Probabilistic machine learning and artificial intelligence

Science.gov (United States)

Ghahramani, Zoubin

2015-05-01

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Machine intelligence and knowledge bases

Energy Technology Data Exchange (ETDEWEB)

Furukawa, K

1981-09-01

The basic functions necessary in machine intelligence are a knowledge base and a logic programming language such as PROLOG using deductive reasoning. Recently inductive reasoning based on meta knowledge and default reasoning have been developed. The creative thought model of Lenit is reviewed and the concept of knowledge engineering is introduced. 17 references.

Memory Based Machine Intelligence Techniques in VLSI hardware

OpenAIRE

James, Alex Pappachen

2012-01-01

We briefly introduce the memory based approaches to emulate machine intelligence in VLSI hardware, describing the challenges and advantages. Implementation of artificial intelligence techniques in VLSI hardware is a practical and difficult problem. Deep architectures, hierarchical temporal memories and memory networks are some of the contemporary approaches in this area of research. The techniques attempt to emulate low level intelligence tasks and aim at providing scalable solutions to high ...

Recent advances in intelligent machine technologies

International Nuclear Information System (INIS)

Bartholet, T.G.

1987-01-01

Further developments in intelligent machine technologies have recently been accomplished under sponsorship by the Department of Energy (DOE), the Electric Power Research Institute (EPRI), the U.S. Army and NASA. This paper describes these developments and presents actual results achieved and demonstrated. These projects encompass new developments in manipulators, vision and walking machines. Continuing developments will add increasing degrees of autonomy as appropriate to applications in the fields of nuclear power, space, defense and industrial or commercial marketplaces

BRAIN. Broad Research in Artificial Intelligence and Neuroscience-Are We Safe Enough in the Future of Artificial Intelligence? A Discussion on Machine Ethics and Artificial Intelligence Safety

OpenAIRE

Utku Köse

2018-01-01

Nowadays, there is a serious anxiety on the existence of dangerous intelligent systems and it is not just a science-fiction idea of evil machines like the ones in well-known Terminator movie or any other movies including intelligent robots – machines threatening the existence of humankind. So, there is a great interest in some alternative research works under the topics of Machine Ethics, Artificial Intelligence Safety and the associated research topics like Future of Artificial I...

[Algorithms, machine intelligence, big data : general considerations].

Science.gov (United States)

Radermacher, F J

2015-08-01

We are experiencing astonishing developments in the areas of big data and artificial intelligence. They follow a pattern that we have now been observing for decades: according to Moore's Law,the performance and efficiency in the area of elementary arithmetic operations increases a thousand-fold every 20 years. Although we have not achieved the status where in the singular sense machines have become as "intelligent" as people, machines are becoming increasingly better. The Internet of Things has again helped to massively increase the efficiency of machines. Big data and suitable analytics do the same. If we let these processes simply continue, our civilization may be endangerd in many instances. If the "containment" of these processes succeeds in the context of a reasonable political global governance, a worldwide eco-social market economy, andan economy of green and inclusive markets, many desirable developments that are advantageous for our future may result. Then, at some point in time, the constant need for more and faster innovation may even stop. However, this is anything but certain. We are facing huge challenges.

On Intelligent Design and Planning Method of Process Route Based on Gun Breech Machining Process

Science.gov (United States)

Hongzhi, Zhao; Jian, Zhang

2018-03-01

The paper states an approach of intelligent design and planning of process route based on gun breech machining process, against several problems, such as complex machining process of gun breech, tedious route design and long period of its traditional unmanageable process route. Based on gun breech machining process, intelligent design and planning system of process route are developed by virtue of DEST and VC++. The system includes two functional modules--process route intelligent design and its planning. The process route intelligent design module, through the analysis of gun breech machining process, summarizes breech process knowledge so as to complete the design of knowledge base and inference engine. And then gun breech process route intelligently output. On the basis of intelligent route design module, the final process route is made, edited and managed in the process route planning module.

Ecological Design of Cooperative Human-Machine Interfaces for Safety of Intelligent Transport Systems

Directory of Open Access Journals (Sweden)

Orekhov Aleksandr

2016-01-01

Full Text Available The paper describes research results in the domain of cooperative intelligent transport systems. The requirements for human-machine interface considering safety issue of for intelligent transport systems (ITSare analyzed. Profiling of the requirements to cooperative human-machine interface (CHMI for such systems including requirements to usability and safety is based on a set of standards for ITSs. An approach and design technique of cooperative human-machine interface for ITSs are suggested. The architecture of cloud-based CHMI for intelligent transport systems has been developed. The prototype of software system CHMI4ITSis described.

Machine learning \\& artificial intelligence in the quantum domain

OpenAIRE

Dunjko, Vedran; Briegel, Hans J.

2017-01-01

Quantum information technologies, and intelligent learning systems, are both emergent technologies that will likely have a transforming impact on our society. The respective underlying fields of research -- quantum information (QI) versus machine learning (ML) and artificial intelligence (AI) -- have their own specific challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question to what extent th...

Development and evaluation of intelligent machine tools based on knowledge evolution in M2M environment

International Nuclear Information System (INIS)

Kim, Dong Hoon; Song, Jun Yeob; Lee, Jong Hyun; Cha, Suk Keun

2009-01-01

In the near future, the foreseen improvement in machine tools will be in the form of a knowledge evolution-based intelligent device. The goal of this study is to develop intelligent machine tools having knowledge-evolution capability in Machine to Machine (M2M) wired and wireless environment. The knowledge evolution-based intelligent machine tools are expected to be capable of gathering knowledge autonomously, producing knowledge, understanding knowledge, applying reasoning to knowledge, making new decisions, dialoguing with other machines, etc. The concept of the knowledge-evolution intelligent machine originated from the process of machine control operation by the sense, dialogue and decision of a human expert. The structure of knowledge evolution in M2M and the scheme for a dialogue agent among agent-based modules such as a sensory agent, a dialogue agent and an expert system (decision support agent) are presented in this paper, and work-offset compensation from thermal change and recommendation of cutting condition are performed on-line for knowledge-evolution verification

An intelligent man-machine system for future nuclear power plants

International Nuclear Information System (INIS)

Takizawa, Yoji; Hattori, Yoshiaki; Itoh, Juichiro; Fukumoto, Akira

1994-01-01

The objective of the development of an intelligent man-machine system for future nuclear power plants is enhancement of operational reliability by applying recent advances in cognitive science, artificial intelligence, and computer technologies. To realize this objective, the intelligent man-machine system, aiming to support a knowledge-based decision making process in an operator's supervisory plant control tasks, consists of three main functions, i.e., a cognitive model-based advisor, a robust automatic sequence controller, and an ecological interface. These three functions have been integrated into a console-type nuclear power plant monitoring and control system as a validation test bed. The validation tests in which experienced operator crews participated were carried out in 1991 and 1992. The test results show the usefulness of the support functions and the validity of the system design approach

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.

Social collective intelligence combining the powers of humans and machines to build a smarter society

CERN Document Server

Miorandi, Daniele; Rovatsos, Michael

2014-01-01

The book focuses on Social Collective Intelligence, a term used to denote a class of socio-technical systems that combine, in a coordinated way, the strengths of humans, machines and collectives in terms of competences, knowledge and problem solving capabilities with the communication, computing and storage capabilities of advanced ICT.Social Collective Intelligence opens a number of challenges for researchers in both computer science and social sciences; at the same time it provides an innovative approach to solve challenges in diverse application domains, ranging from health to education

6th International Conference on Pattern Recognition and Machine Intelligence

CERN Document Server

Gawrysiak, Piotr; Kryszkiewicz, Marzena; Rybiński, Henryk

2016-01-01

This book presents valuable contributions devoted to practical applications of Machine Intelligence and Big Data in various branches of the industry. All the contributions are extended versions of presentations delivered at the Industrial Session the 6th International Conference on Pattern Recognition and Machine Intelligence (PREMI 2015) held in Warsaw, Poland at June 30- July 3, 2015, which passed through a rigorous reviewing process. The contributions address real world problems and show innovative solutions used to solve them. This volume will serve as a bridge between researchers and practitioners, as well as between different industry branches, which can benefit from sharing ideas and results.

A REVIEW OF VIBRATION MACHINE DIAGNOSTICS BY USING ARTIFICIAL INTELLIGENCE METHODS

Directory of Open Access Journals (Sweden)

Grover Zurita

2016-09-01

Full Text Available In the industry, gears and rolling bearings failures are one of the foremost causes of breakdown in rotating machines, reducing availability time of the production and resulting in costly systems downtime. Therefore, there are growing demands for vibration condition based monitoring of gears and bearings, and any method in order to improve the effectiveness, reliability, and accuracy of the bearing faults diagnosis ought to be evaluated. In order to perform machine diagnosis efficiently, researchers have extensively investigated different advanced digital signal processing techniques and artificial intelligence methods to accurately extract fault characteristics from vibration signals. The main goal of this article is to present the state-of-the-art development in vibration analysis for machine diagnosis based on artificial intelligence methods.

Intelligent Human Machine Interface Design for Advanced Product Life Cycle Management Systems

OpenAIRE

Ahmed, Zeeshan

2010-01-01

Designing and implementing an intelligent and user friendly human machine interface for any kind of software or hardware oriented application is always be a challenging task for the designers and developers because it is very difficult to understand the psychology of the user, nature of the work and best suit of the environment. This research paper is basically about to propose an intelligent, flexible and user friendly machine interface for Product Life Cycle Management products or PDM Syste...

A Survey on Ambient Intelligence in Health Care.

Science.gov (United States)

Acampora, Giovanni; Cook, Diane J; Rashidi, Parisa; Vasilakos, Athanasios V

2013-12-01

Ambient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people's capabilities by the means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive and anticipatory communications. Such innovative interaction paradigms make ambient intelligence technology a suitable candidate for developing various real life solutions, including in the health care domain. This survey will discuss the emergence of ambient intelligence (AmI) techniques in the health care domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of ambient intelligence, such as smart environments and wearable medical devices. We will summarize of the state of the art artificial intelligence methodologies used for developing AmI system in the health care domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users' goals and intensions) and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths.

Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs.

Science.gov (United States)

Marshall, Thomas; Champagne-Langabeer, Tiffiany; Castelli, Darla; Hoelscher, Deanna

2017-12-01

To present research models based on artificial intelligence and discuss the concept of cognitive computing and eScience as disruptive factors in health and life science research methodologies. The paper identifies big data as a catalyst to innovation and the development of artificial intelligence, presents a framework for computer-supported human problem solving and describes a transformation of research support models. This framework includes traditional computer support; federated cognition using machine learning and cognitive agents to augment human intelligence; and a semi-autonomous/autonomous cognitive model, based on deep machine learning, which supports eScience. The paper provides a forward view of the impact of artificial intelligence on our human-computer support and research methods in health and life science research. By augmenting or amplifying human task performance with artificial intelligence, cognitive computing and eScience research models are discussed as novel and innovative systems for developing more effective adaptive obesity intervention programs.

A Survey on Ambient Intelligence in Health Care

Science.gov (United States)

Acampora, Giovanni; Cook, Diane J.; Rashidi, Parisa; Vasilakos, Athanasios V.

2013-01-01

Ambient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people’s capabilities by the means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive and anticipatory communications. Such innovative interaction paradigms make ambient intelligence technology a suitable candidate for developing various real life solutions, including in the health care domain. This survey will discuss the emergence of ambient intelligence (AmI) techniques in the health care domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of ambient intelligence, such as smart environments and wearable medical devices. We will summarize of the state of the art artificial intelligence methodologies used for developing AmI system in the health care domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users’ goals and intensions) and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths. PMID:24431472

Syndrome Diagnosis: Human Intuition or Machine Intelligence?

Science.gov (United States)

Braaten, Øivind; Friestad, Johannes

2008-01-01

The aim of this study was to investigate whether artificial intelligence methods can represent objective methods that are essential in syndrome diagnosis. Most syndromes have no external criterion standard of diagnosis. The predictive value of a clinical sign used in diagnosis is dependent on the prior probability of the syndrome diagnosis. Clinicians often misjudge the probabilities involved. Syndromology needs objective methods to ensure diagnostic consistency, and take prior probabilities into account. We applied two basic artificial intelligence methods to a database of machine-generated patients - a ‘vector method’ and a set method. As reference methods we ran an ID3 algorithm, a cluster analysis and a naive Bayes’ calculation on the same patient series. The overall diagnostic error rate for the the vector algorithm was 0.93%, and for the ID3 0.97%. For the clinical signs found by the set method, the predictive values varied between 0.71 and 1.0. The artificial intelligence methods that we used, proved simple, robust and powerful, and represent objective diagnostic methods. PMID:19415142

Integrating human factors and artificial intelligence in the development of human-machine cooperation

NARCIS (Netherlands)

Maanen, P.P. van; Lindenberg, J.; Neericx, M.A.

2005-01-01

Increasing machine intelligence leads to a shift from a mere interactive to a much more complex cooperative human-machine relation requiring a multidisciplinary development approach. This paper presents a generic multidisciplinary cognitive engineering method CE+ for the integration of human factors

Games and Machine Learning: A Powerful Combination in an Artificial Intelligence Course

Science.gov (United States)

Wallace, Scott A.; McCartney, Robert; Russell, Ingrid

2010-01-01

Project MLeXAI [Machine Learning eXperiences in Artificial Intelligence (AI)] seeks to build a set of reusable course curriculum and hands on laboratory projects for the artificial intelligence classroom. In this article, we describe two game-based projects from the second phase of project MLeXAI: Robot Defense--a simple real-time strategy game…

Responding to the will of the machine: Leadership in the age of artificial intelligence

OpenAIRE

NAQVI, Al

2017-01-01

Abstract. The advent of artificial intelligence in the modern economy will revolutionize the workplace of tomorrow. It will alsocreate never-seen-before challenges for leadership. The current leadership theory is extensive but it does not address on how to lead in a workplace composed of intelligent machines. However, it can be observed that leadership theory tends to develop in tandem with the developments in technology - metaphorically termed as will of the machine in this article. Specific...

Machine Learning-based Intelligent Formal Reasoning and Proving System

Science.gov (United States)

Chen, Shengqing; Huang, Xiaojian; Fang, Jiaze; Liang, Jia

2018-03-01

The reasoning system can be used in many fields. How to improve reasoning efficiency is the core of the design of system. Through the formal description of formal proof and the regular matching algorithm, after introducing the machine learning algorithm, the system of intelligent formal reasoning and verification has high efficiency. The experimental results show that the system can verify the correctness of propositional logic reasoning and reuse the propositional logical reasoning results, so as to obtain the implicit knowledge in the knowledge base and provide the basic reasoning model for the construction of intelligent system.

Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care?

Science.gov (United States)

Bini, Stefano A

2018-02-27

This article was presented at the 2017 annual meeting of the American Association of Hip and Knee Surgeons to introduce the members gathered as the audience to the concepts behind artificial intelligence (AI) and the applications that AI can have in the world of health care today. We discuss the origin of AI, progress to machine learning, and then discuss how the limits of machine learning lead data scientists to develop artificial neural networks and deep learning algorithms through biomimicry. We will place all these technologies in the context of practical clinical examples and show how AI can act as a tool to support and amplify human cognitive functions for physicians delivering care to increasingly complex patients. The aim of this article is to provide the reader with a basic understanding of the fundamentals of AI. Its purpose is to demystify this technology for practicing surgeons so they can better understand how and where to apply it. Copyright © 2018 Elsevier Inc. All rights reserved.

AN INTELLIGENT CONVERSATION AGENT FOR HEALTH CARE DOMAIN

Directory of Open Access Journals (Sweden)

K. Karpagam

2014-04-01

Full Text Available Human Computer Interaction is one of the pervasive application areas of computer science to develop with multimodal interaction for information sharings. The conversation agent acts as the major core area for developing interfaces between a system and user with applied AI for proper responses. In this paper, the interactive system plays a vital role in improving knowledge in the domain of health through the intelligent interface between machine and human with text and speech. The primary aim is to enrich the knowledge and help the user in the domain of health using conversation agent to offer immediate response with human companion feel.

Study on intelligent processing system of man-machine interactive garment frame model

Science.gov (United States)

Chen, Shuwang; Yin, Xiaowei; Chang, Ruijiang; Pan, Peiyun; Wang, Xuedi; Shi, Shuze; Wei, Zhongqian

2018-05-01

A man-machine interactive garment frame model intelligent processing system is studied in this paper. The system consists of several sensor device, voice processing module, mechanical parts and data centralized acquisition devices. The sensor device is used to collect information on the environment changes brought by the body near the clothes frame model, the data collection device is used to collect the information of the environment change induced by the sensor device, voice processing module is used for speech recognition of nonspecific person to achieve human-machine interaction, mechanical moving parts are used to make corresponding mechanical responses to the information processed by data collection device.it is connected with data acquisition device by a means of one-way connection. There is a one-way connection between sensor device and data collection device, two-way connection between data acquisition device and voice processing module. The data collection device is one-way connection with mechanical movement parts. The intelligent processing system can judge whether it needs to interact with the customer, realize the man-machine interaction instead of the current rigid frame model.

Social Intelligence in a Human-Machine Collaboration System

Science.gov (United States)

Nakajima, Hiroshi; Morishima, Yasunori; Yamada, Ryota; Brave, Scott; Maldonado, Heidy; Nass, Clifford; Kawaji, Shigeyasu

In this information society of today, it is often argued that it is necessary to create a new way of human-machine interaction. In this paper, an agent with social response capabilities has been developed to achieve this goal. There are two kinds of information that is exchanged by two entities: objective and functional information (e.g., facts, requests, states of matters, etc.) and subjective information (e.g., feelings, sense of relationship, etc.). Traditional interactive systems have been designed to handle the former kind of information. In contrast, in this study social agents handling the latter type of information are presented. The current study focuses on sociality of the agent from the view point of Media Equation theory. This article discusses the definition, importance, and benefits of social intelligence as agent technology and argues that social intelligence has a potential to enhance the user's perception of the system, which in turn can lead to improvements of the system's performance. In order to implement social intelligence in the agent, a mind model has been developed to render affective expressions and personality of the agent. The mind model has been implemented in a human-machine collaborative learning system. One differentiating feature of the collaborative learning system is that it has an agent that performs as a co-learner with which the user interacts during the learning session. The mind model controls the social behaviors of the agent, thus making it possible for the user to have more social interactions with the agent. The experiment with the system suggested that a greater degree of learning was achieved when the students worked with the co-learner agent and that the co-learner agent with the mind model that expressed emotions resulted in a more positive attitude toward the system.

Behavioral Modeling for Mental Health using Machine Learning Algorithms.

Science.gov (United States)

Srividya, M; Mohanavalli, S; Bhalaji, N

2018-04-03

Mental health is an indicator of emotional, psychological and social well-being of an individual. It determines how an individual thinks, feels and handle situations. Positive mental health helps one to work productively and realize their full potential. Mental health is important at every stage of life, from childhood and adolescence through adulthood. Many factors contribute to mental health problems which lead to mental illness like stress, social anxiety, depression, obsessive compulsive disorder, drug addiction, and personality disorders. It is becoming increasingly important to determine the onset of the mental illness to maintain proper life balance. The nature of machine learning algorithms and Artificial Intelligence (AI) can be fully harnessed for predicting the onset of mental illness. Such applications when implemented in real time will benefit the society by serving as a monitoring tool for individuals with deviant behavior. This research work proposes to apply various machine learning algorithms such as support vector machines, decision trees, naïve bayes classifier, K-nearest neighbor classifier and logistic regression to identify state of mental health in a target group. The responses obtained from the target group for the designed questionnaire were first subject to unsupervised learning techniques. The labels obtained as a result of clustering were validated by computing the Mean Opinion Score. These cluster labels were then used to build classifiers to predict the mental health of an individual. Population from various groups like high school students, college students and working professionals were considered as target groups. The research presents an analysis of applying the aforementioned machine learning algorithms on the target groups and also suggests directions for future work.

Imagining the thinking machine: technological myths and the rise of Artificial Intelligence

OpenAIRE

Natale, Simone; Ballatore, Andrea

2017-01-01

This article discusses the role of technological myths in the development of Artificial Intelligence (AI) technologies from 1950s to the early 1970s. It shows how the rise of AI was accompanied by the construction of a powerful cultural myth: the creation of a thinking machine, which would be able to perfectly simulate the cognitive faculties of the human mind. Based on a content analysis of articles on Artificial Intelligence published in two magazines, the Scientific American and the New Sc...

Imaging, Health Record, and Artificial Intelligence: Hype or Hope?

Science.gov (United States)

Mazzanti, Marco; Shirka, Ervina; Gjergo, Hortensia; Hasimi, Endri

2018-05-10

The review is focused on "digital health", which means advanced analytics based on multi-modal data. The "Health Care Internet of Things", which uses sensors, apps, and remote monitoring could provide continuous clinical information in the cloud that enables clinicians to access the information they need to care for patients everywhere. Greater standardization of acquisition protocols will be needed to maximize the potential gains from automation and machine learning. Recent artificial intelligence applications on cardiac imaging will not be diagnosing patients and replacing doctors but will be augmenting their ability to find key relevant data they need to care for a patient and present it in a concise, easily digestible format. Risk stratification will transition from oversimplified population-based risk scores to machine learning-based metrics incorporating a large number of patient-specific clinical and imaging variables in real-time beyond the limits of human cognition. This will deliver highly accurate and individual personalized risk assessments and facilitate tailored management plans.

An intelligent human-machine system based on an ecological interface design concept

International Nuclear Information System (INIS)

Naito, N.

1995-01-01

It seems both necessary and promising to develop an intelligent human-machine system, considering the objective of the human-machine system and the recent advance in cognitive engineering and artificial intelligence together with the ever-increasing importance of human factor issues in nuclear power plant operation and maintenance. It should support human operators in their knowledge-based behaviour and allow them to cope with unanticipated abnormal events, including recovery from erroneous human actions. A top-down design approach has been adopted based on cognitive work analysis, and (1) an ecological interface, (2) a cognitive model-based advisor and (3) a robust automatic sequence controller have been established. These functions have been integrated into an experimental control room. A validation test was carried out by the participation of experienced operators and engineers. The results showed the usefulness of this system in supporting the operator's supervisory plant control tasks. ((orig.))

Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM).

Science.gov (United States)

Nadiri, Ata Allah; Gharekhani, Maryam; Khatibi, Rahman; Sadeghfam, Sina; Moghaddam, Asghar Asghari

2017-01-01

This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), Neuro-Fuzzy (NF) and Gene Expression Programming (GEP). Each model uses the DRASTIC index, the acronym of 7 geological, hydrological and hydrogeological parameters, which collectively represents intrinsic (or natural) vulnerability and gives a sense of contaminants, such as nitrate-N, penetrating aquifers from the surface. These models are trained to modify or condition their DRASTIC index values by measured nitrate-N concentration. The three AI-techniques often perform similarly but have differences as well and therefore SICM exploits the situation to improve the modeled values by producing a hybrid modeling results through selecting better performing SVM, NF and GEP components. The models of the study area at Ardabil aquifer show that the vulnerability indices by the DRASTIC framework produces sharp fronts but AI models smoothen the fronts and reflect a better correlation with observed nitrate values; SICM improves on the performances of three AI models and cope well with heterogeneity and uncertain parameters. Copyright © 2016 Elsevier B.V. All rights reserved.

Prediction of Compressional, Shear, and Stoneley Wave Velocities from Conventional Well Log Data Using a Committee Machine with Intelligent Systems

Science.gov (United States)

Asoodeh, Mojtaba; Bagheripour, Parisa

2012-01-01

Measurement of compressional, shear, and Stoneley wave velocities, carried out by dipole sonic imager (DSI) logs, provides invaluable data in geophysical interpretation, geomechanical studies and hydrocarbon reservoir characterization. The presented study proposes an improved methodology for making a quantitative formulation between conventional well logs and sonic wave velocities. First, sonic wave velocities were predicted from conventional well logs using artificial neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a committee machine with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search technique while outputs of artificial neural network, fuzzy logic and neuro-fuzzy models were used as inputs of the committee machine. It is capable of improving the accuracy of final prediction through integrating the outputs of aforementioned intelligent systems. The hybrid genetic algorithm-pattern search tool, embodied in the structure of committee machine, assigns a weight factor to each individual intelligent system, indicating its involvement in overall prediction of DSI parameters. This methodology was implemented in Asmari formation, which is the major carbonate reservoir rock of Iranian oil field. A group of 1,640 data points was used to construct the intelligent model, and a group of 800 data points was employed to assess the reliability of the proposed model. The results showed that the committee machine with intelligent systems performed more effectively compared with individual intelligent systems performing alone.

Automated business process management – in times of digital transformation using machine learning or artificial intelligence

Directory of Open Access Journals (Sweden)

Paschek Daniel

2017-01-01

Full Text Available The continuous optimization of business processes is still a challenge for companies. In times of digital transformation, faster changing internal and external framework conditions and new customer expectations for fastest delivery and best quality of goods and many more, companies should set up their internal process at the best way. But what to do if framework conditions changed unexpectedly? The purpose of the paper is to analyse how the digital transformation will impact the Business Process Management (BPM while using methods like machine learning or artificial intelligence. Therefore, the core components will be explained, compared and set up in relation. To identify application areas interviews and analysis will be held up with digital companies. The finding of the paper will be recommendation for action in the field of BPM and process optimization through machine learning and artificial intelligence. The Approach of optimizing and management processes via machine learning and artificial intelligence will support companies to decide which tool will be the best for automated BPM.

Intelligent Machine Learning Approaches for Aerospace Applications

Science.gov (United States)

Sathyan, Anoop

Machine Learning is a type of artificial intelligence that provides machines or networks the ability to learn from data without the need to explicitly program them. There are different kinds of machine learning techniques. This thesis discusses the applications of two of these approaches: Genetic Fuzzy Logic and Convolutional Neural Networks (CNN). Fuzzy Logic System (FLS) is a powerful tool that can be used for a wide variety of applications. FLS is a universal approximator that reduces the need for complex mathematics and replaces it with expert knowledge of the system to produce an input-output mapping using If-Then rules. The expert knowledge of a system can help in obtaining the parameters for small-scale FLSs, but for larger networks we will need to use sophisticated approaches that can automatically train the network to meet the design requirements. This is where Genetic Algorithms (GA) and EVE come into the picture. Both GA and EVE can tune the FLS parameters to minimize a cost function that is designed to meet the requirements of the specific problem. EVE is an artificial intelligence developed by Psibernetix that is trained to tune large scale FLSs. The parameters of an FLS can include the membership functions and rulebase of the inherent Fuzzy Inference Systems (FISs). The main issue with using the GFS is that the number of parameters in a FIS increase exponentially with the number of inputs thus making it increasingly harder to tune them. To reduce this issue, the FLSs discussed in this thesis consist of 2-input-1-output FISs in cascade (Chapter 4) or as a layer of parallel FISs (Chapter 7). We have obtained extremely good results using GFS for different applications at a reduced computational cost compared to other algorithms that are commonly used to solve the corresponding problems. In this thesis, GFSs have been designed for controlling an inverted double pendulum, a task allocation problem of clustering targets amongst a set of UAVs, a fire

m-Health 2.0: New perspectives on mobile health, Machine Learning and Big Data Analytics.

Science.gov (United States)

Istepanian, Robert S H; Al-Anzi, Turki

2018-06-08

Mobile health (m-Health) has been repeatedly called the biggest technological breakthrough of our modern times. Similarly, the concept of big data in the context of healthcare is considered one of the transformative drivers for intelligent healthcare delivery systems. In recent years, big data has become increasingly synonymous with mobile health, however key challenges of 'Big Data and mobile health', remain largely untackled. This is becoming particularly important with the continued deluge of the structured and unstructured data sets generated on daily basis from the proliferation of mobile health applications within different healthcare systems and products globally. The aim of this paper is of twofold. First we present the relevant big data issues from the mobile health (m-Health) perspective. In particular we discuss these issues from the technological areas and building blocks (communications, sensors and computing) of mobile health and the newly defined (m-Health 2.0) concept. The second objective is to present the relevant rapprochement issues of big m-Health data analytics with m-Health. Further, we also present the current and future roles of machine and deep learning within the current smart phone centric m-health model. The critical balance between these two important areas will depend on how different stakeholder from patients, clinicians, healthcare providers, medical and m-health market businesses and regulators will perceive these developments. These new perspectives are essential for better understanding the fine balance between the new insights of how intelligent and connected the future mobile health systems will look like and the inherent risks and clinical complexities associated with the big data sets and analytical tools used in these systems. These topics will be subject for extensive work and investigations in the foreseeable future for the areas of data analytics, computational and artificial intelligence methods applied for mobile health

Routine human-competitive machine intelligence by means of genetic programming

Science.gov (United States)

Koza, John R.; Streeter, Matthew J.; Keane, Martin

2004-01-01

Genetic programming is a systematic method for getting computers to automatically solve a problem. Genetic programming starts from a high-level statement of what needs to be done and automatically creates a computer program to solve the problem. The paper demonstrates that genetic programming (1) now routinely delivers high-return human-competitive machine intelligence; (2) is an automated invention machine; (3) can automatically create a general solution to a problem in the form of a parameterized topology; and (4) has delivered a progression of qualitatively more substantial results in synchrony with five approximately order-of-magnitude increases in the expenditure of computer time. Recent results involving the automatic synthesis of the topology and sizing of analog electrical circuits and controllers demonstrate these points.

Emotional intelligence of mental health nurses.

Science.gov (United States)

van Dusseldorp, Loes R L C; van Meijel, Berno K G; Derksen, Jan J L

2011-02-01

The aim of this study is to gain insight into the level of emotional intelligence of mental health nurses in the Netherlands. The focus in research on emotional intelligence to date has been on a variety of professionals. However, little is known about emotional intelligence in mental health nurses. The emotional intelligence of 98 Dutch nurses caring for psychiatric patients is reported. Data were collected with the Bar-On Emotional Quotient Inventory within a cross-sectional research design. The mean level of emotional intelligence of this sample of professionals is statistically significant higher than the emotional intelligence of the general population. Female nurses score significantly higher than men on the subscales Empathy, Social Responsibility, Interpersonal Relationship, Emotional Self-awareness, Self-Actualisation and Assertiveness. No correlations are found between years of experience and age on the one hand and emotional intelligence on the other hand. The results of this study show that nurses in psychiatric care indeed score above average in the emotional intelligence required to cope with the amount of emotional labour involved in daily mental health practice. The ascertained large range in emotional intelligence scores among the mental health nurses challenges us to investigate possible implications which higher or lower emotional intelligence levels may have on the quality of care. For instance, a possible relation between the level of emotional intelligence and the quality of the therapeutic nurse-patient relationship or the relation between the level of emotional intelligence and the manner of coping with situations characterised by a great amount of emotional labour (such as caring for patients who self-harm or are suicidal). © 2010 The Authors. Journal compilation © 2010 Blackwell Publishing Ltd.

From curve fitting to machine learning an illustrative guide to scientific data analysis and computational intelligence

CERN Document Server

Zielesny, Achim

2016-01-01

This successful book provides in its second edition an interactive and illustrative guide from two-dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics. The major topics are extensively outlined with exploratory examples and applications. The primary goal is to be as illustrative as possible without hiding problems and pitfalls but to address them. The character of an illustrative cookbook is complemented with specific sections that address more fundamental questions like the relation between machine learning and human intelligence. All topics are completely demonstrated with the computing platform Mathematica and the Computational Intelligence Packages (CIP), a high-level function library developed with M...

Intelligent Integrated System Health Management

Science.gov (United States)

Figueroa, Fernando

2012-01-01

Intelligent Integrated System Health Management (ISHM) is the management of data, information, and knowledge (DIaK) with the purposeful objective of determining the health of a system (Management: storage, distribution, sharing, maintenance, processing, reasoning, and presentation). Presentation discusses: (1) ISHM Capability Development. (1a) ISHM Knowledge Model. (1b) Standards for ISHM Implementation. (1c) ISHM Domain Models (ISHM-DM's). (1d) Intelligent Sensors and Components. (2) ISHM in Systems Design, Engineering, and Integration. (3) Intelligent Control for ISHM-Enabled Systems

Nonvolatile Memory Materials for Neuromorphic Intelligent Machines.

Science.gov (United States)

Jeong, Doo Seok; Hwang, Cheol Seong

2018-04-18

Recent progress in deep learning extends the capability of artificial intelligence to various practical tasks, making the deep neural network (DNN) an extremely versatile hypothesis. While such DNN is virtually built on contemporary data centers of the von Neumann architecture, physical (in part) DNN of non-von Neumann architecture, also known as neuromorphic computing, can remarkably improve learning and inference efficiency. Particularly, resistance-based nonvolatile random access memory (NVRAM) highlights its handy and efficient application to the multiply-accumulate (MAC) operation in an analog manner. Here, an overview is given of the available types of resistance-based NVRAMs and their technological maturity from the material- and device-points of view. Examples within the strategy are subsequently addressed in comparison with their benchmarks (virtual DNN in deep learning). A spiking neural network (SNN) is another type of neural network that is more biologically plausible than the DNN. The successful incorporation of resistance-based NVRAM in SNN-based neuromorphic computing offers an efficient solution to the MAC operation and spike timing-based learning in nature. This strategy is exemplified from a material perspective. Intelligent machines are categorized according to their architecture and learning type. Also, the functionality and usefulness of NVRAM-based neuromorphic computing are addressed. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Machine learning based Intelligent cognitive network using fog computing

Science.gov (United States)

Lu, Jingyang; Li, Lun; Chen, Genshe; Shen, Dan; Pham, Khanh; Blasch, Erik

2017-05-01

In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to distribute the limited radio spectrum resources more efficiently. The CRN framework can analyze the time-sensitive signal data close to the signal source using fog computing with different types of machine learning techniques. Depending on the computational capabilities of the fog nodes, different features and machine learning techniques are chosen to optimize spectrum allocation. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.

