructed from a plurality of interconnectable construction elements, wherein each construction element has a number of connection elements for connecting the construction element with another construction element. The met......A method of generating a computer readable model of a geometrical object constructed from a plurality of interconnectable construction elements, wherein each construction element has a number of connection elements for connecting the construction element with another construction element....... The method comprises encoding a first and a second one of the construction elements as corresponding data structures, each representing the connection elements of the corresponding construction element, and each of the connection elements having associated with it a predetermined connection type. The method...... further comprises determining a first connection element of the first construction element and a second connection element of the second construction element located in a predetermined proximity of each other; and retrieving connectivity information of the corresponding connection types of the first...

The quality and readability of internet information regarding clavicle fractures.

Science.gov (United States)

Zhang, Dafang; Schumacher, Charles; Harris, Mitchel Byron

2016-03-01

The internet has become a major source of health information for patients. However, there has been little scrutiny of health information available on the internet to the public. Our objectives were to evaluate the quality and readability of information available on the internet regarding clavicle fractures and whether they changed with academic affiliation of the website or with complexity of the search term. Through a prospective evaluation of 3 search engines using 3 different search terms of varying complexity ("broken collarbone," "collarbone fracture," and "clavicle fracture"), we evaluated 91 website hits for quality and readability. Websites were specifically analyzed by search term and by website type. Information quality was evaluated on a four-point scale, and information readability was assessed using the Flesch-Kincaid score for reading grade level. The average quality score for our website hits was low, and the average reading grade level was far above the recommended level. Academic websites offered significantly higher quality information, whereas commercial websites offered significantly lower quality information. The use of more complex search terms yielded information of higher reading grade level but not higher quality. Current internet information regarding clavicle fractures is of low quality and low readability. Higher quality information utilizing more accessible language on clavicle fractures is needed on the internet. It is important to be aware of the information accessible to patients prior to their presentation to our clinics. Patients should be advised to visit websites with academic affiliations and to avoid commercial websites. Copyright © 2015 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

[Global analysis of the readability of the informed consent forms used in public hospitals of Spain].

Science.gov (United States)

Mariscal-Crespo, M I; Coronado-Vázquez, M V; Ramirez-Durán, M V

To analyse the readability of informed consent forms (ICF) used in Public Hospitals throughout Spain, with the aim of checking their function of providing comprehensive information to people who are making any health decision no matter where they are in Spain. A descriptive study was performed on a total of 11,339 ICF received from all over Spanish territory, of which 1617 ICF were collected from 4 web pages of Health Portal and the rest (9722) were received through email and/or telephone contact from March 2012 to February 2013. The readability level was studied using the Inflesz tool. A total of 372 ICF were selected and analysed using simple random sampling. The Inflesz scale and the Flesch-Szigriszt index were used to analyse the readability. The readability results showed that 62.4% of the ICF were rated as a "little difficult", the 23.4% as "normal", and the 13.4% were rated as "very difficult". The highest readability means using the Flesch index were scored in Andalusia with a mean of 56.99 (95% CI; 55.42-58.57) and Valencia with a mean of 51.93 (95% CI; 48.4-55.52). The lowest readability means were in Galicia with a mean of 40.77 (95% CI; 9.83-71.71) and Melilla, mean=41.82 (95% CI; 35.5-48.14). The readability level of Spanish informed consent forms must be improved because their scores using readability tools could not be classified in normal scales. Furthermore, there was very wide variability among Spanish ICF, which showed a lack of equity in information access among Spanish citizens. Copyright © 2017 SECA. Publicado por Elsevier España, S.L.U. All rights reserved.

Readability assessment of online patient education materials provided by the European Association of Urology.

Science.gov (United States)

Betschart, Patrick; Zumstein, Valentin; Bentivoglio, Maico; Engeler, Daniel; Schmid, Hans-Peter; Abt, Dominik

2017-12-01

To assess the readability of the web-based patient education material provided by the European Association of Urology. English patient education materials (PEM) as available in May 2017 were obtained from the EAU website. Each topic was analyzed separately using six well-established readability assessment tools, including Flesch-Kincaid Grade Level (FKGL), SMOG Grade Level (SMOG), Coleman-Liau Index (CLI), Gunning Fog Index (GFI), Flesch Reading Ease Formula (FRE) and Fry Readability Graph (FRG). A total of 17 main topics were identified of which separate basic and in-depth information is provided for 14 topics. Calculation of grade levels (FKGL, SMOG, CLI, GFI) showed readability scores of 7th-13th grade for basic information, 8th-15th grade for in-depth information and 7th-15th grade for single PEM. Median FRE score was 54 points (range 45-65) for basic information and 56 points (41-64) for in-depth information. The FRG as a graphical assessment revealed only 13 valid results with an approximate 8th-17th grade level. The EAU provides carefully worked out PEM for 17 urological topics. Although improved readability compared to similar analyses was found, a simplification of certain chapters might be helpful to facilitate better patient understanding.

Application of the Disruption Predictor Feature Developer to developing a machine-portable disruption predictor

Science.gov (United States)

Parsons, Matthew; Tang, William; Feibush, Eliot

2016-10-01

Plasma disruptions pose a major threat to the operation of tokamaks which confine a large amount of stored energy. In order to effectively mitigate this damage it is necessary to predict an oncoming disruption with sufficient warning time to take mitigative action. Machine learning approaches to this problem have shown promise but require further developments to address (1) the need for machine-portable predictors and (2) the availability of multi-dimensional signal inputs. Here we demonstrate progress in these two areas by applying the Disruption Predictor Feature Developer to data from JET and NSTX, and discuss topics of focus for ongoing work in support of ITER. The author is also supported under the Fulbright U.S. Student Program as a graduate student in the department of Nuclear, Plasma and Radiological Engineering at the University of Illinois at Urbana-Champaign.

Readability assessment of Internet-based patient education materials related to endoscopic sinus surgery.

Science.gov (United States)

Cherla, Deepa V; Sanghvi, Saurin; Choudhry, Osamah J; Liu, James K; Eloy, Jean Anderson

2012-08-01

Numerous professional societies, clinical practices, and hospitals provide Internet-based patient education materials (PEMs) to the general public, but not all of this information is written at a reading level appropriate for the average patient. The National Institutes of Health and the US Department of Health and Human Services recommend that PEMs be written at or below the sixth-grade level. Our purpose was to assess the readability of endoscopic sinus surgery (ESS)-related PEMs available on the Internet and compare readability levels of PEMs provided by three sources: professional societies, clinical practices, and hospitals. A descriptive and correlational design was used for this study. The readability of 31 ESS-related PEMs was assessed with four different readability indices: Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease Score (FRES), Simple Measure of Gobbledygook (SMOG), and Gunning Frequency of Gobbledygook (Gunning FOG). Averages were evaluated against national recommendations and between each source using analysis of variance and t tests. The majority of PEMs (96.8%) were written above the recommended sixth-grade reading level, based on FKGL (P Internet-based PEMs related to ESS, regardless of source type, were written well above the recommended sixth-grade level. Materials from the hospitals/university-affiliated websites had lower readability scores, but were still above recommended levels. Web-based PEMs pertaining to ESS should be written with the average patient in mind. Copyright © 2012 The American Laryngological, Rhinological, and Otological Society, Inc.

Readability of Patient Education Materials From the Web Sites of Orthopedic Implant Manufacturers.

Science.gov (United States)

Yi, Meghan M; Yi, Paul H; Hussein, Khalil I; Cross, Michael B; Della Valle, Craig J

2017-12-01

Prior studies indicate that orthopedic patient education materials are written at a level that is too high for the average patient. The purpose of this study was to assess the readability of online patient education materials provided by orthopedic implant manufacturers. All patient education articles available in 2013 from the web sites of the 5 largest orthopedic implant manufacturers were identified. Each article was evaluated with the Flesch-Kincaid (FK) readability test. The number of articles with readability ≤ the eighth-grade level (average reading ability of US adults) and the sixth-grade level (recommended level for patient education materials) was determined. Mean readability levels of each company's articles were compared using analysis of variance (significance set at P articles were reviewed from the 5 largest implant manufacturers. The mean overall FK grade level was 10.9 (range, 3.8-16.1). Only 58 articles (10%) were written ≤ the eighth-grade level, and only 13 (2.2%) were ≤ the sixth-grade level. The mean FK grade level was significantly different among groups (Smith & Nephew = 12.0, Stryker = 11.6, Biomet = 11.3, DePuy = 10.6, Zimmer = 10.1; P education materials from implant manufacturers are written at a level too high to be comprehended by the average patient. Future efforts should be made to improve the readability of orthopedic patient education materials. Copyright © 2017 Elsevier Inc. All rights reserved.

Integrated Features by Administering the Support Vector Machine (SVM of Translational Initiations Sites in Alternative Polymorphic Contex

Directory of Open Access Journals (Sweden)

Nurul Arneida Husin

2012-04-01

Full Text Available Many algorithms and methods have been proposed for classification problems in bioinformatics. In this study, the discriminative approach in particular support vector machines (SVM is employed to recognize the studied TIS patterns. The applied discriminative approach is used to learn about some discriminant functions of samples that have been labelled as positive or negative. After learning, the discriminant functions are employed to decide whether a new sample is true or false. In this study, support vector machines (SVM is employed to recognize the patterns for studied translational initiation sites in alternative weak context. The method has been optimized with the best parameters selected; c=100, E=10-6 and ex=2 for non linear kernel function. Results show that with top 5 features and non linear kernel, the best prediction accuracy achieved is 95.8%. J48 algorithm is applied to compare with SVM with top 15 features and the results show a good prediction accuracy of 95.8%. This indicates that the top 5 features selected by the IGR method and that are performed by SVM are sufficient to use in the prediction of TIS in weak contexts.

Readability of internet-sourced patient education material related to “labour analgesia”

Science.gov (United States)

Boztas, Nilay; Omur, Dilek; Ozbılgın, Sule; Altuntas, Gözde; Piskin, Ersan; Ozkardesler, Sevda; Hanci, Volkan

2017-01-01

Abstract We evaluated the readability of Internet-sourced patient education materials (PEMs) related to “labour analgesia.” In addition to assessing the readability of websites, we aimed to compare commercial, personal, and academic websites. We used the most popular search engine (http://www.google.com) in our study. The first 100 websites in English that resulted from a search for the key words “labour analgesia” were scanned. Websites that were not in English, graphs, pictures, videos, tables, figures and list formats in the text, all punctuation, the number of words in the text is less than 100 words, feedback forms not related to education, (Uniform Resource Locator) URL websites, author information, references, legal disclaimers, and addresses and telephone numbers were excluded. The texts included in the study were assessed using the Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), and Gunning Frequency of Gobbledygook (FOG) readability formulae. The number of Latin words within the text was determined. Analysis of 300-word sections of the texts revealed that the mean FRES was 47.54 ± 12.54 (quite difficult), mean FKGL and SMOG were 11.92 ± 2.59 and 10.57 ± 1.88 years of education, respectively, and mean Gunning FOG was 14.71 ± 2.76 (very difficult). Within 300-word sections, the mean number of Latin words was identified as 16.56 ± 6.37. In our study, the readability level of Internet-sourced PEM related to “labour analgesia” was identified to be quite high indicating poor readability. PMID:29137057

Readability Formulas as Applied to College Economics Textbooks.

Science.gov (United States)

McConnell, Campbell R.

1982-01-01

Determines from empirical information on the application of four readability formulas to a group of widely used college economics textbooks that there is no consistency in the absolute reading levels or the rank orderings of these books. (AEA)

Readability of Malaria Medicine Information Leaflets in Nigeria

African Journals Online (AJOL)

Erah

2010-12-18

Dec 18, 2010 ... for malaria medicines information leaflets available in Nigeria was 13.69 ± 1.70. This value is equivalent ... Health promotion and behaviour change communication ... their rational use at the community level. Readability is the ...

Analysis of Readability and Interest of Marketing Education Textbooks: Implications for Special Needs Learners.

Science.gov (United States)

Jones, Karen H.; And Others

1993-01-01

The readability, reading ease, interest level, and writing style of 20 current textbooks in secondary marketing education were evaluated. Readability formulas consistently identified lower reading levels for special needs education, human interest scores were not very reliable information sources, and writing style was also a weak variable. (JOW)

A Readability Analysis of Selected Introductory Economics.

Science.gov (United States)

Gallagher, Daniel J.; Thompson, G. Rodney

1981-01-01

To aid secondary school and college level economics teachers as they select textbooks for introductory economics courses, this article recounts how teachers can use the Flesch Reading Ease Test to measure readability. Data are presented on application of the Flesch Reading Ease Test to 15 introductory economics textbooks. (Author/DB)

The Role of Auditory Features Within Slot-Themed Social Casino Games and Online Slot Machine Games.

Science.gov (United States)

Bramley, Stephanie; Gainsbury, Sally M

2015-12-01

Over the last few years playing social casino games has become a popular entertainment activity. Social casino games are offered via social media platforms and mobile apps and resemble gambling activities. However, social casino games are not classified as gambling as they can be played for free, outcomes may not be determined by chance, and players receive no monetary payouts. Social casino games appear to be somewhat similar to online gambling activities in terms of their visual and auditory features, but to date little research has investigated the cross over between these games. This study examines the auditory features of slot-themed social casino games and online slot machine games using a case study design. An example of each game type was played on three separate occasions during which, the auditory features (i.e., music, speech, sound effects, and the absence of sound) within the games were logged. The online slot-themed game was played in demo mode. This is the first study to provide a qualitative account of the role of auditory features within a slot-themed social casino game and an online slot machine game. Our results found many similarities between how sound is utilised within the two games. Therefore the sounds within these games may serve functions including: setting the scene for gaming, creating an image, demarcating space, interacting with visual features, prompting players to act, communicating achievements to players, providing reinforcement, heightening player emotions and the gaming experience. As a result this may reduce the ability of players to make a clear distinction between these two activities, which may facilitate migration between games.

Online Patient Resources for Liposuction: A Comparative Analysis of Readability.

Science.gov (United States)

Vargas, Christina R; Ricci, Joseph A; Chuang, Danielle J; Lee, Bernard T

2016-03-01

As patients strive to become informed about health care, inadequate functional health literacy is a significant barrier. Nearly half of American adults have poor or marginal health literacy skills and the National Institutes of Health and American Medical Association have recommended that patient information should be written at a sixth grade level. The aim of this study is to identify the most commonly used online patient information about liposuction and to evaluate its readability relative to average American literacy. An internet search of "liposuction" was performed and the 10 most popular websites identified. User and location data were disabled and sponsored results excluded. All relevant, patient-directed articles were downloaded and formatted into plain text. Articles were then analyzed using 10 established readability tests. A comparison group was constructed to identify the most popular online consumer information about tattooing. Mean readability scores and specific article characteristics were compared. A total of 80 articles were collected from websites about liposuction. Readability analysis revealed an overall 13.6 grade reading level (range, 10-16 grade); all articles exceeded the target sixth grade level. Consumer websites about tattooing were significantly easier to read, with a mean 7.8 grade level. These sites contained significantly fewer characters per word and words per sentence, as well as a smaller proportion of complex, long, and unfamiliar words. Online patient resources about liposuction are potentially too difficult for a large number of Americans to understand. Liposuction websites are significantly harder to read than consumer websites about tattooing. Aesthetic surgeons are advised to discuss with patients resources they use and guide patients to appropriate information for their skill level.

Measuring the Readability of Children's Trade Books.

Science.gov (United States)

Popp, Helen M.; Porter, Douglas

In order to utilize interesting children's trade books in a systematic reading program, two readability formulas were devised based on a selection of children's trade books. Children's scores on selections from these books and judges' rankings were compared. The judges' decisions were considered to be highly credible and were used as the criterion…

Readability and Content Assessment of Informed Consent Forms for Phase II-IV Clinical Trials in China.

Science.gov (United States)

Wen, Gaiyan; Liu, Xinchun; Huang, Lihua; Shu, Jingxian; Xu, Nana; Chen, Ruifang; Huang, Zhijun; Yang, Guoping; Wang, Xiaomin; Xiang, Yuxia; Lu, Yao; Yuan, Hong

2016-01-01

To explore the readability and content integrity of informed consent forms (ICFs) used in China and to compare the quality of Chinese local ICFs with that of international ICFs. The length, readability and content of 155 consent documents from phase II-IV drug clinical trials from the Third Xiangya Hospital Ethics Committee from November 2009 to January 2015 were evaluated. Reading difficulty was tested using a readability formula adapted for the Chinese language. An ICF checklist containing 27 required elements was successfully constructed to evaluate content integrity. The description of alternatives to participation was assessed. The quality of ICFs from different sponsorships were also compared. Among the 155 evaluable trials, the ICFs had a median length of 5286 words, corresponding to 7 pages. The median readability score was 4.31 (4.02-4.41), with 63.9% at the 2nd level and 36.1% at the 3rd level. Five of the 27 elements were frequently neglected. The average score for the description of alternatives to participation was 1.06, and 27.7% of the ICFs did not mention any alternatives. Compared with Chinese local ICFs, international ICFs were longer, more readable and contained more of the required elements (P readability and content integrity than Chinese local ICFs. More efforts should thus be made to improve the quality of consent documents in China.

Xeml Lab: a tool that supports the design of experiments at a graphical interface and generates computer-readable metadata files, which capture information about genotypes, growth conditions, environmental perturbations and sampling strategy.

Science.gov (United States)

Hannemann, Jan; Poorter, Hendrik; Usadel, Björn; Bläsing, Oliver E; Finck, Alex; Tardieu, Francois; Atkin, Owen K; Pons, Thijs; Stitt, Mark; Gibon, Yves

2009-09-01

Data mining depends on the ability to access machine-readable metadata that describe genotypes, environmental conditions, and sampling times and strategy. This article presents Xeml Lab. The Xeml Interactive Designer provides an interactive graphical interface at which complex experiments can be designed, and concomitantly generates machine-readable metadata files. It uses a new eXtensible Mark-up Language (XML)-derived dialect termed XEML. Xeml Lab includes a new ontology for environmental conditions, called Xeml Environment Ontology. However, to provide versatility, it is designed to be generic and also accepts other commonly used ontology formats, including OBO and OWL. A review summarizing important environmental conditions that need to be controlled, monitored and captured as metadata is posted in a Wiki (http://www.codeplex.com/XeO) to promote community discussion. The usefulness of Xeml Lab is illustrated by two meta-analyses of a large set of experiments that were performed with Arabidopsis thaliana during 5 years. The first reveals sources of noise that affect measurements of metabolite levels and enzyme activities. The second shows that Arabidopsis maintains remarkably stable levels of sugars and amino acids across a wide range of photoperiod treatments, and that adjustment of starch turnover and the leaf protein content contribute to this metabolic homeostasis.

Measuring the readability of sustainability reports: : A corpus-based analysis through standard formulae and NLP

NARCIS (Netherlands)

Smeuninx, N.; De Clerck, B.; Aerts, Walter

2016-01-01

This study characterises and problematises the language of corporate reporting along region, industry, genre, and content lines by applying readability formulae and more advanced natural language processing (NLP)–based analysis to a manually assembled 2.75-million-word corpus. Readability formulae

Assessment of readability, quality and popularity of online information on ureteral stents.

Science.gov (United States)

Mozafarpour, Sarah; Norris, Briony; Borin, James; Eisner, Brian H

2018-02-12

To evaluate the quality and readability of online information on ureteral stents. Google.com was queried using the search terms "ureteric stent", "ureteral stent", "double J stent" and, "Kidney stent" derived from Google AdWords. Website popularity was determined using Google Rank and the Alexa tool. Website quality assessment was performed using the following criteria: Journal of the American Medical Association (JAMA) benchmarks, Health on the Net (HON) criteria, and a customized DISCERN questionnaire. The customized DISCERN questionnaire was developed by combining the short validated DISCERN questionnaire with additional stent-specific items including definition, placement, complications, limitations, removal and "when to seek help". Scores related to stent items were considered as the "stent score" (SS). Readability was evaluated using five readability tests. Thirty-two websites were included. The mean customized DISCERN score and "stent score" were 27.1 ± 7.1 (maximum possible score = 59) and 14.6 ± 3.8 (maximum possible score = 24), respectively. A minority of websites adequately addressed "stent removal" and "when to seek medical attention". Only two websites (6.3%) had HON certification (drugs.com, radiologyinfo.org) and only one website (3.3%) met all JAMA criteria (bradyurology.blogspot.com). Readability level was higher than the American Medical Association recommendation of sixth-grade level for more than 75% of the websites. There was no correlation between Google rank, Alexa rank, and the quality scores (P > 0.05). Among the 32 most popular websites on the topic of ureteral stents, online information was highly variable. The readability of many of the websites was far higher than standard recommendations and the online information was questionable in many cases. These findings suggest a need for improved online resources in order to better educate patients about ureteral stents and also should inform physicians that popular websites may

Readability Formulas and User Perceptions of Electronic Health Records Difficulty: A Corpus Study.

Science.gov (United States)

Zheng, Jiaping; Yu, Hong

2017-03-02

Electronic health records (EHRs) are a rich resource for developing applications to engage patients and foster patient activation, thus holding a strong potential to enhance patient-centered care. Studies have shown that providing patients with access to their own EHR notes may improve the understanding of their own clinical conditions and treatments, leading to improved health care outcomes. However, the highly technical language in EHR notes impedes patients' comprehension. Numerous studies have evaluated the difficulty of health-related text using readability formulas such as Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), and Gunning-Fog Index (GFI). They conclude that the materials are often written at a grade level higher than common recommendations. The objective of our study was to explore the relationship between the aforementioned readability formulas and the laypeople's perceived difficulty on 2 genres of text: general health information and EHR notes. We also validated the formulas' appropriateness and generalizability on predicting difficulty levels of highly complex technical documents. We collected 140 Wikipedia articles on diabetes and 242 EHR notes with diabetes International Classification of Diseases, Ninth Revision code. We recruited 15 Amazon Mechanical Turk (AMT) users to rate difficulty levels of the documents. Correlations between laypeople's perceived difficulty levels and readability formula scores were measured, and their difference was tested. We also compared word usage and the impact of medical concepts of the 2 genres of text. The distributions of both readability formulas' scores (Preadability predictions and laypeople's perceptions were weak. Furthermore, despite being graded at similar levels, documents of different genres were still perceived with different difficulty (Preadability formulas' predictions did not align with perceived difficulty in either text genre. The widely used readability formulas were

Readability of informed consent forms in clinical trials conducted in a skin research center

Science.gov (United States)

Samadi, Aniseh; Asghari, Fariba

2016-01-01

Obtaining informed consents is one of the most fundamental principles in conducting a clinical trial. In order for the consent to be informed, the patient must receive and comprehend the information appropriately. Complexity of the consent form is a common problem that has been shown to be a major barrier to comprehension for many patients. The objective of this study was to assess the readability of different templates of informed consent forms (ICFs) used in clinical trials in the Center for Research and Training in Skin Diseases and Leprosy (CRTSDL), Tehran, Iran. This study was conducted on ICFs of 45 clinical trials of the CRTSDL affiliated with Tehran University of Medical Sciences. ICFs were tested for reading difficulty, using the readability assessments formula adjusted for the Persian language including the Flesch–Kincaid reading ease score, Flesch–Kincaid grade level, and Gunning fog index. Mean readability score of the whole text of ICFs as well as their 7 main information parts were calculated. The mean ± SD Flesch Reading Ease score for all ICFs was 31.96 ± 5.62 that is in the difficult range. The mean ± SD grade level was calculated as 10.71 ± 1.8 (8.23–14.09) using the Flesch–Kincaid formula and 14.64 ± 1.22 (12.67–18.27) using the Gunning fog index. These results indicate that the text is expected to be understandable for an average student in the 11th grade, while the ethics committee recommend grade level 8 as the standard readability level for ICFs. The results showed that the readability scores of ICFs assessed in our study were not in the acceptable range. This means they were too complex to be understood by the general population. Ethics committees must examine the simplicity and readability of ICFs used in clinical trials. PMID:27471590

Cauda equina syndrome: assessing the readability and quality of patient information on the Internet.

Science.gov (United States)

O'Neill, Shane Ciaran; Baker, Joseph Frederick; Fitzgerald, Conall; Fleming, Christina; Rowan, Fiachra; Byrne, Damien; Synnott, Keith

2014-05-01

A readability and quality control Internet-based study using recognized quality scoring systems. To assess the readability and quality of Internet information relating to cauda equina syndrome accessed through common search engines. Access to health-related Internet information has increased dramatically during the past decade. A significant proportion of this information has been demonstrated to be set at too high a level for general comprehension. Despite this, searching for health-related information is now the third most popular online activity. A total of 125 cauda equina syndrome Web sites were analyzed from the 5 most popular Internet search engines: Google, Bing, Yahoo, Ask, and AOL. Web site authorship was classified: academic, physician, medico-legal, commercial, or discussion/social media. Readability of each Web site was assessed using the Flesch Reading Ease score, the Flesch-Kincaid grade level, and the Gunning Fog Index. Quality was calculated using the DISCERN instrument and The Journal of the American Medical Association benchmark criteria. The presence of HON-code certification was also assessed. Fifty-two individual Web sites were identified and assessed. The majority of Web sites were academic or physician compiled (53.8%; 28/52); however, a significant minority of Web sites were medico-legal related (19.2%; 10/52). Just 13.5% (7/52) of Web sites were at or below the recommended sixth-grade readability level. HON-code certified Web sites achieved significantly greater DISCERN (P = 0.0006) and The Journal of the American Medical Association (P = 0.0002) scores. Internet information relating to cauda equina syndrome is of variable quality and largely set at an inappropriate readability level. Given this variability in quality, health care providers should direct patients to known sources of reliable, readable online information. Identification of reliable sources may be aided by known markers of quality such as HON-code certification.

Validation Study of Waray Text Readability Instrument

Science.gov (United States)

Oyzon, Voltaire Q.; Corrales, Juven B.; Estardo, Wilfredo M., Jr.

2015-01-01

In 2012 the Leyte Normal University developed a computer software--modelled after the Spache Readability Formula (1953) made for English--made to help rank texts that can is used by teachers or research groups on selecting appropriate reading materials to support the DepEd's MTB-MLE program in Region VIII, in the Philippines. However,…

Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis

Directory of Open Access Journals (Sweden)

Mauricio Holguín-Londoño

2016-01-01

Full Text Available Vibration and acoustic analysis actively support the nondestructive and noninvasive fault diagnostics of rotating machines at early stages. Nonetheless, the acoustic signal is less used because of its vulnerability to external interferences, hindering an efficient and robust analysis for condition monitoring (CM. This paper presents a novel methodology to characterize different failure signatures from rotating machines using either acoustic or vibration signals. Firstly, the signal is decomposed into several narrow-band spectral components applying different filter bank methods such as empirical mode decomposition, wavelet packet transform, and Fourier-based filtering. Secondly, a feature set is built using a proposed similarity measure termed cumulative spectral density index and used to estimate the mutual statistical dependence between each bandwidth-limited component and the raw signal. Finally, a classification scheme is carried out to distinguish the different types of faults. The methodology is tested in two laboratory experiments, including turbine blade degradation and rolling element bearing faults. The robustness of our approach is validated contaminating the signal with several levels of additive white Gaussian noise, obtaining high-performance outcomes that make the usage of vibration, acoustic, and vibroacoustic measurements in different applications comparable. As a result, the proposed fault detection based on filter bank similarity features is a promising methodology to implement in CM of rotating machinery, even using measurements with low signal-to-noise ratio.

Development of SMOG-Cro readability formula for healthcare communication and patient education.

Science.gov (United States)

Brangan, Sanja

2015-03-01

Effective communication shows a positive impact on patient satisfaction, compliance and medical outcomes, at the same time reducing the healthcare costs. Written information for patients needs to correspond to health literacy levels of the intended audiences. Readability formulas correlate well with the reading and comprehension tests but are considered an easier and quicker method to estimate a text difficulty. SMOG readability formula designed for English language needs to be modified if used for texts in other languages. The aim of this study was to develop a readability formula based on SMOG, that could be used to estimate text difficulty of written materials for patients in Croatian language. Contras- tive analysis of English and Croatian language covering a corpus of almost 100,000 running words showed clear linguis- tic differences in the number of polysyllabic words. The new formula, named SMOG-Cro, is presented as an equation: SMOG-Cro = 2 + √4+ syllables, with the score showing the number of years of education a person needs to be able to understand a piece of writing. The presented methodology could help in the development of readability formulas for other languages. We hope the results of this study are soon put into practice for more effective healthcare communication and patient education, and for development of a health literacy assessment tool in Croatian language.

Improving the human readability of Arden Syntax medical logic modules using a concept-oriented terminology and object-oriented programming expressions.

Science.gov (United States)

Choi, Jeeyae; Bakken, Suzanne; Lussier, Yves A; Mendonça, Eneida A

2006-01-01

Medical logic modules are a procedural representation for sharing task-specific knowledge for decision support systems. Based on the premise that clinicians may perceive object-oriented expressions as easier to read than procedural rules in Arden Syntax-based medical logic modules, we developed a method for improving the readability of medical logic modules. Two approaches were applied: exploiting the concept-oriented features of the Medical Entities Dictionary and building an executable Java program to replace Arden Syntax procedural expressions. The usability evaluation showed that 66% of participants successfully mapped all Arden Syntax rules to Java methods. These findings suggest that these approaches can play an essential role in the creation of human readable medical logic modules and can potentially increase the number of clinical experts who are able to participate in the creation of medical logic modules. Although our approaches are broadly applicable, we specifically discuss the relevance to concept-oriented nursing terminologies and automated processing of task-specific nursing knowledge.