Software for Intelligent System Health Management

Science.gov (United States)

Trevino, Luis C.

2004-01-01

This viewgraph presentation describes the characteristics and advantages of autonomy and artificial intelligence in systems health monitoring. The presentation lists technologies relevant to Intelligent System Health Management (ISHM), and some potential applications.

Soft computing in machine learning

CERN Document Server

Park, Jooyoung; Inoue, Atsushi

2014-01-01

As users or consumers are now demanding smarter devices, intelligent systems are revolutionizing by utilizing machine learning. Machine learning as part of intelligent systems is already one of the most critical components in everyday tools ranging from search engines and credit card fraud detection to stock market analysis. You can train machines to perform some things, so that they can automatically detect, diagnose, and solve a variety of problems. The intelligent systems have made rapid progress in developing the state of the art in machine learning based on smart and deep perception. Using machine learning, the intelligent systems make widely applications in automated speech recognition, natural language processing, medical diagnosis, bioinformatics, and robot locomotion. This book aims at introducing how to treat a substantial amount of data, to teach machines and to improve decision making models. And this book specializes in the developments of advanced intelligent systems through machine learning. It...

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining.

Science.gov (United States)

Salehi, Mojtaba; Bahreininejad, Ardeshir

2011-08-01

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.

Time-series prediction and applications a machine intelligence approach

CERN Document Server

Konar, Amit

2017-01-01

This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at...

The SP Theory of Intelligence as a Foundation for the Development of a General, Human-Level Thinking Machine

OpenAIRE

Wolff, J Gerard

2016-01-01

This paper summarises how the "SP theory of intelligence" and its realisation in the "SP computer model" simplifies and integrates concepts across artificial intelligence and related areas, and thus provides a promising foundation for the development of a general, human-level thinking machine, in accordance with the main goal of research in artificial general intelligence. The key to this simplification and integration is the powerful concept of "multiple alignment", borrowed and adapted from...

Combining human and machine intelligence to derive agents' behavioral rules for groundwater irrigation

Science.gov (United States)

Hu, Yao; Quinn, Christopher J.; Cai, Ximing; Garfinkle, Noah W.

2017-11-01

For agent-based modeling, the major challenges in deriving agents' behavioral rules arise from agents' bounded rationality and data scarcity. This study proposes a "gray box" approach to address the challenge by incorporating expert domain knowledge (i.e., human intelligence) with machine learning techniques (i.e., machine intelligence). Specifically, we propose using directed information graph (DIG), boosted regression trees (BRT), and domain knowledge to infer causal factors and identify behavioral rules from data. A case study is conducted to investigate farmers' pumping behavior in the Midwest, U.S.A. Results show that four factors identified by the DIG algorithm- corn price, underlying groundwater level, monthly mean temperature and precipitation- have main causal influences on agents' decisions on monthly groundwater irrigation depth. The agent-based model is then developed based on the behavioral rules represented by three DIGs and modeled by BRTs, and coupled with a physically-based groundwater model to investigate the impacts of agents' pumping behavior on the underlying groundwater system in the context of coupled human and environmental systems.

Intelligent machines in the twenty-first century: foundations of inference and inquiry.

Science.gov (United States)

Knuth, Kevin H

2003-12-15

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have

Artificial Intelligence.

Science.gov (United States)

Wash, Darrel Patrick

1989-01-01

Making a machine seem intelligent is not easy. As a consequence, demand has been rising for computer professionals skilled in artificial intelligence and is likely to continue to go up. These workers develop expert systems and solve the mysteries of machine vision, natural language processing, and neural networks. (Editor)

A cloud platform for remote diagnosis of breast cancer in mammography by fusion of machine and human intelligence

Science.gov (United States)

Jiang, Guodong; Fan, Ming; Li, Lihua

2016-03-01

Mammography is the gold standard for breast cancer screening, reducing mortality by about 30%. The application of a computer-aided detection (CAD) system to assist a single radiologist is important to further improve mammographic sensitivity for breast cancer detection. In this study, a design and realization of the prototype for remote diagnosis system in mammography based on cloud platform were proposed. To build this system, technologies were utilized including medical image information construction, cloud infrastructure and human-machine diagnosis model. Specifically, on one hand, web platform for remote diagnosis was established by J2EE web technology. Moreover, background design was realized through Hadoop open-source framework. On the other hand, storage system was built up with Hadoop distributed file system (HDFS) technology which enables users to easily develop and run on massive data application, and give full play to the advantages of cloud computing which is characterized by high efficiency, scalability and low cost. In addition, the CAD system was realized through MapReduce frame. The diagnosis module in this system implemented the algorithms of fusion of machine and human intelligence. Specifically, we combined results of diagnoses from doctors' experience and traditional CAD by using the man-machine intelligent fusion model based on Alpha-Integration and multi-agent algorithm. Finally, the applications on different levels of this system in the platform were also discussed. This diagnosis system will have great importance for the balanced health resource, lower medical expense and improvement of accuracy of diagnosis in basic medical institutes.

Games and machine learning: a powerful combination in an artificial intelligence course

Science.gov (United States)

Wallace, Scott A.; McCartney, Robert; Russell, Ingrid

2010-03-01

Project MLeXAI (Machine Learning eXperiences in Artificial Intelligence (AI)) seeks to build a set of reusable course curriculum and hands on laboratory projects for the artificial intelligence classroom. In this article, we describe two game-based projects from the second phase of project MLeXAI: Robot Defense - a simple real-time strategy game and Checkers - a classic turn-based board game. From the instructors' prospective, we examine aspects of design and implementation as well as the challenges and rewards of using the curricula. We explore students' responses to the projects via the results of a common survey. Finally, we compare the student perceptions from the game-based projects to non-game based projects from the first phase of Project MLeXAI.

Emotional intelligence of mental health nurses

NARCIS (Netherlands)

Jan Derksen; prof Berno van Meijel; Loes van Dusseldorp

2011-01-01

Aims. The aim of this study is to gain insight into the level of emotional intelligence of mental health nurses in the Netherlands. Background. The focus in research on emotional intelligence to date has been on a variety of professionals. However, little is known about emotional intelligence in

Social collective intelligence: combining the powers of humans and machines to build a smarter society

NARCIS (Netherlands)

Miorandi, Daniele; Maltese, Vincenzo; Rovatsos, Michael; Nijholt, Antinus; Stewart, James

2014-01-01

The book focuses on Social Collective Intelligence, a term used to denote a class of socio-technical systems that combine, in a coordinated way, the strengths of humans, machines and collectives in terms of competences, knowledge and problem solving capabilities with the communication, computing and

LARA. Localization of an automatized refueling machine by acoustical sounding in breeder reactors - implementation of artificial intelligence techniques

International Nuclear Information System (INIS)

Lhuillier, C.; Malvache, P.

1987-01-01

The automatic control of the machine which handles the nuclear subassemblies in fast neutron reactors requires autonomous perception and decision tools. An acoustical device allows the machine to position in the work area. Artificial intelligence techniques are implemented to interpret the data: pattern recognition, scene analysis. The localization process is managed by an expert system. 6 refs.; 8 figs

Intelligent Machine Vision for Automated Fence Intruder Detection Using Self-organizing Map

OpenAIRE

Veldin A. Talorete Jr.; Sherwin A Guirnaldo

2017-01-01

This paper presents an intelligent machine vision for automated fence intruder detection. A series of still captured images that contain fence events using Internet Protocol cameras was used as input data to the system. Two classifiers were used; the first is to classify human posture and the second one will classify intruder location. The system classifiers were implemented using Self-Organizing Map after the implementation of several image segmentation processes. The human posture classifie...

Advanced intelligent systems

CERN Document Server

Ryoo, Young; Jang, Moon-soo; Bae, Young-Chul

2014-01-01

Intelligent systems have been initiated with the attempt to imitate the human brain. People wish to let machines perform intelligent works. Many techniques of intelligent systems are based on artificial intelligence. According to changing and novel requirements, the advanced intelligent systems cover a wide spectrum: big data processing, intelligent control, advanced robotics, artificial intelligence and machine learning. This book focuses on coordinating intelligent systems with highly integrated and foundationally functional components. The book consists of 19 contributions that features social network-based recommender systems, application of fuzzy enforcement, energy visualization, ultrasonic muscular thickness measurement, regional analysis and predictive modeling, analysis of 3D polygon data, blood pressure estimation system, fuzzy human model, fuzzy ultrasonic imaging method, ultrasonic mobile smart technology, pseudo-normal image synthesis, subspace classifier, mobile object tracking, standing-up moti...

Intelligent judgements over health risks in a spatial agent-based model.

Science.gov (United States)

Abdulkareem, Shaheen A; Augustijn, Ellen-Wien; Mustafa, Yaseen T; Filatova, Tatiana

2018-03-20

Millions of people worldwide are exposed to deadly infectious diseases on a regular basis. Breaking news of the Zika outbreak for instance, made it to the main media titles internationally. Perceiving disease risks motivate people to adapt their behavior toward a safer and more protective lifestyle. Computational science is instrumental in exploring patterns of disease spread emerging from many individual decisions and interactions among agents and their environment by means of agent-based models. Yet, current disease models rarely consider simulating dynamics in risk perception and its impact on the adaptive protective behavior. Social sciences offer insights into individual risk perception and corresponding protective actions, while machine learning provides algorithms and methods to capture these learning processes. This article presents an innovative approach to extend agent-based disease models by capturing behavioral aspects of decision-making in a risky context using machine learning techniques. We illustrate it with a case of cholera in Kumasi, Ghana, accounting for spatial and social risk factors that affect intelligent behavior and corresponding disease incidents. The results of computational experiments comparing intelligent with zero-intelligent representations of agents in a spatial disease agent-based model are discussed. We present a spatial disease agent-based model (ABM) with agents' behavior grounded in Protection Motivation Theory. Spatial and temporal patterns of disease diffusion among zero-intelligent agents are compared to those produced by a population of intelligent agents. Two Bayesian Networks (BNs) designed and coded using R and are further integrated with the NetLogo-based Cholera ABM. The first is a one-tier BN1 (only risk perception), the second is a two-tier BN2 (risk and coping behavior). We run three experiments (zero-intelligent agents, BN1 intelligence and BN2 intelligence) and report the results per experiment in terms of

Intelligence-Augmented Rat Cyborgs in Maze Solving.

Directory of Open Access Journals (Sweden)

Yipeng Yu

Full Text Available Cyborg intelligence is an emerging kind of intelligence paradigm. It aims to deeply integrate machine intelligence with biological intelligence by connecting machines and living beings via neural interfaces, enhancing strength by combining the biological cognition capability with the machine computational capability. Cyborg intelligence is considered to be a new way to augment living beings with machine intelligence. In this paper, we build rat cyborgs to demonstrate how they can expedite the maze escape task with integration of machine intelligence. We compare the performance of maze solving by computer, by individual rats, and by computer-aided rats (i.e. rat cyborgs. They were asked to find their way from a constant entrance to a constant exit in fourteen diverse mazes. Performance of maze solving was measured by steps, coverage rates, and time spent. The experimental results with six rats and their intelligence-augmented rat cyborgs show that rat cyborgs have the best performance in escaping from mazes. These results provide a proof-of-principle demonstration for cyborg intelligence. In addition, our novel cyborg intelligent system (rat cyborg has great potential in various applications, such as search and rescue in complex terrains.

Intelligence-Augmented Rat Cyborgs in Maze Solving.

Science.gov (United States)

Yu, Yipeng; Pan, Gang; Gong, Yongyue; Xu, Kedi; Zheng, Nenggan; Hua, Weidong; Zheng, Xiaoxiang; Wu, Zhaohui

2016-01-01

Cyborg intelligence is an emerging kind of intelligence paradigm. It aims to deeply integrate machine intelligence with biological intelligence by connecting machines and living beings via neural interfaces, enhancing strength by combining the biological cognition capability with the machine computational capability. Cyborg intelligence is considered to be a new way to augment living beings with machine intelligence. In this paper, we build rat cyborgs to demonstrate how they can expedite the maze escape task with integration of machine intelligence. We compare the performance of maze solving by computer, by individual rats, and by computer-aided rats (i.e. rat cyborgs). They were asked to find their way from a constant entrance to a constant exit in fourteen diverse mazes. Performance of maze solving was measured by steps, coverage rates, and time spent. The experimental results with six rats and their intelligence-augmented rat cyborgs show that rat cyborgs have the best performance in escaping from mazes. These results provide a proof-of-principle demonstration for cyborg intelligence. In addition, our novel cyborg intelligent system (rat cyborg) has great potential in various applications, such as search and rescue in complex terrains.

Thermal Error Test and Intelligent Modeling Research on the Spindle of High Speed CNC Machine Tools

Science.gov (United States)

Luo, Zhonghui; Peng, Bin; Xiao, Qijun; Bai, Lu

2018-03-01

Thermal error is the main factor affecting the accuracy of precision machining. Through experiments, this paper studies the thermal error test and intelligent modeling for the spindle of vertical high speed CNC machine tools in respect of current research focuses on thermal error of machine tool. Several testing devices for thermal error are designed, of which 7 temperature sensors are used to measure the temperature of machine tool spindle system and 2 displacement sensors are used to detect the thermal error displacement. A thermal error compensation model, which has a good ability in inversion prediction, is established by applying the principal component analysis technology, optimizing the temperature measuring points, extracting the characteristic values closely associated with the thermal error displacement, and using the artificial neural network technology.

ANN-PSO Integrated Optimization Methodology for Intelligent Control of MMC Machining

Science.gov (United States)

Chandrasekaran, Muthumari; Tamang, Santosh

2017-08-01

Metal Matrix Composites (MMC) show improved properties in comparison with non-reinforced alloys and have found increased application in automotive and aerospace industries. The selection of optimum machining parameters to produce components of desired surface roughness is of great concern considering the quality and economy of manufacturing process. In this study, a surface roughness prediction model for turning Al-SiCp MMC is developed using Artificial Neural Network (ANN). Three turning parameters viz., spindle speed ( N), feed rate ( f) and depth of cut ( d) were considered as input neurons and surface roughness was an output neuron. ANN architecture having 3 -5 -1 is found to be optimum and the model predicts with an average percentage error of 7.72 %. Particle Swarm Optimization (PSO) technique is used for optimizing parameters to minimize machining time. The innovative aspect of this work is the development of an integrated ANN-PSO optimization method for intelligent control of MMC machining process applicable to manufacturing industries. The robustness of the method shows its superiority for obtaining optimum cutting parameters satisfying desired surface roughness. The method has better convergent capability with minimum number of iterations.

Considerations upon the Machine Learning Technologies

OpenAIRE

Alin Munteanu; Cristina Ofelia Sofran

2006-01-01

Artificial intelligence offers superior techniques and methods by which problems from diverse domains may find an optimal solution. The Machine Learning technologies refer to the domain of artificial intelligence aiming to develop the techniques allowing the computers to “learn”. Some systems based on Machine Learning technologies tend to eliminate the necessity of the human intelligence while the others adopt a man-machine collaborative approach.

Public Health Intelligence: Learning From the Ebola Crisis

Science.gov (United States)

Weber, David Jay

2015-01-01

Today’s public health crises, as exemplified by the Ebola outbreak, lead to dramatic calls to action that typically include improved electronic monitoring systems to better prepare for, and respond to, similar occurrences in the future. Even a preliminary public health informatics evaluation of the current Ebola crisis exposes the need for enhanced coordination and sharing of trustworthy public health intelligence. We call for a consumer-centric model of public health intelligence and the formation of a national center to guide public health intelligence gathering and synthesis. Sharing accurate and actionable information with government agencies, health care practitioners, policymakers, and, critically, the general public, will mark a shift from doing public health surveillance on people to doing public health surveillance for people. PMID:26180978

Intelligent mechatronics; Intelligent mechatronics

Energy Technology Data Exchange (ETDEWEB)

Hashimoto, H. [The University of Tokyo, Tokyo (Japan). Institute of Industrial Science

1995-10-01

Intelligent mechatronics (IM) was explained as follows: a study of IM essentially targets realization of a robot namely, but in the present stage the target is a creation of new values by intellectualization of machine, that is, a combination of the information infrastructure and the intelligent machine system. IM is also thought to be constituted of computers positively used and micromechatronics. The paper next introduces examples of IM study, mainly those the author is concerned with as shown below: sensor gloves, robot hands, robot eyes, tele operation, three-dimensional object recognition, mobile robot, magnetic bearing, construction of remote controlled unmanned dam, robot network, sensitivity communication using neuro baby, etc. 27 figs.

Biomimetics in Intelligent Sensor and Actuator Automation Systems

Science.gov (United States)

Bruckner, Dietmar; Dietrich, Dietmar; Zucker, Gerhard; Müller, Brit

Intelligent machines are really an old mankind's dream. With increasing technological development, the requirements for intelligent devices also increased. However, up to know, artificial intelligence (AI) lacks solutions to the demands of truly intelligent machines that have no problems to integrate themselves into daily human environments. Current hardware with a processing power of billions of operations per second (but without any model of human-like intelligence) could not substantially contribute to the intelligence of machines when compared with that of the early AI times. There are great results, of course. Machines are able to find the shortest path between far apart cities on the map; algorithms let you find information described only by few key words. But no machine is able to get us a cup of coffee from the kitchen yet.

Considerations upon the Machine Learning Technologies

Directory of Open Access Journals (Sweden)

Alin Munteanu

2006-01-01

Full Text Available Artificial intelligence offers superior techniques and methods by which problems from diverse domains may find an optimal solution. The Machine Learning technologies refer to the domain of artificial intelligence aiming to develop the techniques allowing the computers to “learn”. Some systems based on Machine Learning technologies tend to eliminate the necessity of the human intelligence while the others adopt a man-machine collaborative approach.

Artificial Intelligence

CERN Document Server

Warwick, Kevin

2011-01-01

if AI is outside your field, or you know something of the subject and would like to know more then Artificial Intelligence: The Basics is a brilliant primer.' - Nick Smith, Engineering and Technology Magazine November 2011 Artificial Intelligence: The Basics is a concise and cutting-edge introduction to the fast moving world of AI. The author Kevin Warwick, a pioneer in the field, examines issues of what it means to be man or machine and looks at advances in robotics which have blurred the boundaries. Topics covered include: how intelligence can be defined whether machines can 'think' sensory

Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh-Bonab plain aquifer, Iran

Science.gov (United States)

Fijani, Elham; Nadiri, Ata Allah; Asghari Moghaddam, Asghar; Tsai, Frank T.-C.; Dixon, Barnali

2013-10-01

Contamination of wells with nitrate-N (NO3-N) poses various threats to human health. Contamination of groundwater is a complex process and full of uncertainty in regional scale. Development of an integrative vulnerability assessment methodology can be useful to effectively manage (including prioritization of limited resource allocation to monitor high risk areas) and protect this valuable freshwater source. This study introduces a supervised committee machine with artificial intelligence (SCMAI) model to improve the DRASTIC method for groundwater vulnerability assessment for the Maragheh-Bonab plain aquifer in Iran. Four different AI models are considered in the SCMAI model, whose input is the DRASTIC parameters. The SCMAI model improves the committee machine artificial intelligence (CMAI) model by replacing the linear combination in the CMAI with a nonlinear supervised ANN framework. To calibrate the AI models, NO3-N concentration data are divided in two datasets for the training and validation purposes. The target value of the AI models in the training step is the corrected vulnerability indices that relate to the first NO3-N concentration dataset. After model training, the AI models are verified by the second NO3-N concentration dataset. The results show that the four AI models are able to improve the DRASTIC method. Since the best AI model performance is not dominant, the SCMAI model is considered to combine the advantages of individual AI models to achieve the optimal performance. The SCMAI method re-predicts the groundwater vulnerability based on the different AI model prediction values. The results show that the SCMAI outperforms individual AI models and committee machine with artificial intelligence (CMAI) model. The SCMAI model ensures that no water well with high NO3-N levels would be classified as low risk and vice versa. The study concludes that the SCMAI model is an effective model to improve the DRASTIC model and provides a confident estimate of the

Support vector machines applications

CERN Document Server

Guo, Guodong

2014-01-01

Support vector machines (SVM) have both a solid mathematical background and good performance in practical applications. This book focuses on the recent advances and applications of the SVM in different areas, such as image processing, medical practice, computer vision, pattern recognition, machine learning, applied statistics, business intelligence, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications, especially some recent advances.

The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line

Directory of Open Access Journals (Sweden)

Xiaomin Xu

2015-01-01

Full Text Available Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR. According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO, which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability.

Big Data Analytics and Machine Intelligence Capability Development at NASA Langley Research Center: Strategy, Roadmap, and Progress

Science.gov (United States)

Ambur, Manjula Y.; Yagle, Jeremy J.; Reith, William; McLarney, Edward

2016-01-01

In 2014, a team of researchers, engineers and information technology specialists at NASA Langley Research Center developed a Big Data Analytics and Machine Intelligence Strategy and Roadmap as part of Langley's Comprehensive Digital Transformation Initiative, with the goal of identifying the goals, objectives, initiatives, and recommendations need to develop near-, mid- and long-term capabilities for data analytics and machine intelligence in aerospace domains. Since that time, significant progress has been made in developing pilots and projects in several research, engineering, and scientific domains by following the original strategy of collaboration between mission support organizations, mission organizations, and external partners from universities and industry. This report summarizes the work to date in Data Intensive Scientific Discovery, Deep Content Analytics, and Deep Q&A projects, as well as the progress made in collaboration, outreach, and education. Recommendations for continuing this success into future phases of the initiative are also made.

Hybrid-augmented intelligence:collaboration and cognition

Institute of Scientific and Technical Information of China (English)

Nan-ning ZHENG; Zi-yi LIU; Peng-ju REN; Yong-qiang MA; Shi-tao CHEN; Si-yu YU; Jian-ru XUE

2017-01-01

The long-term goal of artificial intelligence (AI) is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems that humans are facing, no matter how intelligent machines are, they are unable to completely replace humans. Therefore, it is necessary to introduce human cognitive capabilities or human-like cognitive models into AI systems to develop a new form of AI, that is, hybrid-augmented intelligence. This form of AI or machine intelligence is a feasible and important developing model. Hybrid-augmented intelligence can be divided into two basic models:one is human-in-the-loop augmented intelligence with human-computer collaboration, and the other is cognitive computing based augmented intelligence, in which a cognitive model is embedded in the machine learning system. This survey describes a basic framework for human-computer collaborative hybrid-augmented intelligence, and the basic elements of hybrid-augmented intelligence based on cognitive computing. These elements include intuitive reasoning, causal models, evolution of memory and knowledge, especially the role and basic principles of intuitive reasoning for complex problem solving, and the cognitive learning framework for visual scene understanding based on memory and reasoning. Several typical applications of hybrid-augmented intelligence in related fields are given.

Relationship between nurses’ spiritual intelligence with hardiness and general health

Directory of Open Access Journals (Sweden)

Fatemeh Akbarizadeh

2012-01-01

Full Text Available Background: Nursing is one of the stressful jobs that affect nurse's general health. The aim of this study was assessment relationship between Spiritual intelligence, Hardiness and General health among nurses in the hospital of Bushehr in 1388. Methods: Cross- sectional study designed and 125 nurses who have been working in different wards of the hospital enrolled in the study. Data was collected using Spiritual intelligence, Hardiness, General health and characteristics demographic questionnaires. Correlation, t-test, ANOVA, Tukey and regression analysis was applied using SPSS-16 soft ware. Results: The results showed there was significant relationship between spiritual intelligence and hardiness (P<0.005, spiritual intelligence and General health (P<0.005, hardiness and General health (P<0.001. Among the demographic characteristics including age, gender, working section, marital status, job experiences, and education only working section showed significantly correlated with patience (P<0.005. Conclusion: Improvement of spiritual intelligence and reinforcement of hardiness could help to increase the general health of nurses.

Intelligence in youth and health at age 50

Science.gov (United States)

Wraw, Christina; Deary, Ian J.; Gale, Catharine R.; Der, Geoff

2015-01-01

Background The link between intelligence in youth and all-cause mortality in later-life is well established. To better understand this relationship, the current study examines the links between pre-morbid intelligence and a number of specific health outcomes at age 50 using the NLSY-1979 cohort. Methods Participants were the 5793 participants in the NLSY-79 who responded to questions about health outcomes at age 50. Sixteen health outcomes were examined: two were summary measures (physical health and functional limitation), 9 were diagnosed illness conditions, 4 were self-reported conditions, and one was a measure of general health status. Linear and logistic regressions were used, as appropriate, to examine the relationship between intelligence in youth and the health outcomes. Age, sex and both childhood and adult SES, and its sub-components – income, education, & occupational prestige – are all adjusted for separately. Results & conclusion Higher pre-morbid intelligence is linked with better physical health at age 50, and a lower risk for a number of chronic health conditions. For example, a 1 SD higher score in IQ was significantly associated with increased odds of having good, very good, or excellent health, with an odds ratio of 1.70 (C.I. 1.55–1.86). Thirteen of the illness outcomes were significantly and negatively associated with IQ in youth; the odds ratios ranged from 0.85 for diabetes/high blood sugar to 0.65 for stroke, per one standard deviation higher score in IQ. Adjustment for childhood SES led to little attenuation but adult SES partially mediated the relationship for a number of conditions. Mediation by adult SES was not consistently explained by any one of its components—income, education, and occupation status. The current findings contribute to our understanding of lower intelligence as a risk factor for poor health and how this may contribute to health inequalities. PMID:26766880

Computational intelligence techniques in health care

CERN Document Server

Zhou, Wengang; Satheesh, P

2016-01-01

This book presents research on emerging computational intelligence techniques and tools, with a particular focus on new trends and applications in health care. Healthcare is a multi-faceted domain, which incorporates advanced decision-making, remote monitoring, healthcare logistics, operational excellence and modern information systems. In recent years, the use of computational intelligence methods to address the scale and the complexity of the problems in healthcare has been investigated. This book discusses various computational intelligence methods that are implemented in applications in different areas of healthcare. It includes contributions by practitioners, technology developers and solution providers.

Artificial intelligence in medicine.

Science.gov (United States)

Hamet, Pavel; Tremblay, Johanne

2017-04-01

Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application. Copyright © 2017. Published by Elsevier Inc.

Space applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 2: Space projects overview

Science.gov (United States)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities, and their related ground support functions are studied so that informed decisions can be made on which aspects of ARAMIS to develop. The space project breakdowns, which are used to identify tasks ('functional elements'), are described. The study method concentrates on the production of a matrix relating space project tasks to pieces of ARAMIS.

Artificial Intelligence and Moral intelligence

Directory of Open Access Journals (Sweden)

Laura Pana

2008-07-01

Full Text Available We discuss the thesis that the implementation of a moral code in the behaviour of artificial intelligent systems needs a specific form of human and artificial intelligence, not just an abstract intelligence. We present intelligence as a system with an internal structure and the structural levels of the moral system, as well as certain characteristics of artificial intelligent agents which can/must be treated as 1- individual entities (with a complex, specialized, autonomous or selfdetermined, even unpredictable conduct, 2- entities endowed with diverse or even multiple intelligence forms, like moral intelligence, 3- open and, even, free-conduct performing systems (with specific, flexible and heuristic mechanisms and procedures of decision, 4 – systems which are open to education, not just to instruction, 5- entities with “lifegraphy”, not just “stategraphy”, 6- equipped not just with automatisms but with beliefs (cognitive and affective complexes, 7- capable even of reflection (“moral life” is a form of spiritual, not just of conscious activity, 8 – elements/members of some real (corporal or virtual community, 9 – cultural beings: free conduct gives cultural value to the action of a ”natural” or artificial being. Implementation of such characteristics does not necessarily suppose efforts to design, construct and educate machines like human beings. The human moral code is irremediably imperfect: it is a morality of preference, of accountability (not of responsibility and a morality of non-liberty, which cannot be remedied by the invention of ethical systems, by the circulation of ideal values and by ethical (even computing education. But such an imperfect morality needs perfect instruments for its implementation: applications of special logic fields; efficient psychological (theoretical and technical attainments to endow the machine not just with intelligence, but with conscience and even spirit; comprehensive technical

Integrating Symbolic and Statistical Methods for Testing Intelligent Systems Applications to Machine Learning and Computer Vision

Energy Technology Data Exchange (ETDEWEB)

Jha, Sumit Kumar [University of Central Florida, Orlando; Pullum, Laura L [ORNL; Ramanathan, Arvind [ORNL

2016-01-01

Embedded intelligent systems ranging from tiny im- plantable biomedical devices to large swarms of autonomous un- manned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping.In this paper, we present a new testing methodology for studying the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.

Self-Adaptive Systems for Machine Intelligence

CERN Document Server

He, Haibo

2011-01-01

This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide application

Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

Energy Technology Data Exchange (ETDEWEB)

Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)

2014-08-15

The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.

Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

International Nuclear Information System (INIS)

Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka

2014-01-01

The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology

Intelligent Decision Technologies : Proceedings of the 4th International Conference on Intelligent Decision Technologies

CERN Document Server

Watanabe, Toyohide; Phillips-Wren, Gloria; Howlett, Robert; Jain, Lakhmi

2012-01-01

The Intelligent Decision Technologies (IDT) International Conference encourages an interchange of research on intelligent systems and intelligent technologies that enhance or improve decision making. The focus of IDT is interdisciplinary and includes research on all aspects of intelligent decision technologies, from fundamental development to real applications. IDT has the potential to expand their support of decision making in such areas as finance, accounting, marketing, healthcare, medical and diagnostic systems, military decisions, production and operation, networks, traffic management, crisis response, human-machine interfaces, financial and stock market monitoring and prediction, and robotics. Intelligent decision systems implement advances in intelligent agents, fuzzy logic, multi-agent systems, artificial neural networks, and genetic algorithms, among others.  Emerging areas of active research include virtual decision environments, social networking, 3D human-machine interfaces, cognitive interfaces,...

Generative Artificial Intelligence : Philosophy and Theory of Artificial Intelligence

NARCIS (Netherlands)

van der Zant, Tijn; Kouw, Matthijs; Schomaker, Lambertus; Mueller, Vincent C.

2013-01-01

The closed systems of contemporary Artificial Intelligence do not seem to lead to intelligent machines in the near future. What is needed are open-ended systems with non-linear properties in order to create interesting properties for the scaffolding of an artificial mind. Using post-structuralistic

Health care leader competencies and the relevance of emotional intelligence.

Science.gov (United States)

Weiszbrod, Twila

2015-01-01

As health care leader competencies continue to be refined and emphasized in health care administration educational programs, the "soft skills" of emotional intelligence have often been implied, but not included explicitly. The purpose of this study was to better understand what relationship, if any, could be identified between health care leader competencies and emotional intelligence. A quantitative correlational method of study was used, utilizing self-assessments and 360-degree assessments of both constructs. There were 43 valid participants in the study, representing the various types of health care delivery systems. Correlational analysis suggested there was a positive relationship; for each unit of increase in emotional intelligence, there was a 0.6 increase in overall health care leadership competence. This study did not suggest causation, but instead suggested that including the study and development of emotional intelligence in health care administration programs could have a positive impact on the degree of leader competence in graduates. Some curricula suggestions were provided, and further study was recommended.

Machine Ethics: Creating an Ethical Intelligent Agent

OpenAIRE

Anderson, Michael; Anderson, Susan Leigh

2007-01-01

The newly emerging field of machine ethics (Anderson and Anderson 2006) is concerned with adding an ethical dimension to machines. Unlike computer ethics -- which has traditionally focused on ethical issues surrounding humans' use of machines -- machine ethics is concerned with ensuring that the behavior of machines toward human users, and perhaps other machines as well, is ethically acceptable. In this article we discuss the importance of machine ethics, the need for machines that represent ...

When Machines Think: Radiology's Next Frontier.

Science.gov (United States)

Dreyer, Keith J; Geis, J Raymond

2017-12-01

Artificial intelligence (AI), machine learning, and deep learning are terms now seen frequently, all of which refer to computer algorithms that change as they are exposed to more data. Many of these algorithms are surprisingly good at recognizing objects in images. The combination of large amounts of machine-consumable digital data, increased and cheaper computing power, and increasingly sophisticated statistical models combine to enable machines to find patterns in data in ways that are not only cost-effective but also potentially beyond humans' abilities. Building an AI algorithm can be surprisingly easy. Understanding the associated data structures and statistics, on the other hand, is often difficult and obscure. Converting the algorithm into a sophisticated product that works consistently in broad, general clinical use is complex and incompletely understood. To show how these AI products reduce costs and improve outcomes will require clinical translation and industrial-grade integration into routine workflow. Radiology has the chance to leverage AI to become a center of intelligently aggregated, quantitative, diagnostic information. Centaur radiologists, formed as a synergy of human plus computer, will provide interpretations using data extracted from images by humans and image-analysis computer algorithms, as well as the electronic health record, genomics, and other disparate sources. These interpretations will form the foundation of precision health care, or care customized to an individual patient. © RSNA, 2017.