Machine Learning for Medical Imaging.

Science.gov (United States)

Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L

2017-01-01

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. © RSNA, 2017.

Readability assessment of online patient education materials from academic otolaryngology-head and neck surgery departments.

Science.gov (United States)

Svider, Peter F; Agarwal, Nitin; Choudhry, Osamah J; Hajart, Aaron F; Baredes, Soly; Liu, James K; Eloy, Jean Anderson

2013-01-01

The aim of this study was to compare the readability of online patient education materials among academic otolaryngology departments in the mid-Atlantic region, with the purpose of determining whether these commonly used online resources were written at a level readily understood by the average American. A readability analysis of online patient education materials was performed using several commonly used readability assessments including the Flesch Reading Ease Score, the Flesch-Kincaid Grade Level, Simple Measure of Gobbledygook, Gunning Frequency of Gobbledygook, the New Dale-Chall Test, the Coleman-Liau Index, the New Fog Count, the Raygor Readability Estimate, the FORCAST test, and the Fry Graph. Most patient education materials from these programs were written at or above an 11th grade reading level, considerably above National Institutes of Health guidelines for recommended difficulty. Patient educational materials from academic otolaryngology Web sites are written at too difficult a reading level for a significant portion of patients and can be simplified. Copyright © 2013 Elsevier Inc. All rights reserved.

Readability Revisited? The Implications of Text Complexity

Science.gov (United States)

Wray, David; Janan, Dahlia

2013-01-01

The concept of readability has had a variable history, moving from a position where it was considered as a very important topic for those responsible for producing texts and matching those texts to the abilities and needs of learners, to its current declining visibility in the education literature. Some important work has been coming from the USA…

Informed consent recall and comprehension in orthodontics: traditional vs improved readability and processability methods.

Science.gov (United States)

Kang, Edith Y; Fields, Henry W; Kiyak, Asuman; Beck, F Michael; Firestone, Allen R

2009-10-01

Low general and health literacy in the United States means informed consent documents are not well understood by most adults. Methods to improve recall and comprehension of informed consent have not been tested in orthodontics. The purposes of this study were to evaluate (1) recall and comprehension among patients and parents by using the American Association of Orthodontists' (AAO) informed consent form and new forms incorporating improved readability and processability; (2) the association between reading ability, anxiety, and sociodemographic variables and recall and comprehension; and (3) how various domains (treatment, risk, and responsibility) of information are affected by the forms. Three treatment groups (30 patient-parent pairs in each) received an orthodontic case presentation and either the AAO form, an improved readability form (MIC), or an improved readability and processability (pairing audio and visual cues) form (MIC + SS). Structured interviews were transcribed and coded to evaluate recall and comprehension. Significant relationships among patient-related variables and recall and comprehension explained little of the variance. The MIC + SS form significantly improved patient recall and parent recall and comprehension. Recall was better than comprehension, and parents performed better than patients. The MIC + SS form significantly improved patient treatment comprehension and risk recall and parent treatment recall and comprehension. Patients and parents both overestimated their understanding of the materials. Improving the readability of consent materials made little difference, but combining improved readability and processability benefited both patients' recall and parents' recall and comprehension compared with the AAO form.

23 CFR Appendix A to Part 1313 - Tamper Resistant Driver's License

Science.gov (United States)

2010-04-01

...) Block graphics. (15) Security fonts and graphics with known hidden flaws. (16) Card stock, layer with colors. (17) Micro-graphics. (18) Retroflective security logos. (19) Machine readable technologies such... permit that has one or more of the following security features: (1) Ghost image. (2) Ghost graphic. (3...

An evaluation of the readability of drinking water quality reports: a national assessment.

Science.gov (United States)

Roy, Siddhartha; Phetxumphou, Katherine; Dietrich, Andrea M; Estabrooks, Paul A; You, Wen; Davy, Brenda M

2015-09-01

The United States Environmental Protection Agency mandates that community water systems (or water utilities) provide annual consumer confidence reports (CCRs)--water quality reports--to their consumers. These reports encapsulate information regarding sources of water, detected contaminants, regulatory compliance, and educational material. These reports have excellent potential for providing the public with accurate information on the safety of tap water, but there is a lack of research on the degree to which the information can be understood by a large proportion of the population. This study evaluated the readability of a nationally representative sample of 30 CCRs, released between 2011 and 2013. Readability (or 'comprehension difficulty') was evaluated using Flesch-Kincaid readability tests. The analysis revealed that CCRs were written at the 11th-14th grade level, which is well above the recommended 6th-7th grade level for public health communications. The CCR readability ease was found to be equivalent to that of the Harvard Law Review journal. These findings expose a wide chasm that exists between current water quality reports and their effectiveness toward being understandable to US residents. Suggestions for reorienting language and scientific information in CCRs to be easily comprehensible to the public are offered.

Readability Assessment of Online Uveitis Patient Education Materials.

Science.gov (United States)

Ayoub, Samantha; Tsui, Edmund; Mohammed, Taariq; Tseng, Joseph

2017-12-29

To evaluate the readability of online uveitis patient education materials. A Google search in November 2016 was completed using search term "uveitis" and "uveitis inflammation." The top 50 websites with patient-centered information were selected and analyzed for readability using the Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease Score (FRES), Gunning FOG Index (GFI), and Simple Measure of Gobbledygook (SMOG). Statistical analysis was performed with two-tailed t-tests. The mean word count of the top 50 websites was 1162.7 words, and averaged 16.2 words per sentence. For these websites, the mean FRES was 38.0 (range 4-66, SD = 12.0), mean FKGL was 12.3 (range 6.8-19, SD = 2.4), mean SMOG score was 14.4 (range 9.8-19, SD = 1.8), and the mean Gunning FOG index was 14.0 (range 8.6-19, SD = 2.0). The majority of online patient directed uveitis materials are at a higher reading level than that of the average American adult.

Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

Science.gov (United States)

McAllister, Patrick; Zheng, Huiru; Bond, Raymond; Moorhead, Anne

2018-04-01

Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs), initially trained using ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset with MatConvNet package, to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101. Deep features were extracted from CNNs and used to train machine learning classifiers including artificial neural network (ANN), support vector machine (SVM), Random Forest, and Naive Bayes. Results show that using ResNet-152 deep features with SVM with RBF kernel can accurately detect food items with 99.4% accuracy using Food-5K validation food image dataset and 98.8% with Food-5K evaluation dataset using ANN, SVM-RBF, and Random Forest classifiers. Trained with ResNet-152 features, ANN can achieve 91.34%, 99.28% when applied to Food-11 and RawFooT-DB food image datasets respectively and SVM with RBF kernel can achieve 64.98% with Food-101 image dataset. From this research it is clear that using deep CNN features can be used efficiently for diverse food item image classification. The work presented in this research shows that pretrained ResNet-152 features provide sufficient generalisation power when applied to a range of food image classification tasks. Copyright © 2018 Elsevier Ltd. All rights reserved.

A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

Science.gov (United States)

Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

2017-08-01

Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

Enhancing the Radiologist-Patient Relationship through Improved Communication: A Quantitative Readability Analysis in Spine Radiology.

Science.gov (United States)

Hansberry, D R; Donovan, A L; Prabhu, A V; Agarwal, N; Cox, M; Flanders, A E

2017-06-01

More than 75 million Americans have less than adequate health literacy skills according to the National Center for Education Statistics. Readability scores are used as a measure of how well populations read and understand patient education materials. The purpose of this study was to assess the readability of Web sites dedicated to patient education for radiologic spine imaging and interventions. Eleven search terms relevant to radiologic spine imaging were searched on the public Internet, and the top 10 links for each term were collected and analyzed to determine readability scores by using 10 well-validated quantitative readability assessments from patient-centered education Web sites. The search terms included the following: x-ray spine, CT spine, MR imaging spine, lumbar puncture, kyphoplasty, vertebroplasty, discogram, myelogram, cervical spine, thoracic spine, and lumbar spine. Collectively, the 110 articles were written at an 11.3 grade level (grade range, 7.1-16.9). None of the articles were written at the American Medical Association and National Institutes of Health recommended 3rd-to-7th grade reading levels. The vertebroplasty articles were written at a statistically significant ( P readability scores of the articles and the American Medical Association and National Institutes of Health recommended guidelines, it is likely that many patients do not fully benefit from these resources. © 2017 by American Journal of Neuroradiology.

The readability of pediatric patient education materials on the World Wide Web.

Science.gov (United States)

D'Alessandro, D M; Kingsley, P; Johnson-West, J

2001-07-01

Literacy is a national and international problem. Studies have shown the readability of adult and pediatric patient education materials to be too high for average adults. Materials should be written at the 8th-grade level or lower. To determine the general readability of pediatric patient education materials designed for adults on the World Wide Web (WWW). GeneralPediatrics.com (http://www.generalpediatrics.com) is a digital library serving the medical information needs of pediatric health care providers, patients, and families. Documents from 100 different authoritative Web sites designed for laypersons were evaluated using a built-in computer software readability formula (Flesch Reading Ease and Flesch-Kincaid reading levels) and hand calculation methods (Fry Formula and SMOG methods). Analysis of variance and paired t tests determined significance. Eighty-nine documents constituted the final sample; they covered a wide spectrum of pediatric topics. The overall Flesch Reading Ease score was 57.0. The overall mean Fry Formula was 12.0 (12th grade, 0 months of schooling) and SMOG was 12.2. The overall Flesch-Kincaid grade level was significantly lower (Peducation materials on the WWW are not written at an appropriate reading level for the average adult. We propose that a practical reading level and how it was determined be included on all patient education materials on the WWW for general guidance in material selection. We discuss suggestions for improved readability of patient education materials.

Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach.

Science.gov (United States)

Hettige, Nuwan C; Nguyen, Thai Binh; Yuan, Chen; Rajakulendran, Thanara; Baddour, Jermeen; Bhagwat, Nikhil; Bani-Fatemi, Ali; Voineskos, Aristotle N; Mallar Chakravarty, M; De Luca, Vincenzo

2017-07-01

Suicide is a major concern for those afflicted by schizophrenia. Identifying patients at the highest risk for future suicide attempts remains a complex problem for psychiatric interventions. Machine learning models allow for the integration of many risk factors in order to build an algorithm that predicts which patients are likely to attempt suicide. Currently it is unclear how to integrate previously identified risk factors into a clinically relevant predictive tool to estimate the probability of a patient with schizophrenia for attempting suicide. We conducted a cross-sectional assessment on a sample of 345 participants diagnosed with schizophrenia spectrum disorders. Suicide attempters and non-attempters were clearly identified using the Columbia Suicide Severity Rating Scale (C-SSRS) and the Beck Suicide Ideation Scale (BSS). We developed four classification algorithms using a regularized regression, random forest, elastic net and support vector machine models with sociocultural and clinical variables as features to train the models. All classification models performed similarly in identifying suicide attempters and non-attempters. Our regularized logistic regression model demonstrated an accuracy of 67% and an area under the curve (AUC) of 0.71, while the random forest model demonstrated 66% accuracy and an AUC of 0.67. Support vector classifier (SVC) model demonstrated an accuracy of 67% and an AUC of 0.70, and the elastic net model demonstrated and accuracy of 65% and an AUC of 0.71. Machine learning algorithms offer a relatively successful method for incorporating many clinical features to predict individuals at risk for future suicide attempts. Increased performance of these models using clinically relevant variables offers the potential to facilitate early treatment and intervention to prevent future suicide attempts. Copyright © 2017 Elsevier Inc. All rights reserved.

Formalising the Safety of Java, the Java Virtual Machine and Java Card

OpenAIRE

Hartel, Pieter H.; Moreau, Luc

2001-01-01

We review the existing literature on Java safety, emphasizing formal approaches, and the impact of Java safety on small footprint devices such as smart cards. The conclusion is that while a lot of good work has been done, a more concerted effort is needed to build a coherent set of machine readable formal models of the whole of Java and its implementation. This is a formidable task but we believe it is essential to building trust in Java safety, and thence to achieve ITSEC level 6 or Common C...

What parents are reading about laryngomalacia: Quality and readability of internet resources on laryngomalacia.

Science.gov (United States)

Corredera, Erica; Davis, Kara S; Simons, Jeffrey P; Jabbour, Noel

2018-05-01

The goal of this study is to measure the quality and readability of websites related to laryngomalacia, and to compare the quality and readability scores for the sites accessed through the most popular search engines. Laryngomalacia is a common diagnosis in children but is often difficult for parents to comprehend. As information available on the internet is unregulated, the quality and readability of this information may vary. An advanced search on Google, Yahoo, and Bing was conducted using the terms "laryngomalacia" OR "soft larynx" OR "floppy voice box." The first ten websites meeting inclusion and exclusion criteria were evaluated, for each search engine. Quality and readability were assessed using the DISCERN criteria and the Flesch reading ease scoring (FRES) and Flesch-Kincaid grade level (FKGL) tests, respectively. The top 10 hits on each search engine yielded 15 unique web pages. The median DISCERN score (out of a possible high-score of 80) was 48.5 (SD 12.6). The median USA grade-level estimated by the FKGL was 11.3 (SD 1.4). Only one website (6.7%), had a readability score in the optimal range of 6th to 8th grade reading level. DISCERN scores did not correlate with FKGL scores (r = 0.10). Online information discussing laryngomalacia often varies in quality and may not be easily comprehensible to the public. It is important for healthcare professionals to understand the quality of health information accessible to patients as it may influence medical decision-making by patient families. Copyright © 2018 Elsevier B.V. All rights reserved.

How Readability and Topic Incidence Relate to Performance on Mathematics Story Problems in Computer-Based Curricula

Science.gov (United States)

Walkington, Candace; Clinton, Virginia; Ritter, Steven N.; Nathan, Mitchell J.

2015-01-01

Solving mathematics story problems requires text comprehension skills. However, previous studies have found few connections between traditional measures of text readability and performance on story problems. We hypothesized that recently developed measures of readability and topic incidence measured by text-mining tools may illuminate associations…

An experimental study on the readability of the digital images in the furcal bone defects

International Nuclear Information System (INIS)

Oh, Bong Hyeon; Hwang, Eui Hwan; Lee, Sang Rae

1995-01-01

The aim of this study was to evaluate and compare observer performance between conventional radiographs and their digitized images for the detection of bone loss in the bifurcation of mandibular first molar. One dried human mandible with minimal periodontal bone loss around the first molar was selected and serially enlarged 27 step defects were prepared in the bifurcation area. The mandible was radiographed with exposure time of 0.12, 0.20, 0.25, 0.32, 0.40, 0.64 seconds, after each successive step in the preperation and all radiographs were digitized with IBM-PC/32 bit-Dx compatible, video camera (VM-S8200, Hitachi Co., Japan), and color monitor (Multisync 3D, NEC, Japan). Sylvia Image Capture Board for the ADC (analog to digital converter) was used. The following results obtained: 1. In the conventional radiographs, the mean score of the readability was higher at the condition of exposure time with 0.32 second. Also, as the size of artificial lesion was increased, the readability of radiographs was elevated (p<0.05). 2. In the digital images, the mean score of the readability was higher at the condition of exposure time with 0.40 second. Also, as the size of artificial lesion was increased, the readability of digital images was elevated (p<0.05). 3. At the same exposure time, the mean scores of readability were mostly higher in the digitized images. As the exposure time was increased, the digital images were superior to radiographs in readability. 4. As the size of lesion was changed, the digital images were superior to radiographs in detecting small lesion. 5. The coefficient of variation of mean score has no significant difference between digital images and radiographs.

Klasifikasi Topik Keluhan Pelanggan Berdasarkan Tweet dengan Menggunakan Penggabungan Feature Hasil Ekstraksi pada Metode Support Vector Machine (SVM

Directory of Open Access Journals (Sweden)

Enda Esyudha Pratama

2015-12-01

Full Text Available Pemanfaatan twitter sebagai layanan customer serevice perusahaan sudah mulai banyak digunakan, tak terkecuali Speedy. Mekanisme yang ada saat ini untuk proses klasifikasi bentuk dan jenis keluhan serta informasi tentang jumlah keluhan lewat twitter masih dilakukan secara manual. Belum lagi data twitter yang bersifat tidak terstruktur tentunya akan menyulitkan untuk dilakukan analisa dan penggalian informasi dari data tersebut. Berdasarkan permasalahan tersebut, penelitian ini bertujuan untuk memproses data teks dari tweet pengguna twitteryang masuk ke akun @TelkomSpeedy untuk diolah menjadi informasi. Informasi tersebut nantinya digunakan untuk klasifikasi bentuk dan jenis keluhan. Merujuk pada beberapa penelitian terkait, salah satu metode klasifikasi yang paling baik untuk digunakan adalah metode Support Vector Machine (SVM. Konsep dari SVM dapat dijelaskan secara sederhana sebagai usaha mencari hyperplane yang dapat memisahkan dataset sesuai dengan kelasnya. Kelas yang digunakan dalam penelitian kali ini berdasarkan topik keluhan pelanggan yaitu billing, pemasangan/instalasi, putus (disconnect, dan lambat. Faktor penting lainnya dalam hal klasifikasi adalah penentuan feature atau atribut kata yang akan digunakan. Metode feature selection yang digunakan pada penlitian ini adalah term frequency (TF, document frequency (DF, information gain, dan chi-square. Pada penelitian ini juga dilakukan metode penggabungan feature yang telah dihasilkan dari beberapa metode feature selection sebelumnya. Dari hasil penelitian menunjukan bahwa SVM mampu melakukan klasifikasi keluhan dengan baik, hal ini dibuktikan dengan akurasi 82,50% untuk klasifikasi bentuk keluhan dan 86,67% untuk klasifikasi jenis keluhan. Sedangkan untuk kombinasi penggunaan feature dapat meningkatkan akurasi menjadi 83,33% untuk bentuk keluhan dan 89,17% untuk jenis keluhan.   Kata Kunci—customer service, klasifikasi topik keluhan, penggabungan feature, support vector machine

Tools for Assessing Readability of Statistics Teaching Materials

Science.gov (United States)

Lesser, Lawrence; Wagler, Amy

2016-01-01

This article provides tools and rationale for instructors in math and science to make their assessment and curriculum materials (more) readable for students. The tools discussed (MSWord, LexTutor, Coh-Metrix TEA) are readily available linguistic analysis applications that are grounded in current linguistic theory, but present output that can…

Towards human behavior recognition based on spatio temporal features and support vector machines

Science.gov (United States)

Ghabri, Sawsen; Ouarda, Wael; Alimi, Adel M.

2017-03-01

Security and surveillance are vital issues in today's world. The recent acts of terrorism have highlighted the urgent need for efficient surveillance. There is indeed a need for an automated system for video surveillance which can detect identity and activity of person. In this article, we propose a new paradigm to recognize an aggressive human behavior such as boxing action. Our proposed system for human activity detection includes the use of a fusion between Spatio Temporal Interest Point (STIP) and Histogram of Oriented Gradient (HoG) features. The novel feature called Spatio Temporal Histogram Oriented Gradient (STHOG). To evaluate the robustness of our proposed paradigm with a local application of HoG technique on STIP points, we made experiments on KTH human action dataset based on Multi Class Support Vector Machines classification. The proposed scheme outperforms basic descriptors like HoG and STIP to achieve 82.26% us an accuracy value of classification rate.

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

Science.gov (United States)

Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-07-18

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine.

Science.gov (United States)

Yang, Zhangjing; Feng, Piaopiao; Wen, Tian; Wan, Minghua; Hong, Xunning

2017-01-01

Differentiation of glioblastoma multiformes (GBMs) and lymphomas using multi-sequence magnetic resonance imaging (MRI) is an important task that is valuable for treatment planning. However, this task is a challenge because GBMs and lymphomas may have a similar appearance in MRI images. This similarity may lead to misclassification and could affect the treatment results. In this paper, we propose a semi-automatic method based on multi-sequence MRI to differentiate these two types of brain tumors. Our method consists of three steps: 1) the key slice is selected from 3D MRIs and region of interests (ROIs) are drawn around the tumor region; 2) different features are extracted based on prior clinical knowledge and validated using a t-test; and 3) features that are helpful for classification are used to build an original feature vector and a support vector machine is applied to perform classification. In total, 58 GBM cases and 37 lymphoma cases are used to validate our method. A leave-one-out crossvalidation strategy is adopted in our experiments. The global accuracy of our method was determined as 96.84%, which indicates that our method is effective for the differentiation of GBM and lymphoma and can be applied in clinical diagnosis. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Contents and readability of currently used surgical/ procedure ...

African Journals Online (AJOL)

Conclusion: The content of majority of the informed consent forms used in Nigerian tertiary health institutions are poor and their readability scores are not better than those used in developed parts of the world. Health Institutions in Nigeria should revise their informed consent forms to improve their contents and do a usability ...

Integration of Error Compensation of Coordinate Measuring Machines into Feature Measurement: Part I—Model Development

Science.gov (United States)

Calvo, Roque; D’Amato, Roberto; Gómez, Emilio; Domingo, Rosario

2016-01-01

The development of an error compensation model for coordinate measuring machines (CMMs) and its integration into feature measurement is presented. CMMs are widespread and dependable instruments in industry and laboratories for dimensional measurement. From the tip probe sensor to the machine display, there is a complex transformation of probed point coordinates through the geometrical feature model that makes the assessment of accuracy and uncertainty measurement results difficult. Therefore, error compensation is not standardized, conversely to other simpler instruments. Detailed coordinate error compensation models are generally based on CMM as a rigid-body and it requires a detailed mapping of the CMM’s behavior. In this paper a new model type of error compensation is proposed. It evaluates the error from the vectorial composition of length error by axis and its integration into the geometrical measurement model. The non-explained variability by the model is incorporated into the uncertainty budget. Model parameters are analyzed and linked to the geometrical errors and uncertainty of CMM response. Next, the outstanding measurement models of flatness, angle, and roundness are developed. The proposed models are useful for measurement improvement with easy integration into CMM signal processing, in particular in industrial environments where built-in solutions are sought. A battery of implementation tests are presented in Part II, where the experimental endorsement of the model is included. PMID:27690052

Integration of Error Compensation of Coordinate Measuring Machines into Feature Measurement: Part I—Model Development

Directory of Open Access Journals (Sweden)

Roque Calvo

2016-09-01

Full Text Available The development of an error compensation model for coordinate measuring machines (CMMs and its integration into feature measurement is presented. CMMs are widespread and dependable instruments in industry and laboratories for dimensional measurement. From the tip probe sensor to the machine display, there is a complex transformation of probed point coordinates through the geometrical feature model that makes the assessment of accuracy and uncertainty measurement results difficult. Therefore, error compensation is not standardized, conversely to other simpler instruments. Detailed coordinate error compensation models are generally based on CMM as a rigid-body and it requires a detailed mapping of the CMM’s behavior. In this paper a new model type of error compensation is proposed. It evaluates the error from the vectorial composition of length error by axis and its integration into the geometrical measurement model. The non-explained variability by the model is incorporated into the uncertainty budget. Model parameters are analyzed and linked to the geometrical errors and uncertainty of CMM response. Next, the outstanding measurement models of flatness, angle, and roundness are developed. The proposed models are useful for measurement improvement with easy integration into CMM signal processing, in particular in industrial environments where built-in solutions are sought. A battery of implementation tests are presented in Part II, where the experimental endorsement of the model is included.

Electrical machines & drives

CERN Document Server

Hammond, P

1985-01-01

Containing approximately 200 problems (100 worked), the text covers a wide range of topics concerning electrical machines, placing particular emphasis upon electrical-machine drive applications. The theory is concisely reviewed and focuses on features common to all machine types. The problems are arranged in order of increasing levels of complexity and discussions of the solutions are included where appropriate to illustrate the engineering implications. This second edition includes an important new chapter on mathematical and computer simulation of machine systems and revised discussions o

Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

Directory of Open Access Journals (Sweden)

Maolong Xi

2016-01-01

Full Text Available This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO for cancer feature gene selection, coupling support vector machine (SVM for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV. Finally, the BQPSO coupling SVM (BQPSO/SVM, binary PSO coupling SVM (BPSO/SVM, and genetic algorithm coupling SVM (GA/SVM are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.

Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

Science.gov (United States)

Sun, Jun; Liu, Li; Fan, Fangyun; Wu, Xiaojun

2016-01-01

This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms. PMID:27642363

Comparative Readability of Shoulder and Elbow Patient Education Materials within Orthopaedic Websites.

Science.gov (United States)

Beutel, Bryan G; Danna, Natalie R; Melamed, Eitan; Capo, John T

2015-12-01

There is growing concern that the readability of online orthopaedic patient education materials are too difficult for the general public to fully understand. It is recommended that this information be at the sixth grade reading level or lower. This study compared the readability of shoulder and elbow education articles from the American Academy of Orthopaedic Surgeons (AAOS) and American Society for Surgery of the Hand (ASSH) websites. Seventy-six patient education articles from the AAOS and ASSH concerning shoulder and elbow disorders were evaluated. Each article was assessed for the number of years since its last update, word count, percentage of passive sentences, Flesch Reading Ease score, Flesch-Kincaid grade level, Simple Measure of Gobbledygook (SMOG) grade, and New Dale-Chall grade level. Only one article was at or below the sixth grade reading level. The AAOS and ASSH articles had the following respective scores: a mean Flesch Reading Ease score of 54.3 and 51.8, Flesch-Kincaid grade level of 9.4 and 10.3, SMOG grade of 8.5 and 9.4, and New Dale-Chall grade of 10.4 and 11.0. Articles from the AAOS were longer (p education materials regarding the shoulder and elbow on the AAOS and ASSH websites have readability scores above the recommended reading level. These may be too challenging for the majority of patients to read and consequently serve as a barrier to proper patient education. Reducing the percentage of passive sentences may serve as a novel target for improving readability.

Dr Google: The readability and accuracy of patient education websites for Graves' disease treatment.

Science.gov (United States)

Purdy, Amanda C; Idriss, Almoatazbellah; Ahern, Susan; Lin, Elizabeth; Elfenbein, Dawn M

2017-11-01

National guidelines emphasize the importance of incorporating patient preferences into the recommendations for the treatment of Graves' disease. Many patients use the Internet to obtain health information, and search results can affect their treatment decisions. This study compares the readability and accuracy of patient-oriented online resources for the treatment of Graves' disease by website affiliation and treatment modality. A systematic Internet search was used to identify the top websites discussing the treatment of Graves' disease. Readability was measured using 5 standardized tests. Accuracy was assessed by a blinded, expert panel, which scored the accuracy of sites on a scale of 1 to 5. Mean readability and accuracy scores were compared among website affiliations and treatment modalities. We identified 13 unique websites, including 2 academic, 2 government, 5 nonprofit, and 4 private sites. There was a difference in both readability (mean 13.2, range 9.1-15.7, P = .003) and accuracy (mean 4.04, range 2.75-4.50, P = .019) based on website affiliation. Government sites (mean readability 11.1) were easier to read than academic (14.3, P < .01), nonprofit (13.9, P < .01), and private sites (13.5, P < .05). Academic sites (mean accuracy 4.50) were more accurate than private sites (3.56, P < .05). Online patient resources for the treatment of Graves' disease are written at an inappropriately high reading level. Academic sites contain both the most accurate and the most difficult to read information. Private sites represented the majority of our top results but contained the least accurate information. Copyright © 2017 Elsevier Inc. All rights reserved.

Readability of online patient education materials for velopharyngeal insufficiency.

Science.gov (United States)

Xie, Deborah X; Wang, Ray Y; Chinnadurai, Sivakumar

2018-01-01

Evaluate the readability of online and mobile application health information about velopharyngeal insufficiency (VPI). Top website and mobile application results for search terms "velopharyngeal insufficiency", "velopharyngeal dysfunction", "VPI", and "VPD" were analyzed. Readability was determined using 10 algorithms with Readability Studio Professional Edition (Oleander Software Ltd; Vandalia, OH). Subgroup analysis was performed based on search term and article source - academic hospital, general online resource, peer-reviewed journal, or professional organization. 18 unique articles were identified. Overall mean reading grade level was a 12.89 ± 2.9. The highest reading level among these articles was 15.47-approximately the level of a college senior. Articles from "velopharyngeal dysfunction" had the highest mean reading level (13.73 ± 2.11), above "velopharyngeal insufficiency" (12.30 ± 1.56) and "VPI" (11.66 ± 1.70). Articles from peer-reviewed journals had the highest mean reading level (15.35 ± 2.79), while articles from academic hospitals had the lowest (12.81 ± 1.66). There were statistically significant differences in reading levels between the different search terms (P reading level guidelines, online patient education materials for VPI are disseminated with language too complex for most readers. There is also a lack of VPI-related mobile application data available for patients. Patients will benefit if future updates to websites and disseminated patient information are undertaken with health literacy in mind. Future studies will investigate patient comprehension of these materials. Copyright © 2017 Elsevier B.V. All rights reserved.

Readability Analysis of the Package Leaflets for Biological Medicines Available on the Internet Between 2007 and 2013: An Analytical Longitudinal Study.