Computational Foundations of Natural Intelligence.

Science.gov (United States)

van Gerven, Marcel

2017-01-01

New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes. This paper ends by outlining some of the challenges that remain to fulfill the promise of machines that show human-like intelligence.

On The Subject of Thinking Machines

OpenAIRE

Olafenwa , John ,; Olafenwa , Moses

2018-01-01

An investigation of the concepts of thoughts, imagination and consciousness in learning machines.; 68 years ago, Alan Turing proposed the question "Can Machines Think" in his seminal paper [1] titled "Computing Machinery and Intelligence" and he formulated the "Imitation Game" also known as the Turing test as a way to answer this question without referring to a rather ambiguous dictionary definition of the word "Think" We have come a long way to building intelligent machines, in fact, the rat...

What Is Artificial Intelligence Anyway?

Science.gov (United States)

Kurzweil, Raymond

1985-01-01

Examines the past, present, and future status of Artificial Intelligence (AI). Acknowledges the limitations of AI but proposes possible areas of application and further development. Urges a concentration on the unique strengths of machine intelligence rather than a copying of human intelligence. (ML)

Machine learning: Trends, perspectives, and prospects.

Science.gov (United States)

Jordan, M I; Mitchell, T M

2015-07-17

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Copyright © 2015, American Association for the Advancement of Science.

Machine-Learning Algorithms to Code Public Health Spending Accounts.

Science.gov (United States)

Brady, Eoghan S; Leider, Jonathon P; Resnick, Beth A; Alfonso, Y Natalia; Bishai, David

Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with manual classification of public health expenditures to determine if machines could provide a faster, cheaper alternative to manual classification. We used machine-learning algorithms to replicate the process of manually classifying state public health expenditures, using the standardized public health spending categories from the Foundational Public Health Services model and a large data set from the US Census Bureau. We obtained a data set of 1.9 million individual expenditure items from 2000 to 2013. We collapsed these data into 147 280 summary expenditure records, and we followed a standardized method of manually classifying each expenditure record as public health, maybe public health, or not public health. We then trained 9 machine-learning algorithms to replicate the manual process. We calculated recall, precision, and coverage rates to measure the performance of individual and ensembled algorithms. Compared with manual classification, the machine-learning random forests algorithm produced 84% recall and 91% precision. With algorithm ensembling, we achieved our target criterion of 90% recall by using a consensus ensemble of ≥6 algorithms while still retaining 93% coverage, leaving only 7% of the summary expenditure records unclassified. Machine learning can be a time- and cost-saving tool for estimating public health spending in the United States. It can be used with standardized public health spending categories based on the Foundational Public Health Services model to help parse public health expenditure information from other types of health-related spending, provide data that are more comparable across public health organizations, and evaluate the impact of evidence-based public health resource allocation.

Algorithms in ambient intelligence

NARCIS (Netherlands)

Aarts, E.H.L.; Korst, J.H.M.; Verhaegh, W.F.J.; Verhaegh, W.F.J.; Aarts, E.H.L.; Korst, J.H.M.

2004-01-01

In this chapter, we discuss the new paradigm for user-centered computing known as ambient intelligence and its relation with methods and techniques from the field of computational intelligence, including problem solving, machine learning, and expert systems.

The Effect of Moral Intelligence and Mental Health on Happiness of Students

Directory of Open Access Journals (Sweden)

M Pourjamshidi

2016-01-01

Full Text Available The research was examined the effect of moral intelligence and mental health on happiness of students using descriptive and correlational method. Two hundred and twenty-six students were selected through stratified random sampling method from postgraduate students at Bu Ali Sina University. Lennick and Kiel’s Ethical characteristics questionnaire, Goldberg’s Mental Health Inventory and Oxford Happiness Questionnaire were used to gather the data. The data was analyzed by Pearson correlation coefficient test and path analysis through. The findings showed that the direct effect of both moral intelligence and mental health on happiness were positive and statistically significant. Also, direct effect Moral intelligence on mental health was positive and significant. Furthermore, the indirect effect of moral intelligence on happiness through mediating role of mental health was significant. According to the results, it can be suggested that happiness in university students can be enhanced by planning on moral intelligence

Computational Foundations of Natural Intelligence

Directory of Open Access Journals (Sweden)

Marcel van Gerven

2017-12-01

Full Text Available New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes. This paper ends by outlining some of the challenges that remain to fulfill the promise of machines that show human-like intelligence.

Intelligent robots: Do we need them and can they be built?

International Nuclear Information System (INIS)

Mann, R.C.

1993-01-01

For avid watchers of science fiction movies, the mention of robotics and artificial intelligence conjures up images of humanlike machines. Often, news reports of scientific advances that enable machines to behave in a flexible manner for a limited set of tests draw parallels to science fiction robots. The effect of this unfortunate kind of publicity is that the scientific disciplines of robotics and artificial intelligence are sometimes regarded as a playground for slightly crazed scientists trying to create artificial humans. In reality, the fields of robotics and artificial intelligence can best be described by answering a few commonly asked questions: What is an intelligent robot, anyway? Why would we need things like that? Could we build them and make them reliable for certain uses? An example of an intelligent machine, or robot is presented and the question of whether intelligent robots are needed is addressed. The impact of ORNL research on uses for intelligent machines is described

Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agent’s Location Using Hidden Markov Models

Directory of Open Access Journals (Sweden)

Cédric Beaulac

2017-01-01

Full Text Available We propose to use a supervised machine learning technique to track the location of a mobile agent in real time. Hidden Markov Models are used to build artificial intelligence that estimates the unknown position of a mobile target moving in a defined environment. This narrow artificial intelligence performs two distinct tasks. First, it provides real-time estimation of the mobile agent’s position using the forward algorithm. Second, it uses the Baum–Welch algorithm as a statistical learning tool to gain knowledge of the mobile target. Finally, an experimental environment is proposed, namely, a video game that we use to test our artificial intelligence. We present statistical and graphical results to illustrate the efficiency of our method.

Relationships between self-reported physical and mental health and intelligence performance across adulthood.

Science.gov (United States)

Perlmutter, M; Nyquist, L

1990-07-01

One hundred and twenty-seven adults between 20 and 90 years of age were tested on the Wechsler Adult Intelligence Scale for their digit span memory (forward and backward), fluid intelligence (block design and digit symbol), and crystallized intelligence (vocabulary and information), as well as assessed for self-reported health (Cornell Medical Index, Zung Depression Scale, health habits, and self-ratings of physical and mental health). As expected, across the entire age range there was no correlation between age and digit span memory (r = .03), a strong negative correlation between age and fluid intelligence (r = -.78), and a modest positive correlation between age and crystallized intelligence (r = .27). In addition, older adults reported more physical (r = .36) and mental (r = .32) health problems than did younger adults. Of special interest was the finding that both self-reported physical and mental health accounted for significant variance in intelligence performance, particularly in older adults. Moreover, self-reported health accounted for a considerable portion of observed variance, even when age differences in self-reported health were statistically controlled.

[Remote intelligent Brunnstrom assessment system for upper limb rehabilitation for post-stroke based on extreme learning machine].

Science.gov (United States)

Wang, Yue; Yu, Lei; Fu, Jianming; Fang, Qiang

2014-04-01

In order to realize an individualized and specialized rehabilitation assessment of remoteness and intelligence, we set up a remote intelligent assessment system of upper limb movement function of post-stroke patients during rehabilitation. By using the remote rehabilitation training sensors and client data sampling software, we collected and uploaded the gesture data from a patient's forearm and upper arm during rehabilitation training to database of the server. Then a remote intelligent assessment system, which had been developed based on the extreme learning machine (ELM) algorithm and Brunnstrom stage assessment standard, was used to evaluate the gesture data. To evaluate the reliability of the proposed method, a group of 23 stroke patients, whose upper limb movement functions were in different recovery stages, and 4 healthy people, whose upper limb movement functions were normal, were recruited to finish the same training task. The results showed that, compared to that of the experienced rehabilitation expert who used the Brunnstrom stage standard table, the accuracy of the proposed remote Brunnstrom intelligent assessment system can reach a higher level, as 92.1%. The practical effects of surgery have proved that the proposed system could realize the intelligent assessment of upper limb movement function of post-stroke patients remotely, and it could also make the rehabilitation of the post-stroke patients at home or in a community care center possible.

Space applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 4: Application of ARAMIS capabilities to space project functional elements

Science.gov (United States)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities and their related ground support functions are studied, so that informed decisions can be made on which aspects of ARAMIS to develop. The specific tasks which will be required by future space project tasks are identified and the relative merits of these options are evaluated. The ARAMIS options defined and researched span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks.

2015 Chinese Intelligent Systems Conference

CERN Document Server

Du, Junping; Li, Hongbo; Zhang, Weicun; CISC’15

2016-01-01

This book presents selected research papers from the 2015 Chinese Intelligent Systems Conference (CISC’15), held in Yangzhou, China. The topics covered include multi-agent systems, evolutionary computation, artificial intelligence, complex systems, computation intelligence and soft computing, intelligent control, advanced control technology, robotics and applications, intelligent information processing, iterative learning control, and machine learning. Engineers and researchers from academia, industry and the government can gain valuable insights into solutions combining ideas from multiple disciplines in the field of intelligent systems.

A Framework for Intelligent Instructional Systems: An Artificial Intelligence Machine Learning Approach.

Science.gov (United States)

Becker, Lee A.

1987-01-01

Presents and develops a general model of the nature of a learning system and a classification for learning systems. Highlights include the relationship between artificial intelligence and cognitive psychology; computer-based instructional systems; intelligent instructional systems; and the role of the learner's knowledge base in an intelligent…

Mankind, machines and people

Energy Technology Data Exchange (ETDEWEB)

Hugli, A

1984-01-01

The following questions are addressed: is there a difference between machines and men, between human communication and communication with machines. Will we ever reach the point when the dream of artificial intelligence becomes a reality. Will thinking machines be able to replace the human spirit in all its aspects. Social consequences and philosophical aspects are addressed. 8 references.

Software for Intelligent System Health Management (ISHM)

Science.gov (United States)

Trevino, Luis C.

2004-01-01

The slide presentation is a briefing in four areas: overview of health management paradigms; overview of the ARC-Houston Software Engineering Technology Workshop held on April 20-22, 2004; identified technologies relevant to technical themes of intelligent system health management; and the author's thoughts on these topics.

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)

Tuberculosis control, and the where and why of artificial intelligence

Directory of Open Access Journals (Sweden)

Riddhi Doshi

2017-06-01

Full Text Available Countries aiming to reduce their tuberculosis (TB burden by 2035 to the levels envisaged by the World Health Organization End TB Strategy need to innovate, with approaches such as digital health (electronic and mobile health in support of patient care, surveillance, programme management, training and communication. Alongside the large-scale roll-out required for such interventions to make a significant impact, products must stay abreast of advancing technology over time. The integration of artificial intelligence into new software promises to make processes more effective and efficient, endowing them with a potential hitherto unimaginable. Users can benefit from artificial intelligence-enabled pattern recognition software for tasks ranging from reading radiographs to adverse event monitoring, sifting through vast datasets to personalise a patient's care plan or to customise training materials. Many experts forecast the imminent transformation of the delivery of healthcare services. We discuss how artificial intelligence and machine learning could revolutionise the management of TB.

Tuberculosis control, and the where and why of artificial intelligence

Science.gov (United States)

Falzon, Dennis; Thomas, Bruce V.; Temesgen, Zelalem; Sadasivan, Lal; Raviglione, Mario

2017-01-01

Countries aiming to reduce their tuberculosis (TB) burden by 2035 to the levels envisaged by the World Health Organization End TB Strategy need to innovate, with approaches such as digital health (electronic and mobile health) in support of patient care, surveillance, programme management, training and communication. Alongside the large-scale roll-out required for such interventions to make a significant impact, products must stay abreast of advancing technology over time. The integration of artificial intelligence into new software promises to make processes more effective and efficient, endowing them with a potential hitherto unimaginable. Users can benefit from artificial intelligence-enabled pattern recognition software for tasks ranging from reading radiographs to adverse event monitoring, sifting through vast datasets to personalise a patient's care plan or to customise training materials. Many experts forecast the imminent transformation of the delivery of healthcare services. We discuss how artificial intelligence and machine learning could revolutionise the management of TB. PMID:28656130

Tuberculosis control, and the where and why of artificial intelligence.

Science.gov (United States)

Doshi, Riddhi; Falzon, Dennis; Thomas, Bruce V; Temesgen, Zelalem; Sadasivan, Lal; Migliori, Giovanni Battista; Raviglione, Mario

2017-04-01

Countries aiming to reduce their tuberculosis (TB) burden by 2035 to the levels envisaged by the World Health Organization End TB Strategy need to innovate, with approaches such as digital health (electronic and mobile health) in support of patient care, surveillance, programme management, training and communication. Alongside the large-scale roll-out required for such interventions to make a significant impact, products must stay abreast of advancing technology over time. The integration of artificial intelligence into new software promises to make processes more effective and efficient, endowing them with a potential hitherto unimaginable. Users can benefit from artificial intelligence-enabled pattern recognition software for tasks ranging from reading radiographs to adverse event monitoring, sifting through vast datasets to personalise a patient's care plan or to customise training materials. Many experts forecast the imminent transformation of the delivery of healthcare services. We discuss how artificial intelligence and machine learning could revolutionise the management of TB.

Tool set for distributed real-time machine control

Science.gov (United States)

Carrott, Andrew J.; Wright, Christopher D.; West, Andrew A.; Harrison, Robert; Weston, Richard H.

1997-01-01

Demands for increased control capabilities require next generation manufacturing machines to comprise intelligent building elements, physically located at the point where the control functionality is required. Networks of modular intelligent controllers are increasingly designed into manufacturing machines and usable standards are slowly emerging. To implement a control system using off-the-shelf intelligent devices from multi-vendor sources requires a number of well defined activities, including (a) the specification and selection of interoperable control system components, (b) device independent application programming and (c) device configuration, management, monitoring and control. This paper briefly discusses the support for the above machine lifecycle activities through the development of an integrated computing environment populated with an extendable software toolset. The toolset supports machine builder activities such as initial control logic specification, logic analysis, machine modeling, mechanical verification, application programming, automatic code generation, simulation/test, version control, distributed run-time support and documentation. The environment itself consists of system management tools and a distributed object-oriented database which provides storage for the outputs from machine lifecycle activities and specific target control solutions.

Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach.

Science.gov (United States)

Cao, Hongliang; Xin, Ya; Yuan, Qiaoxia

2016-02-01

To predict conveniently the biochar yield from cattle manure pyrolysis, intelligent modeling approach was introduced in this research. A traditional artificial neural networks (ANN) model and a novel least squares support vector machine (LS-SVM) model were developed. For the identification and prediction evaluation of the models, a data set with 33 experimental data was used, which were obtained using a laboratory-scale fixed bed reaction system. The results demonstrated that the intelligent modeling approach is greatly convenient and effective for the prediction of the biochar yield. In particular, the novel LS-SVM model has a more satisfying predicting performance and its robustness is better than the traditional ANN model. The introduction and application of the LS-SVM modeling method gives a successful example, which is a good reference for the modeling study of cattle manure pyrolysis process, even other similar processes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Association between spiritual intelligence and mental and physical health in elderly

Directory of Open Access Journals (Sweden)

Andreia Domingues Pereira

2016-02-01

Full Text Available // // // // // Aims: To analyze the levels of spiritual intelligence, psychological well-being, depressive and anxious symptoms, and mental and physical health in elderly attending nursing homes or social centers and explore associations between all these variables (and with some sociodemographic variables.   Method: In this non-experimental study, the Integrated Spiritual Intelligence Scale, the Older Americans Resources and Services (to evaluate physical and mental health, the Philadelphia Geriatric Center Morale Scale (evaluates psychological well-being, the Geriatric Depression Scale and the Geriatric Anxiety Inventory were applied, in an interview format, to 65 aged citizens (age, M = 83.46; SD = 6.65; female, n = 46; 70.8%.   Results: Most elders perceived their physical health (80.0% and mental health (84.0% as unsatisfactory. An important percentage presented depressive (56.9% and anxiety symptoms (64.6%. The total score of spiritual intelligence was positively correlated with attitudes towards aging and negatively with the total score of depressive symptoms. Conscience (spiritual intelligence was positively associated with attitudes towards aging (psychological well-being and negatively with depressive symptoms. Meaning (spiritual intelligence was positively associated with the total score of psychological well-being and it´s dimensions, solitude/dissatisfaction, and agitation, and negatively with depressive and anxious symptoms. Grace was positively associated with the total score of psychological well-being and it´s dimension attitudes towards aging and negatively with depressive and anxiety symptoms. Finally, aged citizens living in nursing homes showed lower values of grace and higher values of meaning (spiritual intelligence, depressive and anxious symptoms.   Conclusions: It is of concern the prevalence of unsatisfactory physical and mental health, depression and anxiety. Higher total levels (and in some of the dimensions of

Emotional intelligence and psychological health in a sample of Kuwaiti college students.

Science.gov (United States)

Alkhadher, Othman

2007-06-01

This summary investigated correlations between emotional intelligence and psychological health amongst 191 Kuwaiti undergraduate students in psychology, 98 men and 93 women (M age=20.6 yr., SD=2.8). There were two measures of emotional intelligence, one based on the ability model, the Arabic Test for Emotional Intelligence, and the other on the mixed model, the Emotional Intelligence Questionnaire. Participants' psychological health was assessed using scales from the Personality Assessment Inventory. A weak relationship between the two types of emotional intelligence was found. A correlation for scores on the Emotional Intelligence Questionnaire with the Personality Assessment Inventory was found but not with those of the Arabic Test for Emotional Intelligence. Regression analysis indicated scores on Managing Emotions and Self-awareness accounted for most of the variance in the association with the Personality Assessment Inventory. Significant sex differences were found only on the Arabic Test for Emotional Intelligence; women scored higher than men. On Emotional Intelligence Questionnaire measures, men had significantly higher means on Managing Emotions and Self-motivation. However, no significant differences were found between the sexes on the Total Emotional Intelligence Questionnaire scores.

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.

The Nigerian health care system: Need for integrating adequate medical intelligence and surveillance systems

Directory of Open Access Journals (Sweden)

Menizibeya Osain Welcome

2011-01-01

Full Text Available Objectives : As an important element of national security, public health not only functions to provide adequate and timely medical care but also track, monitor, and control disease outbreak. The Nigerian health care had suffered several infectious disease outbreaks year after year. Hence, there is need to tackle the problem. This study aims to review the state of the Nigerian health care system and to provide possible recommendations to the worsening state of health care in the country. To give up-to-date recommendations for the Nigerian health care system, this study also aims at reviewing the dynamics of health care in the United States, Britain, and Europe with regards to methods of medical intelligence/surveillance. Materials and Methods : Databases were searched for relevant literatures using the following keywords: Nigerian health care, Nigerian health care system, and Nigerian primary health care system. Additional keywords used in the search were as follows: United States (OR Europe health care dynamics, Medical Intelligence, Medical Intelligence systems, Public health surveillance systems, Nigerian medical intelligence, Nigerian surveillance systems, and Nigerian health information system. Literatures were searched in scientific databases Pubmed and African Journals OnLine. Internet searches were based on Google and Search Nigeria. Results : Medical intelligence and surveillance represent a very useful component in the health care system and control diseases outbreak, bioattack, etc. There is increasing role of automated-based medical intelligence and surveillance systems, in addition to the traditional manual pattern of document retrieval in advanced medical setting such as those in western and European countries. Conclusion : The Nigerian health care system is poorly developed. No adequate and functional surveillance systems are developed. To achieve success in health care in this modern era, a system well grounded in routine

The role of mindfulness and spiritual intelligence in students' mental health

Directory of Open Access Journals (Sweden)

Ebrahim Nemati

2017-06-01

Full Text Available Studies show that mental disorders are highly prevalent among students. Therefore, the present study aimed to examine the role of mindfulness and spiritual intelligence in the students’ mental health studying at university of medical sciences. The study population included all undergraduate and medicine students. A total of 393 female and male students (193 medical and 200 non-medical students were selected through randomly. General Health Questionnaire (GHQ and spiritual intelligence and mindfulness questionnaire were used to evaluate the participants. The results revealed the negative correlation of mental health with mindfulness and spiritual intelligence and a positive correlation between mindfulness and dimensions of spiritual intelligence. Also, the dimension of spiritual life (43.1% and mindfulness (31% had a significant negative effect on the explained variance of the students’ mental health. Analysis of variance showed that the scales of mindfulness, perception of existence, somatic symptoms, and anxiety were higher among women. Therefore, the students can be more capable of coping with existing traumas and pressures by boosting their spirituality, consciousness, and mindfulness.

Development and Experimental Evaluation of Machine-Learning Techniques for an Intelligent Hairy Scalp Detection System

Directory of Open Access Journals (Sweden)

Wei-Chien Wang

2018-05-01

Full Text Available Deep learning has become the most popular research subject in the fields of artificial intelligence (AI and machine learning. In October 2013, MIT Technology Review commented that deep learning was a breakthrough technology. Deep learning has made progress in voice and image recognition, image classification, and natural language processing. Prior to deep learning, decision tree, linear discriminant analysis (LDA, support vector machines (SVM, k-nearest neighbors algorithm (K-NN, and ensemble learning were popular in solving classification problems. In this paper, we applied the previously mentioned and deep learning techniques to hairy scalp images. Hairy scalp problems are usually diagnosed by non-professionals in hair salons, and people with such problems may be advised by these non-professionals. Additionally, several common scalp problems are similar; therefore, non-experts may provide incorrect diagnoses. Hence, scalp problems have worsened. In this work, we implemented and compared the deep-learning method, the ImageNet-VGG-f model Bag of Words (BOW, with machine-learning classifiers, and histogram of oriented gradients (HOG/pyramid histogram of oriented gradients (PHOG with machine-learning classifiers. The tools from the classification learner apps were used for hairy scalp image classification. The results indicated that deep learning can achieve an accuracy of 89.77% when the learning rate is 1 × 10−4, and this accuracy is far higher than those achieved by BOW with SVM (80.50% and PHOG with SVM (53.0%.

Reviewing the current state of machine learning for artificial intelligence with regards to the use of contextual information

OpenAIRE

Kinch, Martin W.; Melis, Wim J.C.; Keates, Simeon

2017-01-01

This paper will consider the current state of Machine Learning for Artificial Intelligence, more specifically for applications, such as: Speech Recognition, Game Playing and Image Processing. The artificial world tends to make limited use of context in comparison to what currently happens in human life, while it would benefit from improvements in this area. Additionally, the process of transferring knowledge between application domains is another important area where artificial system can imp...

陕西电信天翼3G双模智能机市场发展策略研究%Shanxi Telecom Tianyi 3G Dual-mode Intelligent Machine Market Development Strategy Research

Institute of Scientific and Technical Information of China (English)

贾琳

2013-01-01

中国电信陕西公司天翼3G智能终端销售在2011年至2012年间快速上升，但2012年下半年，智能机发展出现阶段性波动，增长率降低；同时竞争对手发力双模智能机，快速挖转中国移动用户。分析和论证发展天翼3G双模智能机的必要性，并从产品、价格、渠道、宣传和促销4方面提出了天翼3G双模智能机发展的具体策略，对于陕西电信进一步加快智能机的发展，拓展社会开放渠道，保持3G市场领先优势具有重要意义。%3G intelligent terminal sales of Shaanxi China Telecom Tianyi form 2011 to 2012 years the rises rapidly,but the second half of 2012, intelligent machine development stage of volatility, growth rate decreased,rival force of dual-mode intelligent machine, fast digging to China Mobile users at the same time. This paper focuses on a telecommunications enterprise employee's point of view, fully aware of the changing market environment and competition strategy, the necessity analysis and demonstration of development day wing 3G dual-mode intelligent machines, and specific strategies day wing development 3G dual-mode intelligent machine is put forward from the product, price, channel, promotion and promotion four aspects, for Shaanxi to further accelerate the development of the telecom intelligent machine, expand social open channel, it is important to keep the 3G market advantage.

Intelligent Integrated Health Management for a System of Systems

Science.gov (United States)

Smith, Harvey; Schmalzel, John; Figueroa, Fernando

2008-01-01

An intelligent integrated health management system (IIHMS) incorporates major improvements over prior such systems. The particular IIHMS is implemented for any system defined as a hierarchical distributed network of intelligent elements (HDNIE), comprising primarily: (1) an architecture (Figure 1), (2) intelligent elements, (3) a conceptual framework and taxonomy (Figure 2), and (4) and ontology that defines standards and protocols. Some definitions of terms are prerequisite to a further brief description of this innovation: A system-of-systems (SoS) is an engineering system that comprises multiple subsystems (e.g., a system of multiple possibly interacting flow subsystems that include pumps, valves, tanks, ducts, sensors, and the like); 'Intelligent' is used here in the sense of artificial intelligence. An intelligent element may be physical or virtual, it is network enabled, and it is able to manage data, information, and knowledge (DIaK) focused on determining its condition in the context of the entire SoS; As used here, 'health' signifies the functionality and/or structural integrity of an engineering system, subsystem, or process (leading to determination of the health of components); 'Process' can signify either a physical process in the usual sense of the word or an element into which functionally related sensors are grouped; 'Element' can signify a component (e.g., an actuator, a valve), a process, a controller, an actuator, a subsystem, or a system; The term Integrated System Health Management (ISHM) is used to describe a capability that focuses on determining the condition (health) of every element in a complex system (detect anomalies, diagnose causes, prognosis of future anomalies), and provide data, information, and knowledge (DIaK) not just data to control systems for safe and effective operation. A major novel aspect of the present development is the concept of intelligent integration. The purpose of intelligent integration, as defined and

Behavior Analysis and the Quest for Machine Intelligence.

Science.gov (United States)

Stephens, Kenneth R.; Hutchison, William R.

1993-01-01

Discusses three approaches to building intelligent systems: artificial intelligence, neural networks, and behavior analysis. BANKET, an object-oriented software system, is explained; a commercial application of BANKET is described; and a collaborative effort between the academic and business communities for the use of BANKET is discussed.…

STANFORD ARTIFICIAL INTELLIGENCE PROJECT.

Science.gov (United States)

ARTIFICIAL INTELLIGENCE , GAME THEORY, DECISION MAKING, BIONICS, AUTOMATA, SPEECH RECOGNITION, GEOMETRIC FORMS, LEARNING MACHINES, MATHEMATICAL MODELS, PATTERN RECOGNITION, SERVOMECHANISMS, SIMULATION, BIBLIOGRAPHIES.

Medical Education Must Move from the Information Age to the Age of Artificial Intelligence.

Science.gov (United States)

Wartman, Steven A; Combs, C Donald

2017-11-01

Changes to the medical profession require medical education reforms that will enable physicians to more effectively enter contemporary practice. Proposals for such reforms abound. Common themes include renewed emphasis on communication, teamwork, risk-management, and patient safety. These reforms are important but insufficient. They do not adequately address the most fundamental change--the practice of medicine is rapidly transitioning from the information age to the age of artificial intelligence. Employers need physicians who: work at the top of their license, have knowledge spanning the health professions and care continuum, effectively leverage data platforms, focus on analyzing outcomes and improving performance, and communicate the meaning of the probabilities generated by massive amounts of data to patients given their unique human complexities.Future medical practice will have four characteristics that must be addressed in medical education: care will be (1) provided in many locations; (2) provided by newly-constituted health care teams; and (3) based on a growing array of data from multiple sources and artificial intelligence applications; and (4) the interface between medicine and machines will need to be skillfully managed. Thus, medical education must make better use of the findings of cognitive psychology, pay more attention to the alignment of humans and machines in education, and increase the use of simulations. Medical education will need to evolve to include systematic curricular attention to the organization of professional effort among health professionals, the use of intelligence tools like machine learning and robots, and a relentless focus on improving performance and patient outcomes. [end of abstract].

The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation.

Science.gov (United States)

Michie, Susan; Thomas, James; Johnston, Marie; Aonghusa, Pol Mac; Shawe-Taylor, John; Kelly, Michael P; Deleris, Léa A; Finnerty, Ailbhe N; Marques, Marta M; Norris, Emma; O'Mara-Eves, Alison; West, Robert

2017-10-18

Behaviour change is key to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. We need to make better use of the vast amount of accumulating evidence from behaviour change intervention (BCI) evaluations and promote the uptake of that evidence into a wide range of contexts. The scale and complexity of the task of synthesising and interpreting this evidence, and increasing evidence timeliness and accessibility, will require increased computer support. The Human Behaviour-Change Project (HBCP) will use Artificial Intelligence and Machine Learning to (i) develop and evaluate a 'Knowledge System' that automatically extracts, synthesises and interprets findings from BCI evaluation reports to generate new insights about behaviour change and improve prediction of intervention effectiveness and (ii) allow users, such as practitioners, policy makers and researchers, to easily and efficiently query the system to get answers to variants of the question 'What works, compared with what, how well, with what exposure, with what behaviours (for how long), for whom, in what settings and why?'. The HBCP will: a) develop an ontology of BCI evaluations and their reports linking effect sizes for given target behaviours with intervention content and delivery and mechanisms of action, as moderated by exposure, populations and settings; b) develop and train an automated feature extraction system to annotate BCI evaluation reports using this ontology; c) develop and train machine learning and reasoning algorithms to use the annotated BCI evaluation reports to predict effect sizes for particular combinations of behaviours, interventions, populations and settings; d) build user and machine interfaces for interrogating and updating the knowledge base; and e) evaluate all the above in terms of performance and utility. The HBCP aims to revolutionise our ability to synthesise, interpret and deliver

Combining human and machine processes (CHAMP)

Science.gov (United States)

Sudit, Moises; Sudit, David; Hirsch, Michael

2015-05-01

Machine Reasoning and Intelligence is usually done in a vacuum, without consultation of the ultimate decision-maker. The late consideration of the human cognitive process causes some major problems in the use of automated systems to provide reliable and actionable information that users can trust and depend to make the best Course-of-Action (COA). On the other hand, if automated systems are created exclusively based on human cognition, then there is a danger of developing systems that don't push the barrier of technology and are mainly done for the comfort level of selected subject matter experts (SMEs). Our approach to combining human and machine processes (CHAMP) is based on the notion of developing optimal strategies for where, when, how, and which human intelligence should be injected within a machine reasoning and intelligence process. This combination is based on the criteria of improving the quality of the output of the automated process while maintaining the required computational efficiency for a COA to be actuated in timely fashion. This research addresses the following problem areas: • Providing consistency within a mission: Injection of human reasoning and intelligence within the reliability and temporal needs of a mission to attain situational awareness, impact assessment, and COA development. • Supporting the incorporation of data that is uncertain, incomplete, imprecise and contradictory (UIIC): Development of mathematical models to suggest the insertion of a cognitive process within a machine reasoning and intelligent system so as to minimize UIIC concerns. • Developing systems that include humans in the loop whose performance can be analyzed and understood to provide feedback to the sensors.

Principles of artificial intelligence

CERN Document Server

Nilsson, Nils J

1980-01-01

A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of th

Design of Control System for Kiwifruit Automatic Grading Machine

Directory of Open Access Journals (Sweden)

Xingjian Zuo

2013-05-01

Full Text Available The kiwifruit automatic grading machine is an important machine for postharvest processing of kiwifruit, and the control system ensures that the machine realizes intelligence. The control system for the kiwifruit automatic grading machine designed in this paper comprises a host computer and a slave microcontroller. The host computer provides a visual grading interface for the machine with a LabVIEW software, the slave microcontroller adopts an STC89C52 microcontroller as its core, and C language is used to write programs for controlling a position sensor module, push-pull type electromagnets, motor driving modules and a power supply for controlling the operation of the machine as well as the rise or descend of grading baffle plates. The ideal control effect is obtained through test, and the intelligent operation of the machine is realized.

Intelligent robot action planning

Energy Technology Data Exchange (ETDEWEB)

Vamos, T; Siegler, A

1982-01-01

Action planning methods used in intelligent robot control are discussed. Planning is accomplished through environment understanding, environment representation, task understanding and planning, motion analysis and man-machine communication. These fields are analysed in detail. The frames of an intelligent motion planning system are presented. Graphic simulation of the robot's environment and motion is used to support the planning. 14 references.

A systematic approach to the application of Automation, Robotics, and Machine Intelligence Systems /ARAMIS/ to future space projects

Science.gov (United States)

Smith, D. B. S.

1982-01-01

The potential applications of Automation, Robotics, and Machine Intelligence Systems (ARAMIS) to space projects are investigated, through a systematic method. In this method selected space projects are broken down into space project tasks, and 69 of these tasks are selected for study. Candidate ARAMIS options are defined for each task. The relative merits of these options are evaluated according to seven indices of performance. Logical sequences of ARAMIS development are also defined. Based on this data, promising applications of ARAMIS are

Malware and Disease: Lessons from Cyber Intelligence for Public Health Surveillance.

Science.gov (United States)

Smith, Frank L

2016-01-01

Malicious software and infectious diseases are similar is several respects, as are the functional requirements for surveillance and intelligence to defend against these threats. Given these similarities, this article compares and contrasts the actors, relationships, and norms at work in cyber intelligence and disease surveillance. Historical analysis reveals that civilian cyber defense is more decentralized, private, and voluntary than public health in the United States. Most of these differences are due to political choices rather than technical necessities. In particular, political resistance to government institutions has shaped cyber intelligence over the past 30 years, which is a troubling sign for attempts to improve disease surveillance through local, state, and federal health departments. Information sharing about malware is also limited, despite information technology being integral to cyberspace. Such limits suggest that automation through electronic health records will not automatically improve public health surveillance. Still, certain aspects of information sharing and analysis for cyber defense are worth emulating or, at the very least, learning from to help detect and manage health threats.

Artificial Intelligence in Cardiology.

Science.gov (United States)

Johnson, Kipp W; Torres Soto, Jessica; Glicksberg, Benjamin S; Shameer, Khader; Miotto, Riccardo; Ali, Mohsin; Ashley, Euan; Dudley, Joel T

2018-06-12

Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

An Intelligent and Interactive Simulation and Tutoring Environment for Exploring and Learning Simple Machines

Science.gov (United States)

Myneni, Lakshman Sundeep

Students in middle school science classes have difficulty mastering physics concepts such as energy and work, taught in the context of simple machines. Moreover, students' naive conceptions of physics often remain unchanged after completing a science class. To address this problem, I developed an intelligent tutoring system, called the Virtual Physics System (ViPS), which coaches students through problem solving with one class of simple machines, pulley systems. The tutor uses a unique cognitive based approach to teaching simple machines, and includes innovations in three areas. (1) It employs a teaching strategy that focuses on highlighting links among concepts of the domain that are essential for conceptual understanding yet are seldom learned by students. (2) Concepts are taught through a combination of effective human tutoring techniques (e.g., hinting) and simulations. (3) For each student, the system identifies which misconceptions he or she has, from a common set of student misconceptions gathered from domain experts, and tailors tutoring to match the correct line of scientific reasoning regarding the misconceptions. ViPS was implemented as a platform on which students can design and simulate pulley system experiments, integrated with a constraint-based tutor that intervenes when students make errors during problem solving to teach them and to help them. ViPS has a web-based client-server architecture, and has been implemented using Java technologies. ViPS is different from existing physics simulations and tutoring systems due to several original features. (1). It is the first system to integrate a simulation based virtual experimentation platform with an intelligent tutoring component. (2) It uses a novel approach, based on Bayesian networks, to help students construct correct pulley systems for experimental simulation. (3) It identifies student misconceptions based on a novel decision tree applied to student pretest scores, and tailors tutoring to

The expressive stance: intentionality, expression, and machine art

OpenAIRE

Linson, Adam

2013-01-01

This paper proposes a new interpretive stance for interpreting artistic works and performances that is relevant to artificial intelligence research but also has broader implications. Termed the expressive stance, this stance makes intelligible a critical distinction between present-day machine art and human art, but allows for the possibility that future machine art could find a place alongside our own. The expressive stance is elaborated as a response to Daniel Dennett's notion of the intent...

Machine Translation in Post-Contemporary Era

Science.gov (United States)

Lin, Grace Hui Chin

2010-01-01

This article focusing on translating techniques via personal computer or laptop reports updated artificial intelligence progresses before 2010. Based on interpretations and information for field of MT [Machine Translation] by Yorick Wilks' book, "Machine Translation, Its scope and limits," this paper displays understandable theoretical frameworks…

Teaching machine learning to design students

NARCIS (Netherlands)

Vlist, van der B.J.J.; van de Westelaken, H.F.M.; Bartneck, C.; Hu, J.; Ahn, R.M.C.; Barakova, E.I.; Delbressine, F.L.M.; Feijs, L.M.G.; Pan, Z.; Zhang, X.; El Rhalibi, A.

2008-01-01

Machine learning is a key technology to design and create intelligent systems, products, and related services. Like many other design departments, we are faced with the challenge to teach machine learning to design students, who often do not have an inherent affinity towards technology. We

Intelligent Vehicle Health Management

Science.gov (United States)

Paris, Deidre E.; Trevino, Luis; Watson, Michael D.

2005-01-01

As a part of the overall goal of developing Integrated Vehicle Health Management systems for aerospace vehicles, the NASA Faculty Fellowship Program (NFFP) at Marshall Space Flight Center has performed a pilot study on IVHM principals which integrates researched IVHM technologies in support of Integrated Intelligent Vehicle Management (IIVM). IVHM is the process of assessing, preserving, and restoring system functionality across flight and ground systems (NASA NGLT 2004). The framework presented in this paper integrates advanced computational techniques with sensor and communication technologies for spacecraft that can generate responses through detection, diagnosis, reasoning, and adapt to system faults in support of INM. These real-time responses allow the IIVM to modify the affected vehicle subsystem(s) prior to a catastrophic event. Furthermore, the objective of this pilot program is to develop and integrate technologies which can provide a continuous, intelligent, and adaptive health state of a vehicle and use this information to improve safety and reduce costs of operations. Recent investments in avionics, health management, and controls have been directed towards IIVM. As this concept has matured, it has become clear the INM requires the same sensors and processing capabilities as the real-time avionics functions to support diagnosis of subsystem problems. New sensors have been proposed, in addition, to augment the avionics sensors to support better system monitoring and diagnostics. As the designs have been considered, a synergy has been realized where the real-time avionics can utilize sensors proposed for diagnostics and prognostics to make better real-time decisions in response to detected failures. IIVM provides for a single system allowing modularity of functions and hardware across the vehicle. The framework that supports IIVM consists of 11 major on-board functions necessary to fully manage a space vehicle maintaining crew safety and mission

Evaluation of Machine Learning Methods for LHC Optics Measurements and Corrections Software

CERN Document Server

AUTHOR|(CDS)2206853; Henning, Peter

The field of artificial intelligence is driven by the goal to provide machines with human-like intelligence. However modern science is currently facing problems with high complexity that cannot be solved by humans in the same timescale as by machines. Therefore there is a demand on automation of complex tasks. To identify the category of tasks which can be performed by machines in the domain of optics measurements and correction on the Large Hadron Collider (LHC) is one of the central research subjects of this thesis. The application of machine learning methods and concepts of artificial intelligence can be found in various industry and scientific branches. In High Energy Physics these concepts are mostly used in offline analysis of experiments data and to perform regression tasks. In Accelerator Physics the machine learning approach has not found a wide application yet. Therefore potential tasks for machine learning solutions can be specified in this domain. The appropriate methods and their suitability for...

Quantum Machine Learning

OpenAIRE

Romero García, Cristian

2017-01-01

[EN] In a world in which accessible information grows exponentially, the selection of the appropriate information turns out to be an extremely relevant problem. In this context, the idea of Machine Learning (ML), a subfield of Artificial Intelligence, emerged to face problems in data mining, pattern recognition, automatic prediction, among others. Quantum Machine Learning is an interdisciplinary research area combining quantum mechanics with methods of ML, in which quantum properties allow fo...

Support vector machine in machine condition monitoring and fault diagnosis

Science.gov (United States)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

Screen-Printed Washable Electronic Textiles as Self-Powered Touch/Gesture Tribo-Sensors for Intelligent Human-Machine Interaction.

Science.gov (United States)

Cao, Ran; Pu, Xianjie; Du, Xinyu; Yang, Wei; Wang, Jiaona; Guo, Hengyu; Zhao, Shuyu; Yuan, Zuqing; Zhang, Chi; Li, Congju; Wang, Zhong Lin

2018-05-22

Multifunctional electronic textiles (E-textiles) with embedded electric circuits hold great application prospects for future wearable electronics. However, most E-textiles still have critical challenges, including air permeability, satisfactory washability, and mass fabrication. In this work, we fabricate a washable E-textile that addresses all of the concerns and shows its application as a self-powered triboelectric gesture textile for intelligent human-machine interfacing. Utilizing conductive carbon nanotubes (CNTs) and screen-printing technology, this kind of E-textile embraces high conductivity (0.2 kΩ/sq), high air permeability (88.2 mm/s), and can be manufactured on common fabric at large scales. Due to the advantage of the interaction between the CNTs and the fabrics, the electrode shows excellent stability under harsh mechanical deformation and even after being washed. Moreover, based on a single-electrode mode triboelectric nanogenerator and electrode pattern design, our E-textile exhibits highly sensitive touch/gesture sensing performance and has potential applications for human-machine interfacing.

Load Forecasting with Artificial Intelligence on Big Data

OpenAIRE

Glauner, Patrick; State, Radu

2016-01-01

In the domain of electrical power grids, there is a particular interest in time series analysis using artificial intelligence. Machine learning is the branch of artificial intelligence giving computers the ability to learn patterns from data without being explicitly programmed. Deep Learning is a set of cutting-edge machine learning algorithms that are inspired by how the human brain works. It allows to self-learn feature hierarchies from the data rather than modeling hand-crafted features. I...

Simple and Multivariate Relationships Between Spiritual Intelligence with General Health and Happiness.

Science.gov (United States)

Amirian, Mohammad-Elyas; Fazilat-Pour, Masoud

2016-08-01

The present study examined simple and multivariate relationships of spiritual intelligence with general health and happiness. The employed method was descriptive and correlational. King's Spiritual Quotient scales, GHQ-28 and Oxford Happiness Inventory, are filled out by a sample consisted of 384 students, which were selected using stratified random sampling from the students of Shahid Bahonar University of Kerman. Data are subjected to descriptive and inferential statistics including correlations and multivariate regressions. Bivariate correlations support positive and significant predictive value of spiritual intelligence toward general health and happiness. Further analysis showed that among the Spiritual Intelligence' subscales, Existential Critical Thinking Predicted General Health and Happiness, reversely. In addition, happiness was positively predicted by generation of personal meaning and transcendental awareness. The findings are discussed in line with the previous studies and the relevant theoretical background.

Systematic approach to the application of automation, robotics, and machine intelligence systems (aramis) to future space projects

Energy Technology Data Exchange (ETDEWEB)

Smith, D B.S.

1983-01-01

The potential applications of automation, robotics and machine intelligence systems (ARAMIS) to space projects are investigated, through a systematic method. In this method selected space projects are broken down into space project tasks, and 69 of these tasks are selected for study. Candidate ARAMIS options are defined for each task. The relative merits of these options are evaluated according to seven indices of performance. Logical sequences of ARAMIS development are also defined. Based on this data, promising applications of ARAMIS are identified for space project tasks. General conclusions and recommendations for further study are also presented. 6 references.

Emerging Paradigms in Machine Learning

CERN Document Server

Jain, Lakhmi; Howlett, Robert

2013-01-01

This  book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The  multidisciplinary nature of machine learning makes it a very fascinating and popular area for research.  The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems.  Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary ...

Sensor guided control and navigation with intelligent machines. Final technical report

Energy Technology Data Exchange (ETDEWEB)

Ghosh, Bijoy K.

2001-03-26

This item constitutes the final report on ''Visionics: An integrated approach to analysis and design of intelligent machines.'' The report discusses dynamical systems approach to problems in robust control of possibly time-varying linear systems, problems in vision and visually guided control, and, finally, applications of these control techniques to intelligent navigation with a mobile platform. Robust design of a controller for a time-varying system essentially deals with the problem of synthesizing a controller that can adapt to sudden changes in the parameters of the plant and can maintain stability. The approach presented is to design a compensator that simultaneously stabilizes each and every possible mode of the plant as the parameters undergo sudden and unexpected changes. Such changes can in fact be detected by a visual sensor and, hence, visually guided control problems are studied as a natural consequence. The problem here is to detect parameters of the plant and maintain st ability in the closed loop using a ccd camera as a sensor. The main result discussed in the report is the role of perspective systems theory that was developed in order to analyze such a detection and control problem. The robust control algorithms and the visually guided control algorithms are applied in the context of a PUMA 560 robot arm control where the goal is to visually locate a moving part on a mobile turntable. Such problems are of paramount importance in manufacturing with a certain lack of structure. Sensor guided control problems are extended to problems in robot navigation using a NOMADIC mobile platform with a ccd and a laser range finder as sensors. The localization and map building problems are studied with the objective of navigation in an unstructured terrain.

Challenging problems and solutions in intelligent systems

CERN Document Server

Grzegorzewski, Przemysław; Kacprzyk, Janusz; Owsiński, Jan; Penczek, Wojciech; Zadrożny, Sławomir

2016-01-01

This volume presents recent research, challenging problems and solutions in Intelligent Systems– covering the following disciplines: artificial and computational intelligence, fuzzy logic and other non-classic logics, intelligent database systems, information retrieval, information fusion, intelligent search (engines), data mining, cluster analysis, unsupervised learning, machine learning, intelligent data analysis, (group) decision support systems, intelligent agents and multi-agent systems, knowledge-based systems, imprecision and uncertainty handling, electronic commerce, distributed systems, etc. The book defines a common ground for sometimes seemingly disparate problems and addresses them by using the paradigm of broadly perceived intelligent systems. It presents a broad panorama of a multitude of theoretical and practical problems which have been successfully dealt with using the paradigm of intelligent computing.

Artificial Intelligence: Threat or Boon to Radiologists?

Science.gov (United States)

Recht, Michael; Bryan, R Nick

2017-11-01

The development and integration of machine learning/artificial intelligence into routine clinical practice will significantly alter the current practice of radiology. Changes in reimbursement and practice patterns will also continue to affect radiology. But rather than being a significant threat to radiologists, we believe these changes, particularly machine learning/artificial intelligence, will be a boon to radiologists by increasing their value, efficiency, accuracy, and personal satisfaction. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Extreme learning machines 2013 algorithms and applications

CERN Document Server

Toh, Kar-Ann; Romay, Manuel; Mao, Kezhi

2014-01-01

In recent years, ELM has emerged as a revolutionary technique of computational intelligence, and has attracted considerable attentions. An extreme learning machine (ELM) is a single layer feed-forward neural network alike learning system, whose connections from the input layer to the hidden layer are randomly generated, while the connections from the hidden layer to the output layer are learned through linear learning methods. The outstanding merits of extreme learning machine (ELM) are its fast learning speed, trivial human intervene and high scalability.   This book contains some selected papers from the International Conference on Extreme Learning Machine 2013, which was held in Beijing China, October 15-17, 2013. This conference aims to bring together the researchers and practitioners of extreme learning machine from a variety of fields including artificial intelligence, biomedical engineering and bioinformatics, system modelling and control, and signal and image processing, to promote research and discu...

Simulation research on the process of large scale ship plane segmentation intelligent workshop

Science.gov (United States)

Xu, Peng; Liao, Liangchuang; Zhou, Chao; Xue, Rui; Fu, Wei

2017-04-01

Large scale ship plane segmentation intelligent workshop is a new thing, and there is no research work in related fields at home and abroad. The mode of production should be transformed by the existing industry 2.0 or part of industry 3.0, also transformed from "human brain analysis and judgment + machine manufacturing" to "machine analysis and judgment + machine manufacturing". In this transforming process, there are a great deal of tasks need to be determined on the aspects of management and technology, such as workshop structure evolution, development of intelligent equipment and changes in business model. Along with them is the reformation of the whole workshop. Process simulation in this project would verify general layout and process flow of large scale ship plane section intelligent workshop, also would analyze intelligent workshop working efficiency, which is significant to the next step of the transformation of plane segmentation intelligent workshop.

Affective Computing and Intelligent Interaction

CERN Document Server

2012-01-01

2012 International Conference on Affective Computing and Intelligent Interaction (ICACII 2012) was the most comprehensive conference focused on the various aspects of advances in Affective Computing and Intelligent Interaction. The conference provided a rare opportunity to bring together worldwide academic researchers and practitioners for exchanging the latest developments and applications in this field such as Intelligent Computing, Affective Computing, Machine Learning, Business Intelligence and HCI.   This volume is a collection of 119 papers selected from 410 submissions from universities and industries all over the world, based on their quality and relevancy to the conference. All of the papers have been peer-reviewed by selected experts.  

Vision Guided Intelligent Robot Design And Experiments

Science.gov (United States)

Slutzky, G. D.; Hall, E. L.

1988-02-01

The concept of an intelligent robot is an important topic combining sensors, manipulators, and artificial intelligence to design a useful machine. Vision systems, tactile sensors, proximity switches and other sensors provide the elements necessary for simple game playing as well as industrial applications. These sensors permit adaption to a changing environment. The AI techniques permit advanced forms of decision making, adaptive responses, and learning while the manipulator provides the ability to perform various tasks. Computer languages such as LISP and OPS5, have been utilized to achieve expert systems approaches in solving real world problems. The purpose of this paper is to describe several examples of visually guided intelligent robots including both stationary and mobile robots. Demonstrations will be presented of a system for constructing and solving a popular peg game, a robot lawn mower, and a box stacking robot. The experience gained from these and other systems provide insight into what may be realistically expected from the next generation of intelligent machines.

Machine Learning and Data Mining Methods in Diabetes Research.

Science.gov (United States)

Kavakiotis, Ioannis; Tsave, Olga; Salifoglou, Athanasios; Maglaveras, Nicos; Vlahavas, Ioannis; Chouvarda, Ioanna

2017-01-01

The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

Machine learning & artificial intelligence in the quantum domain: a review of recent progress.

Science.gov (United States)

Dunjko, Vedran; Briegel, Hans J

2018-03-05

Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research-quantum information versus machine learning (ML) and artificial intelligence (AI)-have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question of the extent to which these fields can indeed learn and benefit from each other. Quantum ML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for ML problems, critical in our 'big data' world. Conversely, ML already permeates many cutting-edge technologies and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement-exploring what ML/AI can do for quantum physics and vice versa-researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent developments and

Identification of COPD patients' health status using an intelligent system in the CHRONIOUS wearable platform.

Science.gov (United States)

Bellos, Christos C; Papadopoulos, Athanasios; Rosso, Roberto; Fotiadis, Dimitrios I

2014-05-01

The CHRONIOUS system offers an integrated platform aiming at the effective management and real-time assessment of the health status of the patient suffering from chronic obstructive pulmonary disease (COPD). An intelligent system is developed for the analysis and the real-time evaluation of patient's condition. A hybrid classifier has been implemented on a personal digital assistant, combining a support vector machine, a random forest, and a rule-based system to provide a more advanced categorization scheme for the early and in real-time characterization of a COPD episode. This is followed by a severity estimation algorithm which classifies the identified pathological situation in different levels and triggers an alerting mechanism to provide an informative and instructive message/advice to the patient and the clinical supervisor. The system has been validated using data collected from 30 patients that have been annotated by experts indicating 1) the severity level of the current patient's health status, and 2) the COPD disease level of the recruited patients according to the GOLD guidelines. The achieved characterization accuracy has been found 94%.

Machine learning approaches to the social determinants of health in the health and retirement study.

Science.gov (United States)

Seligman, Benjamin; Tuljapurkar, Shripad; Rehkopf, David

2018-04-01

Social and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study. A linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length. Prediction, fit, and interpretability were compared across four machine learning methods: linear regression, penalized regressions, random forests, and neural networks. All models had poor out-of-sample prediction. Most machine learning models performed similarly to the simpler models. However, neural networks greatly outperformed the three other methods. Neural networks also had good fit to the data ( R 2 between 0.4-0.6, versus learning models, nine variables were frequently selected or highly weighted as predictors: dental visits, current smoking, self-rated health, serial-seven subtractions, probability of receiving an inheritance, probability of leaving an inheritance of at least $10,000, number of children ever born, African-American race, and gender. Some of the machine learning methods do not improve prediction or fit beyond simpler models, however, neural networks performed well. The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider.

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms.

Science.gov (United States)

Meiring, Gys Albertus Marthinus; Myburgh, Hermanus Carel

2015-12-04

In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.

The Birth of Artificial Intelligence: First Conference on Artificial Intelligence in Paris in 1951?

OpenAIRE

Bruderer , Herbert

2016-01-01

International audience; The 1956 Dartmouth conference is often considered as the cradle of artificial intelligence. There is a controversy on its origin. Some historians of computing believe that Turing or Zuse were the fathers of machine intelligence. However, the first working chess-playing automaton was developed by Torres Quevedo by 1912. Moreover, there was a large and important (but forgotten) European conference on computing and human thinking in Paris in 1951.

Social Media- A source of intelligence

Indian Academy of Sciences (India)

First page Back Continue Last page Graphics. Any technology that produces large amount of data like social media and CDR is a source of intelligence for the LEA. Any technology that produces large amount of data like social media and CDR is a source of intelligence for the LEA. Data Mining, Machine learning, Big Data, ...

IQ Tests Are Not for Machines, Yet

Science.gov (United States)

Dowe, David L.; Hernandez-Orallo, Jose

2012-01-01

Complex, but specific, tasks--such as chess or "Jeopardy!"--are popularly seen as milestones for artificial intelligence (AI). However, they are not appropriate for evaluating the intelligence of machines or measuring the progress in AI. Aware of this delusion, Detterman has recently raised a challenge prompting AI researchers to evaluate their…

1st International Conference on Intelligent Communication, Control and Devices

CERN Document Server

Choudhury, Sushabhan

2017-01-01

The book presents high-quality research papers presented at the first international conference, ICICCD 2016, organised by the Department of Electronics, Instrumentation and Control Engineering of University of Petroleum and Energy Studies, Dehradun on 2nd and 3rd April, 2016. The book is broadly divided into three sections: Intelligent Communication, Intelligent Control and Intelligent Devices. The areas covered under these sections are wireless communication and radio technologies, optical communication, communication hardware evolution, machine-to-machine communication networks, routing techniques, network analytics, network applications and services, satellite and space communications, technologies for e-communication, wireless Ad-Hoc and sensor networks, communications and information security, signal processing for communications, communication software, microwave informatics, robotics and automation, optimization techniques and algorithms, intelligent transport, mechatronics system, guidance and navigat...

A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis

OpenAIRE

Ademujimi , Toyosi ,; Brundage , Michael ,; Prabhu , Vittaldas ,

2017-01-01

Part 6: Intelligent Diagnostics and Maintenance Solutions; International audience; Artificial intelligence applications are increasing due to advances in data collection systems, algorithms, and affordability of computing power. Within the manufacturing industry, machine learning algorithms are often used for improving manufacturing system fault diagnosis. This study focuses on a review of recent fault diagnosis applications in manufacturing that are based on several prominent machine learnin...

Intelligent Machine Vision for Automated Fence Intruder Detection Using Self-organizing Map

Directory of Open Access Journals (Sweden)

Veldin A. Talorete Jr.

2017-03-01

Full Text Available This paper presents an intelligent machine vision for automated fence intruder detection. A series of still captured images that contain fence events using Internet Protocol cameras was used as input data to the system. Two classifiers were used; the first is to classify human posture and the second one will classify intruder location. The system classifiers were implemented using Self-Organizing Map after the implementation of several image segmentation processes. The human posture classifier is in charge of classifying the detected subject’s posture patterns from subject’s silhouette. Moreover, the Intruder Localization Classifier is in charge of classifying the detected pattern’s location classifier will estimate the location of the intruder with respect to the fence using geometric feature from images as inputs. The system is capable of activating the alarm, display the actual image and depict the location of the intruder when an intruder is detected. In detecting intruder posture, the system’s success rate of 88%. Overall system accuracy for day-time intruder localization is 83% and an accuracy of 88% for night-time intruder localization

Minimally Naturalistic Artificial Intelligence

OpenAIRE

Hansen, Steven Stenberg

2017-01-01

The rapid advancement of machine learning techniques has re-energized research into general artificial intelligence. While the idea of domain-agnostic meta-learning is appealing, this emerging field must come to terms with its relationship to human cognition and the statistics and structure of the tasks humans perform. The position of this article is that only by aligning our agents' abilities and environments with those of humans do we stand a chance at developing general artificial intellig...

Teaching Emotional Intelligence to Intensive Care Unit Nurses and their General Health: A Randomized Clinical Trial

Directory of Open Access Journals (Sweden)

F Sharif

2013-07-01

Full Text Available Background: Emotion and how people manage it is an important part of personality that would immensely affect their health. Investigations showed that emotional intelligence is significantly related to and can predict psychological health. Objective: To determine the effect of teaching emotional intelligence to intensive care unit nurses on their general health. Methods: This randomized clinical trial (registered as IRCT201208022812N9 was conducted on 52 of 200 in intensive care unit nurses affiliated to Shiraz University of Medical Sciences. They were recruited through purposeful convenience sampling and then randomly categorized into two groups. The intervention group members were trained in emotional intelligence. Bar-on emotional intelligence and Goldberg's general health questionnaires were administered to each participant before, immediately after, and one month after the intervention. Results: While the mean score of general health for the intervention group decreased from 25.4 before the intervention, to 18.1 immediately after the intervention and to 14.6 one month later, for the control group, it increased from 22.0, to 24.2 and to 26.5, respectively (p<0.001. Conclusion: Teaching emotional intelligence improved the general health of intensive care unit nurses.

Foundations of Intelligent Systems : Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering

CERN Document Server

Li, Tianrui

2012-01-01

Proceedings of The Sixth International Conference on Intelligent System and Knowledge Engineering presents selected papers from the conference ISKE 2011, held December 15-17 in Shanghai, China. This proceedings doesn’t only examine original research and approaches in the broad areas of intelligent systems and knowledge engineering, but also present new methodologies and practices in intelligent computing paradigms. The book introduces the current scientific and technical advances in the fields of artificial intelligence, machine learning, pattern recognition, data mining, information retrieval, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, natural-language processing, etc. Furthermore, new computing methodologies are presented, including cloud computing, service computing and pervasive computing with traditional intelligent methods. The proceedings will be beneficial for both researchers and practitioners who want to utilize intelligent methods in their specific resea...

The cognitive approach to conscious machines

CERN Document Server

Haikonen, Pentti O

2003-01-01

Could a machine have an immaterial mind? The author argues that true conscious machines can be built, but rejects artificial intelligence and classical neural networks in favour of the emulation of the cognitive processes of the brain-the flow of inner speech, inner imagery and emotions. This results in a non-numeric meaning-processing machine with distributed information representation and system reactions. It is argued that this machine would be conscious; it would be aware of its own existence and its mental content and perceive this as immaterial. Novel views on consciousness and the mind-

The resources that matter: fundamental social causes of health disparities and the challenge of intelligence.

Science.gov (United States)

Link, Bruce G; Phelan, Jo C; Miech, Richard; Westin, Emily Leckman

2008-03-01

A robust and very persistent association between indicators of socioeconomic status (SES) and the onset of life-threatening disease is a prominent concern of medical sociology. The persistence of the association over time and its generality across very different places suggests that no fixed set of intervening risk and protective factors can account for the connection. Instead, fundamental-cause theory views SES-related resources of knowledge, money, power prestige, and beneficial social connections as flexible resources that allow people to avoid risks and adopt protective strategies no matter what the risk and protective factors are in a given place or time. Recently, however, intelligence has been proposed as an alternative flexible resource that could fully account for the association between SES and health and thereby find its place as the epidemiologists' "elusive fundamental cause" (Gottfredson 2004). We examine the direct effects of intelligence test scores and adult SES in two data sets containing measures of intelligence, SES, and health. In analyses of prospective data from both the Wisconsin Longitudinal Study and the Health and Retirement Survey, we find little evidence of a direct effect of intelligence on health once adult education and income are held constant. In contrast, the significant effects of education and income on health change very little when intelligence is controlled. Although data limitations do not allow a definitive resolution of the issue, this evidence is inconsistent with the claim that intelligence is the elusive fundamental cause of health disparities, and instead supports the idea that the flexible resources people actively use to gain a health advantage are the SES-related resources of knowledge, money, power, prestige, and beneficial social connections.

Active learning machine learns to create new quantum experiments.

Science.gov (United States)

Melnikov, Alexey A; Poulsen Nautrup, Hendrik; Krenn, Mario; Dunjko, Vedran; Tiersch, Markus; Zeilinger, Anton; Briegel, Hans J

2018-02-06

How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence. In our approach, the projective simulation system is challenged to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments. The artificial intelligence system learns to create a variety of entangled states and improves the efficiency of their realization. In the process, the system autonomously (re)discovers experimental techniques which are only now becoming standard in modern quantum optical experiments-a trait which was not explicitly demanded from the system but emerged through the process of learning. Such features highlight the possibility that machines could have a significantly more creative role in future research.

Artificial Consciousness or Artificial Intelligence

Directory of Open Access Journals (Sweden)

Spanache Florin

2017-05-01

Full Text Available Artificial intelligence is a tool designed by people for the gratification of their own creative ego, so we can not confuse conscience with intelligence and not even intelligence in its human representation with conscience. They are all different concepts and they have different uses. Philosophically, there are differences between autonomous people and automatic artificial intelligence. This is the difference between intelligence and artificial intelligence, autonomous versus automatic. But conscience is above these differences because it is neither conditioned by the self-preservation of autonomy, because a conscience is something that you use to help your neighbor, nor automatic, because one’s conscience is tested by situations which are not similar or subject to routine. So, artificial intelligence is only in science-fiction literature similar to an autonomous conscience-endowed being. In real life, religion with its notions of redemption, sin, expiation, confession and communion will not have any meaning for a machine which cannot make a mistake on its own.

Machine throughput improvement achieved using innovative control technique

International Nuclear Information System (INIS)

Sharma, V.; Acharya, S.; Mittal, K.C.

2012-01-01

In any type of fully or semi automatic machine the control systems plays an important role. The control system on the one hand has to consider the human psychology, intelligence requirement for an operator, and attention needed from him. On the other hand the complexity of the control has also to be understood well before designing a control system that can be handled comfortably and safely by the operator. As far as the user experience/comfort is concerned the design of control system GUI is vital. Considering these two aspects related to the user of the machine it is evident that the control system design is very important because it is has to accommodate the human behaviour and skill sets required/available as well as the capability of the machine under the control of the control system. An intelligently designed control system can enhance the productivity of the machine. (author)

A Machine-to-Machine protocol benchmark for eHealth applications - Use case: Respiratory rehabilitation.

Science.gov (United States)

Talaminos-Barroso, Alejandro; Estudillo-Valderrama, Miguel A; Roa, Laura M; Reina-Tosina, Javier; Ortega-Ruiz, Francisco

2016-06-01

M2M (Machine-to-Machine) communications represent one of the main pillars of the new paradigm of the Internet of Things (IoT), and is making possible new opportunities for the eHealth business. Nevertheless, the large number of M2M protocols currently available hinders the election of a suitable solution that satisfies the requirements that can demand eHealth applications. In the first place, to develop a tool that provides a benchmarking analysis in order to objectively select among the most relevant M2M protocols for eHealth solutions. In the second place, to validate the tool with a particular use case: the respiratory rehabilitation. A software tool, called Distributed Computing Framework (DFC), has been designed and developed to execute the benchmarking tests and facilitate the deployment in environments with a large number of machines, with independence of the protocol and performance metrics selected. DDS, MQTT, CoAP, JMS, AMQP and XMPP protocols were evaluated considering different specific performance metrics, including CPU usage, memory usage, bandwidth consumption, latency and jitter. The results obtained allowed to validate a case of use: respiratory rehabilitation of chronic obstructive pulmonary disease (COPD) patients in two scenarios with different types of requirement: Home-Based and Ambulatory. The results of the benchmark comparison can guide eHealth developers in the choice of M2M technologies. In this regard, the framework presented is a simple and powerful tool for the deployment of benchmark tests under specific environments and conditions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Intelligent Distributed Computing VI : Proceedings of the 6th International Symposium on Intelligent Distributed Computing

CERN Document Server

Badica, Costin; Malgeri, Michele; Unland, Rainer

2013-01-01

This book represents the combined peer-reviewed proceedings of the Sixth International Symposium on Intelligent Distributed Computing -- IDC~2012, of the International Workshop on Agents for Cloud -- A4C~2012 and of the Fourth International Workshop on Multi-Agent Systems Technology and Semantics -- MASTS~2012. All the events were held in Calabria, Italy during September 24-26, 2012. The 37 contributions published in this book address many topics related to theory and applications of intelligent distributed computing and multi-agent systems, including: adaptive and autonomous distributed systems, agent programming, ambient assisted living systems, business process modeling and verification, cloud computing, coalition formation, decision support systems, distributed optimization and constraint satisfaction, gesture recognition, intelligent energy management in WSNs, intelligent logistics, machine learning, mobile agents, parallel and distributed computational intelligence, parallel evolutionary computing, trus...

Cognitive logical systems with artificial intelligence

Energy Technology Data Exchange (ETDEWEB)

Liss, E

1983-09-01

The simulation of cognitive processes for the purpose of the technical development of learning systems with intelligent behavior is a basic object of the young interdisciplinary cognition science which is based upon artificial intelligence, cognitive psychology, computer science, linguistics and pedagogics. Cognitive systems may be described as knowledge-based logical systems. Based on structural and functional principles of intelligent automata and elementary information processing systems with structural learning capability the future process, machine and robot controls, advising units and fifth generation computers may be developed.

Groundhog Day for Medical Artificial Intelligence.