Science.gov (United States)

Piñero-López, María Ángeles; Modamio, Pilar; Lastra, Cecilia F; Mariño, Eduardo L

2016-05-25

The package leaflet included in the packaging of all medicinal products plays an important role in the transmission of medicine-related information to patients. Therefore, in 2009, the European Commission published readability guidelines to try to ensure that the information contained in the package leaflet is understood by patients. The main objective of this study was to calculate and compare the readability levels and length (number of words) of the package leaflets for biological medicines in 2007, 2010, and 2013. The sample of this study included 36 biological medicine package leaflets that were downloaded from the European Medicines Agency website in three different years: 2007, 2010, and 2013. The readability of the selected package leaflets was obtained using the following readability formulas: SMOG grade, Flesch-Kincaid grade level, and Szigriszt's perspicuity index. The length (number of words) of the package leaflets was also measured. Afterwards, the relationship between these quantitative variables (three readability indexes and length) and categorical (or qualitative) variables were analyzed. The categorical variables were the year when the package leaflet was downloaded, the package leaflet section, type of medicine, year of authorization of biological medicine, and marketing authorization holder. The readability values of all the package leaflets exceeded the sixth-grade reading level, which is the recommended value for health-related written materials. No statistically significant differences were found between the three years of study in the readability indexes, although differences were observed in the case of the length (P=.002), which increased over the study period. When the relationship between readability indexes and length and the other variables was analyzed, statistically significant differences were found between package leaflet sections (Preadability indexes (SMOG grade and Flesch-Kincaid grade level: r(2)=.92; SMOG grade and Szigriszt

Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features.

Science.gov (United States)

Szantoi, Zoltan; Escobedo, Francisco J; Abd-Elrahman, Amr; Pearlstine, Leonard; Dewitt, Bon; Smith, Scot

2015-05-01

Mapping of wetlands (marsh vs. swamp vs. upland) is a common remote sensing application.Yet, discriminating between similar freshwater communities such as graminoid/sedge fromremotely sensed imagery is more difficult. Most of this activity has been performed using medium to low resolution imagery. There are only a few studies using highspatial resolutionimagery and machine learning image classification algorithms for mapping heterogeneouswetland plantcommunities. This study addresses this void by analyzing whether machine learning classifierssuch as decisiontrees (DT) and artificial neural networks (ANN) can accurately classify graminoid/sedgecommunities usinghigh resolution aerial imagery and image texture data in the Everglades National Park, Florida.In addition tospectral bands, the normalized difference vegetation index, and first- and second-order texturefeatures derivedfrom the near-infrared band were analyzed. Classifier accuracies were assessed using confusiontablesand the calculated kappa coefficients of the resulting maps. The results indicated that an ANN(multilayerperceptron based on backpropagation) algorithm produced a statistically significantly higheraccuracy(82.04%) than the DT (QUEST) algorithm (80.48%) or the maximum likelihood (80.56%)classifier (αtexture features.

A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods.

Science.gov (United States)

Torija, Antonio J; Ruiz, Diego P

2015-02-01

The prediction of environmental noise in urban environments requires the solution of a complex and non-linear problem, since there are complex relationships among the multitude of variables involved in the characterization and modelling of environmental noise and environmental-noise magnitudes. Moreover, the inclusion of the great spatial heterogeneity characteristic of urban environments seems to be essential in order to achieve an accurate environmental-noise prediction in cities. This problem is addressed in this paper, where a procedure based on feature-selection techniques and machine-learning regression methods is proposed and applied to this environmental problem. Three machine-learning regression methods, which are considered very robust in solving non-linear problems, are used to estimate the energy-equivalent sound-pressure level descriptor (LAeq). These three methods are: (i) multilayer perceptron (MLP), (ii) sequential minimal optimisation (SMO), and (iii) Gaussian processes for regression (GPR). In addition, because of the high number of input variables involved in environmental-noise modelling and estimation in urban environments, which make LAeq prediction models quite complex and costly in terms of time and resources for application to real situations, three different techniques are used to approach feature selection or data reduction. The feature-selection techniques used are: (i) correlation-based feature-subset selection (CFS), (ii) wrapper for feature-subset selection (WFS), and the data reduction technique is principal-component analysis (PCA). The subsequent analysis leads to a proposal of different schemes, depending on the needs regarding data collection and accuracy. The use of WFS as the feature-selection technique with the implementation of SMO or GPR as regression algorithm provides the best LAeq estimation (R(2)=0.94 and mean absolute error (MAE)=1.14-1.16 dB(A)). Copyright © 2014 Elsevier B.V. All rights reserved.

49 CFR 573.4 - Definitions.

Science.gov (United States)

2010-10-01

... readable by machine. If readable by machine, the submitting party must obtain written confirmation from the Office of Defects Investigation immediately prior to submission that the machine is readily available to... addition, all coded information must be accompanied by an explanation of the codes used. Replacement...

Readability of Orthopaedic Patient-reported Outcome Measures: Is There a Fundamental Failure to Communicate?

Science.gov (United States)

Perez, Jorge L; Mosher, Zachary A; Watson, Shawna L; Sheppard, Evan D; Brabston, Eugene W; McGwin, Gerald; Ponce, Brent A

2017-08-01

Patient-reported outcome measures (PROMs) are increasingly used to quantify patients' perceptions of functional ability. The American Medical Association and NIH suggest patient materials be written at or below 6th to 8th grade reading levels, respectively, yet one recent study asserts that few PROMs comply with these recommendations, and suggests that the majority of PROMs are written at too high of a reading level for self-administered patient use. Notably, this study was limited in its use of only one readability algorithm, although there is no commonly accepted, standard readability algorithm for healthcare-related materials. Our study, using multiple readability equations and heeding equal weight to each, hopes to yield a broader, all-encompassing estimate of readability, thereby offering a more accurate assessment of the readability of orthopaedic PROMS. (1) What proportion of orthopaedic-related PROMs and orthopaedic-related portions of the NIH Patient Reported Outcomes Measurement Information System (PROMIS ® ) are written at or below the 6th and 8th grade levels? (2) Is there a correlation between the number of questions in the PROM and reading level? (3) Using systematic edits based on guidelines from the Centers for Medicare and Medicaid Services, what proportion of PROMs achieved American Medical Association and NIH-recommended reading levels? Eighty-six (86) independent, orthopaedic and general wellness PROMs, drawn from commonly referenced orthopaedic websites and prior studies, were chosen for analysis. Additionally, owing to their increasing use in orthopaedics, four relevant short forms, and 11 adult, physical health question banks from the PROMIS ® , were included for analysis. All documents were analyzed for reading grade levels using 19 unique readability algorithms. Descriptive statistics were performed using SPSS Version 22.0. The majority of the independent PROMs (64 of 86; 74%) were written at or below the 6th grade level, with 81 of 86

Readability assessment of internet-based patient education materials related to mammography for breast cancer screening.

Science.gov (United States)

AlKhalili, Rend; Shukla, Pratik A; Patel, Ronak H; Sanghvi, Saurin; Hubbi, Basil

2015-03-01

The US Department of Health and Human Services (USDHHS) recommends that Internet-based patient education materials (IPEMs) be written below the sixth-grade reading level to target the average American adult. This study was designed to determine the readability of IPEMs regarding mammography for breast cancer screening. Three-hundred mammography-related Web sites were reviewed for IPEMs. Forty-two IPEMs that met the Health on the Net Foundation Code of Conduct were assessed for readability level with four readability indices that use existing algorithms based on word and sentence length to quantitatively analyze Internet sources for language intricacy including the following: Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease Score (FRES), Simple Measure of Gobbledygook (SMOG), and Gunning Frequency of Gobbledygook (Gunning FOG; GFOG). Results were compared to national recommendations, and intergroup analysis was performed. No IPEMs (0%) regarding mammography were written at or below the sixth-grade reading level, based on FKGL. The mean readability scores were as follows: FRES, 49.04 ± 10.62; FKGL, 10.71 ± 2.01; SMOG, 13.33 ± 1.67; and Gunning FOG, 14.32 ± 2.18. These scores indicate that the readability of mammography IPEMs is written at a "difficult" level, significantly above the recommended sixth-grade reading level (P < .05) determined by the USDHHS. IPEMs related to mammography are written well above the recommended sixth-grade level and likely reflect other IPEMs in diagnostic radiology. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

The Readability of Electronic Cigarette Health Information and Advice: A Quantitative Analysis of Web-Based Information.

Science.gov (United States)

Park, Albert; Zhu, Shu-Hong; Conway, Mike

2017-01-06

The popularity and use of electronic cigarettes (e-cigarettes) has increased across all demographic groups in recent years. However, little is currently known about the readability of health information and advice aimed at the general public regarding the use of e-cigarettes. The objective of our study was to examine the readability of publicly available health information as well as advice on e-cigarettes. We compared information and advice available from US government agencies, nongovernment organizations, English speaking government agencies outside the United States, and for-profit entities. A systematic search for health information and advice on e-cigarettes was conducted using search engines. We manually verified search results and converted to plain text for analysis. We then assessed readability of the collected documents using 4 readability metrics followed by pairwise comparisons of groups with adjustment for multiple comparisons. A total of 54 documents were collected for this study. All 4 readability metrics indicate that all information and advice on e-cigarette use is written at a level higher than that recommended for the general public by National Institutes of Health (NIH) communication guidelines. However, health information and advice written by for-profit entities, many of which were promoting e-cigarettes, were significantly easier to read. A substantial proportion of potential and current e-cigarette users are likely to have difficulty in fully comprehending Web-based health information regarding e-cigarettes, potentially hindering effective health-seeking behaviors. To comply with NIH communication guidelines, government entities and nongovernment organizations would benefit from improving the readability of e-cigarettes information and advice. ©Albert Park, Shu-Hong Zhu, Mike Conway. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 06.01.2017.

Influence of vocabulary and sentence complexity and passive voice on the readability of consumer-oriented mental health information on the Internet.

Science.gov (United States)

Ownby, Raymond L

2005-01-01

Searching for health care information is one of the most common uses of the Internet by the elderly. Our earlier research showed that health care information websites may present information at levels of readability that are excessively difficult for many potential users. This study investigated the influence of several aspects of readability (vocabulary and sentence complexity and use of passive voice construction) on overall readability at several different levels of readability. Results show that easier to read sites could be differentiated most consistently from more difficult sites by vocabulary complexity. Comparison of the easiest and most difficult sites in several cases showed that sentence complexity and passive voice may also be important. These results can provide guidance for those interested in improving the readability of web sites that provide mental health information for consumers.

An Analysis of the Readability of Financial Accounting Textbooks.

Science.gov (United States)

Smith, Gerald; And Others

1981-01-01

The Flesch formula was used to calculate the readability of 15 financial accounting textbooks. The 15 textbooks represented introductory, intermediate, and advanced levels and also were classified by five different publishers. Two-way analysis of variance and Tukey's post hoc analysis revealed some significant differences. (Author/CT)

Readability of patient information pamphlets in urogynecology.

Science.gov (United States)

Reagan, Krista M L; O'Sullivan, David M; Harvey, David P; Lasala, Christine A

2015-01-01

The purpose of this study was to determine the reading level of frequently used patient information pamphlets and documents in the field of urogynecology. Urogynecology pamphlets were identified from a variety of sources. Readability was determined using 4 different accepted formulas: the Flesch-Kincaid Grade Level, the simple measure of gobbledygook Index, the Coleman-Liau Index, and the Gunning Fog index. The scores were calculated using an online calculator (http://www.readability-score.com). Descriptive statistics were used for analysis. The average of the 4 scores was calculated for each pamphlet. Subsequently, Z-scores were used to standardize the averages between the reading scales. Of the 40 documents reviewed, only a single pamphlet met the National Institutes of Health-recommended reading level. This document was developed by the American Urological Association and was specifically designated as a "Low-Literacy Brochure." The remainder of the patient education pamphlets, from both industry-sponsored and academic-sponsored sources, consistently rated above the recommended reading level for maximum comprehension. The majority of patient education pamphlets, from both industry-sponsored and academic-sponsored sources, are above the reading level recommended by the National Institutes of Health for maximum patient comprehension. Future work should be done to improve the educational resources available to patients by simplifying the verbiage in these documents.

A quantitative readability analysis of patient education resources from gastroenterology society websites.

Science.gov (United States)

Hansberry, David R; Patel, Sahil R; Agarwal, Prateek; Agarwal, Nitin; John, Elizabeth S; John, Ann M; Reynolds, James C

2017-06-01

The lay public frequently access and rely on online information as a source of their medical knowledge. Many medical societies are unaware of national patient education material guidelines and subsequently fail to meet them. The goal of the present study was to evaluate the readability of patient education materials within the medical field of gastroenterology. Two hundred fourteen articles pertaining to patient education materials were evaluated with ten well-established readability scales. The articles were available on the websites for the American College of Gastroenterology (ACG), the American Gastroenterological Association (AGA), the American Society of Gastrointestinal Endoscopy (ASGE), the British Society of Gastroenterology (BSG), and the NIH section National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). One-way analysis of variance (ANOVA) and Tukey's honest significant difference (HSD) post hoc analysis were conducted to determine any differences in level of readability between websites. The 214 articles were written at an 11.8 ± 2.1 grade level with a range of 8.0 to 16.0 grade level. A one-way ANOVA and Tukey's HSD post hoc analysis determined the ACG was written at a significantly (p gastroenterology content.

Machine medical ethics

CERN Document Server

Pontier, Matthijs

2015-01-01

The essays in this book, written by researchers from both humanities and sciences, describe various theoretical and experimental approaches to adding medical ethics to a machine in medical settings. Medical machines are in close proximity with human beings, and getting closer: with patients who are in vulnerable states of health, who have disabilities of various kinds, with the very young or very old, and with medical professionals. In such contexts, machines are undertaking important medical tasks that require emotional sensitivity, knowledge of medical codes, human dignity, and privacy. As machine technology advances, ethical concerns become more urgent: should medical machines be programmed to follow a code of medical ethics? What theory or theories should constrain medical machine conduct? What design features are required? Should machines share responsibility with humans for the ethical consequences of medical actions? How ought clinical relationships involving machines to be modeled? Is a capacity for e...

Readability of Informed Consent Documents at University Counseling Centers

Science.gov (United States)

Lustgarten, Samuel D.; Elchert, Daniel M.; Cederberg, Charles; Garrison, Yunkyoung L.; Ho, Y. C. S.

2017-01-01

The extent to which clients understand the nature and anticipated course of therapy is referred to as informed consent. Counseling psychologists often provide informed consent documents to enhance the education of services and for liability purposes. Professionals in numerous health care settings have evaluated the readability of their informed…

BLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection

KAUST Repository

Kandaswamy, Krishna Kumar

2011-08-17

Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.

BLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection

KAUST Repository

Kandaswamy, Krishna Kumar; Pugalenthi, Ganesan; Hazrati, Mehrnaz Khodam; Kalies, Kai-Uwe; Martinetz, Thomas

2011-01-01

Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.

Readability, complexity, and suitability analysis of online lymphedema resources.

Science.gov (United States)

Tran, Bao Ngoc N; Singh, Mansher; Lee, Bernard T; Rudd, Rima; Singhal, Dhruv

2017-06-01

Over 72% of Americans use online health information to assist in health care decision-making. Previous studies of lymphedema literature have focused only on reading level of patient-oriented materials online. Findings indicate they are too advanced for most patients to comprehend. This, more comprehensive study, expands the previous analysis to include critical elements of health materials beyond readability using assessment tools to report on the complexity and density of data as well as text design, vocabulary, and organization. The top 10 highest ranked websites on lymphedema were identified using the most popular search engine (Google). Website content was analyzed for readability, complexity, and suitability using Simple Measure of Gobbledygook, PMOSE/iKIRSCH, and Suitability Assessment of Materials (SAM), respectively. PMOSE/iKIRSCH and SAM were performed by two independent raters. Fleiss' kappa score was calculated to ensure inter-rater reliability. Online lymphedema literature had a reading grade level of 14.0 (SMOG). Overall complexity score was 6.7 (PMOSE/iKIRSCH) corresponding to "low" complexity and requiring a 8th-12th grade education. Fleiss' kappa score was 80% (P = 0.04, "substantial" agreement). Overall suitability score was 45% (SAM) correlating to the lowest level of "adequate" suitability. Fleiss' kappa score was 76% (P = 0.06, "substantial" agreement). Online resources for lymphedema are above the recommended levels for readability and complexity. The suitability level is barely adequate for the intended audience. Overall, these materials are too sophisticated for the average American adult, whose literacy skills are well documented. Further efforts to revise these materials are needed to improve patient comprehension and understanding. Copyright © 2017 Elsevier Inc. All rights reserved.

Reassessing the Accuracy and Use of Readability Formulae

Science.gov (United States)

Janan, Dahlia; Wray, David

2014-01-01

Purpose: The purpose of the study is to review readability formulae and offer a critique, based on a comparison of the grading of a variety of texts given by six well-known formulae. Methodology: A total of 64 texts in English were selected either by or for native English speaking children aged between six and 11 years. Each text was assessed…

Polyadenylated Sequencing Primers Enable Complete Readability of PCR Amplicons Analyzed by Dideoxynucleotide Sequencing

Directory of Open Access Journals (Sweden)

Martin Beránek

2012-01-01

Full Text Available Dideoxynucleotide DNA sequencing is one of the principal procedures in molecular biology. Loss of an initial part of nucleotides behind the 3' end of the sequencing primer limits the readability of sequenced amplicons. We present a method which extends the readability by using sequencing primers modified by polyadenylated tails attached to their 5' ends. Performing a polymerase chain reaction, we amplified eight amplicons of six human genes (AMELX, APOE, HFE, MBL2, SERPINA1 and TGFB1 ranging from 106 bp to 680 bp. Polyadenylation of the sequencing primers minimized the loss of bases in all amplicons. Complete sequences of shorter products (AMELX 106 bp, SERPINA1 121 bp, HFE 208 bp, APOE 244 bp, MBL2 317 bp were obtained. In addition, in the case of TGFB1 products (366 bp, 432 bp, and 680 bp, respectively, the lengths of sequencing readings were significantly longer if adenylated primers were used. Thus, single strand dideoxynucleotide sequencing with adenylated primers enables complete or near complete readability of short PCR amplicons.

Evaluation of Quality and Readability of Health Information Websites Identified through India's Major Search Engines.

Science.gov (United States)

Raj, S; Sharma, V L; Singh, A J; Goel, S

2016-01-01

Background. The available health information on websites should be reliable and accurate in order to make informed decisions by community. This study was done to assess the quality and readability of health information websites on World Wide Web in India. Methods. This cross-sectional study was carried out in June 2014. The key words "Health" and "Information" were used on search engines "Google" and "Yahoo." Out of 50 websites (25 from each search engines), after exclusion, 32 websites were evaluated. LIDA tool was used to assess the quality whereas the readability was assessed using Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), and SMOG. Results. Forty percent of websites (n = 13) were sponsored by government. Health On the Net Code of Conduct (HONcode) certification was present on 50% (n = 16) of websites. The mean LIDA score (74.31) was average. Only 3 websites scored high on LIDA score. Only five had readability scores at recommended sixth-grade level. Conclusion. Most health information websites had average quality especially in terms of usability and reliability and were written at high readability levels. Efforts are needed to develop the health information websites which can help general population in informed decision making.

Health literacy and the Internet: a study on the readability of Australian online health information.

Science.gov (United States)

Cheng, Christina; Dunn, Matthew

2015-08-01

Almost 80% of Australian Internet users seek out health information online so the readability of this information is important. This study aimed to evaluate the readability of Australian online health information and determine if it matches the average reading level of Australians. Two hundred and fifty-one web pages with information on 12 common health conditions were identified across sectors. Readability was assessed by the Flesch-Kincaid (F-K), Simple Measure of Gobbledygook (SMOG) and Flesch Reading Ease (FRE) formulas, with grade 8 adopted as the average Australian reading level. The average reading grade measured by F-K and SMOG was 10.54 and 12.12 respectively. The mean FRE was 47.54, a 'difficult-to-read' score. Only 0.4% of web pages were written at or below grade 8 according to SMOG. Information on dementia was the most difficult to read overall, while obesity was the most difficult among government websites. The findings suggest that the readability of Australian health websites is above the average Australian levels of reading. A quantifiable guideline is needed to ensure online health information accommodates the reading needs of the general public to effectively use the Internet as an enabler of health literacy. © 2015 Public Health Association of Australia.

Pattern recognition & machine learning

CERN Document Server

Anzai, Y

1992-01-01

This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

Readability Trends of Online Information by the American Academy of Otolaryngology-Head and Neck Surgery Foundation.

Science.gov (United States)

Wong, Kevin; Levi, Jessica R

2017-01-01

Objective Previous studies have shown that patient education materials published by the American Academy of Otolaryngology-Head and Neck Surgery Foundation may be too difficult for the average reader to understand. The purpose of this study was to determine if current educational materials show improvements in readability. Study Design Cross-sectional analysis. Setting The Patient Health Information section of the American Academy of Otolaryngology-Head and Neck Surgery Foundation website. Subjects and Methods All patient education articles were extracted in plain text. Webpage navigation, references, author information, appointment information, acknowledgments, and disclaimers were removed. Follow-up editing was also performed to remove paragraph breaks, colons, semicolons, numbers, percentages, and bullets. Readability grade was calculated with the Flesch-Kincaid Grade Level, Flesch Reading Ease, Gunning-Fog Index, Coleman-Liau Index, Automated Readability Index, and Simple Measure of Gobbledygook. Intra- and interobserver reliability were assessed. Results A total of 126 articles from 7 topics were analyzed. Readability levels across all 6 tools showed that the difficulty of patient education materials exceeded the abilities of an average American. As compared with previous studies, current educational materials by the American Academy of Otolaryngology-Head and Neck Surgery Foundation have shown a decrease in difficulty. Intra- and interobserver reliability were both excellent, with intraclass coefficients of 0.99 and 0.96, respectively. Conclusion Improvements in readability is an encouraging finding and one that is consistent with recent trends toward improved health literacy. Nevertheless, online patient educational material is still too difficult for the average reader. Revisions may be necessary for current materials to benefit a larger readership.

Using Machine Learning to Predict Student Performance

OpenAIRE

Pojon, Murat

2017-01-01

This thesis examines the application of machine learning algorithms to predict whether a student will be successful or not. The specific focus of the thesis is the comparison of machine learning methods and feature engineering techniques in terms of how much they improve the prediction performance. Three different machine learning methods were used in this thesis. They are linear regression, decision trees, and naïve Bayes classification. Feature engineering, the process of modification ...

Accuracy and readability of cardiovascular entries on Wikipedia: are they reliable learning resources for medical students?

Science.gov (United States)

Azer, Samy A; AlSwaidan, Nourah M; Alshwairikh, Lama A; AlShammari, Jumana M

2015-10-06

To evaluate accuracy of content and readability level of English Wikipedia articles on cardiovascular diseases, using quality and readability tools. Wikipedia was searched on the 6 October 2013 for articles on cardiovascular diseases. Using a modified DISCERN (DISCERN is an instrument widely used in assessing online resources), articles were independently scored by three assessors. The readability was calculated using Flesch-Kincaid Grade Level. The inter-rater agreement between evaluators was calculated using the Fleiss κ scale. This study was based on 47 English Wikipedia entries on cardiovascular diseases. The DISCERN scores had a median=33 (IQR=6). Four articles (8.5%) were of good quality (DISCERN score 40-50), 39 (83%) moderate (DISCERN 30-39) and 4 (8.5%) were poor (DISCERN 10-29). Although the entries covered the aetiology and the clinical picture, there were deficiencies in the pathophysiology of diseases, signs and symptoms, diagnostic approaches and treatment. The number of references varied from 1 to 127 references; 25.9±29.4 (mean±SD). Several problems were identified in the list of references and citations made in the articles. The readability of articles was 14.3±1.7 (mean±SD); consistent with the readability level for college students. In comparison, Harrison's Principles of Internal Medicine 18th edition had more tables, less references and no significant difference in number of graphs, images, illustrations or readability level. The overall agreement between the evaluators was good (Fleiss κ 0.718 (95% CI 0.57 to 0.83). The Wikipedia entries are not aimed at a medical audience and should not be used as a substitute to recommended medical resources. Course designers and students should be aware that Wikipedia entries on cardiovascular diseases lack accuracy, predominantly due to errors of omission. Further improvement of the Wikipedia content of cardiovascular entries would be needed before they could be considered a supplementary resource

Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms

Directory of Open Access Journals (Sweden)

Kuan-Cheng Lin

2015-01-01

Full Text Available Rapid advances in information and communication technology have made ubiquitous computing and the Internet of Things popular and practicable. These applications create enormous volumes of data, which are available for analysis and classification as an aid to decision-making. Among the classification methods used to deal with big data, feature selection has proven particularly effective. One common approach involves searching through a subset of the features that are the most relevant to the topic or represent the most accurate description of the dataset. Unfortunately, searching through this kind of subset is a combinatorial problem that can be very time consuming. Meaheuristic algorithms are commonly used to facilitate the selection of features. The artificial fish swarm algorithm (AFSA employs the intelligence underlying fish swarming behavior as a means to overcome optimization of combinatorial problems. AFSA has proven highly successful in a diversity of applications; however, there remain shortcomings, such as the likelihood of falling into a local optimum and a lack of multiplicity. This study proposes a modified AFSA (MAFSA to improve feature selection and parameter optimization for support vector machine classifiers. Experiment results demonstrate the superiority of MAFSA in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original FASA.

An asymptotical machine

Science.gov (United States)

Cristallini, Achille

2016-07-01

A new and intriguing machine may be obtained replacing the moving pulley of a gun tackle with a fixed point in the rope. Its most important feature is the asymptotic efficiency. Here we obtain a satisfactory description of this machine by means of vector calculus and elementary trigonometry. The mathematical model has been compared with experimental data and briefly discussed.

Third Molars on the Internet: A Guide for Assessing Information Quality and Readability.

Science.gov (United States)

Hanna, Kamal; Brennan, David; Sambrook, Paul; Armfield, Jason

2015-10-06

Directing patients suffering from third molars (TMs) problems to high-quality online information is not only medically important, but also could enable better engagement in shared decision making. This study aimed to develop a scale that measures the scientific information quality (SIQ) for online information concerning wisdom tooth problems and to conduct a quality evaluation for online TMs resources. In addition, the study evaluated whether a specific piece of readability software (Readability Studio Professional 2012) might be reliable in measuring information comprehension, and explored predictors for the SIQ Scale. A cross-sectional sample of websites was retrieved using certain keywords and phrases such as "impacted wisdom tooth problems" using 3 popular search engines. The retrieved websites (n=150) were filtered. The retained 50 websites were evaluated to assess their characteristics, usability, accessibility, trust, readability, SIQ, and their credibility using DISCERN and Health on the Net Code (HoNCode). Websites' mean scale scores varied significantly across website affiliation groups such as governmental, commercial, and treatment provider bodies. The SIQ Scale had a good internal consistency (alpha=.85) and was significantly correlated with DISCERN (r=.82, Psoftware estimates were associated with scientific information comprehensiveness measures.

An analysis of the readability of patient information and consent forms used in research studies in anaesthesia in Australia and New Zealand.

Science.gov (United States)

Taylor, H E; Bramley, D E P

2012-11-01

The provision of written information is a component of the informed consent process for research participants. We conducted a readability analysis to test the hypothesis that the language used in patient information and consent forms in anaesthesia research in Australia and New Zealand does not meet the readability standards or expectations of the Good Clinical Practice Guidelines, the National Health and Medical Research Council in Australia and the Health Research Council of New Zealand. We calculated readability scores for 40 patient information and consent forms using the Simple Measure of Gobbledygook and Flesch-Kincaid formulas. The mean grade level of patient information and consent forms when using the Simple Measure of Gobbledygook and Flesch-Kincaid readability formulas was 12.9 (standard deviation of 0.8, 95% confidence interval 12.6 to 13.1) and 11.9 (standard deviation 1.1, 95% confidence interval 11.6 to 12.3), respectively. This exceeds the average literacy and comprehension of the general population in Australia and New Zealand. Complex language decreases readability and negatively impacts on the informed consent process. Care should be exercised when providing written information to research participants to ensure language and readability is appropriate for the audience.

Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization – Extreme learning machine approach

International Nuclear Information System (INIS)

Salcedo-Sanz, S.; Pastor-Sánchez, A.; Prieto, L.; Blanco-Aguilera, A.; García-Herrera, R.