Science.gov (United States)

London, Alex John

2018-05-01

Following a boom in investment and overinflated expectations in the 1980s, artificial intelligence entered a period of retrenchment known as the "AI winter." With advances in the field of machine learning and the availability of large datasets for training various types of artificial neural networks, AI is in another cycle of halcyon days. Although medicine is particularly recalcitrant to change, applications of AI in health care have professionals in fields like radiology worried about the future of their careers and have the public tittering about the prospect of soulless machines making life-and-death decisions. Medicine thus appears to be at an inflection point-a kind of Groundhog Day on which either AI will bring a springtime of improved diagnostic and predictive practices or the shadow of public and professional fear will lead to six more metaphorical weeks of winter in medical AI. © 2018 The Hastings Center.

International Conference on Computational Intelligence 2015

CERN Document Server

Saha, Sujan

2017-01-01

This volume comprises the proceedings of the International Conference on Computational Intelligence 2015 (ICCI15). This book aims to bring together work from leading academicians, scientists, researchers and research scholars from across the globe on all aspects of computational intelligence. The work is composed mainly of original and unpublished results of conceptual, constructive, empirical, experimental, or theoretical work in all areas of computational intelligence. Specifically, the major topics covered include classical computational intelligence models and artificial intelligence, neural networks and deep learning, evolutionary swarm and particle algorithms, hybrid systems optimization, constraint programming, human-machine interaction, computational intelligence for the web analytics, robotics, computational neurosciences, neurodynamics, bioinspired and biomorphic algorithms, cross disciplinary topics and applications. The contents of this volume will be of use to researchers and professionals alike....

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms

Directory of Open Access Journals (Sweden)

Gys Albertus Marthinus Meiring

2015-12-01

Full Text Available In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.

Practical Applications of Intelligent Systems : Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering

CERN Document Server

Li, Tianrui

2012-01-01

Proceedings of The Sixth International Conference on Intelligent System and Knowledge Engineering presents selected papers from the conference ISKE 2011, held December 15-17 in Shanghai, China. This proceedings doesn’t only examine original research and approaches in the broad areas of intelligent systems and knowledge engineering, but also present new methodologies and practices in intelligent computing paradigms. The book introduces the current scientific and technical advances in the fields of artificial intelligence, machine learning, pattern recognition, data mining, information retrieval, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, natural-language processing, etc. Furthermore, new computing methodologies are presented, including cloud computing, service computing and pervasive computing with traditional intelligent methods. The proceedings will be beneficial for both researchers and practitioners who want to utilize intelligent methods in their specific res...

Indonesian Stock Prediction using Support Vector Machine (SVM

Directory of Open Access Journals (Sweden)

Santoso Murtiyanto

2018-01-01

Full Text Available This project is part of developing software to provide predictive information technology-based services artificial intelligence (Machine Intelligence or Machine Learning that will be utilized in the money market community. The prediction method used in this early stages uses the combination of Gaussian Mixture Model and Support Vector Machine with Python programming. The system predicts the price of Astra International (stock code: ASII.JK stock data. The data used was taken during 17 yr period of January 2000 until September 2017. Some data was used for training/modeling (80 % of data and the remainder (20 % was used for testing. An integrated model comprising Gaussian Mixture Model and Support Vector Machine system has been tested to predict stock market of ASII.JK for l d in advance. This model has been compared with the Market Cummulative Return. From the results, it is depicts that the Gaussian Mixture Model-Support Vector Machine based stock predicted model, offers significant improvement over the compared models resulting sharpe ratio of 3.22.

Learning scikit-learn machine learning in Python

CERN Document Server

Garreta, Raúl

2013-01-01

The book adopts a tutorial-based approach to introduce the user to Scikit-learn.If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this the book for you. No previous experience with machine-learning algorithms is required.

Intelligent controller of a flexible hybrid robot machine for ITER assembly and maintenance

International Nuclear Information System (INIS)

Al-saedi, Mazin I.; Wu, Huapeng; Handroos, Heikki

2014-01-01

Highlights: • Studying flexible multibody dynamic of hybrid parallel robot. • Investigating fuzzy-PD controller to control a hybrid flexible hydraulically driven robot. • Investigating ANFIS-PD controller to control a hybrid flexible robot. Compare to traditional PID this method gives better performance. • Using the equilibrium of reaction forces between the parallel and serial parts of hybrid robot to control the serial part hydraulically driven. - Abstract: The assembly and maintenance of International Thermonuclear Experimental Reactor (ITER) vacuum vessel (VV) is highly challenging since the tasks performed by the robot involve welding, material handling, and machine cutting from inside the VV. To fulfill the tasks in ITER application, this paper presents a hybrid redundant manipulator with four DOFs provided by serial kinematic axes and six DOFs by parallel mechanism. Thus, in machining, to achieve greater end-effector trajectory tracking accuracy for surface quality, a robust control of the actuators for the flexible link has to be deduced. In this paper, the intelligent control of a hydraulically driven parallel robot part based on the dynamic model and two control schemes have been investigated: (1) fuzzy-PID self tuning controller composed of the conventional PID control and with fuzzy logic; (2) adaptive neuro-fuzzy inference system-PID (ANFIS-PID) self tuning of the gains of the PID controller, which are implemented independently to control each hydraulic cylinder of the parallel robot based on rod position predictions. The obtained results of the fuzzy-PID and ANFIS-PID self tuning controller can reduce more tracking errors than the conventional PID controller. Subsequently, the serial component of the hybrid robot can be analyzed using the equilibrium of reaction forces at the universal joint connections of the hexa-element. To achieve precise positional control of the end effector for maximum precision machining, the hydraulic cylinder should

Intelligent controller of a flexible hybrid robot machine for ITER assembly and maintenance

Energy Technology Data Exchange (ETDEWEB)

Al-saedi, Mazin I., E-mail: mazin.al-saedi@lut.fi; Wu, Huapeng; Handroos, Heikki

2014-10-15

Highlights: • Studying flexible multibody dynamic of hybrid parallel robot. • Investigating fuzzy-PD controller to control a hybrid flexible hydraulically driven robot. • Investigating ANFIS-PD controller to control a hybrid flexible robot. Compare to traditional PID this method gives better performance. • Using the equilibrium of reaction forces between the parallel and serial parts of hybrid robot to control the serial part hydraulically driven. - Abstract: The assembly and maintenance of International Thermonuclear Experimental Reactor (ITER) vacuum vessel (VV) is highly challenging since the tasks performed by the robot involve welding, material handling, and machine cutting from inside the VV. To fulfill the tasks in ITER application, this paper presents a hybrid redundant manipulator with four DOFs provided by serial kinematic axes and six DOFs by parallel mechanism. Thus, in machining, to achieve greater end-effector trajectory tracking accuracy for surface quality, a robust control of the actuators for the flexible link has to be deduced. In this paper, the intelligent control of a hydraulically driven parallel robot part based on the dynamic model and two control schemes have been investigated: (1) fuzzy-PID self tuning controller composed of the conventional PID control and with fuzzy logic; (2) adaptive neuro-fuzzy inference system-PID (ANFIS-PID) self tuning of the gains of the PID controller, which are implemented independently to control each hydraulic cylinder of the parallel robot based on rod position predictions. The obtained results of the fuzzy-PID and ANFIS-PID self tuning controller can reduce more tracking errors than the conventional PID controller. Subsequently, the serial component of the hybrid robot can be analyzed using the equilibrium of reaction forces at the universal joint connections of the hexa-element. To achieve precise positional control of the end effector for maximum precision machining, the hydraulic cylinder should

Energy Efficiency of Tunnel Boring Machines.

OpenAIRE

Grishenko, Vitaly

2014-01-01

Herrenknecht AG is a German world-leading Tunnel Boring Machines manufacturer showing strong awareness and concern regarding environmental issues. The company supports research on the Energy Efficiency (EE) of their products, aimed at the development of intelligent design for a green Tunnel Boring Machine. The aim of this project is to produce a ’status quo’ report on EE of three types of Tunnel Boring Machines (Hardrock, EPB and Mixshield TBM). In the framework of this research 39 projects a...

Ultrasensitive and Highly Stable Resistive Pressure Sensors with Biomaterial-Incorporated Interfacial Layers for Wearable Health-Monitoring and Human-Machine Interfaces.

Science.gov (United States)

Chang, Hochan; Kim, Sungwoong; Jin, Sumin; Lee, Seung-Woo; Yang, Gil-Tae; Lee, Ki-Young; Yi, Hyunjung

2018-01-10

Flexible piezoresistive sensors have huge potential for health monitoring, human-machine interfaces, prosthetic limbs, and intelligent robotics. A variety of nanomaterials and structural schemes have been proposed for realizing ultrasensitive flexible piezoresistive sensors. However, despite the success of recent efforts, high sensitivity within narrower pressure ranges and/or the challenging adhesion and stability issues still potentially limit their broad applications. Herein, we introduce a biomaterial-based scheme for the development of flexible pressure sensors that are ultrasensitive (resistance change by 5 orders) over a broad pressure range of 0.1-100 kPa, promptly responsive (20 ms), and yet highly stable. We show that employing biomaterial-incorporated conductive networks of single-walled carbon nanotubes as interfacial layers of contact-based resistive pressure sensors significantly enhances piezoresistive response via effective modulation of the interlayer resistance and provides stable interfaces for the pressure sensors. The developed flexible sensor is capable of real-time monitoring of wrist pulse waves under external medium pressure levels and providing pressure profiles applied by a thumb and a forefinger during object manipulation at a low voltage (1 V) and power consumption (<12 μW). This work provides a new insight into the material candidates and approaches for the development of wearable health-monitoring and human-machine interfaces.

Integration of an intelligent systems behavior simulator and a scalable soldier-machine interface

Science.gov (United States)

Johnson, Tony; Manteuffel, Chris; Brewster, Benjamin; Tierney, Terry

2007-04-01

As the Army's Future Combat Systems (FCS) introduce emerging technologies and new force structures to the battlefield, soldiers will increasingly face new challenges in workload management. The next generation warfighter will be responsible for effectively managing robotic assets in addition to performing other missions. Studies of future battlefield operational scenarios involving the use of automation, including the specification of existing and proposed technologies, will provide significant insight into potential problem areas regarding soldier workload. The US Army Tank Automotive Research, Development, and Engineering Center (TARDEC) is currently executing an Army technology objective program to analyze and evaluate the effect of automated technologies and their associated control devices with respect to soldier workload. The Human-Robotic Interface (HRI) Intelligent Systems Behavior Simulator (ISBS) is a human performance measurement simulation system that allows modelers to develop constructive simulations of military scenarios with various deployments of interface technologies in order to evaluate operator effectiveness. One such interface is TARDEC's Scalable Soldier-Machine Interface (SMI). The scalable SMI provides a configurable machine interface application that is capable of adapting to several hardware platforms by recognizing the physical space limitations of the display device. This paper describes the integration of the ISBS and Scalable SMI applications, which will ultimately benefit both systems. The ISBS will be able to use the Scalable SMI to visualize the behaviors of virtual soldiers performing HRI tasks, such as route planning, and the scalable SMI will benefit from stimuli provided by the ISBS simulation environment. The paper describes the background of each system and details of the system integration approach.

Strategies for the Integration of Medical and Health Representation within Law Enforcement Intelligence Fusion Centers

National Research Council Canada - National Science Library

Morrissey, James F

2007-01-01

Terrorism-related intelligence gathering, analysis and information dissemination would be improved and enhanced by including a medical and health element in law enforcement intelligence fusion centers...

Machine health prognostics using the Bayesian-inference-based probabilistic indication and high-order particle filtering framework

Science.gov (United States)

Yu, Jianbo

2015-12-01

Prognostics is much efficient to achieve zero-downtime performance, maximum productivity and proactive maintenance of machines. Prognostics intends to assess and predict the time evolution of machine health degradation so that machine failures can be predicted and prevented. A novel prognostics system is developed based on the data-model-fusion scheme using the Bayesian inference-based self-organizing map (SOM) and an integration of logistic regression (LR) and high-order particle filtering (HOPF). In this prognostics system, a baseline SOM is constructed to model the data distribution space of healthy machine under an assumption that predictable fault patterns are not available. Bayesian inference-based probability (BIP) derived from the baseline SOM is developed as a quantification indication of machine health degradation. BIP is capable of offering failure probability for the monitored machine, which has intuitionist explanation related to health degradation state. Based on those historic BIPs, the constructed LR and its modeling noise constitute a high-order Markov process (HOMP) to describe machine health propagation. HOPF is used to solve the HOMP estimation to predict the evolution of the machine health in the form of a probability density function (PDF). An on-line model update scheme is developed to adapt the Markov process changes to machine health dynamics quickly. The experimental results on a bearing test-bed illustrate the potential applications of the proposed system as an effective and simple tool for machine health prognostics.

Health Informatics via Machine Learning for the Clinical Management of Patients.

Science.gov (United States)

Clifton, D A; Niehaus, K E; Charlton, P; Colopy, G W

2015-08-13

To review how health informatics systems based on machine learning methods have impacted the clinical management of patients, by affecting clinical practice. We reviewed literature from 2010-2015 from databases such as Pubmed, IEEE xplore, and INSPEC, in which methods based on machine learning are likely to be reported. We bring together a broad body of literature, aiming to identify those leading examples of health informatics that have advanced the methodology of machine learning. While individual methods may have further examples that might be added, we have chosen some of the most representative, informative exemplars in each case. Our survey highlights that, while much research is taking place in this high-profile field, examples of those that affect the clinical management of patients are seldom found. We show that substantial progress is being made in terms of methodology, often by data scientists working in close collaboration with clinical groups. Health informatics systems based on machine learning are in their infancy and the translation of such systems into clinical management has yet to be performed at scale.

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

OpenAIRE

Rui Zhao; Ruqiang Yan; Jinjiang Wang; Kezhi Mao

2017-01-01

In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression mode...

Seventh International Conference on Intelligent Systems and Knowledge Engineering - Foundations and Applications of Intelligent Systems

CERN Document Server

Li, Tianrui; Li, Hongbo

2014-01-01

These proceedings present technical papers selected from the 2012 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2012), held on December 15-17 in Beijing. The aim of this conference is to bring together experts from different fields of expertise to discuss the state-of-the-art in Intelligent Systems and Knowledge Engineering, and to present new findings and perspectives on future developments. The proceedings introduce current scientific and technical advances in the fields of artificial intelligence, machine learning, pattern recognition, data mining, knowledge engineering, information retrieval, information theory, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, and natural-language processing, etc. Furthermore they include papers on new intelligent computing paradigms, which combine new computing methodologies, e.g., cloud computing, service computing and pervasive computing with traditional intelligent methods. By presenting new method...

The impact of artificial intelligence on the world economy

OpenAIRE

Kuprevich, T. S.

2017-01-01

In the article the potential benefits and opportunities offered by AI in the world economy are considered. In the course of the research benefits and tendencies of artificial intelligence in the world economy were revealed, the main directions of development and barriers of artificial intelligence adoption are analyzed and revealed. Nowadays artificial intelligence (AI) is going mainstream, driven by machine learning, big data and cloud computing.

Comparison of Spiritual Intelligence and Mental Health in Addicts and Normal Individuals

Directory of Open Access Journals (Sweden)

M Raghibi

2010-09-01

Full Text Available Introduction: Substance abuse is a chronic phenomenon that affects mental and physical health and results in a lot of social, domestic and economic trauma. Methods: The samples included 80 addicts and 80 healthy individuals. The addicts were selected randomly from private and state remedy and rehabilitation centers of Zahedan city. Healthy individuals were matched with addicts in respect to age and sex and also selected randomly. Then, participants were assessed with The Spiritual Intelligence Self‐Report Inventory-24(SISI and General Health Questionnaire (GHQ. The data was analyzed with Pearson correlation coefficient and t test for independent groups. Results: There were significant differences in SISI scores and GHQ scores. Addicts scored lower in two scales. There was a significant positive correlation between SISI scores and GHQ scores and subscales of GHQ (P< 0.01 in two groups. Substance abuse affects mental and physical health. Individuals with lesser spiritual intelligence levels are more prone to addiction and maybe with higher levels of spiritual intelligence, we can prevent individuals from addiction.

Teraflop-scale Incremental Machine Learning

OpenAIRE

Özkural, Eray

2011-01-01

We propose a long-term memory design for artificial general intelligence based on Solomonoff's incremental machine learning methods. We use R5RS Scheme and its standard library with a few omissions as the reference machine. We introduce a Levin Search variant based on Stochastic Context Free Grammar together with four synergistic update algorithms that use the same grammar as a guiding probability distribution of programs. The update algorithms include adjusting production probabilities, re-u...

Man-machine dialogue design and challenges

CERN Document Server

Landragin, Frederic

2013-01-01

This book summarizes the main problems posed by the design of a man-machine dialogue system and offers ideas on how to continue along the path towards efficient, realistic and fluid communication between humans and machines. A culmination of ten years of research, it is based on the author's development, investigation and experimentation covering a multitude of fields, including artificial intelligence, automated language processing, man-machine interfaces and notably multimodal or multimedia interfaces. Contents Part 1. Historical and Methodological Landmarks 1. An Assessment of the Evolution

Support vector machine based diagnostic system for breast cancer using swarm intelligence.

Science.gov (United States)

Chen, Hui-Ling; Yang, Bo; Wang, Gang; Wang, Su-Jing; Liu, Jie; Liu, Da-You

2012-08-01

Breast cancer is becoming a leading cause of death among women in the whole world, meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients. In this paper, a swarm intelligence technique based support vector machine classifier (PSO_SVM) is proposed for breast cancer diagnosis. In the proposed PSO-SVM, the issue of model selection and feature selection in SVM is simultaneously solved under particle swarm (PSO optimization) framework. A weighted function is adopted to design the objective function of PSO, which takes into account the average accuracy rates of SVM (ACC), the number of support vectors (SVs) and the selected features simultaneously. Furthermore, time varying acceleration coefficients (TVAC) and inertia weight (TVIW) are employed to efficiently control the local and global search in PSO algorithm. The effectiveness of PSO-SVM has been rigorously evaluated against the Wisconsin Breast Cancer Dataset (WBCD), which is commonly used among researchers who use machine learning methods for breast cancer diagnosis. The proposed system is compared with the grid search method with feature selection by F-score. The experimental results demonstrate that the proposed approach not only obtains much more appropriate model parameters and discriminative feature subset, but also needs smaller set of SVs for training, giving high predictive accuracy. In addition, Compared to the existing methods in previous studies, the proposed system can also be regarded as a promising success with the excellent classification accuracy of 99.3% via 10-fold cross validation (CV) analysis. Moreover, a combination of five informative features is identified, which might provide important insights to the nature of the breast cancer disease and give an important clue for the physicians to take a closer attention. We believe the promising result can ensure that the physicians make very accurate diagnostic decision in

Basic research on intelligent robotic systems operating in hostile environments: New developments at ORNL

International Nuclear Information System (INIS)

Barhen, J.; Babcock, S.M.; Hamel, W.R.; Oblow, E.M.; Saridis, G.N.; deSaussure, G.; Solomon, A.D.; Weisbin, C.R.

1984-01-01

Robotics and artificial intelligence research carried out within the Center for Engineering Systems Advanced Research (CESAR) is presented. Activities focus on the development and demonstration of a comprehensive methodological framework for intelligent machines operating in unstructured hostile environments. Areas currently being addressed include mathematical modeling of robot dynamics, real-time control, ''world'' modeling, machine perception and strategy planning

Artificial intelligence applications for operation and maintenance

International Nuclear Information System (INIS)

Itoh, M.; Tai, I.; Monta, K.; Sekimizu, K.

1987-01-01

A nuclear power plant as a typical man-machine system of the modern industry needs an efficient human window through which operators can observe every necessary detail of the plant for its safe and reliable operation. Much efforts have been devoted to the development of the computerized operator support systems (COSS). Recent development of artificial intelligence (AI) seems to offer new possibility to strengthen the performance of the COSS such as more powerful diagnosis and procedure synthesis and user friendly man-machine interfaces. From this point of view, a national project of Advanced Man-Machine System Development for Nuclear Power Plants has been carried out. Artificial intelligence application to nuclear power plant operation and maintenance is also selected as a major theme for the promotion of research and development on frontiers in the recently revised long term national program for development and utilization of nuclear energy in JAPAN

FOREWORD: Structural Health Monitoring and Intelligent Infrastructure

Science.gov (United States)

Wu, Zhishen; Fujino, Yozo

2005-06-01

This special issue collects together 19 papers that were originally presented at the First International Conference on Structural Health Monitoring and Intelligent Infrastructure (SHMII-1'2003), held in Tokyo, Japan, on 13-15 November 2003. This conference was organized by the Japan Society of Civil Engineers (JSCE) with partial financial support from the Japan Society for the Promotion of Science (JSPS) and the Ministry of Education, Culture, Sport, Science and Technology, Japan. Many related organizations supported the conference. A total of 16 keynote papers including six state-of-the-art reports from different counties, six invited papers and 154 contributed papers were presented at the conference. The conference was attended by a diverse group of about 300 people from a variety of disciplines in academia, industry and government from all over the world. Structural health monitoring (SHM) and intelligent materials, structures and systems have been the subject of intense research and development in the last two decades and, in recent years, an increasing range of applications in infrastructure have been discovered both for existing structures and for new constructions. SHMII-1'2003 addressed progress in the development of building, transportation, marine, underground and energy-generating structures, and other civilian infrastructures that are periodically, continuously and/or actively monitored where there is a need to optimize their performance. In order to focus the current needs on SHM and intelligent technologies, the conference theme was set as 'Structures/Infrastructures Sustainability'. We are pleased to have the privilege to edit this special issue on SHM and intelligent infrastructure based on SHMII-1'2003. We invited some of the presenters to submit a revised/extended version of their paper that was included in the SHMII-1'2003 proceedings for possible publication in the special issue. Each paper included in this special issue was edited with the same

Optimization of fuel exchange machine operation for boiling water reactors using an artificial intelligence technique

International Nuclear Information System (INIS)

Sekimizu, K.; Araki, T.; Tatemichi, S.I.

1987-01-01

Optimization of fuel assembly exchange machine movements during periodic refueling outage is discussed. The fuel assembly movements during a fuel shuffling were examined, and it was found that the fuel assembly movements consist of two different movement sequences;one is the ''PATH,'' which begins at a discharged fuel assembly and terminates at a fresh fuel assembly, and the other is the ''LOOP,'' where fuel assemblies circulate in the core. It is also shown that fuel-loading patterns during the fuel shuffling can be expressed by the state of each PATH, which is the number of elements already accomplished in the PATH actions. Based on this fact, a scheme to determine a fuel assembly movement sequence within the constraint was formulated using the artificial intelligence language PROLOG. An additional merit to the scheme is that it can simultaneously evaluate fuel assembly movement, due to the control rods and local power range monitor exchange, in addition to normal fuel shuffling. Fuel assembly movements, for fuel shuffling in a 540-MW(electric) boiling water reactor power plant, were calculated by this scheme. It is also shown that the true optimization to minimize the fuel exchange machine movements would be costly to obtain due to the number of alternatives that would need to be evaluated. However, a method to obtain a quasi-optimum solution is suggested

Automation of Knowledge Work in Medicine and Health care: Future and Challenges

OpenAIRE

Farzan Majidfar

2017-01-01

Increment of computing speed, machine learning and human interface, have extended capabilities of artificial intelligence applications to an important stage. It is predicted that use of artificial intelligence (AI) to automate knowledge-based occupations (occupations such as medicine, engineering and law) may have an global enormous economic impact in the near future.Applications based on artificial intelligence are able to improve health and quality of life for millions in the coming years. ...

[Technologies for Complex Intelligent Clinical Data Analysis].

Science.gov (United States)

Baranov, A A; Namazova-Baranova, L S; Smirnov, I V; Devyatkin, D A; Shelmanov, A O; Vishneva, E A; Antonova, E V; Smirnov, V I

2016-01-01

also proposed. Authors have processed anonymized health records from the pediatric center to estimate the proposed methods. The results show the applicability of the information extracted from the texts for solving practical problems. The records ofpatients with allergic, glomerular and rheumatic diseases were used for experimental assessment of the method of automatic diagnostic. Authors have also determined the most appropriate machine learning methods for classification of patients for each group of diseases, as well as the most informative disease signs. It has been found that using additional information extracted from clinical texts, together with structured data helps to improve the quality of diagnosis of chronic diseases. Authors have also obtained pattern combinations of signs of diseases. The proposed methods have been implemented in the intelligent data processing system for a multidisciplinary pediatric center. The experimental results show the availability of the system to improve the quality of pediatric healthcare.

Controls and Health Management Technologies for Intelligent Aerospace Propulsion Systems

Science.gov (United States)

Garg, Sanjay

2004-01-01

With the increased emphasis on aircraft safety, enhanced performance and affordability, and the need to reduce the environmental impact of aircraft, there are many new challenges being faced by the designers of aircraft propulsion systems. The Controls and Dynamics Technology Branch at NASA (National Aeronautics and Space Administration) Glenn Research Center (GRC) in Cleveland, Ohio, is leading and participating in various projects in partnership with other organizations within GRC and across NASA, the U.S. aerospace industry, and academia to develop advanced controls and health management technologies that will help meet these challenges through the concept of an Intelligent Engine. The key enabling technologies for an Intelligent Engine are the increased efficiencies of components through active control, advanced diagnostics and prognostics integrated with intelligent engine control to enhance component life, and distributed control with smart sensors and actuators in an adaptive fault tolerant architecture. This paper describes the current activities of the Controls and Dynamics Technology Branch in the areas of active component control and propulsion system intelligent control, and presents some recent analytical and experimental results in these areas.

A Proposed Intelligent Policy-Based Interface for a Mobile eHealth Environment

Science.gov (United States)

Tavasoli, Amir; Archer, Norm

Users of mobile eHealth systems are often novices, and the learning process for them may be very time consuming. In order for systems to be attractive to potential adopters, it is important that the interface should be very convenient and easy to learn. However, the community of potential users of a mobile eHealth system may be quite varied in their requirements, so the system must be able to adapt easily to suit user preferences. One way to accomplish this is to have the interface driven by intelligent policies. These policies can be refined gradually, using inputs from potential users, through intelligent agents. This paper develops a framework for policy refinement for eHealth mobile interfaces, based on dynamic learning from user interactions.

DEVELOPING A HUMAN CONTROLLED MODEL FOR SAFE ARTIFICIAL INTELLIGENCE SYSTEMS

OpenAIRE

KÖSE, Utku

2018-01-01

Artificial Intelligence is known as one of the most effective research field of nowadays and the future. But rapid rise of Artificial Intelligence and its potential to solve all real world problems autonomously, it has caused also several anxieties. Some scientists think that intelligent systems can reach to a level, which is dangerous for the humankind so because of that some precautions should be taken. So, many sub-research fields like Machine Ethics or Artificial Intelligence Safety have ...

Paradox in AI - AI 2.0: The Way to Machine Consciousness

Science.gov (United States)

Palensky, Peter; Bruckner, Dietmar; Tmej, Anna; Deutsch, Tobias

Artificial Intelligence, the big promise of the last millennium, has apparently made its way into our daily lives. Cell phones with speech control, evolutionary computing in data mining or power grids, optimized via neural network, show its applicability in industrial environments. The original expectation of true intelligence and thinking machines lies still ahead of us. Researchers are, however, optimistic as never before. This paper tries to compare the views, challenges and approaches of several disciplines: engineering, psychology, neuroscience, philosophy. It gives a short introduction to Psychoanalysis, discusses the term consciousness, social implications of intelligent machines, related theories, and expectations and shall serve as a starting point for first attempts of combining these diverse thoughts.

Intelligent robot trends for 1998

Science.gov (United States)

Hall, Ernest L.

1998-10-01

An intelligent robot is a remarkably useful combination of a manipulator, sensors and controls. The use of these machines in factory automation can improve productivity, increase product quality and improve competitiveness. This paper presents a discussion of recent technical and economic trends. Technically, the machines are faster, cheaper, more repeatable, more reliable and safer. The knowledge base of inverse kinematic and dynamic solutions and intelligent controls is increasing. More attention is being given by industry to robots, vision and motion controls. New areas of usage are emerging for service robots, remote manipulators and automated guided vehicles. Economically, the robotics industry now has a 1.1 billion-dollar market in the U.S. and is growing. Feasibility studies results are presented which also show decreasing costs for robots and unaudited healthy rates of return for a variety of robotic applications. However, the road from inspiration to successful application can be long and difficult, often taking decades to achieve a new product. A greater emphasis on mechatronics is needed in our universities. Certainly, more cooperation between government, industry and universities is needed to speed the development of intelligent robots that will benefit industry and society.

[Artificial intelligence to assist clinical diagnosis in medicine].

Science.gov (United States)

Lugo-Reyes, Saúl Oswaldo; Maldonado-Colín, Guadalupe; Murata, Chiharu

2014-01-01

Medicine is one of the fields of knowledge that would most benefit from a closer interaction with Computer studies and Mathematics by optimizing complex, imperfect processes such as differential diagnosis; this is the domain of Machine Learning, a branch of Artificial Intelligence that builds and studies systems capable of learning from a set of training data, in order to optimize classification and prediction processes. In Mexico during the last few years, progress has been made on the implementation of electronic clinical records, so that the National Institutes of Health already have accumulated a wealth of stored data. For those data to become knowledge, they need to be processed and analyzed through complex statistical methods, as it is already being done in other countries, employing: case-based reasoning, artificial neural networks, Bayesian classifiers, multivariate logistic regression, or support vector machines, among other methodologies; to assist the clinical diagnosis of acute appendicitis, breast cancer and chronic liver disease, among a wide array of maladies. In this review we shift through concepts, antecedents, current examples and methodologies of machine learning-assisted clinical diagnosis.

Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century.

Science.gov (United States)

Sadasivam, Rajani Shankar; Cutrona, Sarah L; Kinney, Rebecca L; Marlin, Benjamin M; Mazor, Kathleen M; Lemon, Stephenie C; Houston, Thomas K

2016-03-07

What is the next frontier for computer-tailored health communication (CTHC) research? In current CTHC systems, study designers who have expertise in behavioral theory and mapping theory into CTHC systems select the variables and develop the rules that specify how the content should be tailored, based on their knowledge of the targeted population, the literature, and health behavior theories. In collective-intelligence recommender systems (hereafter recommender systems) used by Web 2.0 companies (eg, Netflix and Amazon), machine learning algorithms combine user profiles and continuous feedback ratings of content (from themselves and other users) to empirically tailor content. Augmenting current theory-based CTHC with empirical recommender systems could be evaluated as the next frontier for CTHC. The objective of our study was to uncover barriers and challenges to using recommender systems in health promotion. We conducted a focused literature review, interviewed subject experts (n=8), and synthesized the results. We describe (1) limitations of current CTHC systems, (2) advantages of incorporating recommender systems to move CTHC forward, and (3) challenges to incorporating recommender systems into CTHC. Based on the evidence presented, we propose a future research agenda for CTHC systems. We promote discussion of ways to move CTHC into the 21st century by incorporation of recommender systems.

Artificial intelligence in cardiology

Directory of Open Access Journals (Sweden)

Srishti Sharma

2017-01-01

Full Text Available Artificial intelligence (AI provides machines with the ability to learn and respond the way humans do and is also referred to as machine learning. The step to building an AI system is to provide the data to learn from so that it can map relations between inputs and outputs and set up parameters such as “weights”/decision boundaries to predict responses for inputs in the future. Then, the model is tested on a second data set. This article outlines the promise this analytic approach has in medicine and cardiology.

Philosophy and Theory of Artificial Intelligence

CERN Document Server

2013-01-01

Can we make machines that think and act like humans or other natural intelligent agents? The answer to this question depends on how we see ourselves and how we see the machines in question. Classical AI and cognitive science had claimed that cognition is computation, and can thus be reproduced on other computing machines, possibly surpassing the abilities of human intelligence. This consensus has now come under threat and the agenda for the philosophy and theory of AI must be set anew, re-defining the relation between AI and Cognitive Science. We can re-claim the original vision of general AI from the technical AI disciplines; we can reject classical cognitive science and replace it with a new theory (e.g. embodied); or we can try to find new ways to approach AI, for example from neuroscience or from systems theory. To do this, we must go back to the basic questions on computing, cognition and ethics for AI. The 30 papers in this volume provide cutting-edge work from leading researchers that define where we s...

Artificial intelligence techniques in Prolog

CERN Document Server

Shoham, Yoav

1993-01-01

Artificial Intelligence Techniques in Prolog introduces the reader to the use of well-established algorithmic techniques in the field of artificial intelligence (AI), with Prolog as the implementation language. The techniques considered cover general areas such as search, rule-based systems, and truth maintenance, as well as constraint satisfaction and uncertainty management. Specific application domains such as temporal reasoning, machine learning, and natural language are also discussed.Comprised of 10 chapters, this book begins with an overview of Prolog, paying particular attention to Prol

The Impact of Emotional Intelligence on Conditions of Trust Among Leaders at the Kentucky Department for Public Health

Directory of Open Access Journals (Sweden)

Jennifer Redmond Knight

2015-03-01

Full Text Available There has been limited leadership research on emotional intelligence and trust in governmental public health settings. The purpose of this study was to identify and seek to understand the relationship between trust and elements of emotional intelligence, including stress management, at the Kentucky Department for Public Health. The Kentucky Department for Public Health (KDPH serves as Kentucky’s state governmental health department. KDPH is led by a Commissioner and composed of seven primary divisions and 25 branches within those divisions. The study was a non-randomized cross-sectional study utilizing electronic surveys that evaluated conditions of trust among staff members and emotional intelligence among supervisors. Pearson correlation coefficients and corresponding p-values are presented to provide the association between emotional intelligence scales and the conditions of trust. Significant positive correlations were observed between supervisors' stress management and the staff members' trust or perception of supervisors' loyalty(r=0.6, p=0.01, integrity(r=0.5, p=0.03, receptivity(r=0.6, p=0.02, promise fulfillment(r=0.6, p=0.02 and availability (r=0.5, p=0.07. This research lays the foundation for emotional intelligence and trust research and leadership training in other governmental public health settings, such as local, other state, national or international organizations. This original research provides metrics to assess the public health workforce with attention to organizational management and leadership constructs. The survey tools could be used in other governmental public health settings in order to develop tailored training opportunities related to emotional intelligence and trust organizations.

Human-in-the-loop Artificial Intelligence

OpenAIRE

Zanzotto, Fabio Massimo

2017-01-01

Little by little, newspapers are revealing the bright future that Artificial Intelligence (AI) is building. Intelligent machines will help everywhere. However, this bright future has a dark side: a dramatic job market contraction before its unpredictable transformation. Hence, in a near future, large numbers of job seekers will need financial support while catching up with these novel unpredictable jobs. This possible job market crisis has an antidote inside. In fact, the rise of AI is sustai...

EU copyright protection of works created by artificial intelligence systems

OpenAIRE

Bøhler, Helene Margrethe

2017-01-01

This thesis is concerned with copyright regulation of works created by artificial intelligence systems. The rapid advances in artificial intelligence are calling into question some of the fundamental assumptions upon which intellectual property law rests. Currently, the European framework of copyright law does not take non-human innovation into account. Meanwhile, advances in artificial intelligence are quickly making machine-generation of creative works a reality. Institutions of the Europea...

Relationship of emotional intelligence and health locus of control among female breast cancer patients in pakistan

International Nuclear Information System (INIS)

Naz, R.; Kamal, A.

2016-01-01

Objective: To investigate relationship between emotional intelligence and health locus of control in married women with breast cancer disease. Study Design: Cross sectional study. Place and Duration of Study: The data was collected from Nuclear Oncology and Radiology Institute (NORI Hospital) Islamabad (n=210) and from Combined Military Hospital (CMH) Rawalpindi (n=101). Data collection was completed between the period from Oct 2013 to Feb 2014. Patients and Methods: The sample was selected using non- probability sampling technique. Collected breast cancer patients sample was n= 311 whose age range was from 18-80 years. A biographical sheet that contain personal and disease information of patient, and two scales were used: Self Report Measure of Emotional Intelligence (Khan and Kamal, 2010), and Multidimensional Health Locus of Control (Wallston, Stein, and Smith, 1994) were used to assess the constructs explored in this study. Results: Results depict that there was significant positive correlation between emotional intelligence (EI), including its sub scales Emotional Self-Regulation Skills (ESRS), Emotional Self Awareness Skills (ESAS), and Interpersonal Skills Scale (ISS) with the Internal Health Locus of Control (IHLOC). Doctors Health Locus of Control (DHLOC) also have significant relationship to emotional intelligence's all sub divisions, whereas external health locus of control including Chance Health Locus of Control (CHLOC) and Powerful Other people Health Locus of Control (PHLOC) both are related to psychological distresses but it was observed in breast cancer population that chance was significantly correlated to ESAS, and ISS and powerful other people locus. Further on group comparison One Way Analysis of Variance (ANOVA) depicted no significant difference on disease stage groups. Conclusion: The strength factors of EI and HLOC are highlighted in current study. It was concluded that Emotional Intelligence (EI) and health locus of control (IHLOC, and

Machine Learning and Radiology

Science.gov (United States)

Wang, Shijun; Summers, Ronald M.

2012-01-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

IVHM Framework for Intelligent Integration for Vehicle Health Management

Science.gov (United States)

Paris, Deidre; Trevino, Luis C.; Watson, Michael D.

2005-01-01

Integrated Vehicle Health Management (IVHM) systems for aerospace vehicles, is the process of assessing, preserving, and restoring system functionality across flight and techniques with sensor and communication technologies for spacecraft that can generate responses through detection, diagnosis, reasoning, and adapt to system faults in support of Integrated Intelligent Vehicle Management (IIVM). These real-time responses allow the IIVM to modify the affected vehicle subsystem(s) prior to a catastrophic event. Furthermore, this framework integrates technologies which can provide a continuous, intelligent, and adaptive health state of a vehicle and use this information to improve safety and reduce costs of operations. Recent investments in avionics, health management, and controls have been directed towards IIVM. As this concept has matured, it has become clear that IIVM requires the same sensors and processing capabilities as the real-time avionics functions to support diagnosis of subsystem problems. New sensors have been proposed, in addition to augment the avionics sensors to support better system monitoring and diagnostics. As the designs have been considered, a synergy has been realized where the real-time avionics can utilize sensors proposed for diagnostics and prognostics to make better real-time decisions in response to detected failures. IIVM provides for a single system allowing modularity of functions and hardware across the vehicle. The framework that supports IIVM consists of 11 major on-board functions necessary to fully manage a space vehicle maintaining crew safety and mission objectives. These systems include the following: Guidance and Navigation; Communications and Tracking; Vehicle Monitoring; Information Transport and Integration; Vehicle Diagnostics; Vehicle Prognostics; Vehicle Mission Planning, Automated Repair and Replacement; Vehicle Control; Human Computer Interface; and Onboard Verification and Validation. Furthermore, the presented

Integrated Systems Health Management for Intelligent Systems

Science.gov (United States)

Figueroa, Fernando; Melcher, Kevin

2011-01-01

The implementation of an integrated system health management (ISHM) capability is fundamentally linked to the management of data, information, and knowledge (DIaK) with the purposeful objective of determining the health of a system. It is akin to having a team of experts who are all individually and collectively observing and analyzing a complex system, and communicating effectively with each other in order to arrive at an accurate and reliable assessment of its health. In this paper, concepts, procedures, and approaches are presented as a foundation for implementing an intelligent systems ]relevant ISHM capability. The capability stresses integration of DIaK from all elements of a system. Both ground-based (remote) and on-board ISHM capabilities are compared and contrasted. The information presented is the result of many years of research, development, and maturation of technologies, and of prototype implementations in operational systems.

Application of Machine Learning to Rotorcraft Health Monitoring

Science.gov (United States)

Cody, Tyler; Dempsey, Paula J.

2017-01-01

Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

Role of artificial intelligence in the care of patients with nonsmall cell lung cancer.

Science.gov (United States)

Rabbani, Mohamad; Kanevsky, Jonathan; Kafi, Kamran; Chandelier, Florent; Giles, Francis J

2018-04-01

Lung cancer is the leading cause of cancer death worldwide. In up to 57% of patients, it is diagnosed at an advanced stage and the 5-year survival rate ranges between 10%-16%. There has been a significant amount of research using machine learning to generate tools using patient data to improve outcomes. This narrative review is based on research material obtained from PubMed up to Nov 2017. The search terms include "artificial intelligence," "machine learning," "lung cancer," "Nonsmall Cell Lung Cancer (NSCLC)," "diagnosis" and "treatment." Recent studies support the use of computer-aided systems and the use of radiomic features to help diagnose lung cancer earlier. Other studies have looked at machine learning (ML) methods that offer prognostic tools to doctors and help them in choosing personalized treatment options for their patients based on molecular, genetics and histological features. Combining artificial intelligence approaches into health care may serve as a beneficial tool for patients with NSCLC, and this review outlines these benefits and current shortcomings throughout the continuum of care. We present a review of the various applications of ML methods in NSCLC as it relates to improving diagnosis, treatment and outcomes. © 2018 Stichting European Society for Clinical Investigation Journal Foundation.

Automation of Knowledge Work in Medicine and Health care: Future and Challenges

Directory of Open Access Journals (Sweden)

Farzan Majidfar

2017-07-01

Full Text Available Increment of computing speed, machine learning and human interface, have extended capabilities of artificial intelligence applications to an important stage. It is predicted that use of artificial intelligence (AI to automate knowledge-based occupations (occupations such as medicine, engineering and law may have an global enormous economic impact in the near future.Applications based on artificial intelligence are able to improve health and quality of life for millions in the coming years. Although clinical applications of computer science are slow moving to real-world labs, but there are promising signs that the pace of innovation will improve. In the near future AI based applications by automating knowledge-based work in the field of diagnosis and treatment, nursing and health care, robotic surgery and development of new drugs, will have a transformative effect on the health sector. Therefore many artificial intelligence systems should work closely with health providers and patients to gain their trust. The progress of how smart machines naturally will interact with healthcare professionals, patients and patients' families is very important, yet challenging.In this article, we review the future of  automation of knowledge enabled by AI work in medicine and healthcare in  seven categories including big medical data mining, computer Aided Diagnosis, online consultations, evidence based medicine, health assistance, precision medicine and drug creation. Also challenges of this issue including cultural, organizational, legal and social barriers are described.

Machine and Woodworking Tool Safety. Module SH-24. Safety and Health.

Science.gov (United States)

Center for Occupational Research and Development, Inc., Waco, TX.

This student module on machine and woodworking tool safety is one of 50 modules concerned with job safety and health. This module discusses specific practices and precautions concerned with the efficient operation and use of most machine and woodworking tools in use today. Following the introduction, 13 objectives (each keyed to a page in the…

Using Machine Learning for Land Suitability Classification

African Journals Online (AJOL)

User

West African Journal of Applied Ecology, vol. ... evidence for the utility of machine learning methods in land suitability classification especially MCS methods. ... Artificial intelligence tools. ..... Numerical values of index for the various classes.

Pathways of evolution for man and machine

Science.gov (United States)

Mclaughlin, W. I.

1983-01-01

A simplified model of the future is introduced in order to examine the relations between man, machine, and extraterrestrial intelligence. The observed lack of extraterrestrials on earth is used as a boundary condition for the model. Two exobiological conclusions are obtained: (1) advanced extraterrestrials exist but are difficult to observe or to distinguish from natural phenomena, (2) lower level, man-like extraterrestrials, which are potentially observable, are rare in the Universe because they are rapidly replaced in the evolutinary sequence by the intelligent machines which they create. The decline of man is inferred and may take place as soon as 100 years from now or as long as 100,000 years from the present time. Thus, we are a short-lived species, having a life expectancy of only a million years. A note is appended on the rationale and methods of the search for extraterrestrial intelligence in light of the above calculations. The Tau Ceti and Epsilon Eridani systems present favourable opportunities for a radio-search experiment in 1987 and 1988, respectively.

Intelligence artificielle, linguistique et cognition

OpenAIRE

Sabah, Gérard

2017-01-01

Après un historique rapide retraçant l’évolution des façons de concevoir le traitement automatique des langues dans la perspective « cognitiviste » de l’intelligence artificielle, nous évoquerons les divers types de connaissances considérées en informatique linguistique. L’article présentera ensuite les limites de l’intelligence artificielle actuelle, et analysera les raisons de ces limites. Après avoir souligné l’importance de la langue tant dans la communication homme-machine que pour le dé...

Intelligent Control of UPFC for Enhancing Transient Stability on Multi-Machine Power Systems

Directory of Open Access Journals (Sweden)

Hassan Barati

2010-01-01

Full Text Available One of the benefit of FACTS devices is increase of stability in power systems with control active and reactive power at during the fault in power system. Although, the power system stabilizers (PSSs have been one of the most common controls used to damp out oscillations, this device may not produce enough damping especially to inter-area mode and therefore, there is an increasing interest in using FACTS devices to aid in damping of these oscillations. In This paper, UPFC is used for damping oscillations and to enhance the transient stability performance of power systems. The controller parameters are designed using an efficient version of the Takagi-Sugeno fuzzy control scheme. The function based Takagi-Sugeno-Kang (TSK fuzzy controller uses. For optimization parameters of fuzzy PI controller, the GA, PSO and HGAPSO algorithms are used. The computer simulation results, the effect of UPFC with conventional PI controller, fuzzy PI controller and intelligent controllers (GA, PSO and HGAPSO for damping the local-mode and inter-area mode of under large and small disturbances in the four-machine two-area power system evaluated and compared.

Artificial versus Natural Intelligence: An Adendum to the Philosophy ...

African Journals Online (AJOL)

in modern science that is causing waves in the philosophy of mind. Can there be artificial minds? Can machines be made to think? Can machines be conscious? Is it possible for artificial intelligence to replace the human brain? These and similar questions pervade most discussions and philosophical polemics on the issue ...

Exploring an Emotional Intelligence Model With Psychiatric Mental Health Nurses.

Science.gov (United States)

Sims, Traci T

A lack of emotional skills may affect a nurse's personal well-being and have negative effects on patient outcomes. To compare psychiatric-mental health nurses' (PMHN) scores on the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) to a normed population and compare the emotional intelligence (EI) scores of PMHNs using two tools, MSCEIT and Self-Rated Emotional Intelligence Scale (SREIS). Comparative descriptive and correlational study. PMHNs in the study had a higher mean EI compared with that of 5,000 participants in the normed MSCEIT sample. Significant weak correlations were seen between the perceiving and understanding emotion branches of the MSCEIT and SREIS. The current study added data about a sample of PMHN's EI levels in the United States, which may encourage dialog about EI among PMHNs. Future research is needed to examine the relationship between self-report EI tools (e.g., SREIS) and performance tools (e.g., MSCEIT) to determine if they are measuring the same construct.

Automation and robotics technology for intelligent mining systems

Science.gov (United States)

Welsh, Jeffrey H.

1989-01-01

The U.S. Bureau of Mines is approaching the problems of accidents and efficiency in the mining industry through the application of automation and robotics to mining systems. This technology can increase safety by removing workers from hazardous areas of the mines or from performing hazardous tasks. The short-term goal of the Automation and Robotics program is to develop technology that can be implemented in the form of an autonomous mining machine using current continuous mining machine equipment. In the longer term, the goal is to conduct research that will lead to new intelligent mining systems that capitalize on the capabilities of robotics. The Bureau of Mines Automation and Robotics program has been structured to produce the technology required for the short- and long-term goals. The short-term goal of application of automation and robotics to an existing mining machine, resulting in autonomous operation, is expected to be accomplished within five years. Key technology elements required for an autonomous continuous mining machine are well underway and include machine navigation systems, coal-rock interface detectors, machine condition monitoring, and intelligent computer systems. The Bureau of Mines program is described, including status of key technology elements for an autonomous continuous mining machine, the program schedule, and future work. Although the program is directed toward underground mining, much of the technology being developed may have applications for space systems or mining on the Moon or other planets.

Do Executive Function and Impulsivity Predict Adolescent Health Behaviour after Accounting for Intelligence? Findings from the ALSPAC Cohort.

Science.gov (United States)

Stautz, Kaidy; Pechey, Rachel; Couturier, Dominique-Laurent; Deary, Ian J; Marteau, Theresa M

2016-01-01

Executive function, impulsivity, and intelligence are correlated markers of cognitive resource that predict health-related behaviours. It is unknown whether executive function and impulsivity are unique predictors of these behaviours after accounting for intelligence. Data from 6069 participants from the Avon Longitudinal Study of Parents and Children were analysed to investigate whether components of executive function (selective attention, attentional control, working memory, and response inhibition) and impulsivity (parent-rated) measured between ages 8 and 10, predicted having ever drunk alcohol, having ever smoked, fruit and vegetable consumption, physical activity, and overweight at age 13, after accounting for intelligence at age 8 and childhood socioeconomic characteristics. Higher intelligence predicted having drunk alcohol, not smoking, greater fruit and vegetable consumption, and not being overweight. After accounting for intelligence, impulsivity predicted alcohol use (odds ratio = 1.10; 99% confidence interval = 1.02, 1.19) and smoking (1.22; 1.11, 1.34). Working memory predicted not being overweight (0.90; 0.81, 0.99). After accounting for intelligence, executive function predicts overweight status but not health-related behaviours in early adolescence, whilst impulsivity predicts the onset of alcohol and cigarette use, all with small effects. This suggests overlap between executive function and intelligence as predictors of health behaviour in this cohort, with trait impulsivity accounting for additional variance.

Soft computing in intelligent control

CERN Document Server

Jung, Jin-Woo; Kubota, Naoyuki

2014-01-01

Nowadays, people have tendency to be fond of smarter machines that are able to collect data, make learning, recognize things, infer meanings, communicate with human and perform behaviors. Thus, we have built advanced intelligent control affecting all around societies; automotive, rail, aerospace, defense, energy, healthcare, telecoms and consumer electronics, finance, urbanization. Consequently, users and consumers can take new experiences through the intelligent control systems. We can reshape the technology world and provide new opportunities for industry and business, by offering cost-effective, sustainable and innovative business models. We will have to know how to create our own digital life. The intelligent control systems enable people to make complex applications, to implement system integration and to meet society’s demand for safety and security. This book aims at presenting the research results and solutions of applications in relevance with intelligent control systems. We propose to researchers ...

Intelligent Wireless Sensor Networks for System Health Monitoring

Science.gov (United States)

Alena, Rick

2011-01-01

Wireless sensor networks (WSN) based on the IEEE 802.15.4 Personal Area Network (PAN) standard are finding increasing use in the home automation and emerging smart energy markets. The network and application layers, based on the ZigBee 2007 Standard, provide a convenient framework for component-based software that supports customer solutions from multiple vendors. WSNs provide the inherent fault tolerance required for aerospace applications. The Discovery and Systems Health Group at NASA Ames Research Center has been developing WSN technology for use aboard aircraft and spacecraft for System Health Monitoring of structures and life support systems using funding from the NASA Engineering and Safety Center and Exploration Technology Development and Demonstration Program. This technology provides key advantages for low-power, low-cost ancillary sensing systems particularly across pressure interfaces and in areas where it is difficult to run wires. Intelligence for sensor networks could be defined as the capability of forming dynamic sensor networks, allowing high-level application software to identify and address any sensor that joined the network without the use of any centralized database defining the sensors characteristics. The IEEE 1451 Standard defines methods for the management of intelligent sensor systems and the IEEE 1451.4 section defines Transducer Electronic Datasheets (TEDS), which contain key information regarding the sensor characteristics such as name, description, serial number, calibration information and user information such as location within a vehicle. By locating the TEDS information on the wireless sensor itself and enabling access to this information base from the application software, the application can identify the sensor unambiguously and interpret and present the sensor data stream without reference to any other information. The application software is able to read the status of each sensor module, responding in real-time to changes of

The 1990 Goddard Conference on Space Applications of Artificial Intelligence

Science.gov (United States)

Rash, James L. (Editor)

1990-01-01

The papers presented at the 1990 Goddard Conference on Space Applications of Artificial Intelligence are given. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The proceedings fall into the following areas: Planning and Scheduling, Fault Monitoring/Diagnosis, Image Processing and Machine Vision, Robotics/Intelligent Control, Development Methodologies, Information Management, and Knowledge Acquisition.

Artificial Intelligence and Urbanization: The Rise of the Elysium City

OpenAIRE

MUNOZ, J. Mark; NAQVI, Al

2017-01-01

Abstract. From ancient times, Greek religion introduced Elysium as a heavenly place to which admission was exclusively reserved for mortals related to gods, heroes, and those blessed by gods. We argue that the rise of artificial intelligence technology will lead to the creation of Elysium cities. Elysium cities agents will be technologists, technocrats, intelligent machines, and wealthy capitalists. These cities will be the first embracers of the artificial intelligence technology and will do...

International Conference on Frontiers of Intelligent Computing : Theory and Applications

CERN Document Server

Udgata, Siba; Biswal, Bhabendra

2014-01-01

This volume contains the papers presented at the Second International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA-2013) held during 14-16 November 2013 organized by Bhubaneswar Engineering College (BEC), Bhubaneswar, Odisha, India. It contains 63 papers focusing on application of intelligent techniques which includes evolutionary computation techniques like genetic algorithm, particle swarm optimization techniques, teaching-learning based optimization etc  for various engineering applications such as data mining, Fuzzy systems, Machine Intelligence and ANN, Web technologies and Multimedia applications and Intelligent computing and Networking etc.

16th International Conference on Intelligent Systems Design and Applications

CERN Document Server

Abraham, Ajith; Gamboa, Dorabela; Novais, Paulo

2017-01-01

This book comprises selected papers from the 16th International Conference on Intelligent Systems Design and Applications (ISDA’16), which was held in Porto, Portugal from December 1 to16, 2016. ISDA 2016 was jointly organized by the Portugual-based Instituto Superior de Engenharia do Porto and the US-based Machine Intelligence Research Labs (MIR Labs) to serve as a forum for the dissemination of state-of-the-art research and development of intelligent systems, intelligent technologies, and applications. The papers included address a wide variety of themes ranging from theories to applications of intelligent systems and computational intelligence area and provide a valuable resource for students and researchers in academia and industry alike. .

Genesis machines

CERN Document Server

Amos, Martyn

2014-01-01

Silicon chips are out. Today's scientists are using real, wet, squishy, living biology to build the next generation of computers. Cells, gels and DNA strands are the 'wetware' of the twenty-first century. Much smaller and more intelligent, these organic computers open up revolutionary possibilities. Tracing the history of computing and revealing a brave new world to come, Genesis Machines describes how this new technology will change the way we think not just about computers - but about life itself.

Neuroscience-Inspired Artificial Intelligence.

Science.gov (United States)

Hassabis, Demis; Kumaran, Dharshan; Summerfield, Christopher; Botvinick, Matthew

2017-07-19

The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals. We conclude by highlighting shared themes that may be key for advancing future research in both fields. Copyright © 2017. Published by Elsevier Inc.

2nd International Conference on Robot Intelligence Technology and Applications

CERN Document Server

Matson, Eric; Myung, Hyun; Xu, Peter; Karray, Fakhri

2014-01-01

We are facing a new technological challenge on how to store and retrieve knowledge and manipulate intelligence for autonomous services by intelligent systems which should be capable of carrying out real world tasks autonomously. To address this issue, robot researchers have been developing intelligence technology (InT) for “robots that think” which is in the focus of this book. The book covers all aspects of intelligence from perception at sensor level and reasoning at cognitive level to behavior planning at execution level for each low level segment of the machine. It also presents the technologies for cognitive reasoning, social interaction with humans, behavior generation, ability to cooperate with other robots, ambience awareness, and an artificial genome that can be passed on to other robots. These technologies are to materialize cognitive intelligence, social intelligence, behavioral intelligence, collective intelligence, ambient intelligence and genetic intelligence. The book aims at serving resear...

Eighth International Conference on Intelligent Systems and Knowledge Engineering

CERN Document Server

Li, Tianrui; ISKE 2013; Foundations of Intelligent Systems; Knowledge Engineering and Management; Practical Applications of Intelligent Systems

2014-01-01

"Foundations of Intelligent Systems" presents selected papers from the 2013 International Conference on Intelligent Systems and Knowledge Engineering (ISKE2013). The aim of this conference is to bring together experts from different expertise areas to discuss the state-of-the-art in Intelligent Systems and Knowledge Engineering, and to present new research results and perspectives on future development. The topics in this volume include, but not limited to: Artificial Intelligence Theories, Pattern Recognition, Intelligent System Models, Speech Recognition, Computer Vision, Multi-Agent Systems, Machine Learning, Soft Computing and Fuzzy Systems, Biological Inspired Computation, Game Theory, Cognitive Systems and Information Processing, Computational Intelligence, etc. The proceedings are benefit for both researchers and practitioners who want to utilize intelligent methods in their specific research fields. Dr. Zhenkun Wen is a Professor at the College of Computer and Software Engineering, Shenzhen University...

A fuzzy logic intelligent diagnostic system for spacecraft integrated vehicle health management

Science.gov (United States)

Wu, G. Gordon

1995-01-01

Due to the complexity of future space missions and the large amount of data involved, greater autonomy in data processing is demanded for mission operations, training, and vehicle health management. In this paper, we develop a fuzzy logic intelligent diagnostic system to perform data reduction, data analysis, and fault diagnosis for spacecraft vehicle health management applications. The diagnostic system contains a data filter and an inference engine. The data filter is designed to intelligently select only the necessary data for analysis, while the inference engine is designed for failure detection, warning, and decision on corrective actions using fuzzy logic synthesis. Due to its adaptive nature and on-line learning ability, the diagnostic system is capable of dealing with environmental noise, uncertainties, conflict information, and sensor faults.

Do we develop public health leaders?- association between public health competencies and emotional intelligence: a cross-sectional study.

Science.gov (United States)

Czabanowska, Katarzyna; Malho, André; Schröder-Bäck, Peter; Popa, Daniela; Burazeri, Genc

2014-04-17

Professional development of public health leaders requires a form of instruction which is competency-based to help them develop the abilities to address complex and evolving demands of health care systems. Concurrently, emotional intelligence (EI) is a key to organisational success. Our aim was twofold: i) to assess the relationship between the level of self-assessed public health and EI competencies among Master of European Public Health (MEPH) students and graduates at Maastricht University, and; ii) to determine the relationship between different groups of public health competencies and specific EI skills. A cross-sectional study was conducted including all recent MEPH graduates and students from 2009-2012, out of 67 eligible candidates N = 51 were contacted and N = 33 responded (11 males and 22 females; overall response: 64.7%).Two validated tools were employed: i) public health competencies self-assessment questionnaire, and; ii) Assessing Emotions Scale. Females scored higher than males in all seven domains of the self-assessed key public health competencies (NS) and emotional intelligence competences (P = 0.022). Overall, the mean value of public health competencies was the lowest in students with "staff" preferences and the highest among students with mixed job preferences (P leadership.

Towards a Future Reallocation of Work between Humans and Machines – Taxonomy of Tasks and Interaction Types in the Context of Machine Learning

OpenAIRE

Traumer, Fabian; Oeste-Reiß, Sarah; Leimeister, Jan Marco

2017-01-01

In today’s race for competitive advantages, more and more companies implement innovations in artificial intelligence and machine learning (ML). Although these machines take over tasks that have been executed by humans, they will not make human workforce obsolete. To leverage the potentials of ML, collaboration between humans and machines is necessary. Before collaboration processes can be developed, a classification of tasks in the field of ML is needed. Therefore, we present a taxonomy for t...

Artificial intelligence applications in information and communication technologies

CERN Document Server

Bouguila, Nizar

2015-01-01

This book presents various recent applications of Artificial Intelligence in Information and Communication Technologies such as Search and Optimization methods, Machine Learning, Data Representation and Ontologies, and Multi-agent Systems. The main aim of this book is to help Information and Communication Technologies (ICT) practitioners in managing efficiently their platforms using AI tools and methods and to provide them with sufficient Artificial Intelligence background to deal with real-life problems.  .

Snap-drift neural computing for intelligent diagnostic feedback

OpenAIRE

Habte, Samson

2017-01-01

Information and communication technologies have been playing a crucial role in improving the efficiency and effectiveness of learning and teaching in higher education. Two decades ago, research studies were focused on how to use artificial intelligence techniques to imitate teachers or tutors in delivering learning sessions. Machine learning techniques have been applied in several research studies to construct a student model in the context of intelligent tutoring systems. However, the usage ...

Artificial Intelligence to Assist Clinical Diagnosis in Medicine

Directory of Open Access Journals (Sweden)

Saúl Oswaldo Lugo-Reyes

2014-03-01

Full Text Available Medicine is one of the elds of knowledge that would most bene t from a closer interaction with Computer studies and Mathematics by optimizing complex, imperfect processes such as differential diagnosis; this is the domain of Machine Learning, a branch of Arti cial Intelligence that builds and studies systems capable of learning from a set of training data, in order to optimize classi cation and prediction processes. In Mexico during the last few years, progress has been made on the implementation of electronic clinical records, so that the National Institutes of Health already have accumulated a wealth of stored data. For those data to become knowledge, they need to be processed and analyzed through complex statistical methods, as it is already being done in other countries, employing: case-based reasoning, artificial neural networks, Bayesian classi ers, multivariate logistic regression, or support vector machines, among other methodologies; to assist the clinical diagnosis of acute appendicitis, breast cancer and chronic liver disease, among a wide array of maladies. In this review we sift through concepts, antecedents, current examples and methodologies of machine learning-assisted clinical diagnosis.

Do Executive Function and Impulsivity Predict Adolescent Health Behaviour after Accounting for Intelligence? Findings from the ALSPAC Cohort.

Directory of Open Access Journals (Sweden)

Kaidy Stautz

Full Text Available Executive function, impulsivity, and intelligence are correlated markers of cognitive resource that predict health-related behaviours. It is unknown whether executive function and impulsivity are unique predictors of these behaviours after accounting for intelligence.Data from 6069 participants from the Avon Longitudinal Study of Parents and Children were analysed to investigate whether components of executive function (selective attention, attentional control, working memory, and response inhibition and impulsivity (parent-rated measured between ages 8 and 10, predicted having ever drunk alcohol, having ever smoked, fruit and vegetable consumption, physical activity, and overweight at age 13, after accounting for intelligence at age 8 and childhood socioeconomic characteristics.Higher intelligence predicted having drunk alcohol, not smoking, greater fruit and vegetable consumption, and not being overweight. After accounting for intelligence, impulsivity predicted alcohol use (odds ratio = 1.10; 99% confidence interval = 1.02, 1.19 and smoking (1.22; 1.11, 1.34. Working memory predicted not being overweight (0.90; 0.81, 0.99.After accounting for intelligence, executive function predicts overweight status but not health-related behaviours in early adolescence, whilst impulsivity predicts the onset of alcohol and cigarette use, all with small effects. This suggests overlap between executive function and intelligence as predictors of health behaviour in this cohort, with trait impulsivity accounting for additional variance.

Machine learning and radiology.

Science.gov (United States)

Wang, Shijun; Summers, Ronald M

2012-07-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.

A computational proof of concept of a machine-intelligent artificial pancreas using Lyapunov stability and differential game theory.

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Greenwood, Nigel J C; Gunton, Jenny E

2014-07-01

This study demonstrated the novel application of a "machine-intelligent" mathematical structure, combining differential game theory and Lyapunov-based control theory, to the artificial pancreas to handle dynamic uncertainties. Realistic type 1 diabetes (T1D) models from the literature were combined into a composite system. Using a mixture of "black box" simulations and actual data from diabetic medical histories, realistic sets of diabetic time series were constructed for blood glucose (BG), interstitial fluid glucose, infused insulin, meal estimates, and sometimes plasma insulin assays. The problem of underdetermined parameters was side stepped by applying a variant of a genetic algorithm to partial information, whereby multiple candidate-personalized models were constructed and then rigorously tested using further data. These formed a "dynamic envelope" of trajectories in state space, where each trajectory was generated by a hypothesis on the hidden T1D system dynamics. This dynamic envelope was then culled to a reduced form to cover observed dynamic behavior. A machine-intelligent autonomous algorithm then implemented game theory to construct real-time insulin infusion strategies, based on the flow of these trajectories through state space and their interactions with hypoglycemic or near-hyperglycemic states. This technique was tested on 2 simulated participants over a total of fifty-five 24-hour days, with no hypoglycemic or hyperglycemic events, despite significant uncertainties from using actual diabetic meal histories with 10-minute warnings. In the main case studies, BG was steered within the desired target set for 99.8% of a 16-hour daily assessment period. Tests confirmed algorithm robustness for ±25% carbohydrate error. For over 99% of the overall 55-day simulation period, either formal controller stability was achieved to the desired target or else the trajectory was within the desired target. These results suggest that this is a stable, high

16th UK Workshop on Computational Intelligence

CERN Document Server

Gegov, Alexander; Jayne, Chrisina; Shen, Qiang

2017-01-01

The book is a timely report on advanced methods and applications of computational intelligence systems. It covers a long list of interconnected research areas, such as fuzzy systems, neural networks, evolutionary computation, evolving systems and machine learning. The individual chapters are based on peer-reviewed contributions presented at the 16th Annual UK Workshop on Computational Intelligence, held on September 7-9, 2016, in Lancaster, UK. The book puts a special emphasis on novels methods and reports on their use in a wide range of applications areas, thus providing both academics and professionals with a comprehensive and timely overview of new trends in computational intelligence.

Health-promoting vending machines: evaluation of a pediatric hospital intervention.

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Van Hulst, Andraea; Barnett, Tracie A; Déry, Véronique; Côté, Geneviève; Colin, Christine

2013-01-01

Taking advantage of a natural experiment made possible by the placement of health-promoting vending machines (HPVMs), we evaluated the impact of the intervention on consumers' attitudes toward and practices with vending machines in a pediatric hospital. Vending machines offering healthy snacks, meals, and beverages were developed to replace four vending machines offering the usual high-energy, low-nutrition fare. A pre- and post-intervention evaluation design was used; data were collected through exit surveys and six-week follow-up telephone surveys among potential vending machine users before (n=293) and after (n=226) placement of HPVMs. Chi-2 statistics were used to compare pre- and post-intervention participants' responses. More than 90% of pre- and post-intervention participants were satisfied with their purchase. Post-intervention participants were more likely to state that nutritional content and appropriateness of portion size were elements that influenced their purchase. Overall, post-intervention participants were more likely than pre-intervention participants to perceive as healthy the options offered by the hospital vending machines. Thirty-three percent of post-intervention participants recalled two or more sources of information integrated in the HPVM concept. No differences were found between pre- and post-intervention participants' readiness to adopt healthy diets. While the HPVM project had challenges as well as strengths, vending machines offering healthy snacks are feasible in hospital settings.