2014-01-01

Highlights: • A novel approach for short-term wind speed prediction is presented. • The system is formed by a coral reefs optimization algorithm and an extreme learning machine. • Feature selection is carried out with the CRO to improve the ELM performance. • The method is tested in real wind farm data in USA, for the period 2007–2008. - Abstract: This paper presents a novel approach for short-term wind speed prediction based on a Coral Reefs Optimization algorithm (CRO) and an Extreme Learning Machine (ELM), using meteorological predictive variables from a physical model (the Weather Research and Forecast model, WRF). The approach is based on a Feature Selection Problem (FSP) carried out with the CRO, that must obtain a reduced number of predictive variables out of the total available from the WRF. This set of features will be the input of an ELM, that finally provides the wind speed prediction. The CRO is a novel bio-inspired approach, based on the simulation of reef formation and coral reproduction, able to obtain excellent results in optimization problems. On the other hand, the ELM is a new paradigm in neural networks’ training, that provides a robust and extremely fast training of the network. Together, these algorithms are able to successfully solve this problem of feature selection in short-term wind speed prediction. Experiments in a real wind farm in the USA show the excellent performance of the CRO–ELM approach in this FSP wind speed prediction problem

Evaluation of Quality and Readability of Health Information Websites Identified through India’s Major Search Engines

Directory of Open Access Journals (Sweden)

S. Raj

2016-01-01

Full Text Available Background. The available health information on websites should be reliable and accurate in order to make informed decisions by community. This study was done to assess the quality and readability of health information websites on World Wide Web in India. Methods. This cross-sectional study was carried out in June 2014. The key words “Health” and “Information” were used on search engines “Google” and “Yahoo.” Out of 50 websites (25 from each search engines, after exclusion, 32 websites were evaluated. LIDA tool was used to assess the quality whereas the readability was assessed using Flesch Reading Ease Score (FRES, Flesch-Kincaid Grade Level (FKGL, and SMOG. Results. Forty percent of websites (n=13 were sponsored by government. Health On the Net Code of Conduct (HONcode certification was present on 50% (n=16 of websites. The mean LIDA score (74.31 was average. Only 3 websites scored high on LIDA score. Only five had readability scores at recommended sixth-grade level. Conclusion. Most health information websites had average quality especially in terms of usability and reliability and were written at high readability levels. Efforts are needed to develop the health information websites which can help general population in informed decision making.

Logic without unique readability - a study of semantic and syntactic ambiguity

DEFF Research Database (Denmark)

Bentzen, Martin Mose

One of the main reasons for introducing a formal language is to remove ambiguity, the possibility of assigning several meanings to a linguistic expression. Typically, this is achieved through ensuring unique readability of formulas by using brackets (or another convention, such as Polish notation...... not hold true universally. Whereas e.g. scope ambiguities in natural languages have been studied extensively, ambiguous formal languages have not been the focus of in depth research. Here, we lift the assumption of unique readability by omitting the brackets from propositional logic, making it possible...... to formally distinguish between syntactic and semantic ambiguity. A valuation then amounts to a semantic disambiguation, and rather than a unique valuation (truth value), there is a set of valuations corresponding to ways a formula could have been constructed. We show what happens to familiar concepts...

Identification and characterization of plastid-type proteins from sequence-attributed features using machine learning

Science.gov (United States)

2013-01-01

Background Plastids are an important component of plant cells, being the site of manufacture and storage of chemical compounds used by the cell, and contain pigments such as those used in photosynthesis, starch synthesis/storage, cell color etc. They are essential organelles of the plant cell, also present in algae. Recent advances in genomic technology and sequencing efforts is generating a huge amount of DNA sequence data every day. The predicted proteome of these genomes needs annotation at a faster pace. In view of this, one such annotation need is to develop an automated system that can distinguish between plastid and non-plastid proteins accurately, and further classify plastid-types based on their functionality. We compared the amino acid compositions of plastid proteins with those of non-plastid ones and found significant differences, which were used as a basis to develop various feature-based prediction models using similarity-search and machine learning. Results In this study, we developed separate Support Vector Machine (SVM) trained classifiers for characterizing the plastids in two steps: first distinguishing the plastid vs. non-plastid proteins, and then classifying the identified plastids into their various types based on their function (chloroplast, chromoplast, etioplast, and amyloplast). Five diverse protein features: amino acid composition, dipeptide composition, the pseudo amino acid composition, Nterminal-Center-Cterminal composition and the protein physicochemical properties are used to develop SVM models. Overall, the dipeptide composition-based module shows the best performance with an accuracy of 86.80% and Matthews Correlation Coefficient (MCC) of 0.74 in phase-I and 78.60% with a MCC of 0.44 in phase-II. On independent test data, this model also performs better with an overall accuracy of 76.58% and 74.97% in phase-I and phase-II, respectively. The similarity-based PSI-BLAST module shows very low performance with about 50% prediction

Readability Levels of the Reading Passages in the ITED: Final Report. Iowa Testing Programs Research Report. Number 6.

Science.gov (United States)

Forsyth, Robert

The readability level of passages from three subtests of the Iowa Tests of Educational Development (ITED), Forms X-6 and Y-6, were compared with the readability level of passages selected from the Des Moines Resister, Reader's Digest, Time, Newsweek, Saturday Review, and 18 high school textbooks from the fields of social studies, science, and…

Reply to "Further Issues in Determining the Readability of Self-Report Items: Comment on McHugh and Behar (2009)"

Science.gov (United States)

McHugh, R. Kathryn; Behar, Evelyn

2012-01-01

In his commentary on our previously published article "Readability of Self-Report Measures of Depression and Anxiety," J. Schinka (2012) argued for the importance of considering readability of patient materials and highlighted limitations of existing methodologies for this assessment. Schinka's commentary articulately described the weaknesses of…

Readability of self-illuminated signs obscured by black fuel-fire smoke.

Science.gov (United States)

1980-07-01

This study, using black fuel-fire generated smoke, is a partial replication of an earlier study using an inert white smoke as the obscuring agent in the study of the readability of smoke-obscured, self-illuminated emergency exit signs. : The results ...

Specific Features of Chip Making and Work-piece Surface Layer Formation in Machining Thermal Coatings

Directory of Open Access Journals (Sweden)

V. M. Yaroslavtsev

2016-01-01

Full Text Available A wide range of unique engineering structural and performance properties inherent in metallic composites characterizes wear- and erosion-resistant high-temperature coatings made by thermal spraying methods. This allows their use both in manufacturing processes to enhance the wear strength of products, which have to operate under the cyclic loading, high contact pressures, corrosion and high temperatures and in product renewal.Thermal coatings contribute to the qualitative improvement of the technical level of production and product restoration using the ceramic composite materials. However, the possibility to have a significantly increased product performance, reduce their factory labour hours and materials/output ratio in manufacturing and restoration is largely dependent on the degree of the surface layer quality of products at their finishing stage, which is usually provided by different kinds of machining.When machining the plasma-sprayed thermal coatings, a removing process of the cut-off layer material is determined by its distinctive features such as a layered structure, high internal stresses, low ductility material, high tendency to the surface layer strengthening and rehardening, porosity, high abrasive properties, etc. When coatings are machined these coating properties result in specific characteristics of chip formation and conditions for formation of the billet surface layer.The chip formation of plasma-sprayed coatings was studied at micro-velocities using an experimental tool-setting microscope-based setup, created in BMSTU. The setup allowed simultaneous recording both the individual stages (phases of the chip formation process and the operating force factors.It is found that formation of individual chip elements comes with the multiple micro-cracks that cause chipping-off the small particles of material. The emerging main crack in the cut-off layer of material leads to separation of the largest chip element. Then all the stages

How well are health information websites displayed on mobile phones? Implications for the readability of health information.

Science.gov (United States)

Cheng, Christina; Dunn, Matthew

2017-03-01

Issue addressed More than 87% of Australians own a mobile phone with Internet access and 82% of phone owners use their smartphones to search for health information, indicating that mobile phones may be a powerful tool for building health literacy. Yet, online health information has been found to be above the reading ability of the general population. As reading on a smaller screen may further complicate the readability of information, this study aimed to examine how health information is displayed on mobile phones and its implications for readability. Methods Using a cross-sectional design with convenience sampling, a sample of 270 mobile webpages with information on 12 common health conditions was generated for analysis, they were categorised based on design and position of information display. Results The results showed that 71.48% of webpages were mobile-friendly but only 15.93% were mobile-friendly webpages designed in a way to optimise readability, with a paging format and queried information displayed for immediate viewing. Conclusion With inadequate evidence and lack of consensus on how webpage design can best promote reading and comprehension, it is difficult to draw a conclusion on the effect of current mobile health information presentation on readability. So what? Building mobile-responsive websites should be a priority for health information providers and policy-makers. Research efforts are urgently required to identify how best to enhance readability of mobile health information and fully capture the capabilities of mobile phones as a useful device to increase health literacy.

The role of readability in effective health communication: an experiment using a Japanese health information text on chronic suppurative otitis media.

Science.gov (United States)

Sakai, Yukiko

2013-09-01

This study identifies the most significant readability factors and examines ways of improving and evaluating Japanese health information text in terms of ease of reading and understanding. Six different Japanese texts were prepared based on an original short text written by a medical doctor for a hospital web site intended for laypersons regarding chronic suppurative otitis media. Four were revised for single readability factor (syntax, vocabulary, or text structure) and two were modified in all three factors. Using a web-based survey, 270 high school students read one of the seven texts, including the original, completed two kinds of comprehension tests, and answered questions on their impressions of the text's readability. Significantly higher comprehension test scores were shown in the true or false test for a mixed text that presented important information first for better text structure. They were also found in the cloze test for a text using common vocabulary and a cohesive mixed text. Vocabulary could be a critical single readability factor when presumably combined with better text structure. Using multiple evaluation methods can help assess comprehensive readability. The findings on improvement and evaluation methods of readability can be applied to support effective health communication. © 2013 The authors. Health Information and Libraries Journal © 2013 Health Libraries Group Health Information and Libraries Journal.

Combining extreme learning machines using support vector machines for breast tissue classification.

Science.gov (United States)

Daliri, Mohammad Reza

2015-01-01

In this paper, we present a new approach for breast tissue classification using the features derived from electrical impedance spectroscopy. This method is composed of a feature extraction method, feature selection phase and a classification step. The feature extraction phase derives the features from the electrical impedance spectra. The extracted features consist of the impedivity at zero frequency (I0), the phase angle at 500 KHz, the high-frequency slope of phase angle, the impedance distance between spectral ends, the area under spectrum, the normalised area, the maximum of the spectrum, the distance between impedivity at I0 and the real part of the maximum frequency point and the length of the spectral curve. The system uses the information theoretic criterion as a strategy for feature selection and the combining extreme learning machines (ELMs) for the classification phase. The results of several ELMs are combined using the support vector machines classifier, and the result of classification is reported as a measure of the performance of the system. The results indicate that the proposed system achieves high accuracy in classification of breast tissues using the electrical impedance spectroscopy.

Readability of Spanish language online information for the initial treatment of burns.

Science.gov (United States)

Votta, Kaitlyn; Metivier, Meghan; Romo, Stephanie; Garrigan, Hannah; Drexler, Alana; Nodoushani, Ariana; Sheridan, Robert

2018-06-01

This study's aim is to identify the most popular online resources for burn treatment information available in the Spanish language, and to evaluate the readability of this information. The phrase "tratamiento de quemaduras" (burn treatment) was entered into search engines Google and Bing on 9/15/2014 and 9/13/2017. The top 12 Spanish web results on each site were identified and analyzed using Readability Studio Professional Edition v2012.1. The software generated a "mean grade reading level" for each article, or the grade of students that could be expected to understand the article's language. 21 distinct articles were identified at T1 and 17 at T2, with seven overlapping between T1 and T2. The average grade reading level of all the websites ranged from 7.8 to 13.8 at T1 (approximately 8th grade to sophomore year of college) and 7.8 to 12.2 at T2. No websites were within 1 standard deviation of the American Medical Association recommended 6th grade reading level. With readability showing little improvement during the past three years, providers should be aware of the complexity of online literature, and the potential complications this presents to patients. Additionally, burn centers should prioritize generating more accessible information for the Spanish speaking public. Copyright © 2017 Elsevier Ltd and ISBI. All rights reserved.

Critical Analysis of the Quality, Readability, and Technical Aspects of Online Information Provided for Neck-Lifts.

Science.gov (United States)

Rayess, Hani; Zuliani, Giancarlo F; Gupta, Amar; Svider, Peter F; Folbe, Adam J; Eloy, Jean Anderson; Carron, Michael A

2017-03-01

The number of patients using the internet to obtain health information is growing. This material is unregulated and heterogeneous and can influence patient decisions. To compare the quality, readability, and technical aspects of online information about neck-lifts provided by private practice websites vs academic medical centers and reference sources. In this cross-sectional analysis conducted between November 2015 and January 2016, a Google search of the term neck-lift was performed, and the first 45 websites were evaluated. The websites were categorized as private practice vs other. Private websites (PWs) included sites created by private practice physicians. Other websites (OWs) were created by academic medical centers or reference sources. Quality, readability, and technical aspects of online websites related to neck-lifts. Quality was assessed using the DISCERN criteria and the Health on the Net principles (HONcode). Readability was assessed using 7 validated and widely used criteria. Consensus US reading grade level readability was provided by a website (readabilityformulas.com). Twelve technical aspects were evaluated based on criteria specified by medical website creators. Forty-five websites (8 OWs [18%] and 37 PWs [82%]) were analyzed. There was a significant difference in quality between OWs and PWs based on the DISCERN criteria and HONcode principles. The DISCERN overall mean (SD) scores were 2.3 (0.5) for OWs and 1.3 (0.3) for PWs (P analysis, the mean (SD) was 8.6 (1.8) (range, 5-11) for OW, and the mean (SD) was 5.8 (1.7) (range, 2-9) for PW. The mean (SD) readability consensus reading grade level scores were 11.7 (1.9) for OWs and 10.6 (1.9) for PWs. Of a total possible score of 12, the mean (SD) technical scores were 6.3 (1.8) (range, 4-9) for OWs and 6.4 (1.5) (range, 3-9) for PWs. Compared with PWs, OWs had a significantly higher quality score based on both the DISCERN criteria and HONcode principles. The mean readability for OWs and PWs was

Readability Levels of Health-Based Websites: From Content to Comprehension

Science.gov (United States)

Schutten, Mary; McFarland, Allison

2009-01-01

Three of the national health education standards include decision-making, accessing information and analyzing influences. WebQuests are a popular inquiry-oriented method used by secondary teachers to help students achieve these content standards. While WebQuests support higher level thinking skills, the readability level of the information on the…

Spanish Readability Formulas for Elementary-Level Texts: A Validation Study.

Science.gov (United States)

Parker, Richard I.; Hasbrouck, Jan E.; Weaver, Laurie

2001-01-01

Uses two formulas developed for Spanish language text to analyze 9 stories that were read by 36 Spanish-speaking second graders with limited English proficiency. Finds that the Spanish readability formulas only weakly predicted student performance, indicating the need to pursue broader, qualitative indices of difficulty for Spanish text. (SG)

Quality and readability of websites for patient information on tonsillectomy and sleep apnea.

Science.gov (United States)

Chi, Ethan; Jabbour, Noel; Aaronson, Nicole Leigh

2017-07-01

Tonsillectomy is a common treatment for obstructive sleep apnea (OSA). The Internet allows patients direct access to medical information. Since information on the Internet is largely unregulated, quality and readability are variable. This study evaluates the quality and readability of the most likely visited websites presenting information on sleep apnea and tonsillectomy. The three most popular search engines (Google, Bing, Yahoo) were queried with the phrase "sleep apnea AND tonsillectomy." The DISCERN instrument was used to assess quality of information. Readability was evaluated using the Flesch-Kincaid Reading Grade Level (FKGL) and Flesch Reading Ease Score (FRES). Out of the maximum of 80, the average DISCERN quality score for the websites was 55.1 (SD- 12.3, Median- 60.5). The mean score for FRES was 42.3 (SD- 15.9, Median- 45.5), which falls in the range defined as difficult. No website was above the optimal score of 65. The mean score for the FKGL was US grade-level of 10.7 (SD- 1.6, Median- 11.6). Only 4(27%) websites were in the optimal range of 6-8. There was very weak correlation between FRES and DISCERN (r = 0.07) and FKGL and DISCERN (r = 0.21). Tonsillectomy is one of the most common surgeries in the US. However, the internet information readily available to patients varies in quality. Additionally, much of the information is above the recommended grade level for comprehension by the public. By being aware of what information patients are reading online, physicians can better explain treatments and address misunderstandings. Physicians may consider using similar methods to test the readability for their own resources for patient education. Copyright © 2017 Elsevier B.V. All rights reserved.

Classification of Alzheimer's disease patients with hippocampal shape wrapper-based feature selection and support vector machine

Science.gov (United States)

Young, Jonathan; Ridgway, Gerard; Leung, Kelvin; Ourselin, Sebastien

2012-02-01

It is well known that hippocampal atrophy is a marker of the onset of Alzheimer's disease (AD) and as a result hippocampal volumetry has been used in a number of studies to provide early diagnosis of AD and predict conversion of mild cognitive impairment patients to AD. However, rates of atrophy are not uniform across the hippocampus making shape analysis a potentially more accurate biomarker. This study studies the hippocampi from 226 healthy controls, 148 AD patients and 330 MCI patients obtained from T1 weighted structural MRI images from the ADNI database. The hippocampi are anatomically segmented using the MAPS multi-atlas segmentation method, and the resulting binary images are then processed with SPHARM software to decompose their shapes as a weighted sum of spherical harmonic basis functions. The resulting parameterizations are then used as feature vectors in Support Vector Machine (SVM) classification. A wrapper based feature selection method was used as this considers the utility of features in discriminating classes in combination, fully exploiting the multivariate nature of the data and optimizing the selected set of features for the type of classifier that is used. The leave-one-out cross validated accuracy obtained on training data is 88.6% for classifying AD vs controls and 74% for classifying MCI-converters vs MCI-stable with very compact feature sets, showing that this is a highly promising method. There is currently a considerable fall in accuracy on unseen data indicating that the feature selection is sensitive to the data used, however feature ensemble methods may overcome this.

Readability of patient discharge instructions with and without the use of electronically available disease-specific templates.

Science.gov (United States)

Mueller, Stephanie K; Giannelli, Kyla; Boxer, Robert; Schnipper, Jeffrey L

2015-07-01

Low health literacy is common, leading to patient vulnerability during hospital discharge, when patients rely on written health instructions. We aimed to examine the impact of the use of electronic, patient-friendly, templated discharge instructions on the readability of discharge instructions provided to patients at discharge. We performed a retrospective cohort study of 233 patients discharged from a large tertiary care hospital to their homes following the implementation of a web-based "discharge module," which included the optional use of diagnosis-specific templated discharge instructions. We compared the readability of discharge instructions, as measured by the Flesch Reading Ease Level test (FREL, on a 0-100 scale, with higher scores indicating greater readability) and the Flesch-Kincaid Grade Level test (FKGL, measured in grade levels), between discharges that used templated instructions (with or without modification) versus discharges that used clinician-generated instructions (with or without available templated instructions for the specific discharge diagnosis). Templated discharge instructions were provided to patients in 45% of discharges. Of the 55% of patients that received clinician-generated discharge instructions, the majority (78.1%) had no available templated instruction for the specific discharge diagnosis. Templated discharge instructions had higher FREL scores (71 vs. 57, P readability (a higher FREL score and a lower FKGL score) than the use of clinician-generated discharge instructions. The main reason for clinicians to create discharge instructions was the lack of available templates for the patient's specific discharge diagnosis. Use of electronically available templated discharge instructions may be a viable option to improve the readability of written material provided to patients at discharge, although the library of available templates requires expansion. © The Author 2015. Published by Oxford University Press on behalf of the

A Novel Approach for Automatic Machining Feature Recognition with Edge Blend Feature

OpenAIRE

Keong Chen Wong; Yusof Yusri

2017-01-01

This paper presents an algorithm for efficiently recognizing and determining the convexity of an edge blend feature. The algorithm first recognizes all of the edge blend features from the Boundary Representation of a part; then a series of convexity test have been run on the recognized edge blend features. The novelty of the presented algorithm lies in, instead of each recognized blend feature is suppressed as most of researchers did, the recognized blend features of this research are gone th...

Multiple-output support vector machine regression with feature selection for arousal/valence space emotion assessment.

Science.gov (United States)

Torres-Valencia, Cristian A; Álvarez, Mauricio A; Orozco-Gutiérrez, Alvaro A

2014-01-01

Human emotion recognition (HER) allows the assessment of an affective state of a subject. Until recently, such emotional states were described in terms of discrete emotions, like happiness or contempt. In order to cover a high range of emotions, researchers in the field have introduced different dimensional spaces for emotion description that allow the characterization of affective states in terms of several variables or dimensions that measure distinct aspects of the emotion. One of the most common of such dimensional spaces is the bidimensional Arousal/Valence space. To the best of our knowledge, all HER systems so far have modelled independently, the dimensions in these dimensional spaces. In this paper, we study the effect of modelling the output dimensions simultaneously and show experimentally the advantages in modeling them in this way. We consider a multimodal approach by including features from the Electroencephalogram and a few physiological signals. For modelling the multiple outputs, we employ a multiple output regressor based on support vector machines. We also include an stage of feature selection that is developed within an embedded approach known as Recursive Feature Elimination (RFE), proposed initially for SVM. The results show that several features can be eliminated using the multiple output support vector regressor with RFE without affecting the performance of the regressor. From the analysis of the features selected in smaller subsets via RFE, it can be observed that the signals that are more informative into the arousal and valence space discrimination are the EEG, Electrooculogram/Electromiogram (EOG/EMG) and the Galvanic Skin Response (GSR).

Classification of breast masses in ultrasound images using self-adaptive differential evolution extreme learning machine and rough set feature selection.

Science.gov (United States)

Prabusankarlal, Kadayanallur Mahadevan; Thirumoorthy, Palanisamy; Manavalan, Radhakrishnan

2017-04-01

A method using rough set feature selection and extreme learning machine (ELM) whose learning strategy and hidden node parameters are optimized by self-adaptive differential evolution (SaDE) algorithm for classification of breast masses is investigated. A pathologically proven database of 140 breast ultrasound images, including 80 benign and 60 malignant, is used for this study. A fast nonlocal means algorithm is applied for speckle noise removal, and multiresolution analysis of undecimated discrete wavelet transform is used for accurate segmentation of breast lesions. A total of 34 features, including 29 textural and five morphological, are applied to a [Formula: see text]-fold cross-validation scheme, in which more relevant features are selected by quick-reduct algorithm, and the breast masses are discriminated into benign or malignant using SaDE-ELM classifier. The diagnosis accuracy of the system is assessed using parameters, such as accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), Matthew's correlation coefficient (MCC), and area ([Formula: see text]) under receiver operating characteristics curve. The performance of the proposed system is also compared with other classifiers, such as support vector machine and ELM. The results indicated that the proposed SaDE algorithm has superior performance with [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] compared to other classifiers.

Introduction to machine learning.

Science.gov (United States)

Baştanlar, Yalin; Ozuysal, Mustafa

2014-01-01

The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.

Principal Components of Superhigh-Dimensional Statistical Features and Support Vector Machine for Improving Identification Accuracies of Different Gear Crack Levels under Different Working Conditions

Directory of Open Access Journals (Sweden)

Dong Wang

2015-01-01

Full Text Available Gears are widely used in gearbox to transmit power from one shaft to another. Gear crack is one of the most frequent gear fault modes found in industry. Identification of different gear crack levels is beneficial in preventing any unexpected machine breakdown and reducing economic loss because gear crack leads to gear tooth breakage. In this paper, an intelligent fault diagnosis method for identification of different gear crack levels under different working conditions is proposed. First, superhigh-dimensional statistical features are extracted from continuous wavelet transform at different scales. The number of the statistical features extracted by using the proposed method is 920 so that the extracted statistical features are superhigh dimensional. To reduce the dimensionality of the extracted statistical features and generate new significant low-dimensional statistical features, a simple and effective method called principal component analysis is used. To further improve identification accuracies of different gear crack levels under different working conditions, support vector machine is employed. Three experiments are investigated to show the superiority of the proposed method. Comparisons with other existing gear crack level identification methods are conducted. The results show that the proposed method has the highest identification accuracies among all existing methods.

An experimental study on the readability of digital images in the furcal bone defects

Energy Technology Data Exchange (ETDEWEB)

Kang, Hyung Wuk; Hwang, Eui Hwan; Lee, Sang Rae [Kyunghee University College of Medicine, Seoul (Korea, Republic of)

2003-06-15

To evaluate and compare the efficacy of digital radiographic images in the detection of bone loss at the bifurcation area of the mandibular first molar with traditional film-based periapical radiographs. One dried human mandible with minimal periodontal bone loss around the first molar was selected and an artificial alveolar bone defect at the bifurcation area was serially prepared over 18 steps. Images were taken using a direct CCD-based system and with F-speed periapical films. The images were evaluated by seven interpreters (3 radiologists, 3 periodontologists, and 1 general dentist) using a 5-point confidence rating scale. The readability of both periapical radiographs and digital image increased as the size of the artificial lesion and exposure time increased (p<0.05). Periapical radiographs offered greater readability of smaller bone defects than digital images, and the coefficient of variation of mean score between periapical radiographs and digital images showed a significant difference. The experimental results indicate that a significant difference in the coefficient of variation of mean score exists between periapical radiographs and digital images, and that traditional film-based periapical images offer greater readability of smaller bone defects than digital images can presently offer.

An experimental study on the readability of digital images in the furcal bone defects

International Nuclear Information System (INIS)

Kang, Hyung Wuk; Hwang, Eui Hwan; Lee, Sang Rae

2003-01-01

To evaluate and compare the efficacy of digital radiographic images in the detection of bone loss at the bifurcation area of the mandibular first molar with traditional film-based periapical radiographs. One dried human mandible with minimal periodontal bone loss around the first molar was selected and an artificial alveolar bone defect at the bifurcation area was serially prepared over 18 steps. Images were taken using a direct CCD-based system and with F-speed periapical films. The images were evaluated by seven interpreters (3 radiologists, 3 periodontologists, and 1 general dentist) using a 5-point confidence rating scale. The readability of both periapical radiographs and digital image increased as the size of the artificial lesion and exposure time increased (p<0.05). Periapical radiographs offered greater readability of smaller bone defects than digital images, and the coefficient of variation of mean score between periapical radiographs and digital images showed a significant difference. The experimental results indicate that a significant difference in the coefficient of variation of mean score exists between periapical radiographs and digital images, and that traditional film-based periapical images offer greater readability of smaller bone defects than digital images can presently offer.

Readability and quality assessment of internet-based patient education materials related to laryngeal cancer.

Science.gov (United States)

Narwani, Vishal; Nalamada, Keerthana; Lee, Michael; Kothari, Prasad; Lakhani, Raj

2016-04-01

Patients are increasingly using the internet to access health-related information. The purpose of this study was to assess the readability and quality of laryngeal cancer-related websites. Patient education materials were identified by performing an internet search using 3 search engines. Readability was assessed using Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), and Gunning Fog Index (GFI). The DISCERN instrument was utilized to assess quality of health information. A total of 54 websites were included in the analysis. The mean readability scores were as follows: FRES, 48.2 (95% confidence interval [CI] = 44.8-51.6); FKGL, 10.9 (95% CI = 10.3-11.5); and GFI, 13.8 (95% CI = 11.3-16.3). These scores suggest that, on average, online information about patients with laryngeal cancer is written at an advanced level. The mean DISCERN score was 49.8 (95% CI = 45.4-54.2), suggesting that online information is of variable quality. Our study suggests much of the laryngeal cancer information available online is of suboptimal quality and written at a level too difficult for the average adult to read comfortably. © 2015 Wiley Periodicals, Inc.

Readability of Written Materials for CKD Patients: A Systematic Review.

Science.gov (United States)

Morony, Suzanne; Flynn, Michaela; McCaffery, Kirsten J; Jansen, Jesse; Webster, Angela C

2015-06-01

The "average" patient has a literacy level of US grade 8 (age 13-14 years), but this may be lower for people with chronic kidney disease (CKD). Current guidelines suggest that patient education materials should be pitched at a literacy level of around 5th grade (age 10-11 years). This study aims to evaluate the readability of written materials targeted at patients with CKD. Systematic review. Patient information materials aimed at adults with CKD and written in English. Patient education materials designed to be printed and read, sourced from practices in Australia and online at all known websites run by relevant international CKD organizations during March 2014. Quantitative analysis of readability using Lexile Analyzer and Flesch-Kincaid tools. We analyzed 80 materials. Both Lexile Analyzer and Flesch-Kincaid analyses suggested that most materials required a minimum of grade 9 (age 14-15 years) schooling to read them. Only 5% of materials were pitched at the recommended level (grade 5). Readability formulas have inherent limitations and do not account for visual information. We did not consider other media through which patients with CKD may access information. Although the study covered materials from the United States, United Kingdom, and Australia, all non-Internet materials were sourced locally, and it is possible that some international paper-based materials were missed. Generalizability may be limited due to exclusion of non-English materials. These findings suggest that patient information materials aimed at patients with CKD are pitched above the average patient's literacy level. This issue is compounded by cognitive decline in patients with CKD, who may have lower literacy than the average patient. It suggests that information providers need to consider their audience more carefully when preparing patient information materials, including user testing with a low-literacy patient population. Copyright © 2015 National Kidney Foundation, Inc. Published by

[Systematic Readability Analysis of Medical Texts on Websites of German University Clinics for General and Abdominal Surgery].

Science.gov (United States)

Esfahani, B Janghorban; Faron, A; Roth, K S; Grimminger, P P; Luers, J C

2016-12-01

Background: Besides the function as one of the main contact points, websites of hospitals serve as medical information portals. As medical information texts should be understood by any patients independent of the literacy skills and educational level, online texts should have an appropriate structure to ease understandability. Materials and Methods: Patient information texts on websites of clinics for general surgery at German university hospitals (n = 36) were systematically analysed. For 9 different surgical topics representative medical information texts were extracted from each website. Using common readability tools and 5 different readability indices the texts were analysed concerning their readability and structure. The analysis was furthermore stratified in relation to geographical regions in Germany. Results: For the definite analysis the texts of 196 internet websites could be used. On average the texts consisted of 25 sentences and 368 words. The reading analysis tools congruously showed that all texts showed a rather low readability demanding a high literacy level from the readers. Conclusion: Patient information texts on German university hospital websites are difficult to understand for most patients. To fulfill the ambition of informing the general population in an adequate way about medical issues, a revision of most medical texts on websites of German surgical hospitals is recommended. Georg Thieme Verlag KG Stuttgart · New York.