The effect of age on fluid intelligence is fully mediated by physical health.

Science.gov (United States)

Bergman, Ingvar; Almkvist, Ove

2013-01-01

The present study investigated the extent to which the effect of age on cognitive ability is predicted by individual differences in physical health. The sample consisted of 118 volunteer subjects who were healthy and ranging in age from 26 to 91. The examinations included a clinical investigation, magnetic resonance imaging (MRI) brain neuroimaging, and a comprehensive neuropsychological assessment. The effect of age on fluid IQ with and without visual spatial praxis and on crystallized IQ was tested whether being fully-, partially- or non-mediated by physical health. Structural equation analyses showed that the best and most parsimonious fit to the data was provided by models that were fully mediated for fluid IQ without praxis, non-mediated for crystallized IQ and partially mediated for fluid IQ with praxis. The diseases of the circulatory and nervous systems were the major mediators. It was concluded from the pattern of findings that the effect of age on fluid intelligence is fully mediated by physical health, while crystallized intelligence is non-mediated and visual spatial praxis is partially mediated, influenced mainly by direct effects of age. Our findings imply that improving health by acting against the common age-related circulatory- and nervous system diseases and risk factors will oppose the decline in fluid intelligence with age. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

1st International Conference on Intelligent Computing, Communication and Devices

CERN Document Server

Patnaik, Srikanta; Ichalkaranje, Nikhil

2015-01-01

In the history of mankind, three revolutions which impact the human life are the tool-making revolution, agricultural revolution and industrial revolution. They have transformed not only the economy and civilization but the overall development of the society. Probably, intelligence revolution is the next revolution, which the society will perceive in the next 10 years. ICCD-2014 covers all dimensions of intelligent sciences, i.e. Intelligent Computing, Intelligent Communication and Intelligent Devices. This volume covers contributions from Intelligent Communication which are from the areas such as Communications and Wireless Ad Hoc & Sensor Networks, Speech & Natural Language Processing, including Signal, Image and Video Processing and Mobile broadband and Optical networks, which are the key to the ground-breaking inventions to intelligent communication technologies. Secondly, Intelligent Device is any type of equipment, instrument, or machine that has its own computing capability. Contributions from ...

Students' perspectives on promoting healthful food choices from campus vending machines: a qualitative interview study.

Science.gov (United States)

Ali, Habiba I; Jarrar, Amjad H; Abo-El-Enen, Mostafa; Al Shamsi, Mariam; Al Ashqar, Huda

2015-05-28

Increasing the healthfulness of campus food environments is an important step in promoting healthful food choices among college students. This study explored university students' suggestions on promoting healthful food choices from campus vending machines. It also examined factors influencing students' food choices from vending machines. Peer-led semi-structured individual interviews were conducted with 43 undergraduate students (33 females and 10 males) recruited from students enrolled in an introductory nutrition course in a large national university in the United Arab Emirates. Interviews were audiotaped, transcribed, and coded to generate themes using N-Vivo software. Accessibility, peer influence, and busy schedules were the main factors influencing students' food choices from campus vending machines. Participants expressed the need to improve the nutritional quality of the food items sold in the campus vending machines. Recommendations for students' nutrition educational activities included placing nutrition tips on or beside the vending machines and using active learning methods, such as competitions on nutrition knowledge. The results of this study have useful applications in improving the campus food environment and nutrition education opportunities at the university to assist students in making healthful food choices.

4th International Conference on Robot Intelligence Technology and Applications

CERN Document Server

Karray, Fakhri; Jo, Jun; Sincak, Peter; Myung, Hyun

2017-01-01

This book covers all aspects of robot intelligence from perception at sensor level and reasoning at cognitive level to behavior planning at execution level for each low level segment of the machine. It also presents the technologies for cognitive reasoning, social interaction with humans, behavior generation, ability to cooperate with other robots, ambience awareness, and an artificial genome that can be passed on to other robots. These technologies are to materialize cognitive intelligence, social intelligence, behavioral intelligence, collective intelligence, ambient intelligence and genetic intelligence. The book aims at serving researchers and practitioners with a timely dissemination of the recent progress on robot intelligence technology and its applications, based on a collection of papers presented at the 4th International Conference on Robot Intelligence Technology and Applications (RiTA), held in Bucheon, Korea, December 14 - 16, 2015. For better readability, this edition has the total of 49 article...

3rd International Conference on Robot Intelligence Technology and Applications

CERN Document Server

Yang, Weimin; Jo, Jun; Sincak, Peter; Myung, Hyun

2015-01-01

This book covers all aspects of robot intelligence from perception at sensor level and reasoning at cognitive level to behavior planning at execution level for each low level segment of the machine. It also presents the technologies for cognitive reasoning, social interaction with humans, behavior generation, ability to cooperate with other robots, ambience awareness, and an artificial genome that can be passed on to other robots. These technologies are to materialize cognitive intelligence, social intelligence, behavioral intelligence, collective intelligence, ambient intelligence and genetic intelligence. The book aims at serving researchers and practitioners with a timely dissemination of the recent progress on robot intelligence technology and its applications, based on a collection of papers presented at the 3rd International Conference on Robot Intelligence Technology and Applications (RiTA), held in Beijing, China, November 6 - 8, 2014. For better readability, this edition has the total 74 papers group...

Magic in the machine: a computational magician's assistant

OpenAIRE

Williams, Howard; McOwan, Peter W.

2014-01-01

A human magician blends science, psychology, and performance to create a magical effect. In this paper we explore what can be achieved when that human intelligence is replaced or assisted by machine intelligence. Magical effects are all in some form based on hidden mathematical, scientific, or psychological principles; often the parameters controlling these underpinning techniques are hard for a magician to blend to maximize the magical effect required. The complexity is often caused by inter...

Magic in the machine: a computational magician's assistant

OpenAIRE

Howard eWilliams; Peter eMcOwan

2014-01-01

A human magician blends science, psychology and performance to create a magical effect. In this paper we explore what can be achieved when that human intelligence is replaced or assisted by machine intelligence. Magical effects are all in some form based on hidden mathematical, scientific or psychological principles; often the parameters controlling these underpinning techniques are hard for a magician to blend to maximise the magical effect required. The complexity is often caused by interac...

Optimizing Distributed Machine Learning for Large Scale EEG Data Set

Directory of Open Access Journals (Sweden)

M Bilal Shaikh

2017-06-01

Full Text Available Distributed Machine Learning (DML has gained its importance more than ever in this era of Big Data. There are a lot of challenges to scale machine learning techniques on distributed platforms. When it comes to scalability, improving the processor technology for high level computation of data is at its limit, however increasing machine nodes and distributing data along with computation looks as a viable solution. Different frameworks   and platforms are available to solve DML problems. These platforms provide automated random data distribution of datasets which miss the power of user defined intelligent data partitioning based on domain knowledge. We have conducted an empirical study which uses an EEG Data Set collected through P300 Speller component of an ERP (Event Related Potential which is widely used in BCI problems; it helps in translating the intention of subject w h i l e performing any cognitive task. EEG data contains noise due to waves generated by other activities in the brain which contaminates true P300Speller. Use of Machine Learning techniques could help in detecting errors made by P300 Speller. We are solving this classification problem by partitioning data into different chunks and preparing distributed models using Elastic CV Classifier. To present a case of optimizing distributed machine learning, we propose an intelligent user defined data partitioning approach that could impact on the accuracy of distributed machine learners on average. Our results show better average AUC as compared to average AUC obtained after applying random data partitioning which gives no control to user over data partitioning. It improves the average accuracy of distributed learner due to the domain specific intelligent partitioning by the user. Our customized approach achieves 0.66 AUC on individual sessions and 0.75 AUC on mixed sessions, whereas random / uncontrolled data distribution records 0.63 AUC.

[Artificial intelligence in psychiatry-an overview].

Science.gov (United States)

Meyer-Lindenberg, A

2018-06-18

Artificial intelligence and the underlying methods of machine learning and neuronal networks (NN) have made dramatic progress in recent years and have allowed computers to reach superhuman performance in domains that used to be thought of as uniquely human. In this overview, the underlying methodological developments that made this possible are briefly delineated and then the applications to psychiatry in three domains are discussed: precision medicine and biomarkers, natural language processing and artificial intelligence-based psychotherapeutic interventions. In conclusion, some of the risks of this new technology are mentioned.

[INVITED] Computational intelligence for smart laser materials processing

Science.gov (United States)

Casalino, Giuseppe

2018-03-01

Computational intelligence (CI) involves using a computer algorithm to capture hidden knowledge from data and to use them for training ;intelligent machine; to make complex decisions without human intervention. As simulation is becoming more prevalent from design and planning to manufacturing and operations, laser material processing can also benefit from computer generating knowledge through soft computing. This work is a review of the state-of-the-art on the methodology and applications of CI in laser materials processing (LMP), which is nowadays receiving increasing interest from world class manufacturers and 4.0 industry. The focus is on the methods that have been proven effective and robust in solving several problems in welding, cutting, drilling, surface treating and additive manufacturing using the laser beam. After a basic description of the most common computational intelligences employed in manufacturing, four sections, namely, laser joining, machining, surface, and additive covered the most recent applications in the already extensive literature regarding the CI in LMP. Eventually, emerging trends and future challenges were identified and discussed.

A Rather Intelligent Language Teacher.

Science.gov (United States)

Cerri, Stefano; Breuker, Joost

1981-01-01

Characteristics of DART (Didactic Augmented Recursive Transition), an ATN-based system for writing intelligent computer assisted instruction (ICAI) programs that is available on the PLATO system are described. DART allows writing programs in an ATN dialect, compiling them in machine code for the PLATO system, and executing them as if the original…

Application of Structural Equations Modeling to assess relationship among Emotional Intelligence, General Health and Occupational Accidents

OpenAIRE

MOAMMAD KHANDAN; AMIR KAVOUSI; ALIREZA KOOHPAEI

2015-01-01

ORIGINAL ARTICLEEmotional intelligence (EI) has been subject of significant amounts of literature over the past two decades. However, little has been contributed to how emotional intelligence may be practically applied to enhance both accident prevention program and general health in workplaces. Purpose of this paper is to survey relationship among these variables in working society of Iran in 2014. As well as identify practical approaches to application of emotional intelligence skills to ma...

Predicting asthma exacerbations using artificial intelligence.

Science.gov (United States)

Finkelstein, Joseph; Wood, Jeffrey

2013-01-01

Modern telemonitoring systems identify a serious patient deterioration when it already occurred. It would be much more beneficial if the upcoming clinical deterioration were identified ahead of time even before a patient actually experiences it. The goal of this study was to assess artificial intelligence approaches which potentially can be used in telemonitoring systems for advance prediction of changes in disease severity before they actually occur. The study dataset was based on daily self-reports submitted by 26 adult asthma patients during home telemonitoring consisting of 7001 records. Two classification algorithms were employed for building predictive models: naïve Bayesian classifier and support vector machines. Using a 7-day window, a support vector machine was able to predict asthma exacerbation to occur on the day 8 with the accuracy of 0.80, sensitivity of 0.84 and specificity of 0.80. Our study showed that methods of artificial intelligence have significant potential in developing individualized decision support for chronic disease telemonitoring systems.

Monitoring osseointegration and developing intelligent systems (Conference Presentation)

Science.gov (United States)

Salvino, Liming W.

2017-05-01

Effective monitoring of structural and biological systems is an extremely important research area that enables technology development for future intelligent devices, platforms, and systems. This presentation provides an overview of research efforts funded by the Office of Naval Research (ONR) to establish structural health monitoring (SHM) methodologies in the human domain. Basic science efforts are needed to utilize SHM sensing, data analysis, modeling, and algorithms to obtain the relevant physiological and biological information for human-specific health and performance conditions. This overview of current research efforts is based on the Monitoring Osseointegrated Prosthesis (MOIP) program. MOIP develops implantable and intelligent prosthetics that are directly anchored to the bone of residual limbs. Through real-time monitoring, sensing, and responding to osseointegration of bones and implants as well as interface conditions and environment, our research program aims to obtain individualized actionable information for implant failure identification, load estimation, infection mitigation and treatment, as well as healing assessment. Looking ahead to achieve ultimate goals of SHM, we seek to expand our research areas to cover monitoring human, biological and engineered systems, as well as human-machine interfaces. Examples of such include 1) brainwave monitoring and neurological control, 2) detecting and evaluating brain injuries, 3) monitoring and maximizing human-technological object teaming, and 4) closed-loop setups in which actions can be triggered automatically based on sensors, actuators, and data signatures. Finally, some ongoing and future collaborations across different disciplines for the development of knowledge automation and intelligent systems will be discussed.

Introducing AI into MEMS can lead us to brain-computer interfaces and super-human intelligence

OpenAIRE

Sanders, David

2009-01-01

Last year, I spoke about the progress being made in machine intelligence (Sanders, 2008c) and with sensors and networks of sensors (Sanders, 2008b). Earlier this year (in this journal) I spoke about ambient-intelligence, rapid-prototyping and the role of humans in the factories of the future (Sanders, 2009a). I addressed new applications and technologies such as merging machines with human beings, micro-electromechanics, electro-mechanical systems that can be personalized, smarter than human ...

Intelligent robot trends and predictions for the new millennium

Science.gov (United States)

Hall, Ernest L.; Mundhenk, Terrell N.

1999-08-01

An intelligent robot is a remarkably useful combination of a manipulator, sensors and controls. The current use of these machines in outer space, medicine, hazardous materials, defense applications and industry is being pursued with vigor but little funding. In factory automation such robotics machines can improve productivity, increase product quality and improve competitiveness. The computer and the robot have both been developed during recent times. The intelligent robot combines both technologies and requires a thorough understanding and knowledge of mechatronics. In honor of the new millennium, this paper will present a discussion of futuristic trends and predictions. However, in keeping with technical tradition, a new technique for 'Follow the Leader' will also be presented in the hope of it becoming a new, useful and non-obvious technique.

Connection machine: a computer architecture based on cellular automata

Energy Technology Data Exchange (ETDEWEB)

Hillis, W D

1984-01-01

This paper describes the connection machine, a programmable computer based on cellular automata. The essential idea behind the connection machine is that a regular locally-connected cellular array can be made to behave as if the processing cells are connected into any desired topology. When the topology of the machine is chosen to match the topology of the application program, the result is a fast, powerful computing engine. The connection machine was originally designed to implement knowledge retrieval operations in artificial intelligence programs, but the hardware and the programming techniques are apparently applicable to a much larger class of problems. A machine with 100000 processing cells is currently being constructed. 27 references.

Stress as a mediator between work-family conflict and psychological health among the nursing staff: Moderating role of emotional intelligence.

Science.gov (United States)

Sharma, Jyoti; Dhar, Rajib Lochan; Tyagi, Akansha

2016-05-01

The study examined the extent to which work-family conflicts cause stress among nursing staff and its subsequent impact on their psychological health. It also examined if the emotional intelligence level of the nursing staff acted as a moderator between their level of stress and psychological health. A survey was carried out on 693 nursing staff associated with 33 healthcare institutions in Uttarakhand, India. A hierarchical multiple regression analysis was carried out to understand the relationships shared by independent (work-family conflicts) and dependent (psychological health) constructs with the mediator (stress) as well as the moderator (emotional intelligence). The results revealed that stress acted as a mediator between work-family conflict of the nursing staff and their psychological health. However, their emotional intelligence level acted as a moderator between their stress level and psychological health. To conclude, the crucial roles of emotional intelligence in controlling the impact of stress on psychological health along with the practical as well as theoretical implications are also discussed. Copyright © 2015 Elsevier Inc. All rights reserved.

Visible Machine Learning for Biomedicine.

Science.gov (United States)

Yu, Michael K; Ma, Jianzhu; Fisher, Jasmin; Kreisberg, Jason F; Raphael, Benjamin J; Ideker, Trey

2018-06-14

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Here, we argue for "visible" approaches that guide model structure with experimental biology. Copyright © 2018. Published by Elsevier Inc.

A comprehensive review on intelligent surveillance systems

Directory of Open Access Journals (Sweden)

Sutrisno Warsono Ibrahim

2016-05-01

Full Text Available Intelligent surveillance system (ISS has received growing attention due to the increasing demand on security and safety. ISS is able to automatically analyze image, video, audio or other type of surveillance data without or with limited human intervention. The recent developments in sensor devices, computer vision, and machine learning have an important role in enabling such intelligent system. This paper aims to provide general overview of intelligent surveillance system and discuss some possible sensor modalities and their fusion scenarios such as visible camera (CCTV, infrared camera, thermal camera and radar. This paper also discusses main processing steps in ISS: background-foreground segmentation, object detection and classification, tracking, and behavioral analysis.

An Intelligent System For Arabic Text Categorization

NARCIS (Netherlands)

Syiam, M.M.; Tolba, Mohamed F.; Fayed, Z.T.; Abdel-Wahab, Mohamed S.; Ghoniemy, Said A.; Habib, Mena Badieh

Text Categorization (classification) is the process of classifying documents into a predefined set of categories based on their content. In this paper, an intelligent Arabic text categorization system is presented. Machine learning algorithms are used in this system. Many algorithms for stemming and

Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

Science.gov (United States)

Abbasi, Maryam; El Hanandeh, Ali

2016-10-01

Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg. Copyright © 2016 Elsevier Ltd. All rights reserved.

Machine learning in cardiovascular medicine: are we there yet?

Science.gov (United States)

Shameer, Khader; Johnson, Kipp W; Glicksberg, Benjamin S; Dudley, Joel T; Sengupta, Partho P

2018-01-19

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Artificial intelligence and applications relevant to nuclear industries

International Nuclear Information System (INIS)

Haridasan, G.; Das, Debashis

1987-01-01

Possible areas of application of artificial intelligence systems such as machine vision systems and expert systems are indicated. The work underway in this field at the Bhabha Atomic Research Centre, Bombay is briefly mentioned. (M.G.B.)

BWR shutdown analyzer using artificial intelligence (AI) techniques

International Nuclear Information System (INIS)

Cain, D.G.

1986-01-01

A prototype alarm system for detecting abnormal reactor shutdowns based on artificial intelligence technology is described. The system incorporates knowledge about Boiling Water Reactor (BWR) plant design and component behavior, as well as knowledge required to distinguish normal, abnormal, and ATWS accident conditions. The system was developed using a software tool environment for creating knowledge-based applications on a LISP machine. To facilitate prototype implementation and evaluation, a casual simulation of BWR shutdown sequences was developed and interfaced with the alarm system. An intelligent graphics interface for execution and control is described. System performance considerations and general observations relating to artificial intelligence application to nuclear power plant problems are provided

Public health intelligence and the detection of potential pandemics.

Science.gov (United States)

French, Martin; Mykhalovskiy, Eric

2013-02-01

This article considers contemporary developments in public health intelligence (PHI), especially their focus on health events of pandemic potential. It argues that the sociological study of PHI can yield important insights for the sociology of pandemics. PHI aims to detect health events as (or even before) they unfold. Whilst its apparatuses envelope traditional public health activities, such as epidemiological surveillance, they increasingly extend to non-traditional public health activities such as data-mining in electronically mediated social networks. With a focus on non-traditional PHI activities, the article first situates the study of PHI in relation to the sociology of public health. It then discusses the conceptualisation and actualisation of pandemics, reflecting on how public health professionals and organisations must equip themselves with diverse allies in order to realise the claims they make about pandemic phenomena. Finally, using the analytic tools of actor-network theory, sites for future empirical research that can contribute to the sociology of pandemics are suggested. © 2012 The Authors. Sociology of Health & Illness © 2012 Foundation for the Sociology of Health & Illness/Blackwell Publishing Ltd.

Reinforcement and Systemic Machine Learning for Decision Making

CERN Document Server

Kulkarni, Parag

2012-01-01

Reinforcement and Systemic Machine Learning for Decision Making There are always difficulties in making machines that learn from experience. Complete information is not always available-or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm-creating new learning applications and, ultimately, more intelligent machines. The first book of its kind in this new an

Using Emotional Intelligence and Social Support to Predict Job Performance of Health Educators

Science.gov (United States)

Branscum, Paul; Haider, Taj; Brown, David; Sharma, Manoj

2016-01-01

Background: The theory of emotional intelligence (EI) has been developed to evaluate and highlight the importance of emotional health, especially on job performance. Purpose: No study has examined EI's role on the performance of public health educators; therefore, this study examined the role of EI and social support on the performance of health…

Machine learning methods for clinical forms analysis in mental health.

Science.gov (United States)

Strauss, John; Peguero, Arturo Martinez; Hirst, Graeme

2013-01-01

In preparation for a clinical information system implementation, the Centre for Addiction and Mental Health (CAMH) Clinical Information Transformation project completed multiple preparation steps. An automated process was desired to supplement the onerous task of manual analysis of clinical forms. We used natural language processing (NLP) and machine learning (ML) methods for a series of 266 separate clinical forms. For the investigation, documents were represented by feature vectors. We used four ML algorithms for our examination of the forms: cluster analysis, k-nearest neigh-bours (kNN), decision trees and support vector machines (SVM). Parameters for each algorithm were optimized. SVM had the best performance with a precision of 64.6%. Though we did not find any method sufficiently accurate for practical use, to our knowledge this approach to forms has not been used previously in mental health.

Big Data and Machine Learning in Plastic Surgery: A New Frontier in Surgical Innovation.

Science.gov (United States)

Kanevsky, Jonathan; Corban, Jason; Gaster, Richard; Kanevsky, Ari; Lin, Samuel; Gilardino, Mirko

2016-05-01

Medical decision-making is increasingly based on quantifiable data. From the moment patients come into contact with the health care system, their entire medical history is recorded electronically. Whether a patient is in the operating room or on the hospital ward, technological advancement has facilitated the expedient and reliable measurement of clinically relevant health metrics, all in an effort to guide care and ensure the best possible clinical outcomes. However, as the volume and complexity of biomedical data grow, it becomes challenging to effectively process "big data" using conventional techniques. Physicians and scientists must be prepared to look beyond classic methods of data processing to extract clinically relevant information. The purpose of this article is to introduce the modern plastic surgeon to machine learning and computational interpretation of large data sets. What is machine learning? Machine learning, a subfield of artificial intelligence, can address clinically relevant problems in several domains of plastic surgery, including burn surgery; microsurgery; and craniofacial, peripheral nerve, and aesthetic surgery. This article provides a brief introduction to current research and suggests future projects that will allow plastic surgeons to explore this new frontier of surgical science.

Throwing Down the Visual Intelligence Gauntlet

KAUST Repository

Tan, Cheston

2013-01-01

In recent years, scientific and technological advances have produced artificial systems that have matched or surpassed human capabilities in narrow domains such as face detection and optical character recognition. However, the problem of producing truly intelligent machines still remains far from being solved. In this chapter, we first describe some of these recent advances, and then review one approach to moving beyond these limited successes – the neuromorphic approach of studying and reverse-engineering the networks of neurons in the human brain (specifically, the visual system). Finally, we discuss several possible future directions in the quest for visual intelligence.

Identifying student stuck states in programmingassignments using machine learning

OpenAIRE

Lindell, Johan

2014-01-01

Intelligent tutors are becoming more popular with the increased use of computersand hand held devices in the education sphere. An area of research isinvestigating how machine learning can be used to improve the precision andfeedback of the tutor. This thesis compares machine learning clustering algorithmswith various distance functions in an attempt to cluster together codesnapshots of students solving a programming task. It investigates whethera general non-problem specific implementation of...

Fantastic Journey through Minds and Machines.

Science.gov (United States)

Muir, Michael

Intended for learners with a basic familiarity with the Logo programming language, this manual is designed to introduce them to artificial intelligence and enhance their programming capabilities. Nine chapters discuss the following features of Logo: (1) MAZE.MASTER, a look at robots and how sensors make machines aware of their environment; (2)…

IoT Security Techniques Based on Machine Learning

OpenAIRE

Xiao, Liang; Wan, Xiaoyue; Lu, Xiaozhen; Zhang, Yanyong; Wu, Di

2018-01-01

Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services have to protect user privacy and address attacks such as spoofing attacks, denial of service attacks, jamming and eavesdropping. In this article, we investigate the attack model for IoT systems, and review the IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning and reinforcement learning. We focus on the machine le...

International Conference on Intelligent and Interactive Systems and Applications

CERN Document Server

Patnaik, Srikanta; Yu, Zhengtao

2017-01-01

This book provides the latest research findings and developments in the field of interactive intelligent systems, addressing diverse areas such as autonomous systems, Internet and cloud computing, pattern recognition and vision systems, mobile computing and intelligent networking, and e-enabled systems. It gathers selected papers from the International Conference on Intelligent and Interactive Systems and Applications (IISA2016) held on June 25–26, 2016 in Shanghai, China. Interactive intelligent systems are among the most important multi-disciplinary research and development domains of artificial intelligence, human–computer interaction, machine learning and new Internet-based technologies. Accordingly, these systems embrace a considerable number of application areas such as autonomous systems, expert systems, mobile systems, recommender systems, knowledge-based and semantic web-based systems, virtual communication environments, and decision support systems, to name a few. To date, research on interactiv...

2nd International Conference on Intelligent Computing and Applications

CERN Document Server

Dash, Subhransu; Das, Swagatam; Panigrahi, Bijaya

2017-01-01

Second International Conference on Intelligent Computing and Applications was the annual research conference aimed to bring together researchers around the world to exchange research results and address open issues in all aspects of Intelligent Computing and Applications. The main objective of the second edition of the conference for the scientists, scholars, engineers and students from the academia and the industry is to present ongoing research activities and hence to foster research relations between the Universities and the Industry. The theme of the conference unified the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in computational intelligence and bridges theoretical research concepts with applications. The conference covered vital issues ranging from intelligent computing, soft computing, and communication to machine learning, industrial automation, process technology and robotics. This conference also provided variety of opportunities for ...

INTEGRATING MACHINE TRANSLATION AND SPEECH SYNTHESIS COMPONENT FOR ENGLISH TO DRAVIDIAN LANGUAGE SPEECH TO SPEECH TRANSLATION SYSTEM

Directory of Open Access Journals (Sweden)

J. SANGEETHA

2015-02-01

Full Text Available This paper provides an interface between the machine translation and speech synthesis system for converting English speech to Tamil text in English to Tamil speech to speech translation system. The speech translation system consists of three modules: automatic speech recognition, machine translation and text to speech synthesis. Many procedures for incorporation of speech recognition and machine translation have been projected. Still speech synthesis system has not yet been measured. In this paper, we focus on integration of machine translation and speech synthesis, and report a subjective evaluation to investigate the impact of speech synthesis, machine translation and the integration of machine translation and speech synthesis components. Here we implement a hybrid machine translation (combination of rule based and statistical machine translation and concatenative syllable based speech synthesis technique. In order to retain the naturalness and intelligibility of synthesized speech Auto Associative Neural Network (AANN prosody prediction is used in this work. The results of this system investigation demonstrate that the naturalness and intelligibility of the synthesized speech are strongly influenced by the fluency and correctness of the translated text.

SAI Intelligent Systems Conference (IntelliSys) 2015

CERN Document Server

Kapoor, Supriya; Bhatia, Rahul

2016-01-01

This book is a remarkable collection of chapters covering a wider range of topics, including unsupervised text mining, anomaly and Intrusion Detection, Self-reconfiguring Robotics, application of Fuzzy Logic to development aid, Design and Optimization, Context-Aware Reasoning, DNA Sequence Assembly and Multilayer Perceptron Networks. The twenty-one chapters present extended results from the SAI Intelligent Systems Conference (IntelliSys) 2015 and have been selected based on high recommendations during IntelliSys 2015 review process. This book presents innovative research and development carried out presently in fields of knowledge representation and reasoning, machine learning, and particularly in intelligent systems in a more broad sense. It provides state - of - the - art intelligent methods and techniques for solving real world problems along with a vision of the future research.

Quantum Machine Learning

Science.gov (United States)

Biswas, Rupak

2018-01-01

Quantum computing promises an unprecedented ability to solve intractable problems by harnessing quantum mechanical effects such as tunneling, superposition, and entanglement. The Quantum Artificial Intelligence Laboratory (QuAIL) at NASA Ames Research Center is the space agency's primary facility for conducting research and development in quantum information sciences. QuAIL conducts fundamental research in quantum physics but also explores how best to exploit and apply this disruptive technology to enable NASA missions in aeronautics, Earth and space sciences, and space exploration. At the same time, machine learning has become a major focus in computer science and captured the imagination of the public as a panacea to myriad big data problems. In this talk, we will discuss how classical machine learning can take advantage of quantum computing to significantly improve its effectiveness. Although we illustrate this concept on a quantum annealer, other quantum platforms could be used as well. If explored fully and implemented efficiently, quantum machine learning could greatly accelerate a wide range of tasks leading to new technologies and discoveries that will significantly change the way we solve real-world problems.

Brain-machine and brain-computer interfaces.

Science.gov (United States)

Friehs, Gerhard M; Zerris, Vasilios A; Ojakangas, Catherine L; Fellows, Mathew R; Donoghue, John P

2004-11-01

The idea of connecting the human brain to a computer or machine directly is not novel and its potential has been explored in science fiction. With the rapid advances in the areas of information technology, miniaturization and neurosciences there has been a surge of interest in turning fiction into reality. In this paper the authors review the current state-of-the-art of brain-computer and brain-machine interfaces including neuroprostheses. The general principles and requirements to produce a successful connection between human and artificial intelligence are outlined and the authors' preliminary experience with a prototype brain-computer interface is reported.

The Moral Standing of Machines: Towards a Relational and Non-Cartesian Moral Hermeneutics

NARCIS (Netherlands)

Coeckelbergh, Mark

2014-01-01

Should we give moral standing to machines? In this paper, I explore the implications of a relational approach to moral standing for thinking about machines, in particular autonomous, intelligent robots. I show how my version of this approach, which focuses on moral relations and on the conditions of

Alzheimer's disease and intelligence.

Science.gov (United States)

Yeo, R A; Arden, R; Jung, R E

2011-06-01

A significant body of evidence has accumulated suggesting that individual variation in intellectual ability, whether assessed directly by intelligence tests or indirectly through proxy measures, is related to risk of developing Alzheimer's disease (AD) in later life. Important questions remain unanswered, however, such as the specificity of risk for AD vs. other forms of dementia, and the specific links between premorbid intelligence and development of the neuropathology characteristic of AD. Lower premorbid intelligence has also emerged as a risk factor for greater mortality across myriad health and mental health diagnoses. Genetic covariance contributes importantly to these associations, and pleiotropic genetic effects may impact diverse organ systems through similar processes, including inefficient design and oxidative stress. Through such processes, the genetic underpinnings of intelligence, specifically, mutation load, may also increase the risk of developing AD. We discuss how specific neurobiologic features of relatively lower premorbid intelligence, including reduced metabolic efficiency, may facilitate the development of AD neuropathology. The cognitive reserve hypothesis, the most widely accepted account of the intelligence-AD association, is reviewed in the context of this larger literature.

Blindness in designing intelligent systems

Science.gov (United States)

Denning, Peter J.

1988-01-01

New investigations of the foundations of artificial intelligence are challenging the hypothesis that problem solving is the cornerstone of intelligence. New distinctions among three domains of concern for humans--description, action, and commitment--have revealed that the design process for programmable machines, such as expert systems, is based on descriptions of actions and induces blindness to nonanalytic action and commitment. Design processes focusing in the domain of description are likely to yield programs like burearcracies: rigid, obtuse, impersonal, and unable to adapt to changing circumstances. Systems that learn from their past actions, and systems that organize information for interpretation by human experts, are more likely to be successful in areas where expert systems have failed.

Artificial intelligence (AI) systems for interpreting complex medical datasets.

Science.gov (United States)

Altman, R B

2017-05-01

Advances in machine intelligence have created powerful capabilities in algorithms that find hidden patterns in data, classify objects based on their measured characteristics, and associate similar patients/diseases/drugs based on common features. However, artificial intelligence (AI) applications in medical data have several technical challenges: complex and heterogeneous datasets, noisy medical datasets, and explaining their output to users. There are also social challenges related to intellectual property, data provenance, regulatory issues, economics, and liability. © 2017 ASCPT.

Effect of the Intelligent Health Messenger Box on health care professionals' knowledge, attitudes, and practice related to hand hygiene and hand bacteria counts.