Health literacy and contraception: a readability evaluation of contraceptive instructions for condoms, spermicides and emergency contraception in the USA.

Science.gov (United States)

El-Ibiary, Shareen Y; Youmans, Sharon L

2007-03-01

To assess readability of over-the-counter (OTC) contraceptive product instructions currently available, compare the results with previous studies from a decade ago, and review the implications for health care providers, in particular pharmacists counseling on OTC contraceptives. A sample of contraceptive instructions was submitted to a readability analysis using four standard readability formulas. Products included condoms, spermicides, and emergency contraception instruction pamphlets. Reading grade levels for condoms ranged from 6th to 12th grade. The average reading levels for the spermicides were 9th-10th grade and for the emergency contraceptives 10th-12th grade. These results were consistent with those of similar studies performed a decade ago. Consumers need to have at least a high school reading level in order to comprehend current product instructions. Very little has changed in the past decade regarding readability of OTC contraceptive patient instructions, despite calls to simplify written instructions. Healthcare providers, in particular pharmacists, must be aware of these disparities to enhance patient education and advocate for simpler reading materials.

Readability and quality assessment of websites related to microtia and aural atresia.

Science.gov (United States)

Alamoudi, Uthman; Hong, Paul

2015-02-01

Many parents and children utilize the Internet for health-related information, but the quality of these websites can vary. The objective of this study was to assess the quality and readability of microtia and aural atresia related websites. The search engine Google was queried with the terms 'microtia' and 'aural atresia.' The first 30 results were evaluated, and those websites containing original information written in English were reviewed. Quality of content was assessed with the DISCERN instrument, and readability was assessed with the Flesch-Kincaid Reading Grade Level (FKGL) and the Flesch Reading Ease Score (FRES) tests. Each website was also reviewed for ownership and the date of last update. Sixteen microtia and 14 aural atresia websites were included for full review. The mean DISCERN score for microtia websites was 54.4 (SD=8.3), and for aural atresia websites it was 47.6 (SD=10.7), which indicates 'good' and 'fair' quality of content, respectively. Readability assessments showed an average reading level requiring a grade 10 education on FKGL, and only one microtia (6.3%) and one aural atresia (7.1%) websites were deemed to be at 'reasonable' reading level on FRES. High-quality websites that are considered easily comprehensible to the general public were lacking. Since parents and children may use websites when making treatment decisions, physicians should be aware of the quality of health information pertaining to their area of expertise available on the Internet. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Critical feature analysis of a radiotherapy machine

International Nuclear Information System (INIS)

Rae, Andrew; Jackson, Daniel; Ramanan, Prasad; Flanz, Jay; Leyman, Didier

2005-01-01

The software implementation of the emergency shutdown feature in a major radiotherapy system was analyzed, using a directed form of code review based on module dependences. Dependences between modules are labelled by particular assumptions; this allows one to trace through the code, and identify those fragments responsible for critical features. An 'assumption tree' is constructed in parallel, showing the assumptions which each module makes about others. The root of the assumption tree is the critical feature of interest, and its leaves represent assumptions which, if not valid, might cause the critical feature to fail. The analysis revealed some unexpected assumptions that motivated improvements to the code

In vivo classification of human skin burns using machine learning and quantitative features captured by optical coherence tomography

Science.gov (United States)

Singla, Neeru; Srivastava, Vishal; Singh Mehta, Dalip

2018-02-01

We report the first fully automated detection of human skin burn injuries in vivo, with the goal of automatic surgical margin assessment based on optical coherence tomography (OCT) images. Our proposed automated procedure entails building a machine-learning-based classifier by extracting quantitative features from normal and burn tissue images recorded by OCT. In this study, 56 samples (28 normal, 28 burned) were imaged by OCT and eight features were extracted. A linear model classifier was trained using 34 samples and 22 samples were used to test the model. Sensitivity of 91.6% and specificity of 90% were obtained. Our results demonstrate the capability of a computer-aided technique for accurately and automatically identifying burn tissue resection margins during surgical treatment.

About machine-readable travel documents

International Nuclear Information System (INIS)

Vaudenay, S; Vuagnoux, M

2007-01-01

Passports are documents that help immigration officers to identify people. In order to strongly authenticate their data and to automatically identify people, they are now equipped with RFID chips. These contain private information, biometrics, and a digital signature by issuing authorities. Although they substantially increase security at the border controls, they also come with new security and privacy issues. In this paper, we survey existing protocols and their weaknesses

About machine-readable travel documents

Energy Technology Data Exchange (ETDEWEB)

Vaudenay, S; Vuagnoux, M [EPFL, Lausanne (Switzerland)

2007-07-15

Passports are documents that help immigration officers to identify people. In order to strongly authenticate their data and to automatically identify people, they are now equipped with RFID chips. These contain private information, biometrics, and a digital signature by issuing authorities. Although they substantially increase security at the border controls, they also come with new security and privacy issues. In this paper, we survey existing protocols and their weaknesses.

Identifying product order with restricted Boltzmann machines

Science.gov (United States)

Rao, Wen-Jia; Li, Zhenyu; Zhu, Qiong; Luo, Mingxing; Wan, Xin

2018-03-01

Unsupervised machine learning via a restricted Boltzmann machine is a useful tool in distinguishing an ordered phase from a disordered phase. Here we study its application on the two-dimensional Ashkin-Teller model, which features a partially ordered product phase. We train the neural network with spin configuration data generated by Monte Carlo simulations and show that distinct features of the product phase can be learned from nonergodic samples resulting from symmetry breaking. Careful analysis of the weight matrices inspires us to define a nontrivial machine-learning motivated quantity of the product form, which resembles the conventional product order parameter.

Third Molars on the Internet: A Guide for Assessing Information Quality and Readability

Science.gov (United States)

Brennan, David; Sambrook, Paul; Armfield, Jason

2015-01-01

Background Directing patients suffering from third molars (TMs) problems to high-quality online information is not only medically important, but also could enable better engagement in shared decision making. Objectives This study aimed to develop a scale that measures the scientific information quality (SIQ) for online information concerning wisdom tooth problems and to conduct a quality evaluation for online TMs resources. In addition, the study evaluated whether a specific piece of readability software (Readability Studio Professional 2012) might be reliable in measuring information comprehension, and explored predictors for the SIQ Scale. Methods A cross-sectional sample of websites was retrieved using certain keywords and phrases such as “impacted wisdom tooth problems” using 3 popular search engines. The retrieved websites (n=150) were filtered. The retained 50 websites were evaluated to assess their characteristics, usability, accessibility, trust, readability, SIQ, and their credibility using DISCERN and Health on the Net Code (HoNCode). Results Websites’ mean scale scores varied significantly across website affiliation groups such as governmental, commercial, and treatment provider bodies. The SIQ Scale had a good internal consistency (alpha=.85) and was significantly correlated with DISCERN (r=.82, Preadability grade (10.3, SD 1.9) was above the recommended level, and was significantly correlated with the Scientific Information Comprehension Scale (r=.45. PReadability Studio software estimates were associated with scientific information comprehensiveness measures. PMID:26443470

Assessing the Readability of Geoscience Textbooks, Laboratory Manuals, and Supplemental Materials

Science.gov (United States)

Hippensteel, Scott P.

2015-01-01

Reading materials used in undergraduate science classes have not received the same attention in the literature as those used in secondary schools. Additionally, reports critical of college textbooks and their prose are common. To assess both problems and determine the readability of assignments and texts used by geoscience faculty at the…

Readability of Igbo Language Textbook in Use in Nigerian Secondary Schools

Science.gov (United States)

Eze, Nneka Justina

2015-01-01

This study assessed the readability of Igbo language textbook in use in Nigerian secondary schools. Five Igbo Language textbook were evaluated. The study employed an evaluation research design. The study was conducted in South Eastern Geopolitical zone of Nigeria which is predominantly the Igbo tribe of Nigeria. Four hundred secondary school…

25 CFR 547.11 - What are the minimum technical standards for money and credit handling?

Science.gov (United States)

2010-04-01

... GAMES § 547.11 What are the minimum technical standards for money and credit handling? This section... interface is: (i) Involved in the play of a game; (ii) In audit mode, recall mode or any test mode; (iii...) For machine-readable vouchers and coupons, a bar code or other form of machine readable representation...

Refueling machine for a nuclear reactor

International Nuclear Information System (INIS)

Kowalski, E.F.; Hornak, L.P.; Swidwa, K.J.

1981-01-01

An improved refuelling machine for inserting and removing fuel assemblies from a nuclear reactor is described which has been designed to increase the reliability of such machines. The system incorporates features which enable the refuelling operation to be performed more efficiently and economically. (U.K.)

Quality and readability of online patient information regarding sclerotherapy for venous malformations.

Science.gov (United States)

Pass, Jonathan H; Patel, Amani H; Stuart, Sam; Barnacle, Alex M; Patel, Premal A

2018-05-01

Patients often use the internet as a source of information about their condition and treatments. However, this information is unregulated and varies in quality. To evaluate the readability and quality of online information for pediatric and adult patients and caregivers regarding sclerotherapy for venous malformations. "Venous malformation sclerotherapy" was entered into Google, and results were reviewed until 20 sites that satisfied predefined inclusion criteria were identified. Scientific and non-patient-focused web pages were excluded. Readability was assessed using the Flesch Reading Ease Score and American Medical Association reading difficulty recommendations and quality was assessed using Journal of the American Medical Association standards and assessing if the site displayed HONcode (Health on the Net Code) certification. Assessment of the breadth of relevant information was made using a predefined checklist. Forty-nine search engine results were reviewed before 20 sites were identified for analysis. Average Flesch Reading Ease Score was 44 (range: 24.2-70.1), representing a "fairly difficult" reading level. None of the sites had a Flesch Reading Ease Score meeting the American Medical Association recommendation of 80-90. Only one site met all four Journal of the American Medical Association quality criteria (average: 2.1). None of the sites displayed a HONcode seal. The information most frequently found was: sclerotherapy is performed by radiologists, multiple treatments may be needed and surgery is an alternative treatment. Online information regarding sclerotherapy for venous malformations is heterogeneous in quality and breadth of information, and does not meet readability recommendations for patient information. Radiologists should be aware of and account for this when meeting patients.

Mechanical design of machine components

CERN Document Server

Ugural, Ansel C

2015-01-01

Mechanical Design of Machine Components, Second Edition strikes a balance between theory and application, and prepares students for more advanced study or professional practice. It outlines the basic concepts in the design and analysis of machine elements using traditional methods, based on the principles of mechanics of materials. The text combines the theory needed to gain insight into mechanics with numerical methods in design. It presents real-world engineering applications, and reveals the link between basic mechanics and the specific design of machine components and machines. Divided into three parts, this revised text presents basic background topics, deals with failure prevention in a variety of machine elements and covers applications in design of machine components as well as entire machines. Optional sections treating special and advanced topics are also included.Key Features of the Second Edition:Incorporates material that has been completely updated with new chapters, problems, practical examples...

Superconducting machines. Chapter 4

International Nuclear Information System (INIS)

Appleton, A.D.

1977-01-01

A brief account is given of the principles of superconductivity and superconductors. The properties of Nb-Ti superconductors and the method of flux stabilization are described. The basic features of superconducting d.c. machines are illustrated by the use of these machines for ship propulsion, steel-mill drives, industrial drives, aluminium production, and other d.c. power supplies. Superconducting a.c. generators and their design parameters are discussed. (U.K.)

Power Electronics and Electric Machines Facilities | Transportation

Science.gov (United States)

Research | NREL Facilities Power Electronics and Electric Machines Facilities NREL's power electronics and electric machines thermal management experimentation facilities feature a wide range of four researchers in discussion around a piece of laboratory equipment. Power electronics researchers

Readability, complexity, and suitability of online resources for mastectomy and lumpectomy.

Science.gov (United States)

Tran, Bao Ngoc N; Singh, Mansher; Singhal, Dhruv; Rudd, Rima; Lee, Bernard T

2017-05-15

Nearly half of American adults have low or marginal health literacy. This negatively affects patients' participation, decision-making, satisfaction, and overall outcomes especially when there is a mismatch between information provided and the skills of the intended audience. Recommendations that patient information be written below the sixth grade level have been made for over three decades. This study compares online resources for mastectomy versus lumpectomy using expanded metrics including readability level, complexity, and density of data and overall suitability for public consumption. The 10 highest ranked Web sites for mastectomy and lumpectomy were identified using the largest Internet engine (Google). Each Web site was assessed for readability (Simple Measure of Gobbledygook), complexity (PMOSE/iKIRSCH), and suitability (Suitability Assessment of Materials). Scores were analyzed by each Web site and overall. Readability analysis showed a significant reading grade level difference between mastectomy and lumpectomy online information (15.4 and 13.9, P = 0.04, respectively). Complexity analysis via PMOSE/iKIRSCH revealed a mean score of 6.5 for mastectomy materials corresponding to "low" complexity and eighth to 12 th grade education. Lumpectomy literature had a lower PMOSE/iKIRSCH score of 5.8 corresponding to a "very low" complexity and fourth to eighth grade education (P = 0.05). Suitability assessment showed mean values of 41% and 46% (P = 0.83) labeled as the lowest level of "adequacy" for mastectomy and lumpectomy materials, respectively. Inter-rater reliability was high for both complexity and suitability analysis. Online resources for the surgical treatment of breast cancer are above the recommended reading grade level. The suitability level is barely adequate indicating a need for revision. Online resources for mastectomy have a higher reading grade level than do materials for lumpectomy and tend to be more complex. Copyright © 2017 Elsevier

Readability of written medicine information materials in Arabic language: expert and consumer evaluation.

Science.gov (United States)

Al Aqeel, Sinaa; Abanmy, Norah; Aldayel, Abeer; Al-Khalifa, Hend; Al-Yahya, Maha; Diab, Mona

2018-02-27

Written Medicine Information (WMI) is one of the sources that patients use to obtain information concerning medicine. This paper aims to assess the readability of two types of WMIs in Arabic language based on vocabulary use and sentence structure using a panel of experts and consumers. This is a descriptive study. Two different types of materials, including the online text from King Abdullah Bin Abdulaziz Arabic Health Encyclopaedia (KAAHE) and medication leaflets submitted by the manufacturers to the Saudi Food and Drug Authority (SFDA) were evaluated. We selected a group of sentences from each WMI. The readability was assessed by experts (n = 5) and consumers (n = 5). The sentence readability of each measured using a specific criteria and rated as 1 = easy, 2 = intermediate, or 3 = difficult. A total of 4476 sentences (SFDA 2231; KAHEE 2245) extracted from websites or patient information leaflets on 50 medications and evaluated. The majority of the vocabulary and sentence structure was considered easy by both expert (SFDA: 68%; KAAHE: 76%) and consumer (SFDA: 76%; KAAHE: 84%) groups. The sentences with difficult or intermediate vocabulary and sentence structure are derived primarily from the precautions and side effects sections. The SFDA and KAAHE WMIs are easy to read and understand as judged by our study sample. However; there is room for improvement, especially in sections related to the side effects and precautions.

Man machine interaction for operator information systems : a general purpose display package on PC/AT

International Nuclear Information System (INIS)

Chandra, A.K.; Dubey, B.P.; Deshpande, S.V.; Vaidya, U.W.; Khandekar, A.B.

1991-01-01

Several operator information systems for nuclear plants have been developed at Reactor Control Division of BARC and these have involved extensive operator interaction to extract the maximum information from the systems. Each of these systems used a different scheme for operator interaction. A composite package has now been developed on PC/AT with EGA/VGA for use with any system to obviate the necessity to develop new software for each project. This permits information to be displayed in various formats viz. trend and history curves, tabular data, bar graphs and core matrix (both for 235 and 500 MWe cores). It also allows data to be printed and plotted using multi colour plotter. This package thus integrates all the features of the earlier systems. It also integrates the operator interaction scheme. It uses window based pull down menus to select parameters to be fed into a particular display format. Within any display format the operator has significant flexibility to modify the selected parameters using context dependent soft keys. The package also allows data to be retrieved in machine readable form. This report describes the various user friendly functions implemented and also the design of the system software. (author). 1 tab., 10 fig., 3 refs

Prostate Cancer Information Available in Health-Care Provider Offices: An Analysis of Content, Readability, and Cultural Sensitivity.

Science.gov (United States)

Choi, Seul Ki; Seel, Jessica S; Yelton, Brooks; Steck, Susan E; McCormick, Douglas P; Payne, Johnny; Minter, Anthony; Deutchki, Elizabeth K; Hébert, James R; Friedman, Daniela B

2018-07-01

Prostate cancer (PrCA) is the most common cancer affecting men in the United States, and African American men have the highest incidence among men in the United States. Little is known about the PrCA-related educational materials being provided to patients in health-care settings. Content, readability, and cultural sensitivity of materials available in providers' practices in South Carolina were examined. A total of 44 educational materials about PrCA and associated sexual dysfunction was collected from 16 general and specialty practices. The content of the materials was coded, and cultural sensitivity was assessed using the Cultural Sensitivity Assessment Tool. Flesch Reading Ease, Flesch-Kincaid Grade Level, and the Simple Measure of Gobbledygook were used to assess readability. Communication with health-care providers (52.3%), side effects of PrCA treatment (40.9%), sexual dysfunction and its treatment (38.6%), and treatment options (34.1%) were frequently presented. All materials had acceptable cultural sensitivity scores; however, 2.3% and 15.9% of materials demonstrated unacceptable cultural sensitivity regarding format and visual messages, respectively. Readability of the materials varied. More than half of the materials were written above a high-school reading level. PrCA-related materials available in health-care practices may not meet patients' needs regarding content, cultural sensitivity, and readability. A wide range of educational materials that address various aspects of PrCA, including treatment options and side effects, should be presented in plain language and be culturally sensitive.

CANDU 9 fuelling machine carriage

Energy Technology Data Exchange (ETDEWEB)

Ullrich, D J; Slavik, J F [Atomic Energy of Canada Ltd., Saskatoon, SK (Canada)

1997-12-31

Continuous, on-power refuelling is a key feature of all CANDU reactor designs and is essential to maintaining high station capacity factors. The concept of a fuelling machine carriage can be traced to the early CANDU designs, such as the Douglas Point Nuclear Generating Station. In the CANDU 9 480NU unit, the combination of a mobile carriage and a proven fuelling machine head design comprises an effective means of transporting fuel between the reactor and the fuel transfer ports. It is a suitable alternative to the fuelling machine bridge system that has been utilized in the CANDU 6 reactor units. The CANDU 9 480NU fuel handling system successfully combines features that meet the project requirements with respect to fuelling performance, functionality, seismic qualification and the use of proven components. The design incorporates improvements based on experience and applicable current technologies. (author). 4 figs.

CANDU 9 fuelling machine carriage

International Nuclear Information System (INIS)

Ullrich, D.J.; Slavik, J.F.

1996-01-01

Continuous, on-power refuelling is a key feature of all CANDU reactor designs and is essential to maintaining high station capacity factors. The concept of a fuelling machine carriage can be traced to the early CANDU designs, such as the Douglas Point Nuclear Generating Station. In the CANDU 9 480NU unit, the combination of a mobile carriage and a proven fuelling machine head design comprises an effective means of transporting fuel between the reactor and the fuel transfer ports. It is a suitable alternative to the fuelling machine bridge system that has been utilized in the CANDU 6 reactor units. The CANDU 9 480NU fuel handling system successfully combines features that meet the project requirements with respect to fuelling performance, functionality, seismic qualification and the use of proven components. The design incorporates improvements based on experience and applicable current technologies. (author). 4 figs

Textbook Readability and Student Performance in Online Introductory Corporate Finance Classes

Science.gov (United States)

Peng, Chien-Chih

2015-01-01

This paper examines whether the choice of a more readable textbook can improve student performance in online introductory corporate finance classes. The ordinary least squares regression model is employed to analyze a sample of 206 students during the period from 2008 to 2012. The results of this study show that the student's age, student's major,…

Achieving precision in high density batch mode micro-electro-discharge machining

International Nuclear Information System (INIS)

Richardson, Mark T; Gianchandani, Yogesh B

2008-01-01

This paper reports a parametric study of batch mode micro-electro-discharge machining (µEDM) of high density features in stainless steel. Lithographically fabricated copper tools with single cross, parallel line and 8 × 8 circle/square array features of 5–100 µm width and 5–75 µm spacing were used to quantify trends in machining tolerance and the impact of debris accumulation. As the tool feature density is increased, debris accumulation effects begin to dominate, eventually degrading both tool and workpiece. Two independent techniques for mitigating this debris buildup are separately investigated. The first is a passivation coating which suppresses spurious discharges triggered from the sidewalls of the machining tool. By this method, the mean tool wear rate decreases from a typical of about 34% to 1.7% and machining non-uniformity reduces from 4.9 µm to 1.1 µm across the workpiece. The second technique involves a two-step machining process that enhances the hydrodynamic removal of machining debris compared to standard methods. This improves surface and edge finish, machining time and tool wear

Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

Directory of Open Access Journals (Sweden)

Peek Andrew S

2007-06-01

Full Text Available Abstract Background RNA interference (RNAi is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM approach was used to quantitatively model RNA interference activities. Results Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative. Conclusion The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall t-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid

An analysis of the readability characteristics of oral health information literature available to the public in Tasmania, Australia.

Science.gov (United States)

Barnett, Tony; Hoang, Ha; Furlan, Ashlea

2016-03-17

The effectiveness of print-based health promotion materials is dependent on their readability. This study aimed to assess the characteristics of print-based oral health information literature publically available in Tasmania, Australia. Oral health education brochures were collected from 11 dental clinics across Tasmania and assessed for structure and format, content and readability. Reading level was calculated using three widely-used measures: Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease, and Simple Measure of Gobbledygook (SMOG) reading grade level. The FKGL of the 67 brochures sampled ranged from grade 3 to 13. The grade level for government health department brochures (n = 14) ranged from grade 4 to 11 (5.6 ± 1.8). Reading levels for materials produced by commercial sources (n = 22) ranged from 3 to 13 (8.3 ± 2.1), those from professional associations (n = 22) ranged from grade 7 to 11 (8.9 ± 0.9) and brochures produced by other sources (n = 9) ranged from 5 to 10 (7.6 ± 1.5). The SMOG test was positively correlated with the FKGL (rs = 0.92, p readability characteristics differed. Many brochures required a reading skill level higher than that suited to a large proportion of the Tasmanian population. Readability and other characteristics of oral health education materials should be assessed to ensure their suitability for use with patients, especially those suspected of having low literacy skills.

Breast cancer molecular subtype classifier that incorporates MRI features.

Science.gov (United States)

Sutton, Elizabeth J; Dashevsky, Brittany Z; Oh, Jung Hun; Veeraraghavan, Harini; Apte, Aditya P; Thakur, Sunitha B; Morris, Elizabeth A; Deasy, Joseph O

2016-07-01

To use features extracted from magnetic resonance (MR) images and a machine-learning method to assist in differentiating breast cancer molecular subtypes. This retrospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study received Institutional Review Board (IRB) approval. We identified 178 breast cancer patients between 2006-2011 with: 1) ERPR + (n = 95, 53.4%), ERPR-/HER2 + (n = 35, 19.6%), or triple negative (TN, n = 48, 27.0%) invasive ductal carcinoma (IDC), and 2) preoperative breast MRI at 1.5T or 3.0T. Shape, texture, and histogram-based features were extracted from each tumor contoured on pre- and three postcontrast MR images using in-house software. Clinical and pathologic features were also collected. Machine-learning-based (support vector machines) models were used to identify significant imaging features and to build models that predict IDC subtype. Leave-one-out cross-validation (LOOCV) was used to avoid model overfitting. Statistical significance was determined using the Kruskal-Wallis test. Each support vector machine fit in the LOOCV process generated a model with varying features. Eleven out of the top 20 ranked features were significantly different between IDC subtypes with P machine-learning-based predictive model using features extracted from MRI that can distinguish IDC subtypes with significant predictive power. J. Magn. Reson. Imaging 2016;44:122-129. © 2016 Wiley Periodicals, Inc.

Grammar-based feature generation for time-series prediction

CERN Document Server

De Silva, Anthony Mihirana

2015-01-01

This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method ...

Reconstructing Readability: Recent Developments and Recommendations in the Analysis of Text Difficulty

Science.gov (United States)

Benjamin, Rebekah George

2012-01-01

Largely due to technological advances, methods for analyzing readability have increased significantly in recent years. While past researchers designed hundreds of formulas to estimate the difficulty of texts for readers, controversy has surrounded their use for decades, with criticism stemming largely from their application in creating new texts…

Clustering Categories in Support Vector Machines

DEFF Research Database (Denmark)

Carrizosa, Emilio; Nogales-Gómez, Amaya; Morales, Dolores Romero

2017-01-01

The support vector machine (SVM) is a state-of-the-art method in supervised classification. In this paper the Cluster Support Vector Machine (CLSVM) methodology is proposed with the aim to increase the sparsity of the SVM classifier in the presence of categorical features, leading to a gain in in...

Classifying smoking urges via machine learning.

Science.gov (United States)

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-12-01

Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights

Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification

Directory of Open Access Journals (Sweden)

Mustafa Serter Uzer

2013-01-01

Full Text Available This paper offers a hybrid approach that uses the artificial bee colony (ABC algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications.

Research Translation Strategies to Improve the Readability of Workplace Health Promotion Resources

Science.gov (United States)

Wallace, Alison; Joss, Nerida

2016-01-01

Without deliberate and resourced translation, research evidence is unlikely to inform policy and practice. This paper describes the processes and practical solutions used to translate evaluation research findings to improve the readability of print materials in a large scale worksite health programme. It is argued that a knowledge brokering and…

Remote filter handling machine for Sizewell B

International Nuclear Information System (INIS)

Barker, D.

1993-01-01

Two Filter Handling machines (FHM) have been supplied to Nuclear Electric plc for use at Sizewell B Power Station. These machines have been designed and built following ALARP principles with the functional objective being to remove radioactive filter cartridges from a filter housing and replace them with clean filter cartridges. Operation of the machine is achieved by the prompt of each distinct task via an industrial computer or the prompt of a full cycle using the automatic mode. The design of the machine features many aspects demonstrating ALARP while keeping the machine simple, robust and easy to maintain. (author)

Nano Mechanical Machining Using AFM Probe

Science.gov (United States)

Mostofa, Md. Golam

Complex miniaturized components with high form accuracy will play key roles in the future development of many products, as they provide portability, disposability, lower material consumption in production, low power consumption during operation, lower sample requirements for testing, and higher heat transfer due to their very high surface-to-volume ratio. Given the high market demand for such micro and nano featured components, different manufacturing methods have been developed for their fabrication. Some of the common technologies in micro/nano fabrication are photolithography, electron beam lithography, X-ray lithography and other semiconductor processing techniques. Although these methods are capable of fabricating micro/nano structures with a resolution of less than a few nanometers, some of the shortcomings associated with these methods, such as high production costs for customized products, limited material choices, necessitate the development of other fabricating techniques. Micro/nano mechanical machining, such an atomic force microscope (AFM) probe based nano fabrication, has, therefore, been used to overcome some the major restrictions of the traditional processes. This technique removes material from the workpiece by engaging micro/nano size cutting tool (i.e. AFM probe) and is applicable on a wider range of materials compared to the photolithographic process. In spite of the unique benefits of nano mechanical machining, there are also some challenges with this technique, since the scale is reduced, such as size effects, burr formations, chip adhesions, fragility of tools and tool wear. Moreover, AFM based machining does not have any rotational movement, which makes fabrication of 3D features more difficult. Thus, vibration-assisted machining is introduced into AFM probe based nano mechanical machining to overcome the limitations associated with the conventional AFM probe based scratching method. Vibration-assisted machining reduced the cutting forces

Readability of sports medicine-related patient education materials from the American Academy of Orthopaedic Surgeons and the American Orthopaedic Society for Sports Medicine.

Science.gov (United States)

Ganta, Abhishek; Yi, Paul H; Hussein, Khalil; Frank, Rachel M

2014-04-01

Although studies have revealed high readability levels of orthopedic patient education materials, no study has evaluated sports medicine-related patient education materials. We conducted a study to assess the readability of sports medicine-related patient education materials from the American Academy of Orthopaedic Surgeons (AAOS) and the American Orthopaedic Society for Sports Medicine (AOSSM). All sports medicine patient education articles available online in 2012 from the AAOS and the AOSSM, including the Stop Sports Injuries Campaign (STOP), were identified, and their readability was assessed with the Flesch-Kinkaid (FK) readability test. Mean overall FK grade level of the 170 articles reviewed (104 from AAOS, 36 from AOSSM, 30 from STOP) was 10.2. Mean FK levels for the 3 sources were 9.5 (AAOS), 11.0 (AOSSM), and 11.5 (STOP) (P = .16). Fifteen (8.8%) of the 170 articles had a readability level at or below eighth grade (average reading level of US adults); only 2 (1.2%) of the 170 articles were at or below the recommended sixth-grade level. The majority of sports medicine-related patient education materials from AAOS and AOSSM had reading levels higher than recommended, indicating that the majority of the patient population may find it difficult to comprehend these articles.

Design of a 10 MJ fast discharging homopolar machine

International Nuclear Information System (INIS)

Stillwagon, R.E.; Thullen, P.

1977-01-01

The design of a fast discharging homopolar machine is described. The machine capacity is 10 MJ with a 30 ms energy delivery time. The salient features of the machine are relatively high terminal voltage, fast discharge time, high power density and high efficiency. The machine integrates several new technologies including high surface speeds, large superconducting magnets and current collection at high density

Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.

Science.gov (United States)

Zhang, Jieru; Ju, Ying; Lu, Huijuan; Xuan, Ping; Zou, Quan

2016-01-01

Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.

Readability and Content Assessment of Informed Consent Forms for Medical Procedures in Croatia.

Science.gov (United States)

Vučemilo, Luka; Borovečki, Ana

2015-01-01

High quality of informed consent form is essential for adequate information transfer between physicians and patients. Current status of medical procedure consent forms in clinical practice in Croatia specifically in terms of the readability and the content is unknown. The aim of this study was to assess the readability and the content of informed consent forms for diagnostic and therapeutic procedures used with patients in Croatia. 52 informed consent forms from six Croatian hospitals on the secondary and tertiary health-care level were tested for reading difficulty using Simple Measure of Gobbledygook (SMOG) formula adjusted for Croatian language and for qualitative analysis of the content. The averaged SMOG grade of analyzed informed consent forms was 13.25 (SD 1.59, range 10-19). Content analysis revealed that informed consent forms included description of risks in 96% of the cases, benefits in 81%, description of procedures in 78%, alternatives in 52%, risks and benefits of alternatives in 17% and risks and benefits of not receiving treatment or undergoing procedures in 13%. Readability of evaluated informed consent forms is not appropriate for the general population in Croatia. The content of the forms failed to include in high proportion of the cases description of alternatives, risks and benefits of alternatives, as well as risks and benefits of not receiving treatments or undergoing procedures. Data obtained from this research could help in development and improvement of informed consent forms in Croatia especially now when Croatian hospitals are undergoing the process of accreditation.

Readability and Content Assessment of Informed Consent Forms for Medical Procedures in Croatia

Science.gov (United States)

Vučemilo, Luka; Borovečki, Ana

2015-01-01

Background High quality of informed consent form is essential for adequate information transfer between physicians and patients. Current status of medical procedure consent forms in clinical practice in Croatia specifically in terms of the readability and the content is unknown. The aim of this study was to assess the readability and the content of informed consent forms for diagnostic and therapeutic procedures used with patients in Croatia. Methods 52 informed consent forms from six Croatian hospitals on the secondary and tertiary health-care level were tested for reading difficulty using Simple Measure of Gobbledygook (SMOG) formula adjusted for Croatian language and for qualitative analysis of the content. Results The averaged SMOG grade of analyzed informed consent forms was 13.25 (SD 1.59, range 10–19). Content analysis revealed that informed consent forms included description of risks in 96% of the cases, benefits in 81%, description of procedures in 78%, alternatives in 52%, risks and benefits of alternatives in 17% and risks and benefits of not receiving treatment or undergoing procedures in 13%. Conclusions Readability of evaluated informed consent forms is not appropriate for the general population in Croatia. The content of the forms failed to include in high proportion of the cases description of alternatives, risks and benefits of alternatives, as well as risks and benefits of not receiving treatments or undergoing procedures. Data obtained from this research could help in development and improvement of informed consent forms in Croatia especially now when Croatian hospitals are undergoing the process of accreditation. PMID:26376183

Optimization of line configuration and balancing for flexible machining lines

Science.gov (United States)

Liu, Xuemei; Li, Aiping; Chen, Zurui

2016-05-01

Line configuration and balancing is to select the type of line and allot a given set of operations as well as machines to a sequence of workstations to realize high-efficiency production. Most of the current researches for machining line configuration and balancing problems are related to dedicated transfer lines with dedicated machine workstations. With growing trends towards great product variety and fluctuations in market demand, dedicated transfer lines are being replaced with flexible machining line composed of identical CNC machines. This paper deals with the line configuration and balancing problem for flexible machining lines. The objective is to assign operations to workstations and find the sequence of execution, specify the number of machines in each workstation while minimizing the line cycle time and total number of machines. This problem is subject to precedence, clustering, accessibility and capacity constraints among the features, operations, setups and workstations. The mathematical model and heuristic algorithm based on feature group strategy and polychromatic sets theory are presented to find an optimal solution. The feature group strategy and polychromatic sets theory are used to establish constraint model. A heuristic operations sequencing and assignment algorithm is given. An industrial case study is carried out, and multiple optimal solutions in different line configurations are obtained. The case studying results show that the solutions with shorter cycle time and higher line balancing rate demonstrate the feasibility and effectiveness of the proposed algorithm. This research proposes a heuristic line configuration and balancing algorithm based on feature group strategy and polychromatic sets theory which is able to provide better solutions while achieving an improvement in computing time.

Relationships between abstract features and methodological quality explained variations of social media activity derived from systematic reviews about psoriasis interventions.

Science.gov (United States)

Ruano, J; Aguilar-Luque, M; Isla-Tejera, B; Alcalde-Mellado, P; Gay-Mimbrera, J; Hernandez-Romero, José Luis; Sanz-Cabanillas, J L; Maestre-López, B; González-Padilla, M; Carmona-Fernández, P J; Gómez-García, F; García-Nieto, A Vélez

2018-05-24

The aim of this study was to describe the relationship among abstract structure, readability, and completeness, and how these features may influence social media activity and bibliometric results, considering systematic reviews (SRs) about interventions in psoriasis classified by methodological quality. Systematic literature searches about psoriasis interventions were undertaken on relevant databases. For each review, methodological quality was evaluated using the Assessing the Methodological Quality of Systematic Reviews (AMSTAR) tool. Abstract extension, structure, readability, and quality and completeness of reporting were analyzed. Social media activity, which consider Twitter and Facebook mention counts, as well as Mendeley readers and Google scholar citations were obtained for each article. Analyses were conducted to describe any potential influence of abstract characteristics on review's social media diffusion. We classified 139 intervention SRs as displaying high/moderate/low methodological quality. We observed that abstract readability of SRs has been maintained high for last 20 years, although there are some differences based on their methodological quality. Free-format abstracts were most sensitive to the increase of text readability as compared with more structured abstracts (IMRAD or 8-headings), yielding opposite effects on their quality and completeness depending on the methodological quality: a worsening in low quality reviews and an improvement in those of high-quality. Both readability indices and PRISMA for Abstract total scores showed an inverse relationship with social media activity and bibliometric results in high methodological quality reviews but not in those of lower quality. Our results suggest that increasing abstract readability must be specially considered when writing free-format summaries of high-quality reviews, because this fact correlates with an improvement of their completeness and quality, and this may help to achieve broader

Homopolar machine design

International Nuclear Information System (INIS)

Thullen, P.

1978-01-01

A general conceptual design for a disc-type homopolar machine is presented. This machine uses a superconducting, air-core, solenoidal field winding with a peak field of 8 T. A total energy of 500 MJ is stored in two counter-rotating disc rotors that operate at a surface speed of 200 m/s. Terminal voltages of 500 to 2000 V are obtained over the range of designs studied. Brush systems to collect 3 MA are investigated. Various brush materials are discussed to determine their usefulness in this application. Sufficient information on operating characteristics in high-power applications is only available for copper-graphite brushes. The use of sliding brushes for terminal voltage regulation is discussed. This feature cannot provide a great deal of flexibility in this particular application although it may be useful during start-up. The brush system is the most demanding feature of this design. Few systems in the million ampere range have been constructed, consequently, it is not possible to predict the behavior of this brush system with great certainty. A detailed design of the brushes should be undertaken. It is estimated that the cost of such a machine will range from 0.5 to 1.5 cents per joule

An Individual Claims History Simulation Machine

Directory of Open Access Journals (Sweden)

Andrea Gabrielli

2018-03-01

Full Text Available The aim of this project is to develop a stochastic simulation machine that generates individual claims histories of non-life insurance claims. This simulation machine is based on neural networks to incorporate individual claims feature information. We provide a fully calibrated stochastic scenario generator that is based on real non-life insurance data. This stochastic simulation machine allows everyone to simulate their own synthetic insurance portfolio of individual claims histories and back-test thier preferred claims reserving method.

Machine vision based quality inspection of flat glass products

Science.gov (United States)

Zauner, G.; Schagerl, M.

2014-03-01

This application paper presents a machine vision solution for the quality inspection of flat glass products. A contact image sensor (CIS) is used to generate digital images of the glass surfaces. The presented machine vision based quality inspection at the end of the production line aims to classify five different glass defect types. The defect images are usually characterized by very little `image structure', i.e. homogeneous regions without distinct image texture. Additionally, these defect images usually consist of only a few pixels. At the same time the appearance of certain defect classes can be very diverse (e.g. water drops). We used simple state-of-the-art image features like histogram-based features (std. deviation, curtosis, skewness), geometric features (form factor/elongation, eccentricity, Hu-moments) and texture features (grey level run length matrix, co-occurrence matrix) to extract defect information. The main contribution of this work now lies in the systematic evaluation of various machine learning algorithms to identify appropriate classification approaches for this specific class of images. In this way, the following machine learning algorithms were compared: decision tree (J48), random forest, JRip rules, naive Bayes, Support Vector Machine (multi class), neural network (multilayer perceptron) and k-Nearest Neighbour. We used a representative image database of 2300 defect images and applied cross validation for evaluation purposes.

Attention: A Machine Learning Perspective

DEFF Research Database (Denmark)

Hansen, Lars Kai

2012-01-01

We review a statistical machine learning model of top-down task driven attention based on the notion of ‘gist’. In this framework we consider the task to be represented as a classification problem with two sets of features — a gist of coarse grained global features and a larger set of low...

Readability of consumer health information on the internet: a comparison of U.S. government-funded and commercially funded websites.

Science.gov (United States)

Risoldi Cochrane, Zara; Gregory, Philip; Wilson, Amy

2012-01-01

The Internet has become an extremely prevalent means of communicating health information to consumers. Guidelines for selecting reliable health information websites give preference to U.S. government sites over commercially funded sites. However, these websites are not useful to consumers unless they are able to read and understand them. The authors' objective was to compare the readability of Internet health information intended for consumers found on U.S. government-funded websites versus that found on commercially funded websites. Consumer health websites were identified through a systematic Internet search. Webpages for 10 common health topics were extracted from each website. Readability of webpages was determined by 3 validated measures: Flesch Reading Ease, Flesch-Kincaid Reading Level, and SMOG Formula. Mean readability of government-funded and commercially funded websites was compared using the Mann-Whitney U test. Commercially funded websites were significantly more difficult to read as measured by Flesch Reading Ease (49.7 vs. 55.6 for government-funded sites, p = .002) and Flesch-Kincaid Reading Level (10.1 vs. 9.3, p = .012). There was no significant difference according to SMOG Formula (12.8 vs. 13.2, p = .150). The overall readability of Internet health information intended for consumers was poor. Efforts should be made to ensure that health information communicated via the Internet is easy for consumers to read and understand.

Classifying images using restricted Boltzmann machines and convolutional neural networks

Science.gov (United States)

Zhao, Zhijun; Xu, Tongde; Dai, Chenyu

2017-07-01

To improve the feature recognition ability of deep model transfer learning, we propose a hybrid deep transfer learning method for image classification based on restricted Boltzmann machines (RBM) and convolutional neural networks (CNNs). It integrates learning abilities of two models, which conducts subject classification by exacting structural higher-order statistics features of images. While the method transfers the trained convolutional neural networks to the target datasets, fully-connected layers can be replaced by restricted Boltzmann machine layers; then the restricted Boltzmann machine layers and Softmax classifier are retrained, and BP neural network can be used to fine-tuned the hybrid model. The restricted Boltzmann machine layers has not only fully integrated the whole feature maps, but also learns the statistical features of target datasets in the view of the biggest logarithmic likelihood, thus removing the effects caused by the content differences between datasets. The experimental results show that the proposed method has improved the accuracy of image classification, outperforming other methods on Pascal VOC2007 and Caltech101 datasets.

Readability of Educational Materials to Support Parent Sexual Communication With Their Children and Adolescents.

Science.gov (United States)

Ballonoff Suleiman, Ahna; Lin, Jessica S; Constantine, Norman A

2016-05-01

Sexual communication is a principal means of transmitting sexual values, expectations, and knowledge from parents to their children and adolescents. Many parents seek information and guidance to support talking with their children about sex and sexuality. Parent education materials can deliver this guidance but must use appropriate readability levels to facilitate comprehension and motivation. This study appraised the readability of educational materials to support parent sexual communication with their children. Fifty brochures, pamphlets, and booklets were analyzed using the Flesch-Kincaid, Gunning Fog, and Simple Measure of Gobbledygook (SMOG) index methods. Mean readability grade-level scores were 8.3 (range = 4.5-12.8), 9.7 (range = 5.5-14.9), and 10.1 (range = 6.7-13.9), respectively. Informed by National Institutes of Health-recommended 6th to 7th grade levels and American Medical Association-recommended 5th to 6th grade levels, percentages falling at or below the 7.0 grade level were calculated as 38%, 12%, and 2% and those falling at or below the 6.0 grade level were calculated as 12%, 2%, and 0% based on the Flesch-Kincaid, Gunning Fog, and SMOG methods, respectively. These analyses indicate that the majority of educational materials available online to support parents' communication with their children about sex and sexuality do not meet the needs of many or most parents. Efforts to improve the accessibility of these materials are warranted.

DNA-based machines.

Science.gov (United States)

Wang, Fuan; Willner, Bilha; Willner, Itamar

2014-01-01

The base sequence in nucleic acids encodes substantial structural and functional information into the biopolymer. This encoded information provides the basis for the tailoring and assembly of DNA machines. A DNA machine is defined as a molecular device that exhibits the following fundamental features. (1) It performs a fuel-driven mechanical process that mimics macroscopic machines. (2) The mechanical process requires an energy input, "fuel." (3) The mechanical operation is accompanied by an energy consumption process that leads to "waste products." (4) The cyclic operation of the DNA devices, involves the use of "fuel" and "anti-fuel" ingredients. A variety of DNA-based machines are described, including the construction of "tweezers," "walkers," "robots," "cranes," "transporters," "springs," "gears," and interlocked cyclic DNA structures acting as reconfigurable catenanes, rotaxanes, and rotors. Different "fuels", such as nucleic acid strands, pH (H⁺/OH⁻), metal ions, and light, are used to trigger the mechanical functions of the DNA devices. The operation of the devices in solution and on surfaces is described, and a variety of optical, electrical, and photoelectrochemical methods to follow the operations of the DNA machines are presented. We further address the possible applications of DNA machines and the future perspectives of molecular DNA devices. These include the application of DNA machines as functional structures for the construction of logic gates and computing, for the programmed organization of metallic nanoparticle structures and the control of plasmonic properties, and for controlling chemical transformations by DNA machines. We further discuss the future applications of DNA machines for intracellular sensing, controlling intracellular metabolic pathways, and the use of the functional nanostructures for drug delivery and medical applications.

Aligning Theme and Information Structure to Improve the Readability of Technical Writing

Science.gov (United States)

Moore, N. A. J.

2006-01-01

The readability of technical writing, and technical manuals in particular, especially for second language readers, can be noticeably improved by pairing Theme with Given and Rheme with New. This allows for faster processing of text and easier access to the "method of development" of the text. Typical Theme-Rheme patterns are described, and the…

Availability and Readability of Online Patient Education Materials Regarding Regional Anesthesia Techniques for Perioperative Pain Management.

Science.gov (United States)

Kumar, Gunjan; Howard, Steven K; Kou, Alex; Kim, T Edward; Butwick, Alexander J; Mariano, Edward R

2017-10-01

Patient education materials (PEM) should be written at a sixth-grade reading level or lower. We evaluated the availability and readability of online PEM related to regional anesthesia and compared the readability and content of online PEM produced by fellowship and nonfellowship institutions. With IRB exemption, we constructed a cohort of online regional anesthesia PEM by searching Websites from North American academic medical centers supporting a regional anesthesiology and acute pain medicine fellowships and used a standardized Internet search engine protocol to identify additional nonfellowship Websites with regional anesthesia PEM based on relevant keywords. Readability metrics were calculated from PEM using the TextStat 0.1.4 textual analysis package for Python 2.7 and compared between institutions with and without a fellowship program. The presence of specific descriptive PEM elements related to regional anesthesia was also compared between groups. PEM from 17 fellowship and 15 nonfellowship institutions were included in analyses. The mean (SD) Flesch-Kincaid Grade Level for PEM from the fellowship group was 13.8 (2.9) vs 10.8 (2.0) for the nonfellowship group (p = 0.002). We observed no other differences in readability metrics between fellowship and nonfellowship institutions. Fellowship-based PEM less commonly included descriptions of the following risks: local anesthetic systemic toxicity (p = 0.033) and injury due to an insensate extremity (p = 0.003). Available online PEM related to regional anesthesia are well above the recommended reading level. Further, fellowship-based PEM posted are at a higher reading level than PEM posted by nonfellowship institutions and are more likely to omit certain risk descriptions. 2016 American Academy of Pain Medicine. This work is written by US Government employees and is in the public domain in the US.

Beauty and the Beast - on the readability of object-oriented example programs

DEFF Research Database (Denmark)

Börstler, Jürgen; Caspersen, Michael E.; Nordström, Marie

2016-01-01

Some solutions to a programming problem are more beautiful, elegant, and simple than others and thus more understandable for students. But why is it so, and can we quantify the notion of understandability of programs? We review desirable properties of program examples from a cognitive and a measu...... and exemplify a readability measure for software. An application of this measure to a set of object-oriented textbook examples shows encouraging results which we hope will ignite further research in this area.......Some solutions to a programming problem are more beautiful, elegant, and simple than others and thus more understandable for students. But why is it so, and can we quantify the notion of understandability of programs? We review desirable properties of program examples from a cognitive...... and a measurement point of view. It can be argued that some cognitive aspects of example programs are captured by common software measures, but we argue that they are not sucient to capture the most important aspects of understandability. A key aspect of understandability is readability. The authors propose...

A Practical Method to Increase the Frequency Readability for Vibration Signals

Directory of Open Access Journals (Sweden)

Jean Loius Ntakpe

2016-10-01

Full Text Available Damage detection and nondestructive evaluation of mechanical and civil engineering structures are nowadays very important to assess the integrity and ensure the reliability of structures. Thus, frequency evaluation becomes a crucial issue, since this modal parameter is mainly used in structural integrity assessment. The herein presented study highligts the possibility of increasing the frequency readability by involving a simple and cost-effective method.

Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening

Science.gov (United States)

Tu, Shu-Ju; Wang, Chih-Wei; Pan, Kuang-Tse; Wu, Yi-Cheng; Wu, Chen-Te

2018-03-01

Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clinical trials of lung cancer screening have shown that the false-positive rate is high and positive predictive value is low. To address these problems, a technical approach is greatly needed for accurate malignancy differentiation among these early-detected nodules. We studied the clinical feasibility of an additional protocol of localized thin-section CT for further assessment on recalled patients from lung cancer screening tests. Our approach of localized thin-section CT was integrated with radiomics features extraction and machine learning classification which was supervised by pathological diagnosis. Localized thin-section CT images of 122 nodules were retrospectively reviewed and 374 radiomics features were extracted. In this study, 48 nodules were benign and 74 malignant. There were nine patients with multiple nodules and four with synchronous multiple malignant nodules. Different machine learning classifiers with a stratified ten-fold cross-validation were used and repeated 100 times to evaluate classification accuracy. Of the image features extracted from the thin-section CT images, 238 (64%) were useful in differentiating between benign and malignant nodules. These useful features include CT density (p  =  0.002 518), sigma (p  =  0.002 781), uniformity (p  =  0.032 41), and entropy (p  =  0.006 685). The highest classification accuracy was 79% by the logistic classifier. The performance metrics of this logistic classification model was 0.80 for the positive predictive value, 0.36 for the false-positive rate, and 0.80 for the area under the receiver operating characteristic curve. Our approach of direct risk classification supervised by the pathological diagnosis with localized thin-section CT and radiomics feature extraction may support clinical physicians in determining

SU-F-R-08: Can Normalization of Brain MRI Texture Features Reduce Scanner-Dependent Effects in Unsupervised Machine Learning?

Energy Technology Data Exchange (ETDEWEB)

Ogden, K; O’Dwyer, R [SUNY Upstate Medical University, Syracuse, NY (United States); Bradford, T [Syracuse University, Syracuse, NY (United States); Cussen, L [Rochester Institute of Technology, Rochester, NY (United States)

2016-06-15

Purpose: To reduce differences in features calculated from MRI brain scans acquired at different field strengths with or without Gadolinium contrast. Methods: Brain scans were processed for 111 epilepsy patients to extract hippocampus and thalamus features. Scans were acquired on 1.5 T scanners with Gadolinium contrast (group A), 1.5T scanners without Gd (group B), and 3.0 T scanners without Gd (group C). A total of 72 features were extracted. Features were extracted from original scans and from scans where the image pixel values were rescaled to the mean of the hippocampi and thalami values. For each data set, cluster analysis was performed on the raw feature set and for feature sets with normalization (conversion to Z scores). Two methods of normalization were used: The first was over all values of a given feature, and the second by normalizing within the patient group membership. The clustering software was configured to produce 3 clusters. Group fractions in each cluster were calculated. Results: For features calculated from both the non-rescaled and rescaled data, cluster membership was identical for both the non-normalized and normalized data sets. Cluster 1 was comprised entirely of Group A data, Cluster 2 contained data from all three groups, and Cluster 3 contained data from only groups 1 and 2. For the categorically normalized data sets there was a more uniform distribution of group data in the three Clusters. A less pronounced effect was seen in the rescaled image data features. Conclusion: Image Rescaling and feature renormalization can have a significant effect on the results of clustering analysis. These effects are also likely to influence the results of supervised machine learning algorithms. It may be possible to partly remove the influence of scanner field strength and the presence of Gadolinium based contrast in feature extraction for radiomics applications.

SU-F-R-08: Can Normalization of Brain MRI Texture Features Reduce Scanner-Dependent Effects in Unsupervised Machine Learning?

International Nuclear Information System (INIS)

Ogden, K; O’Dwyer, R; Bradford, T; Cussen, L

2016-01-01

Purpose: To reduce differences in features calculated from MRI brain scans acquired at different field strengths with or without Gadolinium contrast. Methods: Brain scans were processed for 111 epilepsy patients to extract hippocampus and thalamus features. Scans were acquired on 1.5 T scanners with Gadolinium contrast (group A), 1.5T scanners without Gd (group B), and 3.0 T scanners without Gd (group C). A total of 72 features were extracted. Features were extracted from original scans and from scans where the image pixel values were rescaled to the mean of the hippocampi and thalami values. For each data set, cluster analysis was performed on the raw feature set and for feature sets with normalization (conversion to Z scores). Two methods of normalization were used: The first was over all values of a given feature, and the second by normalizing within the patient group membership. The clustering software was configured to produce 3 clusters. Group fractions in each cluster were calculated. Results: For features calculated from both the non-rescaled and rescaled data, cluster membership was identical for both the non-normalized and normalized data sets. Cluster 1 was comprised entirely of Group A data, Cluster 2 contained data from all three groups, and Cluster 3 contained data from only groups 1 and 2. For the categorically normalized data sets there was a more uniform distribution of group data in the three Clusters. A less pronounced effect was seen in the rescaled image data features. Conclusion: Image Rescaling and feature renormalization can have a significant effect on the results of clustering analysis. These effects are also likely to influence the results of supervised machine learning algorithms. It may be possible to partly remove the influence of scanner field strength and the presence of Gadolinium based contrast in feature extraction for radiomics applications.

Voltammetric electronic tongue and support vector machines for identification of selected features in Mexican coffee.

Science.gov (United States)

Domínguez, Rocio Berenice; Moreno-Barón, Laura; Muñoz, Roberto; Gutiérrez, Juan Manuel

2014-09-24

This paper describes a new method based on a voltammetric electronic tongue (ET) for the recognition of distinctive features in coffee samples. An ET was directly applied to different samples from the main Mexican coffee regions without any pretreatment before the analysis. The resulting electrochemical information was modeled with two different mathematical tools, namely Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Growing conditions (i.e., organic or non-organic practices and altitude of crops) were considered for a first classification. LDA results showed an average discrimination rate of 88% ± 6.53% while SVM successfully accomplished an overall accuracy of 96.4% ± 3.50% for the same task. A second classification based on geographical origin of samples was carried out. Results showed an overall accuracy of 87.5% ± 7.79% for LDA and a superior performance of 97.5% ± 3.22% for SVM. Given the complexity of coffee samples, the high accuracy percentages achieved by ET coupled with SVM in both classification problems suggested a potential applicability of ET in the assessment of selected coffee features with a simpler and faster methodology along with a null sample pretreatment. In addition, the proposed method can be applied to authentication assessment while improving cost, time and accuracy of the general procedure.

Using human brain activity to guide machine learning.

Science.gov (United States)

Fong, Ruth C; Scheirer, Walter J; Cox, David D

2018-03-29

Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

Designing and Evaluating Patient Education Pamphlets based on Readability Indexes and Comparison with Literacy Levels of Society

Directory of Open Access Journals (Sweden)

Mahdieh Arian

2016-07-01

Full Text Available Background: Hundreds of patient education materials i.e. pamphlets are annually published in healthcare systems following their design, correction, and revision. Aim: to design and evaluate patient education pamphlets based on readability indexes and their comparison with literacy level in society. Method: The average literacy level among 500 patients admitted to two training hospitals in Bojnurd (northeastern Iran was determined in 2014-2015. Afterwards, all patient education pamphlets in both hospitals (n=69 were collected and their readability level was determined. After that, all the pamphlets were re-designed according to the given standards and in line with literacy level in society. The SPSS software (Version 20 was also used to analyze the data. Results: The average level of literacy among 500 patients in both hospitals in the present study was 6.72±4.34 which was placed in grades six and seven in terms of the guide to readability indexes. In line with McLaughlin’s SMOG Readability Formula, the bulk of pamphlets (91.3% were at college level before corrections and revisions based on the given standards, but 23.2% were at a level lower than grade seven following corrections and revisions. Implications for Practice: Evaluation of patient education pamphlets plays an important role in promoting self-care among patients. Due to the novelty of the present study in Iran, the results of this study can contribute to patient education researchers in order to identify the strengths and weaknesses of patient education materials i.e. pamphlets based on scientific indices as well as their revisions and re-developments.

Energy-efficient electrical machines by new materials. Superconductivity in large electrical machines

International Nuclear Information System (INIS)

Frauenhofer, Joachim; Arndt, Tabea; Grundmann, Joern

2013-01-01

The implementation of superconducting materials in high-power electrical machines results in significant advantages regarding efficiency, size and dynamic behavior when compared to conventional machines. The application of HTS (high-temperature superconductors) in electrical machines allows significantly higher power densities to be achieved for synchronous machines. In order to gain experience with the new technology, Siemens carried out a series of development projects. A 400 kW model motor for the verification of a concept for the new technology was followed by a 4000 kV A generator as highspeed machine - as well as a low-speed 4000 kW propeller motor with high torque. The 4000 kVA generator is still employed to carry out long-term tests and to check components. Superconducting machines have significantly lower weight and envelope dimensions compared to conventional machines, and for this reason alone, they utilize resources better. At the same time, operating losses are slashed to about half and the efficiency increases. Beyond this, they set themselves apart as a result of their special features in operation, such as high overload capability, stiff alternating load behavior and low noise. HTS machines provide significant advantages where the reduction of footprint, weight and losses or the improved dynamic behavior results in significant improvements of the overall system. Propeller motors and generators,for ships, offshore plants, in wind turbine and hydroelectric plants and in large power stations are just some examples. HTS machines can therefore play a significant role when it comes to efficiently using resources and energy as well as reducing the CO 2 emissions.

Advances in Machine Technology.

Science.gov (United States)

Clark, William R; Villa, Gianluca; Neri, Mauro; Ronco, Claudio

2018-01-01

Continuous renal replacement therapy (CRRT) machines have evolved into devices specifically designed for critically ill over the past 40 years. In this chapter, a brief history of this evolution is first provided, with emphasis on the manner in which changes have been made to address the specific needs of the critically ill patient with acute kidney injury. Subsequently, specific examples of technology developments for CRRT machines are discussed, including the user interface, pumps, pressure monitoring, safety features, and anticoagulation capabilities. © 2018 S. Karger AG, Basel.

Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods.

Science.gov (United States)

Liang, Ja-Der; Ping, Xiao-Ou; Tseng, Yi-Ju; Huang, Guan-Tarn; Lai, Feipei; Yang, Pei-Ming

2014-12-01

Recurrence of hepatocellular carcinoma (HCC) is an important issue despite effective treatments with tumor eradication. Identification of patients who are at high risk for recurrence may provide more efficacious screening and detection of tumor recurrence. The aim of this study was to develop recurrence predictive models for HCC patients who received radiofrequency ablation (RFA) treatment. From January 2007 to December 2009, 83 newly diagnosed HCC patients receiving RFA as their first treatment were enrolled. Five feature selection methods including genetic algorithm (GA), simulated annealing (SA) algorithm, random forests (RF) and hybrid methods (GA+RF and SA+RF) were utilized for selecting an important subset of features from a total of 16 clinical features. These feature selection methods were combined with support vector machine (SVM) for developing predictive models with better performance. Five-fold cross-validation was used to train and test SVM models. The developed SVM-based predictive models with hybrid feature selection methods and 5-fold cross-validation had averages of the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the ROC curve as 67%, 86%, 82%, 69%, 90%, and 0.69, respectively. The SVM derived predictive model can provide suggestive high-risk recurrent patients, who should be closely followed up after complete RFA treatment. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

A new fully automated TLD badge reader

International Nuclear Information System (INIS)

Kannan, S.; Ratna, P.; Kulkarni, M.S.

2003-01-01

At present personnel monitoring in India is being carried out using a number of manual and semiautomatic TLD badge Readers and the BARC TL dosimeter badge designed during 1970. Of late the manual TLD badge readers are almost completely replaced by semiautomatic readers with a number of performance improvements like use of hot gas heating to reduce the readout time considerably. PC based design with storage of glow curve for every dosimeter, on-line dose computation and printout of dose reports, etc. However the semiautomatic system suffers from the lack of a machine readable ID code on the badge and the physical design of the dosimeter card not readily compatible for automation. This paper describes a fully automated TLD badge Reader developed in the RSS Division, using a new TLD badge with machine readable ID code. The new PC based reader has a built-in reader for reading the ID code, in the form of an array of holes, on the dosimeter card. The reader has a number of self-diagnostic features to ensure a high degree of reliability. (author)

A technique for improving readability of Forrester diagram in system dynamics

Directory of Open Access Journals (Sweden)

Yang Wei-Tzen

2003-01-01

Full Text Available We describe a three-pass algorithm for improving the readability of Forrester Diagram in system dynamics. The first pass converts Forrester Diagram to recurrent hierarchy. The second pass sorts the vertices on each level, with the goal of minimizing crossings. The third pass is a finite tuning of the layout that determines the horizontal positions of vertices. An illustrative example is given to verify the result. .

Toroidal field coils for the PDX machine

International Nuclear Information System (INIS)

Bushnell, C.W.

1975-01-01

This paper describes the engineering design features of the TF coils for the PDX machine. Included are design details of the electrical insulation, water cooling, and coil segment joint which allows access to the central machine area. A discussion of the problems anticipated in the manufacture and the planned solutions are presented

BADMINTON TRAINING MACHINE WITH IMPACT MECHANISM

Directory of Open Access Journals (Sweden)

B. F. YOUSIF

2011-02-01

Full Text Available In the current work, a newly machine was designed and fabricated for badminton training purpose. In the designing process, CATIA software was used to design and simulate the machine components. The design was based on direct impact method to launch the shuttle using spring as the source of the impact. Hook’s law was used theoretically to determine the initial and the maximum lengths of the springs. The main feature of the machine is that can move in two axes (up and down, left and right. For the control system, infra-red sensor and touch switch were adapted in microcontroller. The final product was locally fabricated and proved that the machine can operate properly.

Reliability, Readability and Quality of Online Information about Femoracetabular Impingement

Directory of Open Access Journals (Sweden)

Fatih Küçükdurmaz

2015-07-01

Conclusion: According to our results, the websites intended to attract patients searching for information regarding femoroacetabular impingement are providing a highly accessible, readable information source, but do not appear to apply a comparable amount of rigor to scientific literature or healthcare practitioner websites in regard to matters such as citing sources for information, supplying methodology and including a publication date. This indicates that while these resources are easily accessed by patients, there is potential for them to be a source of misinformation.

On-machine measurement of a slow slide servo diamond-machined 3D microstructure with a curved substrate

International Nuclear Information System (INIS)

Zhu, Wu-Le; Yang, Shunyao; Ju, Bing-Feng; Jiang, Jiacheng; Sun, Anyu

2015-01-01

A scanning tunneling microscope-based multi-axis measuring system is specially developed for the on-machine measurement of three-dimensional (3D) microstructures, to address the quality control difficulty with the traditional off-line measurement process. A typical 3D microstructure of the curved compound eye was diamond-machined by the slow slide servo technique, and then the whole surface was on-machine scanned three-dimensionally based on the tip-tracking strategy by utilizing a spindle, two linear motion stages, and an additional rotary stage. The machined surface profile and its shape deviation were accurately measured on-machine. The distortion of imaged ommatidia on the curved substrate was distinctively evaluated based on the characterized points extracted from the measured surface. Furthermore, the machining errors were investigated in connection with the on-machine measured surface and its characteristic parameters. Through experiments, the proposed measurement system is demonstrated to feature versatile on-machine measurement of 3D microstructures with a curved substrate, which is highly meaningful for quality control in the fabrication field. (paper)

Design, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine

Directory of Open Access Journals (Sweden)

Hosein Nouri-Ahmadabadi

2017-12-01

Full Text Available In this study, an intelligent system based on combined machine vision (MV and Support Vector Machine (SVM was developed for sorting of peeled pistachio kernels and shells. The system was composed of conveyor belt, lighting box, camera, processing unit and sorting unit. A color CCD camera was used to capture images. The images were digitalized by a capture card and transferred to a personal computer for further analysis. Initially, images were converted from RGB color space to HSV color ones. For segmentation of the acquired images, H-component in the HSV color space and Otsu thresholding method were applied. A feature vector containing 30 color features was extracted from the captured images. A feature selection method based on sensitivity analysis was carried out to select superior features. The selected features were presented to SVM classifier. Various SVM models having a different kernel function were developed and tested. The SVM model having cubic polynomial kernel function and 38 support vectors achieved the best accuracy (99.17% and then was selected to use in online decision-making unit of the system. By launching the online system, it was found that limiting factors of the system capacity were related to the hardware parts of the system (conveyor belt and pneumatic valves used in the sorting unit. The limiting factors led to a distance of 8 mm between the samples. The overall accuracy and capacity of the sorter were obtained 94.33% and 22.74 kg/h, respectively. Keywords: Pistachio kernel, Sorting, Machine vision, Sensitivity analysis, Support vector machine

Data Encoding using Periodic Nano-Optical Features

Science.gov (United States)

Vosoogh-Grayli, Siamack

Successful trials have been made through a designed algorithm to quantize, compress and optically encode unsigned 8 bit integer values in the form of images using Nano optical features. The periodicity of the Nano-scale features (Nano-gratings) have been designed and investigated both theoretically and experimentally to create distinct states of variation (three on states and one off state). The use of easy to manufacture and machine readable encoded data in secured authentication media has been employed previously in bar-codes for bi-state (binary) models and in color barcodes for multiple state models. This work has focused on implementing 4 states of variation for unit information through periodic Nano-optical structures that separate an incident wavelength into distinct colors (variation states) in order to create an encoding system. Compared to barcodes and magnetic stripes in secured finite length storage media the proposed system encodes and stores more data. The benefits of multiple states of variation in an encoding unit are 1) increased numerically representable range 2) increased storage density and 3) decreased number of typical set elements for any ergodic or semi-ergodic source that emits these encoding units. A thorough investigation has targeted the effects of the use of multi-varied state Nano-optical features on data storage density and consequent data transmission rates. The results show that use of Nano-optical features for encoding data yields a data storage density of circa 800 Kbits/in2 via the implementation of commercially available high resolution flatbed scanner systems for readout. Such storage density is far greater than commercial finite length secured storage media such as Barcode family with maximum practical density of 1kbits/in2 and highest density magnetic stripe cards with maximum density circa 3 Kbits/in2. The numerically representable range of the proposed encoding unit for 4 states of variation is [0 255]. The number of

Readability Statistics of Patient Information Leaflets in a Speech and Language Therapy Department

Science.gov (United States)

Pothier, Louise; Day, Rachael; Harris, Catherine; Pothier, David D.

2008-01-01

Background: Information leaflets are commonly used in Speech and Language Therapy Departments. Despite widespread use, they can be of variable quality. Aims: To revise current departmental leaflets using the National Health Service (NHS) Toolkit for Producing Patient Information and to test the effect that this has on the readability scores of the…

Prediction Model of Machining Failure Trend Based on Large Data Analysis

Science.gov (United States)

Li, Jirong

2017-12-01

The mechanical processing has high complexity, strong coupling, a lot of control factors in the machining process, it is prone to failure, in order to improve the accuracy of fault detection of large mechanical equipment, research on fault trend prediction requires machining, machining fault trend prediction model based on fault data. The characteristics of data processing using genetic algorithm K mean clustering for machining, machining feature extraction which reflects the correlation dimension of fault, spectrum characteristics analysis of abnormal vibration of complex mechanical parts processing process, the extraction method of the abnormal vibration of complex mechanical parts processing process of multi-component spectral decomposition and empirical mode decomposition Hilbert based on feature extraction and the decomposition results, in order to establish the intelligent expert system for the data base, combined with large data analysis method to realize the machining of the Fault trend prediction. The simulation results show that this method of fault trend prediction of mechanical machining accuracy is better, the fault in the mechanical process accurate judgment ability, it has good application value analysis and fault diagnosis in the machining process.

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data.

Science.gov (United States)

Hepworth, Philip J; Nefedov, Alexey V; Muchnik, Ilya B; Morgan, Kenton L

2012-08-07

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.

Virtual NC machine model with integrated knowledge data

International Nuclear Information System (INIS)

Sidorenko, Sofija; Dukovski, Vladimir

2002-01-01

The concept of virtual NC machining was established for providing a virtual product that could be compared with an appropriate designed product, in order to make NC program correctness evaluation, without real experiments. This concept is applied in the intelligent CAD/CAM system named VIRTUAL MANUFACTURE. This paper presents the first intelligent module that enables creation of the virtual models of existed NC machines and virtual creation of new ones, applying modular composition. Creation of a virtual NC machine is carried out via automatic knowledge data saving (features of the created NC machine). (Author)

Extending the features of RBMK refuelling machine simulator with a training tool based on virtual reality

International Nuclear Information System (INIS)

Khoudiakov, M.; Slonimsky, V.; Mitrofanov, S.

2004-01-01

include a training methodology, simulation models/ malfunctions and VR-models to support the maintenance personnel. That work is to be based on a design and creation of a multi-machine computer complex, software and information support (Data base) development, and developing anew and/or up-grade the technology system models and training support methodology. The paper gives the background for developing the training system, the features and the structure of the system in addition to the current status in the development process. The final system will be delivered to LNPP in November 2004. (Author)

Readability of Orthopaedic Oncology-related Patient Education Materials Available on the Internet.

Science.gov (United States)

Shah, Akash K; Yi, Paul H; Stein, Andrew

2015-12-01

A person's health literacy is one of the most important indicators of a patient's health status. According to national recommendations, patient education materials should be written at no higher than the sixth- to eighth-grade reading level. The purpose of our study was to assess the readability of online patient education materials related to orthopaedic oncology on the websites of the American Academy of Orthopaedic Surgeons (AAOS), American Cancer Society (ACS), Bone and Cancer Foundation (BCF), and National Cancer Institute (NCI). We searched the online patient education libraries of the AAOS, ACS, BCF, and NCI for all articles related to orthopaedic oncology. The Flesch-Kincaid (FK) readability score was calculated for each article and compared between sources. A total of 227 articles were identified with an overall mean FK grade level of 9.8. Stratified by source, the mean FK grade levels were 10.1, 9.6, 11.1, and 9.5 for the AAOS, ACS, BCF, and NCI, respectively (P education materials related to orthopaedic oncology appear to be written at a level above the comprehension ability of the average patient. Copyright 2015 by the American Academy of Orthopaedic Surgeons.

Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning

Directory of Open Access Journals (Sweden)

Arif Mehmood

2017-01-01

Full Text Available This study develops a model for essay scoring and article relevancy. Essay scoring is a costly process when we consider the time spent by an evaluator. It may lead to inequalities of the effort by various evaluators to apply the same evaluation criteria. Bibliometric research uses the evaluation criteria to find relevancy of articles instead. Researchers mostly face relevancy issues while searching articles. Therefore, they classify the articles manually. However, manual classification is burdensome due to time needed for evaluation. The proposed model performs automatic essay evaluation using multi-text features and ensemble machine learning. The proposed method is implemented in two data sets: a Kaggle short answer data set for essay scoring that includes four ranges of disciplines (Science, Biology, English, and English language Arts, and a bibliometric data set having IoT (Internet of Things and non-IoT classes. The efficacy of the model is measured against the Tandalla and AutoP approach using Cohen’s kappa. The model achieves kappa values of 0.80 and 0.83 for the first and second data sets, respectively. Kappa values show that the proposed model has better performance than those of earlier approaches.

Fall Detection Using Smartphone Audio Features.

Science.gov (United States)

Cheffena, Michael

2016-07-01

An automated fall detection system based on smartphone audio features is developed. The spectrogram, mel frequency cepstral coefficents (MFCCs), linear predictive coding (LPC), and matching pursuit (MP) features of different fall and no-fall sound events are extracted from experimental data. Based on the extracted audio features, four different machine learning classifiers: k-nearest neighbor classifier (k-NN), support vector machine (SVM), least squares method (LSM), and artificial neural network (ANN) are investigated for distinguishing between fall and no-fall events. For each audio feature, the performance of each classifier in terms of sensitivity, specificity, accuracy, and computational complexity is evaluated. The best performance is achieved using spectrogram features with ANN classifier with sensitivity, specificity, and accuracy all above 98%. The classifier also has acceptable computational requirement for training and testing. The system is applicable in home environments where the phone is placed in the vicinity of the user.

Toward the Development of a Model to Estimate the Readability of Credentialing-Examination Materials

Science.gov (United States)

Badgett, Barbara A.

2010-01-01

The purpose of this study was to develop a set of procedures to establish readability, including an equation, that accommodates the multiple-choice item format and occupational-specific language related to credentialing examinations. The procedures and equation should be appropriate for learning materials, examination materials, and occupational…

Operating System For Numerically Controlled Milling Machine

Science.gov (United States)

Ray, R. B.

1992-01-01

OPMILL program is operating system for Kearney and Trecker milling machine providing fast easy way to program manufacture of machine parts with IBM-compatible personal computer. Gives machinist "equation plotter" feature, which plots equations that define movements and converts equations to milling-machine-controlling program moving cutter along defined path. System includes tool-manager software handling up to 25 tools and automatically adjusts to account for each tool. Developed on IBM PS/2 computer running DOS 3.3 with 1 MB of random-access memory.

Virtual Machine Language 2.1

Science.gov (United States)

Riedel, Joseph E.; Grasso, Christopher A.

2012-01-01

VML (Virtual Machine Language) is an advanced computing environment that allows spacecraft to operate using mechanisms ranging from simple, time-oriented sequencing to advanced, multicomponent reactive systems. VML has developed in four evolutionary stages. VML 0 is a core execution capability providing multi-threaded command execution, integer data types, and rudimentary branching. VML 1 added named parameterized procedures, extensive polymorphism, data typing, branching, looping issuance of commands using run-time parameters, and named global variables. VML 2 added for loops, data verification, telemetry reaction, and an open flight adaptation architecture. VML 2.1 contains major advances in control flow capabilities for executable state machines. On the resource requirements front, VML 2.1 features a reduced memory footprint in order to fit more capability into modestly sized flight processors, and endian-neutral data access for compatibility with Intel little-endian processors. Sequence packaging has been improved with object-oriented programming constructs and the use of implicit (rather than explicit) time tags on statements. Sequence event detection has been significantly enhanced with multi-variable waiting, which allows a sequence to detect and react to conditions defined by complex expressions with multiple global variables. This multi-variable waiting serves as the basis for implementing parallel rule checking, which in turn, makes possible executable state machines. The new state machine feature in VML 2.1 allows the creation of sophisticated autonomous reactive systems without the need to develop expensive flight software. Users specify named states and transitions, along with the truth conditions required, before taking transitions. Transitions with the same signal name allow separate state machines to coordinate actions: the conditions distributed across all state machines necessary to arm a particular signal are evaluated, and once found true, that

Nonlinear features of the longitudinal instability for high-current machines

International Nuclear Information System (INIS)

Hofmann, I.; Boine-Frankenheim, O.

1999-01-01

We present results from experiments at the GSI machines as well as computer simulation for space charge dominated coasting beams (below transition). It is found that for the high-current machines presently under discussion the actual challenge lies in the nonlinear regime. Experiments are in good agreement with theory and simulation in the linear regime; for the nonlinear regime and long-time evolution rsp. saturation our experimental results show good agreement in some aspects, like wave steepening. To analyze the final momentum distribution we still depend on simulation, which shows that the behavior differs substantially, depending on whether the working point in the impedance plane lies close to the real (resistive dominated) or imaginary (space charge dominated) axis, or in between. For the space-charge-dominated regime (Re Z<< Im Z) it is found by computer simulation that for currents far above the Keil-Schnell threshold self-stabilization occurs by formation of a momentum tail, hence linear instability criteria can be practically ignored. It is shown here that the global impedance distribution is of crucial importance

Fuel element load/unload machine for the PEC reactor

International Nuclear Information System (INIS)

Clayton, K.F.

1984-01-01

GEC Energy Systems Limited are providing two fuel element load/unload machines for use in the Italian fast reactor programme. One will be used in the mechanism test facility (IPM) at Casaccia, to check the salient features of the machine operating in a sodium environment prior to the second machine being installed in the PEC Brasimone Reactor. The machine is used to handle fuel elements, control rods and other reactor components in the sodium-immersed core of the reactor. (U.K.)

Machine Learning Algorithms Utilizing Quantitative CT Features May Predict Eventual Onset of Bronchiolitis Obliterans Syndrome After Lung Transplantation.

Science.gov (United States)

Barbosa, Eduardo J Mortani; Lanclus, Maarten; Vos, Wim; Van Holsbeke, Cedric; De Backer, William; De Backer, Jan; Lee, James

2018-02-19

Long-term survival after lung transplantation (LTx) is limited by bronchiolitis obliterans syndrome (BOS), defined as a sustained decline in forced expiratory volume in the first second (FEV 1 ) not explained by other causes. We assessed whether machine learning (ML) utilizing quantitative computed tomography (qCT) metrics can predict eventual development of BOS. Paired inspiratory-expiratory CT scans of 71 patients who underwent LTx were analyzed retrospectively (BOS [n = 41] versus non-BOS [n = 30]), using at least two different time points. The BOS cohort experienced a reduction in FEV 1 of >10% compared to baseline FEV 1 post LTx. Multifactor analysis correlated declining FEV 1 with qCT features linked to acute inflammation or BOS onset. Student t test and ML were applied on baseline qCT features to identify lung transplant patients at baseline that eventually developed BOS. The FEV 1 decline in the BOS cohort correlated with an increase in the lung volume (P = .027) and in the central airway volume at functional residual capacity (P = .018), not observed in non-BOS patients, whereas the non-BOS cohort experienced a decrease in the central airway volume at total lung capacity with declining FEV 1 (P = .039). Twenty-three baseline qCT parameters could significantly distinguish between non-BOS patients and eventual BOS developers (P machine), we could identify BOS developers at baseline with an accuracy of 85%, using only three qCT parameters. ML utilizing qCT could discern distinct mechanisms driving FEV 1 decline in BOS and non-BOS LTx patients and predict eventual onset of BOS. This approach may become useful to optimize management of LTx patients. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Informed consent and the readability of the written consent form.

Science.gov (United States)

Sivanadarajah, N; El-Daly, I; Mamarelis, G; Sohail, M Z; Bates, P

2017-11-01

Introduction The aim of this study was to objectively ascertain the level of readability of standardised consent forms for orthopaedic procedures. Methods Standardised consent forms (both in summary and detailed formats) endorsed by the British Orthopaedic Association (BOA) were retrieved from orthoconsent.com and assessed for readability. This involved using an online tool to calculate the validated Flesch reading ease score (FRES). This was compared with the FRES for the National Health Service (NHS) Consent Form 1. Data were analysed and interpreted according to the FRES grading table. Results The FRES for Consent Form 1 was 55.6, relating to the literacy expected of an A level student. The mean FRES for the BOA summary consent forms (n=27) was 63.6 (95% confidence interval [CI]: 61.2-66.0) while for the detailed consent forms (n=32), it was 68.9 (95% CI: 67.7-70.0). All BOA detailed forms scored >60, correlating to the literacy expected of a 13-15-year-old. The detailed forms had a higher FRES than the summary forms (p<0.001). Conclusions This study demonstrates that the BOA endorsed standardised consent forms are much easier to read and understand than the NHS Consent Form 1, with the detailed BOA forms being the easiest to read. Despite this, owing to varying literacy levels, a significant proportion of patients may struggle to give informed consent based on the written information provided to them.

Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review

Science.gov (United States)

Zhang, Xue; Acencio, Marcio Luis; Lemke, Ney

2016-01-01

Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research. PMID:27014079

Optimization of machining fixture layout for tolerance requirements ...

African Journals Online (AJOL)

Dimensional accuracy of workpart under machining is strongly influenced by the layout of the fixturing elements like locators and clamps. Setup or geometrical errors in locators result in overall machining error of the feature under consideration. Therefore it is necessary to ensure that the layout is optimized for the desired ...

Prominent feature extraction for review analysis: an empirical study

Science.gov (United States)

Agarwal, Basant; Mittal, Namita

2016-05-01

Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.

Automatic Sleep Staging using Multi-dimensional Feature Extraction and Multi-kernel Fuzzy Support Vector Machine

Directory of Open Access Journals (Sweden)

Yanjun Zhang

2014-01-01

Full Text Available This paper employed the clinical Polysomnographic (PSG data, mainly including all-night Electroencephalogram (EEG, Electrooculogram (EOG and Electromyogram (EMG signals of subjects, and adopted the American Academy of Sleep Medicine (AASM clinical staging manual as standards to realize automatic sleep staging. Authors extracted eighteen different features of EEG, EOG and EMG in time domains and frequency domains to construct the vectors according to the existing literatures as well as clinical experience. By adopting sleep samples self-learning, the linear combination of weights and parameters of multiple kernels of the fuzzy support vector machine (FSVM were learned and the multi-kernel FSVM (MK-FSVM was constructed. The overall agreement between the experts' scores and the results presented was 82.53%. Compared with previous results, the accuracy of N1 was improved to some extent while the accuracies of other stages were approximate, which well reflected the sleep structure. The staging algorithm proposed in this paper is transparent, and worth further investigation.

Modelling injection moulding machines for micro manufacture applications through functional analysis

DEFF Research Database (Denmark)

Fantoni, G.; Tosello, Guido; Gabelloni, D.

2012-01-01

The paper presents the analysis of an injection moulding machine using functional analysis to identify both its critical components and possible working problems when such a machine is employed for the production of polymer-based micro products. The step-by-step procedure starts from the study...... of the process phases of a machine and then it employs functional analysis to decompose the phases and attributes functions to part features. Part features are subsequently analyzed to understand the causal chains bringing either to the desired behaviour or to failures to avoid. The assessment of the design...... solution is finally performed by gathering quantitative data from experiments. The case study investigates the design motivations and functional drivers of a micro injection moulding machine. The analysis allows identifying the correlations between failures and advantages with the design of the machine...

Adaptive machine and its thermodynamic costs

Science.gov (United States)

Allahverdyan, Armen E.; Wang, Q. A.

2013-03-01

We study the minimal thermodynamically consistent model for an adaptive machine that transfers particles from a higher chemical potential reservoir to a lower one. This model describes essentials of the inhomogeneous catalysis. It is supposed to function with the maximal current under uncertain chemical potentials: if they change, the machine tunes its own structure fitting it to the maximal current under new conditions. This adaptation is possible under two limitations: (i) The degree of freedom that controls the machine's structure has to have a stored energy (described via a negative temperature). The origin of this result is traced back to the Le Chatelier principle. (ii) The machine has to malfunction at a constant environment due to structural fluctuations, whose relative magnitude is controlled solely by the stored energy. We argue that several features of the adaptive machine are similar to those of living organisms (energy storage, aging).

Hybridization between multi-objective genetic algorithm and support vector machine for feature selection in walker-assisted gait.

Science.gov (United States)

Martins, Maria; Costa, Lino; Frizera, Anselmo; Ceres, Ramón; Santos, Cristina

2014-03-01

Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, comparatively to a non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information that often is difficult to interpret. This study addresses the problem of selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. For that purpose, it is presented an efficient approach that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports. For comparison purposes, other classification algorithms are verified. Results with healthy subjects show that the main differences are characterized by balance and joints excursion in the sagittal plane. These results, confirmed by clinical evidence, allow concluding that this technique is an efficient feature selection approach. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Application of machine learning on brain cancer multiclass classification

Science.gov (United States)

Panca, V.; Rustam, Z.

2017-07-01

Classification of brain cancer is a problem of multiclass classification. One approach to solve this problem is by first transforming it into several binary problems. The microarray gene expression dataset has the two main characteristics of medical data: extremely many features (genes) and only a few number of samples. The application of machine learning on microarray gene expression dataset mainly consists of two steps: feature selection and classification. In this paper, the features are selected using a method based on support vector machine recursive feature elimination (SVM-RFE) principle which is improved to solve multiclass classification, called multiple multiclass SVM-RFE. Instead of using only the selected features on a single classifier, this method combines the result of multiple classifiers. The features are divided into subsets and SVM-RFE is used on each subset. Then, the selected features on each subset are put on separate classifiers. This method enhances the feature selection ability of each single SVM-RFE. Twin support vector machine (TWSVM) is used as the method of the classifier to reduce computational complexity. While ordinary SVM finds single optimum hyperplane, the main objective Twin SVM is to find two non-parallel optimum hyperplanes. The experiment on the brain cancer microarray gene expression dataset shows this method could classify 71,4% of the overall test data correctly, using 100 and 1000 genes selected from multiple multiclass SVM-RFE feature selection method. Furthermore, the per class results show that this method could classify data of normal and MD class with 100% accuracy.

Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics

Directory of Open Access Journals (Sweden)

Vladimir S. Kublanov

2017-01-01

Full Text Available The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components.

Research on intrusion detection based on Kohonen network and support vector machine

Science.gov (United States)

Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi

2018-05-01

In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.

Assessing the Readability of College Textbooks in Public Speaking: Attending to Entry Level Instruction

Science.gov (United States)

Schneider, David E.

2011-01-01

More research is needed that examines textbooks intended for the entry level college classroom. This study offers valuable information to academics that adopt a public speaking textbook for instruction as well as objective feedback to the collective authors. Readability levels of 22 nationally published textbooks, based on McGlaughlin's (1969)…

Translation Analysis on Civil Engineering Text Produced by Machine Translator

Directory of Open Access Journals (Sweden)

Sutopo Anam

2018-01-01

Full Text Available Translation is extremely needed in communication since people have serious problem in the language used. Translation activity is done by the person in charge for translating the material. Translation activity is also able to be done by machine. It is called machine translation, reflected in the programs developed by programmer. One of them is Transtool. Many people used Transtool for helping them in solving the problem related with translation activities. This paper wants to deliver how important is the Transtool program, how effective is Transtool program and how is the function of Transtool for human business. This study applies qualitative research. The sources of data were document and informant. This study used documentation and in dept-interviewing as the techniques for collecting data. The collected data were analyzed by using interactive analysis. The results of the study show that, first; Transtool program is helpful for people in translating the civil engineering text and it functions as the aid or helper, second; the working of Transtool software program is effective enough and third; the result of translation produced by Transtool is good for short and simple sentences and not readable, not understandable and not accurate for long sentences (compound, complex and compound complex thought the result is informative. The translated material must be edited by the professional translator.

Translation Analysis on Civil Engineering Text Produced by Machine Translator

Science.gov (United States)

Sutopo, Anam

2018-02-01

Translation is extremely needed in communication since people have serious problem in the language used. Translation activity is done by the person in charge for translating the material. Translation activity is also able to be done by machine. It is called machine translation, reflected in the programs developed by programmer. One of them is Transtool. Many people used Transtool for helping them in solving the problem related with translation activities. This paper wants to deliver how important is the Transtool program, how effective is Transtool program and how is the function of Transtool for human business. This study applies qualitative research. The sources of data were document and informant. This study used documentation and in dept-interviewing as the techniques for collecting data. The collected data were analyzed by using interactive analysis. The results of the study show that, first; Transtool program is helpful for people in translating the civil engineering text and it functions as the aid or helper, second; the working of Transtool software program is effective enough and third; the result of translation produced by Transtool is good for short and simple sentences and not readable, not understandable and not accurate for long sentences (compound, complex and compound complex) thought the result is informative. The translated material must be edited by the professional translator.

Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine

Directory of Open Access Journals (Sweden)

Yi-Hung Liu

2014-01-01

Full Text Available In this paper, we propose a robust tactile sensing image recognition scheme for automatic robotic assembly. First, an image reprocessing procedure is designed to enhance the contrast of the tactile image. In the second layer, geometric features and Fourier descriptors are extracted from the image. Then, kernel principal component analysis (kernel PCA is applied to transform the features into ones with better discriminating ability, which is the kernel PCA-based feature fusion. The transformed features are fed into the third layer for classification. In this paper, we design a classifier by combining the multiple kernel learning (MKL algorithm and support vector machine (SVM. We also design and implement a tactile sensing array consisting of 10-by-10 sensing elements. Experimental results, carried out on real tactile images acquired by the designed tactile sensing array, show that the kernel PCA-based feature fusion can significantly improve the discriminating performance of the geometric features and Fourier descriptors. Also, the designed MKL-SVM outperforms the regular SVM in terms of recognition accuracy. The proposed recognition scheme is able to achieve a high recognition rate of over 85% for the classification of 12 commonly used metal parts in industrial applications.

Comparison of Machine Learning Techniques in Inferring Phytoplankton Size Classes

Directory of Open Access Journals (Sweden)

Shuibo Hu

2018-03-01

Full Text Available The size of phytoplankton not only influences its physiology, metabolic rates and marine food web, but also serves as an indicator of phytoplankton functional roles in ecological and biogeochemical processes. Therefore, some algorithms have been developed to infer the synoptic distribution of phytoplankton cell size, denoted as phytoplankton size classes (PSCs, in surface ocean waters, by the means of remotely sensed variables. This study, using the NASA bio-Optical Marine Algorithm Data set (NOMAD high performance liquid chromatography (HPLC database, and satellite match-ups, aimed to compare the effectiveness of modeling techniques, including partial least square (PLS, artificial neural networks (ANN, support vector machine (SVM and random forests (RF, and feature selection techniques, including genetic algorithm (GA, successive projection algorithm (SPA and recursive feature elimination based on support vector machine (SVM-RFE, for inferring PSCs from remote sensing data. Results showed that: (1 SVM-RFE worked better in selecting sensitive features; (2 RF performed better than PLS, ANN and SVM in calibrating PSCs retrieval models; (3 machine learning techniques produced better performance than the chlorophyll-a based three-component method; (4 sea surface temperature, wind stress, and spectral curvature derived from the remote sensing reflectance at 490, 510, and 555 nm were among the most sensitive features to PSCs; and (5 the combination of SVM-RFE feature selection techniques and random forests regression was recommended for inferring PSCs. This study demonstrated the effectiveness of machine learning techniques in selecting sensitive features and calibrating models for PSCs estimations with remote sensing.

A bidirectional brain-machine interface featuring a neuromorphic hardware decoder

Directory of Open Access Journals (Sweden)

Fabio Boi

2016-12-01

Full Text Available Bidirectional brain-machine interfaces (BMIs establish a two-way direct communication link4 between the brain and the external world. A decoder translates recorded neural activity into motor5 commands and an encoder delivers sensory information collected from the environment directly6 to the brain creating a closed-loop system. These two modules are typically integrated in bulky7 external devices. However, the clinical support of patients with severe motor and sensory deficits8 requires compact, low-power, and fully implantable systems that can decode neural signals to9 control external devices. As a first step toward this goal, we developed a modular bidirectional BMI10 setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented11 a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits.12 On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn13 to decode neural signals recorded from the brain into motor outputs controlling the movements14 of an external device. The modularity of the BMI allowed us to tune the individual components15 of the setup without modifying the whole system. In this paper we present the features of16 this modular BMI, and describe how we configured the network of spiking neuron circuits to17 implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm18 that connects bidirectionally the brain of an anesthetized rat with an external object. We show that19 the chip learned the decoding task correctly, allowing the interfaced brain to control the object’s20 trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is21 mature enough for the development of BMI modules that are sufficiently low-power and compact,22 while being highly computationally powerful and adaptive.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.

Science.gov (United States)

Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

2016-01-01

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.

Computer-Based Readability Testing of Information Booklets for German Cancer Patients.

Science.gov (United States)

Keinki, Christian; Zowalla, Richard; Pobiruchin, Monika; Huebner, Jutta; Wiesner, Martin

2018-04-12

Understandable health information is essential for treatment adherence and improved health outcomes. For readability testing, several instruments analyze the complexity of sentence structures, e.g., Flesch-Reading Ease (FRE) or Vienna-Formula (WSTF). Moreover, the vocabulary is of high relevance for readers. The aim of this study is to investigate the agreement of sentence structure and vocabulary-based (SVM) instruments. A total of 52 freely available German patient information booklets on cancer were collected from the Internet. The mean understandability level L was computed for 51 booklets. The resulting values of FRE, WSTF, and SVM were assessed pairwise for agreement with Bland-Altman plots and two-sided, paired t tests. For the pairwise comparison, the mean L values are L FRE  = 6.81, L WSTF  = 7.39, L SVM  = 5.09. The sentence structure-based metrics gave significantly different scores (P < 0.001) for all assessed booklets, confirmed by the Bland-Altman analysis. The study findings suggest that vocabulary-based instruments cannot be interchanged with FRE/WSTF. However, both analytical aspects should be considered and checked by authors to linguistically refine texts with respect to the individual target group. Authors of health information can be supported by automated readability analysis. Health professionals can benefit by direct booklet comparisons allowing for time-effective selection of suitable booklets for patients.

Gram staining with an automatic machine.

Science.gov (United States)

Felek, S; Arslan, A

1999-01-01

This study was undertaken to develop a new Gram-staining machine controlled by a micro-controller and to investigate the quality of slides that were stained in the machine. The machine was designed and produced by the authors. It uses standard 220 V AC. Staining, washing, and drying periods are controlled by a timer built in the micro-controller. A software was made that contains a certain algorithm and time intervals for the staining mode. One-hundred and forty smears were prepared from Escherichia coli, Staphylococcus aureus, Neisseria sp., blood culture, trypticase soy broth, direct pus and sputum smears for comparison studies. Half of the slides in each group were stained with the machine, the other half by hand and then examined by four different microbiologists. Machine-stained slides had a higher clarity and less debris than the hand-stained slides (p stained slides, some Gram-positive organisms showed poor Gram-positive staining features (p Gram staining with the automatic machine increases the staining quality and helps to decrease the work load in a busy diagnostic laboratory.

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

Energy Technology Data Exchange (ETDEWEB)

Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K. [Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT (United Kingdom); McEwen, Jason D., E-mail: dr.michelle.lochner@gmail.com [Mullard Space Science Laboratory, University College London, Surrey RH5 6NT (United Kingdom)

2016-08-01

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

International Nuclear Information System (INIS)

Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K.; McEwen, Jason D.

2016-01-01

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

Model-Agnostic Interpretability of Machine Learning

OpenAIRE

Ribeiro, Marco Tulio; Singh, Sameer; Guestrin, Carlos

2016-01-01

Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learning, and work in the area of interpretable models has found renewed interest. In some applications, such models are as accurate as non-interpretable ones, and thus are preferred f...

Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes.

Science.gov (United States)

Jamal, Salma; Goyal, Sukriti; Shanker, Asheesh; Grover, Abhinav

2016-10-18

Alzheimer's disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer's is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer's towards development of effective AD therapeutics. In the present study, we have used machine learning approach to identify candidate AD associated genes by integrating topological properties of the genes from the protein-protein interaction networks, sequence features and functional annotations. We also used molecular docking approach and screened already known anti-Alzheimer drugs against the novel predicted probable targets of AD and observed that an investigational drug, AL-108, had high affinity for majority of the possible therapeutic targets. Furthermore, we performed molecular dynamics simulations and MM/GBSA calculations on the docked complexes to validate our preliminary findings. To the best of our knowledge, this is the first comprehensive study of its kind for identification of putative Alzheimer-associated genes using machine learning approaches and we propose that such computational studies can improve our understanding on the core etiology of AD which could lead to the development of effective anti-Alzheimer drugs.

Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics

Directory of Open Access Journals (Sweden)

Achmad Widodo

2012-01-01

Full Text Available This paper deals with the maintenance technique for industrial machinery using the artificial neural network so-called self-organizing map (SOM. The aim of this work is to develop intelligent maintenance system for machinery based on an alternative way, namely, thermal images instead of vibration signals. SOM is selected due to its simplicity and is categorized as an unsupervised algorithm. Following the SOM training, machine fault diagnostics is performed by using the pattern recognition technique of machine conditions. The data used in this work are thermal images and vibration signals, which were acquired from machine fault simulator (MFS. It is a reliable tool and is able to simulate several conditions of faulty machine such as unbalance, misalignment, looseness, and rolling element bearing faults (outer race, inner race, ball, and cage defects. Data acquisition were conducted simultaneously by infrared thermography camera and vibration sensors installed in the MFS. The experimental data are presented as thermal image and vibration signal in the time domain. Feature extraction was carried out to obtain salient features sensitive to machine conditions from thermal images and vibration signals. These features are then used to train the SOM for intelligent machine diagnostics process. The results show that SOM can perform intelligent fault diagnostics with plausible accuracies.

Multi-script handwritten character recognition : Using feature descriptors and machine learning

NARCIS (Netherlands)

Surinta, Olarik

2016-01-01

Handwritten character recognition plays an important role in transforming raw visual image data obtained from handwritten documents using for example scanners to a format which is understandable by a computer. It is an important application in the field of pattern recognition, machine learning and

Marketing and vending machine; Marketing to jido hanbaiki

Energy Technology Data Exchange (ETDEWEB)

Onzo, N. [Waseda University, Tokyo (Japan)

1999-08-10

Vending machines in Japan have made original progress and have developed into big business. Annual sales by vending machines are 6 trillion 700 billion yen, which exceeds 6 trillion 100 billion yen sales by convenience stores. Research on vending machines may have advanced on the technical side but almost none on the marketing. In a vending machine that made an appearance in 1980 with the feature of a lottery, the winning probability was approximately one in fifty. In addition to a simple vending function, these machines have a promotion function. Some other machines have an electrical display of a commercial for products inside the machine for the purpose of attracting attention of passersby. This is an advertising function of the machines. In other words, one vending machine is capable of various marketing functions. This precisely means the subjects are numerous in the marketing research on vending machines. In contrast to the present century in which technical innovations have been made for vending machines, the coming 21st century may turn out to be the one in which marketing innovations are the mainstream for them. (NEDO)

Block-Module Electric Machines of Alternating Current

Science.gov (United States)

Zabora, I.

2018-03-01

The paper deals with electric machines having active zone based on uniform elements. It presents data on disk-type asynchronous electric motors with short-circuited rotors, where active elements are made by integrated technique that forms modular elements. Photolithography, spraying, stamping of windings, pressing of core and combined methods are utilized as the basic technological approaches of production. The constructions and features of operation for new electric machine - compatible electric machines-transformers are considered. Induction motors are intended for operation in hermetic plants with extreme conditions surrounding gas, steam-to-gas and liquid environment at a high temperature (to several hundred of degrees).

Using machine learning to classify image features from canine pelvic radiographs

DEFF Research Database (Denmark)

McEvoy, Fintan; Amigo Rubio, Jose Manuel

2013-01-01

As the number of images per study increases in the field of veterinary radiology, there is a growing need for computer-assisted diagnosis techniques. The purpose of this study was to evaluate two machine learning statistical models for automatically identifying image regions that contain the canine...

High resolution micro ultrasonic machining for trimming 3D microstructures

International Nuclear Information System (INIS)

Viswanath, Anupam; Li, Tao; Gianchandani, Yogesh

2014-01-01

This paper reports on the evaluation of a high resolution micro ultrasonic machining (HR-µUSM) process suitable for post fabrication trimming of complex 3D microstructures made from fused silica. Unlike conventional USM, the HR-µUSM process aims for low machining rates, providing high resolution and high surface quality. The machining rate is reduced by keeping the micro-tool tip at a fixed distance from the workpiece and vibrating it at a small amplitude. The surface roughness is improved by an appropriate selection of abrasive particles. Fluidic modeling is performed to study interaction among the vibrating micro-tool tip, workpiece, and the slurry. Using 304 stainless steel (SS304) tool tips of 50 µm diameter, the machining performance of the HR-µUSM process is characterized on flat fused silica substrates. The depths and surface finish of machined features are evaluated as functions of slurry concentrations, separation between the micro-tool and workpiece, and machining time. Under the selected conditions, the HR-µUSM process achieves machining rates as low as 10 nm s −1  averaged over the first minute of machining of a flat virgin sample. This corresponds to a mass removal rate of ≈20 ng min −1 . The average surface roughness, S a , achieved is as low as 30 nm. Analytical and numerical modeling are used to explain the typical profile of the machined features as well as machining rates. The process is used to demonstrate trimming of hemispherical 3D shells made of fused silica. (paper)

High-speed micro-electro-discharge machining.

Energy Technology Data Exchange (ETDEWEB)

Chandrasekar, Srinivasan Dr. (.School of Industrial Engineering, West Lafayette, IN); Moylan, Shawn P. (School of Industrial Engineering, West Lafayette, IN); Benavides, Gilbert Lawrence

2005-09-01

When two electrodes are in close proximity in a dielectric liquid, application of a voltage pulse can produce a spark discharge between them, resulting in a small amount of material removal from both electrodes. Pulsed application of the voltage at discharge energies in the range of micro-Joules results in the continuous material removal process known as micro-electro-discharge machining (micro-EDM). Spark erosion by micro-EDM provides significant opportunities for producing small features and micro-components such as nozzle holes, slots, shafts and gears in virtually any conductive material. If the speed and precision of micro-EDM processes can be significantly enhanced, then they have the potential to be used for a wide variety of micro-machining applications including fabrication of microelectromechanical system (MEMS) components. Toward this end, a better understanding of the impacts the various machining parameters have on material removal has been established through a single discharge study of micro-EDM and a parametric study of small hole making by micro-EDM. The main avenues for improving the speed and efficiency of the micro-EDM process are in the areas of more controlled pulse generation in the power supply and more controlled positioning of the tool electrode during the machining process. Further investigation of the micro-EDM process in three dimensions leads to important design rules, specifically the smallest feature size attainable by the process.

The Improved Relevance Voxel Machine

DEFF Research Database (Denmark)

Ganz, Melanie; Sabuncu, Mert; Van Leemput, Koen

The concept of sparse Bayesian learning has received much attention in the machine learning literature as a means of achieving parsimonious representations of features used in regression and classification. It is an important family of algorithms for sparse signal recovery and compressed sensing....... Hence in its current form it is reminiscent of a greedy forward feature selection algorithm. In this report, we aim to solve the problems of the original RVoxM algorithm in the spirit of [7] (FastRVM).We call the new algorithm Improved Relevance Voxel Machine (IRVoxM). Our contributions...... and enables basis selection from overcomplete dictionaries. One of the trailblazers of Bayesian learning is MacKay who already worked on the topic in his PhD thesis in 1992 [1]. Later on Tipping and Bishop developed the concept of sparse Bayesian learning [2, 3] and Tipping published the Relevance Vector...

SELECTION OF ONTOLOGY FOR WEB SERVICE DESCRIPTION LANGUAGE TO ONTOLOGY WEB LANGUAGE CONVERSION

OpenAIRE

J. Mannar Mannan; M. Sundarambal; S. Raghul

2014-01-01

Semantic web is to extend the current human readable web to encoding some of the semantic of resources in a machine processing form. As a Semantic web component, Semantic Web Services (SWS) uses a mark-up that makes the data into detailed and sophisticated machine readable way. One such language is Ontology Web Language (OWL). Existing conventional web service annotation can be changed to semantic web service by mapping Web Service Description Language (WSDL) with the semantic annotation of O...

Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection

Directory of Open Access Journals (Sweden)

Tiannan Ma

2016-12-01

Full Text Available Accurate forecasting of icing thickness has great significance for ensuring the security and stability of the power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on the fireworks algorithm and weighted least square support vector machine (W-LSSVM. The method of the fireworks algorithm is employed to select the proper input features with the purpose of eliminating redundant influence. In addition, the aim of the W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through simulation experiments using real-world icing data from the monitoring center of the key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

GCP compliance and readability of informed consent forms from an emerging hub for clinical trials

Directory of Open Access Journals (Sweden)

Satish Chandrasekhar Nair

2015-01-01

Full Text Available Background: The rapid expansion of trials in emerging regions has raised valid concerns about research subject protection, particularly related to informed consent. The purpose of this study is to assess informed consent form (ICF compliance with Good Clinical Practice (GCP guidelines and the readability easeof the ICFs in Abu Dhabi, a potential destination for clinical trials in the UAE. Materials and Methods: A multicenter retrospective cross-sectional analysis of 140 ICFs from industry sponsored and non-sponsored studies was conducted by comparing against a local standard ICF. Flesch-Kincaid Reading Scale was used to assess the readability ease of the forms. Results: Non-sponsored studies had signifi cantly lower overall GCP compliance of 55.8% when compared to 79.5% for industry sponsored studies. Only 33% of sponsored and 16% of non-sponsored studies included basic information on the participants′ rights and responsibilities. Flesch-Kincaid Reading ease score for the informed consent forms from industry sponsored studies was signifi cantly higher 48.9 ± 4.8 as compared to 38.5 ± 8.0 for non-sponsored studies, though both were more complex than recommended. Reading Grade Level score was also higher than expected, but scores for the ICFs from the industry sponsored studies were 9.7 ± 0.7, signifi cantly lower as compared to 12.2 ± 1.3 for non-sponsored studies. Conclusion: In spite of the undisputed benefits of conducting research in emerging markets readability, comprehension issues and the lack of basic essential information call for improvements in the ICFs to protect the rights of future research subjects enrolled in clinical trials in the UAE.

Readability of alphanumeric characters having various contrast levels as a function of age and illumination mode.

Science.gov (United States)

1977-07-01

Readability data of alphanumeric characters that vary in figure-to- ground contrast ratio were obtained from 36 subjects; 12 subjects were placed in each of three age groups (20-25 yr, 40-45 yr, and 60-65 yr). Minimum illuminance required to identify...

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

Science.gov (United States)

Cuperlovic-Culf, Miroslava

2018-01-11

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

Science.gov (United States)

Cuperlovic-Culf, Miroslava

2018-01-01

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

Fast torque estimation of in-wheel parallel flux switching machines

NARCIS (Netherlands)

Ilhan, E.; Paulides, J.J.H.; Lomonova, E.

2010-01-01

Parallel ux switching machines (PFSM) come forward in automotive industry as a promising candidate for hybrid truck applications due to their high power density. Torque calculations, i.e cogging and electromagnetic, are important features of these machines, which require a ??nite element model (FEM)

Readability of the Most Commonly Accessed Arthroscopy-Related Online Patient Education Materials.

Science.gov (United States)

Akinleye, Sheriff D; Krochak, Ryan; Richardson, Nicholas; Garofolo, Garret; Culbertson, Maya Deza; Erez, Orry

2018-04-01

To assess the readability and comprehension of written text by the most commonly visited websites containing patient education materials on common conditions that can be treated arthroscopically. We examined 50 websites, assessed independently by 2 orthopaedic surgery residents (S.A. and G.G.), with educational materials on 5 common conditions treated by arthroscopic surgeons: anterior cruciate ligament (ACL) tear, meniscus tear, hip labral tear, shoulder labral tear, and rotator cuff tear. Following a Google search for each condition, we analyzed the 10 most visited websites for each disorder using a widely used and validated tool for assessing the reading levels of written materials (Flesch-Kincaid formula). The average grade reading level of the 50 websites studied was 9.90 with a reading ease of 52.14 ("fairly difficult, high school"). Only 26% of the websites were at or below the national average of an eighth-grade reading level. Of the 5 conditions treated by arthroscopic surgery, ACL tear had the highest average grade reading level at 10.73 ± 1.54, whereas meniscus tear had the lowest at 9.31 ± 1.81. Every condition in this study had an average readability at or above the ninth-grade reading level. The most frequently accessed materials for patients with injuries requiring arthroscopic surgery exceeds the readability recommendations of the American Medical Association and National Institutes of Health, as well as the average reading ability of US adults. Given the fact that these are the most commonly visited websites by the lay public, there needs to be a greater emphasis on tailoring written information to the literacy levels of the patient population. This study emphasizes the discrepancy between the recommended versus the measured reading levels of online patient education materials related to conditions treated by arthroscopic surgeons. The subject matter of these conditions is inherently complex; thus, relying solely on text to inform patients

Evaluation of completeness of selected poison control center data fields.

Science.gov (United States)

Jaramillo, Jeanie E; Marchbanks, Brenda; Willis, Branch; Forrester, Mathias B

2010-08-01

Poison control center data are used in research and surveillance. Due to the large volume of information, these efforts are dependent on data being recorded in machine readable format. However, poison center records include non-machine readable text fields and machine readable coded fields, some of which are duplicative. Duplicating this data increases the chance of inaccurate/incomplete coding. For surveillance efforts to be effective, coding should be complete and accurate. Investigators identified a convenience sample of 964 records and reviewed the substance code determining if it matched its text field. They also reviewed the coded clinical effects and treatments determining if they matched the notes text field. The substance code matched its text field for 91.4% of the substances. The clinical effects and treatments codes matched their text field for 72.6% and 82.4% of occurrences respectively. This under-reporting of clinical effects and treatments has surveillance and public health implications.

Improving Readability of an Evaluation Tool for Low-Income Clients Using Visual Information Processing Theories

Science.gov (United States)

Townsend, Marilyn S.; Sylva, Kathryn; Martin, Anna; Metz, Diane; Wooten-Swanson, Patti

2008-01-01

Literacy is an issue for many low-income audiences. Using visual information processing theories, the goal was improving readability of a food behavior checklist and ultimately improving its ability to accurately capture existing changes in dietary behaviors. Using group interviews, low-income clients (n = 18) evaluated 4 visual styles. The text…

On-line transient stability assessment of large-scale power systems by using ball vector machines

International Nuclear Information System (INIS)

Mohammadi, M.; Gharehpetian, G.B.

2010-01-01

In this paper ball vector machine (BVM) has been used for on-line transient stability assessment of large-scale power systems. To classify the system transient security status, a BVM has been trained for all contingencies. The proposed BVM based security assessment algorithm has very small training time and space in comparison with artificial neural networks (ANN), support vector machines (SVM) and other machine learning based algorithms. In addition, the proposed algorithm has less support vectors (SV) and therefore is faster than existing algorithms for on-line applications. One of the main points, to apply a machine learning method is feature selection. In this paper, a new Decision Tree (DT) based feature selection technique has been presented. The proposed BVM based algorithm has been applied to New England 39-bus power system. The simulation results show the effectiveness and the stability of the proposed method for on-line transient stability assessment procedure of large-scale power system. The proposed feature selection algorithm has been compared with different feature selection algorithms. The simulation results demonstrate the effectiveness of the proposed feature algorithm.

A tubular flux-switching permanent magnet machine

Science.gov (United States)

Wang, J.; Wang, W.; Clark, R.; Atallah, K.; Howe, D.

2008-04-01

The paper describes a novel tubular, three-phase permanent magnet brushless machine, which combines salient features from both switched reluctance and permanent magnet machine technologies. It has no end windings and zero net radial force and offers a high power density and peak force capability, as well as the potential for low manufacturing cost. It is, therefore, eminently suitable for a variety of applications, ranging from free-piston energy converters to active vehicle suspensions.

MoleculeNet: a benchmark for molecular machine learning.

Science.gov (United States)

Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N; Gomes, Joseph; Geniesse, Caleb; Pappu, Aneesh S; Leswing, Karl; Pande, Vijay

2018-01-14

Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm.

Support vector machine for automatic pain recognition

Science.gov (United States)

Monwar, Md Maruf; Rezaei, Siamak

2009-02-01

Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

Readability Assessment of Internet-Based Patient Education Materials Related to Parathyroid Surgery.

Science.gov (United States)

Patel, Chirag R; Sanghvi, Saurin; Cherla, Deepa V; Baredes, Soly; Eloy, Jean Anderson

2015-07-01

Patient education is critical in obtaining informed consent and reducing preoperative anxiety. Written patient education material (PEM) can supplement verbal communication to improve understanding and satisfaction. Published guidelines recommend that health information be presented at or below a sixth-grade reading level to facilitate comprehension. We investigate the grade level of online PEMs regarding parathyroid surgery. A popular internet search engine was used to identify PEM discussing parathyroid surgery. Four formulas were used to calculate readability scores: Flesch Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL), Gunning Frequency of Gobbledygook (GFOG), and Simple Measure of Gobbledygook (SMOG). Thirty web-based articles discussing parathyroid surgery were identified. The average FRE score was 42.8 (±1 standard deviation [SD] 16.3; 95% confidence interval [CI], 36.6-48.8; range, 6.1-71.3). The average FKGL score was 11.7 (±1 SD 3.3; 95% CI, 10.5-12.9; range, 6.1-19.0). The SMOG scores averaged 14.2 (±1 SD 2.6; 95% CI, 13.2-15.2; range, 10.7-21.9), and the GFOG scores averaged 15.0 (±1 SD 3.5; 95% CI, 13.7-16.3; range, 10.6-24.8). Online PEM on parathyroid surgery is written above the recommended sixth-grade reading level. Improving readability of PEM may promote better health education and compliance. © The Author(s) 2015.

Building Customer Churn Prediction Models in Fitness Industry with Machine Learning Methods

OpenAIRE

Shan, Min

2017-01-01

With the rapid growth of digital systems, churn management has become a major focus within customer relationship management in many industries. Ample research has been conducted for churn prediction in different industries with various machine learning methods. This thesis aims to combine feature selection and supervised machine learning methods for defining models of churn prediction and apply them on fitness industry. Forward selection is chosen as feature selection methods. Support Vector ...

Classification of older adults with/without a fall history using machine learning methods.

Science.gov (United States)

Lin Zhang; Ou Ma; Fabre, Jennifer M; Wood, Robert H; Garcia, Stephanie U; Ivey, Kayla M; McCann, Evan D

2015-01-01

Falling is a serious problem in an aged society such that assessment of the risk of falls for individuals is imperative for the research and practice of falls prevention. This paper introduces an application of several machine learning methods for training a classifier which is capable of classifying individual older adults into a high risk group and a low risk group (distinguished by whether or not the members of the group have a recent history of falls). Using a 3D motion capture system, significant gait features related to falls risk are extracted. By training these features, classification hypotheses are obtained based on machine learning techniques (K Nearest-neighbour, Naive Bayes, Logistic Regression, Neural Network, and Support Vector Machine). Training and test accuracies with sensitivity and specificity of each of these techniques are assessed. The feature adjustment and tuning of the machine learning algorithms are discussed. The outcome of the study will benefit the prediction and prevention of falls.

A study of metaheuristic algorithms for high dimensional feature selection on microarray data

Science.gov (United States)

Dankolo, Muhammad Nasiru; Radzi, Nor Haizan Mohamed; Sallehuddin, Roselina; Mustaffa, Noorfa Haszlinna

2017-11-01

Microarray systems enable experts to examine gene profile at molecular level using machine learning algorithms. It increases the potentials of classification and diagnosis of many diseases at gene expression level. Though, numerous difficulties may affect the efficiency of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data pre-processing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper discusses the application of the metaheuristics algorithms for feature selection in microarray dataset. This study reveals that, the algorithms have yield an interesting result with limited resources thereby saving computational expenses of machine learning algorithms.

Health Literacy: Readability of ACC/AHA Online Patient Education Material.

Science.gov (United States)

Kapoor, Karan; George, Praveen; Evans, Matthew C; Miller, Weldon J; Liu, Stanley S

To determine whether the online patient education material offered by the American College of Cardiology (ACC) and the American Heart Association (AHA) is written at a higher level than the 6th-7th grade level recommended by the National Institute of Health (NIH). Online patient education material from each website was subjected to reading grade level (RGL) analysis using the Readability Studio Professional Edition. One-sample t testing was used to compare the mean RGLs obtained from 8 formulas to the NIH-recommended 6.5 grade level and 8th grade national mean. In total, 372 articles from the ACC website and 82 from the AHA were studied. Mean (±SD) RGLs for the 454 articles were 9.6 ± 2.1, 11.2 ± 2.1, 11.9 ± 1.6, 10.8 ± 1.6, 9.7 ± 2.1, 10.8 ± 0.8, 10.5 ± 2.6, and 11.7 ± 3.5 according to the Flesch-Kincaid grade level (FKGL), Simple Measure of Gobbledygook (SMOG Index), Coleman-Liau Index (CLI), Gunning-Fog Index (GFI), New Dale-Chall reading level formula (NDC), FORCAST, Raygor Readability Estimate (RRE), and Fry Graph (Fry), respectively. All analyzed articles had significantly higher RGLs than both the NIH-recommended grade level of 6.5 and the national mean grade level of 8 (p education material provided on the ACC and AHA websites is written above the NIH-recommended 6.5 grade level and 8th grade national mean reading level. Additional studies are required to demonstrate whether lowering the RGL of this material improves outcomes among patients with cardiovascular disease. © 2017 S. Karger AG, Basel.

An Evolutionary Machine Learning Framework for Big Data Sequence Mining

Science.gov (United States)

Kamath, Uday Krishna

2014-01-01

Sequence classification is an important problem in many real-world applications. Unlike other machine learning data, there are no "explicit" features or signals in sequence data that can help traditional machine learning algorithms learn and predict from the data. Sequence data exhibits inter-relationships in the elements that are…

Information retrieval system based on INIS tapes

International Nuclear Information System (INIS)

Pultorak, G.

1976-01-01

An information retrieval system based on the INIS computer tapes is described. It includes the three main elements of a computerized information system: a data base on a machine -readable medium, a collection of queries which represent the information needs from the data - base, and a set of programs by which the actual retrieval is done, according to the user's queries. The system is built for the center's computer, a CDC 3600, and its special features characterize, to a certain degree, the structure of the programs. (author)

Evaluating the Quality, Accuracy, and Readability of Online Resources Pertaining to Hallux Valgus.

Science.gov (United States)

Tartaglione, Jason P; Rosenbaum, Andrew J; Abousayed, Mostafa; Hushmendy, Shazaan F; DiPreta, John A

2016-02-01