Science.gov (United States)

Saffari, Mohsen; Ghanizadeh, Ghader; Fattahipour, Rasoul; Khalaji, Kazem; Pakpour, Amir H; Koenig, Harold G

2016-12-01

We assessed the effectiveness of the Intelligent Health Messenger Box in promoting hand hygiene using a quasiexperimental design. Knowledge, attitudes, and self-reported practices related to hand hygiene as well as hand bacteria counts and amount of liquid soap used were measured. The intervention involved broadcasting preventive audio messages. All outcomes showed significant change after the intervention compared with before. The Intelligent Health Messenger Box can serve as a practical way to improve hand hygiene. Copyright © 2016 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Intelligent model-based diagnostics for vehicle health management

Science.gov (United States)

Luo, Jianhui; Tu, Fang; Azam, Mohammad S.; Pattipati, Krishna R.; Willett, Peter K.; Qiao, Liu; Kawamoto, Masayuki

2003-08-01

The recent advances in sensor technology, remote communication and computational capabilities, and standardized hardware/software interfaces are creating a dramatic shift in the way the health of vehicles is monitored and managed. These advances facilitate remote monitoring, diagnosis and condition-based maintenance of automotive systems. With the increased sophistication of electronic control systems in vehicles, there is a concomitant increased difficulty in the identification of the malfunction phenomena. Consequently, the current rule-based diagnostic systems are difficult to develop, validate and maintain. New intelligent model-based diagnostic methodologies that exploit the advances in sensor, telecommunications, computing and software technologies are needed. In this paper, we will investigate hybrid model-based techniques that seamlessly employ quantitative (analytical) models and graph-based dependency models for intelligent diagnosis. Automotive engineers have found quantitative simulation (e.g. MATLAB/SIMULINK) to be a vital tool in the development of advanced control systems. The hybrid method exploits this capability to improve the diagnostic system's accuracy and consistency, utilizes existing validated knowledge on rule-based methods, enables remote diagnosis, and responds to the challenges of increased system complexity. The solution is generic and has the potential for application in a wide range of systems.

Energy efficient drives and control engineering. Intelligent machine and plant concepts for manufacturing; Energieeffiziente Antriebs- und Steuerungstechnik. Intelligente Maschinen- und Anlagenkonzepte fuer die Fertigung

Energy Technology Data Exchange (ETDEWEB)

Fahrbach, Christian; Frank, Klaus; Haack, Steffen; Schemm, Eberhardt; Wittschen, Wiebke

2010-07-01

The book discusses the potential of intelligent and energy-efficient drive and control concepts. It shows that energy efficient components - pumps, motors, or speed-controlled pump drives - may result in a double-digit reduction of energy consumption. The effect is even more pronounced when the system is opotimized as a whole. If energy-efficient components are combined so that a direct energy flow will result, energy will be converted on demand, i.e. the right amount at the right time. The book also discusses how these design options can be applied in the various phases of the machine life cycle.

A Web Observatory for the Machine Processability of Structured Data on the Web

NARCIS (Netherlands)

Beek, W.; Groth, P.; Schlobach, S.; Hoekstra, R.

2014-01-01

General human intelligence is needed in order to process Linked Open Data (LOD). On the Semantic Web (SW), content is intended to be machine-processable as well. But the extent to which a machine is able to navigate, access, and process the SW has not been extensively researched. We present LOD

i-Car: An Intelligent and Interactive Interface for Driver Assistance ...

African Journals Online (AJOL)

i-Car: An Intelligent and Interactive Interface for Driver Assistance System. ... techniques with pattern recognition, feature extraction, machine learning, object recognition, ... The system uses eye closure based decision algorithm to detect driver ...

[Social intelligence deficits in autistic children and adolescents--subjective theories of psychosocial health care professionals].

Science.gov (United States)

Krech, M; Probst, P

1998-10-01

The paper is concerned with personal theories of health care professionals about deficiencies in social intelligence of autistic persons. In the component-model of social intelligence means the ability of individuals or groups, to interact with each other in social situations. This contains social perception, social behavior as well as social conceptions and refers to emotional, cognitive and normative aspects. 33 interviewees, working as psychologists or teachers in kindergartens, schools or therapy institutions, are questioned by a half-standardized single interview concerning their beliefs about nonverbal social abilities, social perspective taking, and construction of a theory of mind in autistic persons. The major finding is: The impairments can be found in all aspects of social intelligence. Especially emotional handicaps, which are quoted by more than 80% of the interviewees, and low cognitive preconditions of mastering social stimuli, which are quoted by nearly all interviewees, are relevant. The subjective theories of the interviewees are in accordance to the models of parents as well as the models of the leading experts. The professional relationship to autistic persons and the practical experiences of the health care professionals lead to their specific personal theories of deficiencies in social intelligence of autistic people with wide consequences in respect to the professional contact with the autistic children and young adults.

Intelligent Machine Parts with Surface Embedded Sensors

OpenAIRE

Østbø, Niels Peter

2009-01-01

A surface embedded temperature sensor has successfully been fabricated on a customized industrial bolt. The aluminum substrate of the bolt was electrically isolated by plasma electrolytic oxidation followed by the fabrication of a type T thermocouple and finally covered by a wear resistant DLC coating. This bolt is part of our work to develop smart machine parts that are capable of reporting their current physical status under real working conditions enabling both new tools for condition base...

Issues regarding the design and acceptance of intelligent support systems for reactor operators

International Nuclear Information System (INIS)

Bernard, J.A.

1992-01-01

In this paper, factors relevant to the design and acceptance of intelligent support systems for the operation of nuclear power plants are enumerated and discussed. The central premise is that conventional expert systems which encode experiential knowledge in production rules are not a suitable vehicle for the creation of practical operator support systems. The principal difficulty is the need for real-time operation. This in turn means that intelligent support systems will have knowledge bases derived from temporally accurate plant models, inference engines that permit revisions in the search process so as to accommodate revised or new data, and man-machine interfaces that do not require any human input. Such systems will have to be heavily instrumented and the associated knowledge bases will require a hierarchical organization so as to emulate human approaches to analysis. Issues related to operator acceptance of intelligent support tools are then reviewed. Possible applications are described and the relative merits of the machine- and human-centered approaches to the implementation of intelligent support systems are enumerated. The paper concludes with a plea for additional experimental evaluations

Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential.

Science.gov (United States)

Das, Nilakash; Topalovic, Marko; Janssens, Wim

2018-03-01

The application of artificial intelligence in the diagnosis of obstructive lung diseases is an exciting phenomenon. Artificial intelligence algorithms work by finding patterns in data obtained from diagnostic tests, which can be used to predict clinical outcomes or to detect obstructive phenotypes. The purpose of this review is to describe the latest trends and to discuss the future potential of artificial intelligence in the diagnosis of obstructive lung diseases. Machine learning has been successfully used in automated interpretation of pulmonary function tests for differential diagnosis of obstructive lung diseases. Deep learning models such as convolutional neural network are state-of-the art for obstructive pattern recognition in computed tomography. Machine learning has also been applied in other diagnostic approaches such as forced oscillation test, breath analysis, lung sound analysis and telemedicine with promising results in small-scale studies. Overall, the application of artificial intelligence has produced encouraging results in the diagnosis of obstructive lung diseases. However, large-scale studies are still required to validate current findings and to boost its adoption by the medical community.

Artificial intelligence: A joint narrative on potential use in pediatric stem and immune cell therapies and regenerative medicine.

Science.gov (United States)

Sniecinski, Irena; Seghatchian, Jerard

2018-05-09

Artificial Intelligence (AI) reflects the intelligence exhibited by machines and software. It is a highly desirable academic field of many current fields of studies. Leading AI researchers describe the field as "the study and design of intelligent agents". McCarthy invented this term in 1955 and defined it as "the science and engineering of making intelligent machines". The central goals of AI research are reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. In fact the multidisplinary AI field is considered to be rather interdisciplinary covering numerous number of sciences and professions, including computer science, psychology, linguistics, philosophy and neurosciences. The field was founded on the claim that a central intellectual property of humans, intelligence-the sapience of Homo Sapiens "can be so precisely described that a machine can be made to simulate it". This raises philosophical issues about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence. Artificial Intelligence has been the subject of tremendous optimism but has also suffered stunning setbacks. The goal of this narrative is to review the potential use of AI approaches and their integration into pediatric cellular therapies and regenerative medicine. Emphasis is placed on recognition and application of AI techniques in the development of predictive models for personalized treatments with engineered stem cells, immune cells and regenerated tissues in adults and children. These intelligent machines could dissect the whole genome and isolate the immune particularities of individual patient's disease in a matter of minutes and create the treatment that is customized to patient's genetic specificity and immune system capability. AI techniques could be used for optimization of clinical trials of innovative stem cell and gene therapies in pediatric patients

Design and Development of Intelligent Electrodes for Future Digital Health Monitoring: A Review

Science.gov (United States)

Khairuddin, A. M.; Azir, K. N. F. Ku; Kan, P. Eh

2018-03-01

Electrodes are sensors used in electrocardiography (ECG) monitoring system to diagnose heart diseases. Over the years, diverse types of electrodes have been designed and developed to improve ECG monitoring system. However, more recently, with the technological advances and capabilities from the Internet of Things (IoT), cloud computing and data analytics in personalized healthcare, researchers are attempting to design and develop more effective as well as flexible ECG devices by using intelligent electrodes. This paper reviews previous works on electrodes used in electrocardiography (ECG) monitoring devices to identify the key ftures for designing and developing intelligent electrodes in digital health monitoring devices.

Utilising artificial intelligence in software defined wireless sensor network

CSIR Research Space (South Africa)

Matlou, OG

2017-10-01

Full Text Available Software Defined Wireless Sensor Network (SDWSN) is realised by infusing Software Defined Network (SDN) model in Wireless Sensor Network (WSN), Reason for that is to overcome the challenges of WSN. Artificial Intelligence (AI) and machine learning...

Intelligent operation system for nuclear power plants

International Nuclear Information System (INIS)

Morioka, Toshihiko; Fukumoto, Akira; Suto, Osamu; Naito, Norio.

1987-01-01

Nuclear power plants consist of many systems and are operated by skillful operators with plenty of knowledge and experience of nuclear plants. Recently, plant automation or computerized operator support systems have come to be utilized, but the synthetic judgment of plant operation and management remains as human roles. Toshiba is of the opinion that the activities (planning, operation and maintenance) should be integrated, and man-machine interface should be human-friendly. We have begun to develop the intelligent operation system aiming at reducing the operator's role within the fundamental judgment through the use of artificial intelligence. (author)

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

Science.gov (United States)

Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

2018-03-01

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

Machine learning in laboratory medicine: waiting for the flood?

Science.gov (United States)

Cabitza, Federico; Banfi, Giuseppe

2018-03-28

This review focuses on machine learning and on how methods and models combining data analytics and artificial intelligence have been applied to laboratory medicine so far. Although still in its infancy, the potential for applying machine learning to laboratory data for both diagnostic and prognostic purposes deserves more attention by the readership of this journal, as well as by physician-scientists who will want to take advantage of this new computer-based support in pathology and laboratory medicine.

AI Reloaded: Objectives, Potentials, and Challenges of the Novel Field of Brain-Like Artificial Intelligence

Directory of Open Access Journals (Sweden)

Rosemarie Velik

2012-11-01

Full Text Available The general objective of Artificial Intelligence (AI is to make machines – particularly computers – do things that require intelligence when done by humans. In the last 60 years, AI has significantly progressed and today forms an important part of industry and technology. However, despite the many successes, fundamental questions concerning the creation of human-level intelligence in machines still remain open and will probably not be answerable when continuing on the current, mainly mathematic-algorithmically-guided path of AI. With the novel discipline of
Brain-Like Artificial Intelligence, one potential way out of this dilemma has been suggested. Brain-Like AI aims at analyzing and deciphering the working mechanisms of the brain and translating this knowledge into implementable AI architectures with the objective to develop in this way more efficient, flexible, and capable technical systems This article aims at giving a review about this young and still heterogeneous and dynamic research field.

7th International Conference on Intelligent Systems and Knowledge Engineering

CERN Document Server

Li, Tianrui; Li, Hongbo

2014-01-01

These proceedings present technical papers selected from the 2012 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2012), held on December 15-17 in Beijing. The aim of this conference is to bring together experts from different fields of expertise to discuss the state-of-the-art in Intelligent Systems and Knowledge Engineering, and to present new findings and perspectives on future developments. The proceedings introduce current scientific and technical advances in the fields of artificial intelligence, machine learning, pattern recognition, data mining, knowledge engineering, information retrieval, information theory, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, and natural-language processing, etc. Furthermore they include papers on new intelligent computing paradigms, which combine new computing methodologies, e.g., cloud computing, service computing and pervasive computing with traditional intelligent methods. By presenting new method...

7th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2015)

CERN Document Server

Nguyen, Ngoc; Batubara, John; New Trends in Intelligent Information and Database Systems

2015-01-01

Intelligent information and database systems are two closely related subfields of modern computer science which have been known for over thirty years. They focus on the integration of artificial intelligence and classic database technologies to create the class of next generation information systems. The book focuses on new trends in intelligent information and database systems and discusses topics addressed to the foundations and principles of data, information, and knowledge models, methodologies for intelligent information and database systems analysis, design, and implementation, their validation, maintenance and evolution. They cover a broad spectrum of research topics discussed both from the practical and theoretical points of view such as: intelligent information retrieval, natural language processing, semantic web, social networks, machine learning, knowledge discovery, data mining, uncertainty management and reasoning under uncertainty, intelligent optimization techniques in information systems, secu...

International Conference on Computational Intelligence in Data Mining

CERN Document Server

Mohapatra, Durga

2017-01-01

The book presents high quality papers presented at the International Conference on Computational Intelligence in Data Mining (ICCIDM 2016) organized by School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India during December 10 – 11, 2016. The book disseminates the knowledge about innovative, active research directions in the field of data mining, machine and computational intelligence, along with current issues and applications of related topics. The volume aims to explicate and address the difficulties and challenges that of seamless integration of the two core disciplines of computer science. .

The impact of emotional intelligence in health care professionals on caring behaviour towards patients in clinical and long-term care settings: Findings from an integrative review.

Science.gov (United States)

Nightingale, Suzanne; Spiby, Helen; Sheen, Kayleigh; Slade, Pauline

2018-04-01

Over recent years there has been criticism within the United Kingdom's health service regarding a lack of care and compassion, resulting in adverse outcomes for patients. The impact of emotional intelligence in staff on patient health care outcomes has been recently highlighted. Many recruiters now assess emotional intelligence as part of their selection process for health care staff. However, it has been argued that the importance of emotional intelligence in health care has been overestimated. To explore relationships between emotional intelligence in health care professionals, and caring behaviour. To further explore any additional factors related to emotional intelligence that may impact upon caring behaviour. An integrative review design was used. Psychinfo, Medline, CINAHL Plus, Social Sciences Citation Index, Science Citation Index, and Scopus were searched for studies from 1995 to April 2017. Studies providing quantitative or qualitative exploration of how any healthcare professionals' emotional intelligence is linked to caring in healthcare settings were selected. Twenty two studies fulfilled the inclusion criteria. Three main types of health care professional were identified: nurses, nurse leaders, and physicians. Results indicated that the emotional intelligence of nurses was related to both physical and emotional caring, but emotional intelligence may be less relevant for nurse leaders and physicians. Age, experience, burnout, and job satisfaction may also be relevant factors for both caring and emotional intelligence. This review provides evidence that developing emotional intelligence in nurses may positively impact upon certain caring behaviours, and that there may be differences within groups that warrant further investigation. Understanding more about which aspects of emotional intelligence are most relevant for intervention is important, and directions for further large scale research have been identified. Copyright © 2018 Elsevier Ltd. All

Report on {open_quotes}inspection of human subject research in intelligence and intelligence-related projects{close_quotes}

Energy Technology Data Exchange (ETDEWEB)

NONE

1996-01-16

Executive Order 12333, {open_quotes}United States Intelligence Activities,{close_quotes} (1) designates the Department`s intelligence element as a member of the Intelligence Community, and (2) states that no agency within the Intelligence community shall sponsor, contract for or conduct research on human subjects except in accordance with guidelines issued by the Department of Health and Human Services. The Federal policy for the Protection of Human Subjects, which was based on Department of Health and Human Services regulations, was promulgated in Title 10 Code of Federal Regulations Part 745 by the Department of Energy. The purpose of this inspection was to review the internal control procedures used by the Office of Nonproliferation and National Security to manage selected intelligence and intelligence-related projects that involve human subject research.

Artificial Intelligence, Evolutionary Computing and Metaheuristics In the Footsteps of Alan Turing

CERN Document Server

2013-01-01

Alan Turing pioneered many research areas such as artificial intelligence, computability, heuristics and pattern formation.  Nowadays at the information age, it is hard to imagine how the world would be without computers and the Internet. Without Turing's work, especially the core concept of Turing Machine at the heart of every computer, mobile phone and microchip today, so many things on which we are so dependent would be impossible. 2012 is the Alan Turing year -- a centenary celebration of the life and work of Alan Turing. To celebrate Turing's legacy and follow the footsteps of this brilliant mind, we take this golden opportunity to review the latest developments in areas of artificial intelligence, evolutionary computation and metaheuristics, and all these areas can be traced back to Turing's pioneer work. Topics include Turing test, Turing machine, artificial intelligence, cryptography, software testing, image processing, neural networks, nature-inspired algorithms such as bat algorithm and cuckoo sear...

Challenges and Characteristics of Intelligent Autonomy for Internet of Battle Things in Highly Adversarial Environments

OpenAIRE

Kott, Alexander

2018-01-01

Numerous, artificially intelligent, networked things will populate the battlefield of the future, operating in close collaboration with human warfighters, and fighting as teams in highly adversarial environments. This paper explores the characteristics, capabilities and intelligence required of such a network of intelligent things and humans - Internet of Battle Things (IOBT). It will experience unique challenges that are not yet well addressed by the current generation of AI and machine lear...

Humans and Machines in the Evolution of AI in Korea

OpenAIRE

Zhang, Byoung-Tak

2016-01-01

Artificial intelligence in Korea is currently prospering. The media is regularly reporting AI-enabled products such as smart advisors, personal robots, autonomous cars, and human-level intelligence machines. The IT industry is investing in deep learning and AI to maintain the global competitive edge in their services and products. The Ministry of Science, ICT, and Future Planning (MSIP) has launched new funding programs in AI and cognitive science to implement the government’s newly adopted e...

Applications of artificial intelligence in engineering problems

Energy Technology Data Exchange (ETDEWEB)

Sriram, D; Adey, R

1986-01-01

This book presents the papers given at a conference on the use of artificial intelligence in engineering. Topics considered at the conference included Prolog logic, expert systems, knowledge representation and acquisition, knowledge bases, machine learning, robotics, least-square algorithms, vision systems for robots, natural language, probability, mechanical engineering, civil engineering, and electrical engineering.

Third Conference on Artificial Intelligence for Space Applications, part 1

Science.gov (United States)

Denton, Judith S. (Compiler); Freeman, Michael S. (Compiler); Vereen, Mary (Compiler)

1987-01-01

The application of artificial intelligence to spacecraft and aerospace systems is discussed. Expert systems, robotics, space station automation, fault diagnostics, parallel processing, knowledge representation, scheduling, man-machine interfaces and neural nets are among the topics discussed.

Evidence for Busines Intelligence in Health Care: A Literature Review.

Science.gov (United States)

Loewen, Liz; Roudsari, Abdul

2017-01-01

This paper outlines a systematic literature review undertaken to establish current evidence regarding the impact of Business Intelligence (BI) on health system decision making and organizational performance. The review also examined BI implementation factors contributing to these constructs. Following the systematic review, inductive content analysis was used to categorize themes within the eight articles identified. This study demonstrated there is little evidence based literature focused on BI impact on organizational decision making and performance within health care. There was evidence found that BI does improve decision making. Implementation success was found to be dependent on several factors, many of which relate to broader organizational culture and readiness.

The association between intelligence and lifespan is mostly genetic

DEFF Research Database (Denmark)

Arden, Rosalind; Luciano, Michelle; Deary, Ian J

2016-01-01

BACKGROUND: Several studies in the new field of cognitive epidemiology have shown that higher intelligence predicts longer lifespan. This positive correlation might arise from socioeconomic status influencing both intelligence and health; intelligence leading to better health behaviours; and....../or some shared genetic factors influencing both intelligence and health. Distinguishing among these hypotheses is crucial for medicine and public health, but can only be accomplished by studying a genetically informative sample. METHODS: We analysed data from three genetically informative samples...... containing information on intelligence and mortality: Sample 1, 377 pairs of male veterans from the NAS-NRC US World War II Twin Registry; Sample 2, 246 pairs of twins from the Swedish Twin Registry; and Sample 3, 784 pairs of twins from the Danish Twin Registry. The age at which intelligence was measured...

Mechatronic System Design and Intelligent Motion Control of Hydraulic Robots and Machines

DEFF Research Database (Denmark)

Conrad, Finn; Sørensen, Torben

2003-01-01

The paper presents an approach and concept to mechatronic system design and intelligent motion control. The Information Technology (IT) offers software and hardware for improvement of R&D Mechatronic Teams to create products and solutions for industrial applications. The latest progress in IT makes...... integration of an overall design and manufacturing IT- concept feasible and commercially attractive. An IT-tool concept for modelling, simulation and design of mechatronic products and systems is proposed in this paper. It built on results from a Danish mechatronic research program on intelligent motion...

Methodological evolutions in human-machine cooperative problem solving with applications to nuclear plants

International Nuclear Information System (INIS)

Kitamura, Masaharu; Takahashi, Makoto

2002-01-01

A new framework for attaining higher safety of nuclear plants through introducing machine intelligence and robots has been proposed in this paper. The main emphasis of the framework is placed on user-centered human-machine cooperation in solving problems experienced during conducting operation, monitoring and maintenance activities in nuclear plants. In this framework, human operator is supposed to take initiative of actions at any moment of operation. No attempt has been made to replace human experts by machine intelligence and robots. Efforts have been paid to clarify the expertise and behavioral model of human experts so that the developed techniques are consistent with human mental activities in solving highly complicated operational and maintenance problems. Several techniques essential to the functioning of the framework have also been introduced. Modification of environment to provide support information has also been pursued to realize the concept of ubiquitous computing. (author)

Designing a holistic end-to-end intelligent network analysis and security platform

Science.gov (United States)

Alzahrani, M.

2018-03-01

Firewall protects a network from outside attacks, however, once an attack entering a network, it is difficult to detect. Recent significance accidents happened. i.e.: millions of Yahoo email account were stolen and crucial data from institutions are held for ransom. Within two year Yahoo’s system administrators were not aware that there are intruder inside the network. This happened due to the lack of intelligent tools to monitor user behaviour in internal network. This paper discusses a design of an intelligent anomaly/malware detection system with proper proactive actions. The aim is to equip the system administrator with a proper tool to battle the insider attackers. The proposed system adopts machine learning to analyse user’s behaviour through the runtime behaviour of each node in the network. The machine learning techniques include: deep learning, evolving machine learning perceptron, hybrid of Neural Network and Fuzzy, as well as predictive memory techniques. The proposed system is expanded to deal with larger network using agent techniques.

A new type of intelligent wireless sensing network for health monitoring of large-size structures

Science.gov (United States)

Lei, Ying; Liu, Ch.; Wu, D. T.; Tang, Y. L.; Wang, J. X.; Wu, L. J.; Jiang, X. D.

2009-07-01

In recent years, some innovative wireless sensing systems have been proposed. However, more exploration and research on wireless sensing systems are required before wireless systems can substitute for the traditional wire-based systems. In this paper, a new type of intelligent wireless sensing network is proposed for the heath monitoring of large-size structures. Hardware design of the new wireless sensing units is first studied. The wireless sensing unit mainly consists of functional modules of: sensing interface, signal conditioning, signal digitization, computational core, wireless communication and battery management. Then, software architecture of the unit is introduced. The sensing network has a two-level cluster-tree architecture with Zigbee communication protocol. Important issues such as power saving and fault tolerance are considered in the designs of the new wireless sensing units and sensing network. Each cluster head in the network is characterized by its computational capabilities that can be used to implement the computational methodologies of structural health monitoring; making the wireless sensing units and sensing network have "intelligent" characteristics. Primary tests on the measurement data collected by the wireless system are performed. The distributed computational capacity of the intelligent sensing network is also demonstrated. It is shown that the new type of intelligent wireless sensing network provides an efficient tool for structural health monitoring of large-size structures.

Semantics and artificial intelligence in machine translation

Energy Technology Data Exchange (ETDEWEB)

King, M

1981-01-01

The author exemplifies three types of ambiguity that the introduction of semantics or of AI methods might be expected to solve: word sense, structural, and referential ambiguity. From this point of view she examines the works of Schank, Riesbeck, Minsky, Charniak, and Wilks, and she comes to the conclusion that the systems described will not be of much help for the development of operational MT-systems, except within a well-defined, constrained world. The latter aspect is illustrated by the author by means of a description of the Edinburgh Mecho-project. But, as the vast majority of texts destined for MT does not come from a constrained world, such systems will hardly be used as MT production systems. Still, MT-systems like Eurotra give the chance of making intelligent use of AI ideas. 16 references.

Creating value: unifying silos into public health business intelligence.

Science.gov (United States)

Davidson, Arthur J

2014-01-01

Through September 2014, federal investments in health information technology have been unprecedented, with more than 25 billion dollars in incentive funds distributed to eligible hospitals and providers. Over 85 percent of eligible United States hospitals and 60 percent of eligible providers have used certified electronic health record (EHR) technology and received Meaningful Use incentive funds (HITECH Act1). Certified EHR technology could create new public health (PH) value through novel and rapidly evolving data-use opportunities, never before experienced by PH. The long-standing "silo" approach to funding has fragmented PH programs and departments,2 but the components for integrated business intelligence (i.e., tools and applications to help users make informed decisions) and maximally reuse data are available now. Challenges faced by PH agencies on the road to integration are plentiful, but an emphasis on PH systems and services research (PHSSR) may identify gaps and solutions for the PH community to address. Technology and system approaches to leverage this information explosion to support a transformed health care system and population health are proposed. By optimizing this information opportunity, PH can play a greater role in the learning health system.

Artificial Intelligence and Semantics through the Prism of Structural, Post-Structural and Transcendental Approaches.

Science.gov (United States)

Gasparyan, Diana

2016-12-01

There is a problem associated with contemporary studies of philosophy of mind, which focuses on the identification and convergence of human and machine intelligence. This is the problem of machine emulation of sense. In the present study, analysis of this problem is carried out based on concepts from structural and post-structural approaches that have been almost entirely overlooked by contemporary philosophy of mind. If we refer to the basic definitions of "sign" and "meaning" found in structuralism and post-structuralism, we see a fundamental difference between the capabilities of a machine and the human brain engaged in the processing of a sign. This research will exemplify and provide additional evidence to support distinctions between syntactic and semantic aspects of intelligence, an issue widely discussed by adepts of contemporary philosophy of mind. The research will demonstrate that some aspect of a number of ideas proposed in relation to semantics and semiosis in structuralism and post-structuralism are similar to those we find in contemporary analytical studies related to the theory and philosophy of artificial intelligence. The concluding part of the paper offers an interpretation of the problem of formalization of sense, connected to its metaphysical (transcendental) properties.

Changes in Emotional-Social Intelligence, Caring, Leadership and Moral Judgment during Health Science Education Programs

Science.gov (United States)

Larin, Helene; Benson, Gerry; Wessel, Jean; Martin, Lynn; Ploeg, Jenny

2014-01-01

In addition to having academic knowledge and clinical skills, health professionals need to be caring, ethical practitioners able to understand the emotional concerns of their patients and to effect change. The purpose of this study was to determine whether emotional-social intelligence, caring, leadership and moral judgment of health science…

A Novel Artificial Intelligence System for Endotracheal Intubation.

Science.gov (United States)

Carlson, Jestin N; Das, Samarjit; De la Torre, Fernando; Frisch, Adam; Guyette, Francis X; Hodgins, Jessica K; Yealy, Donald M

2016-01-01

Adequate visualization of the glottic opening is a key factor to successful endotracheal intubation (ETI); however, few objective tools exist to help guide providers' ETI attempts toward the glottic opening in real-time. Machine learning/artificial intelligence has helped to automate the detection of other visual structures but its utility with ETI is unknown. We sought to test the accuracy of various computer algorithms in identifying the glottic opening, creating a tool that could aid successful intubation. We collected a convenience sample of providers who each performed ETI 10 times on a mannequin using a video laryngoscope (C-MAC, Karl Storz Corp, Tuttlingen, Germany). We recorded each attempt and reviewed one-second time intervals for the presence or absence of the glottic opening. Four different machine learning/artificial intelligence algorithms analyzed each attempt and time point: k-nearest neighbor (KNN), support vector machine (SVM), decision trees, and neural networks (NN). We used half of the videos to train the algorithms and the second half to test the accuracy, sensitivity, and specificity of each algorithm. We enrolled seven providers, three Emergency Medicine attendings, and four paramedic students. From the 70 total recorded laryngoscopic video attempts, we created 2,465 time intervals. The algorithms had the following sensitivity and specificity for detecting the glottic opening: KNN (70%, 90%), SVM (70%, 90%), decision trees (68%, 80%), and NN (72%, 78%). Initial efforts at computer algorithms using artificial intelligence are able to identify the glottic opening with over 80% accuracy. With further refinements, video laryngoscopy has the potential to provide real-time, direction feedback to the provider to help guide successful ETI.

Discovery machines accelerators for science, technology, health and innovation

CERN Document Server

Australian Academy of Sciences

2016-01-01

Discovery machines: Accelerators for science, technology, health and innovation explores the science of particle accelerators, the machines that supercharge our ability to discover the secrets of nature and have opened up new tools in medicine, energy, manufacturing, and the environment as well as in pure research. Particle accelerators are now an essential ingredient in discovery science because they offer new ways to analyse the world, such as by probing objects with high energy x-rays or colliding them beams of electrons. They also have a huge—but often unnoticed—impact on all our lives; medical imaging, cancer treatment, new materials and even the chips that power our phones and computers have all been transformed by accelerators of various types. Research accelerators also provide fundamental infrastructure that encourages better collaboration between international and domestic scientists, organisations and governments.

An intelligent content discovery technique for health portal content management.

Science.gov (United States)

De Silva, Daswin; Burstein, Frada

2014-04-23

Continuous content management of health information portals is a feature vital for its sustainability and widespread acceptance. Knowledge and experience of a domain expert is essential for content management in the health domain. The rate of generation of online health resources is exponential and thereby manual examination for relevance to a specific topic and audience is a formidable challenge for domain experts. Intelligent content discovery for effective content management is a less researched topic. An existing expert-endorsed content repository can provide the necessary leverage to automatically identify relevant resources and evaluate qualitative metrics. This paper reports on the design research towards an intelligent technique for automated content discovery and ranking for health information portals. The proposed technique aims to improve efficiency of the current mostly manual process of portal content management by utilising an existing expert-endorsed content repository as a supporting base and a benchmark to evaluate the suitability of new content A model for content management was established based on a field study of potential users. The proposed technique is integral to this content management model and executes in several phases (ie, query construction, content search, text analytics and fuzzy multi-criteria ranking). The construction of multi-dimensional search queries with input from Wordnet, the use of multi-word and single-word terms as representative semantics for text analytics and the use of fuzzy multi-criteria ranking for subjective evaluation of quality metrics are original contributions reported in this paper. The feasibility of the proposed technique was examined with experiments conducted on an actual health information portal, the BCKOnline portal. Both intermediary and final results generated by the technique are presented in the paper and these help to establish benefits of the technique and its contribution towards effective

Determination of rock depth using artificial intelligence techniques

Institute of Scientific and Technical Information of China (English)

R. Viswanathan; Pijush Samui

2016-01-01

This article adopts three artificial intelligence techniques, Gaussian Process Regression (GPR), Least Square Support Vector Machine (LSSVM) and Extreme Learning Machine (ELM), for prediction of rock depth (d) at any point in Chennai. GPR, ELM and LSSVM have been used as regression techniques. Latitude and longitude are also adopted as inputs of the GPR, ELM and LSSVM models. The performance of the ELM, GPR and LSSVM models has been compared. The developed ELM, GPR and LSSVM models produce spatial variability of rock depth and offer robust models for the prediction of rock depth.

Application of Artificial Intelligence Techniques for the Control of the Asynchronous Machine

Directory of Open Access Journals (Sweden)

F. Khammar

2016-01-01

Full Text Available The induction machine is experiencing a growing success for two decades by gradually replacing the DC machines and synchronous in many industrial applications. This paper is devoted to the study of advanced methods applied to the command of the asynchronous machine in order to obtain a system of control of high performance. While the criteria for response time, overtaking, and static error can be assured by the techniques of conventional control, the criterion of robustness remains a challenge for researchers. This criterion can be satisfied only by applying advanced techniques of command. After mathematical modeling of the asynchronous machine, it defines the control strategies based on the orientation of the rotor flux. The results of the different simulation tests highlight the properties of robustness of algorithms proposed and suggested to compare the different control strategies.

The role of automation and artificial intelligence

Science.gov (United States)

Schappell, R. T.

1983-07-01

Consideration is given to emerging technologies that are not currently in common use, yet will be mature enough for implementation in a space station. Artificial intelligence (AI) will permit more autonomous operation and improve the man-machine interfaces. Technology goals include the development of expert systems, a natural language query system, automated planning systems, and AI image understanding systems. Intelligent robots and teleoperators will be needed, together with improved sensory systems for the robotics, housekeeping, vehicle control, and spacecraft housekeeping systems. Finally, NASA is developing the ROBSIM computer program to evaluate level of automation, perform parametric studies and error analyses, optimize trajectories and control systems, and assess AI technology.

What makes you clever the puzzle of intelligence

CERN Document Server

Partridge, Derek

2014-01-01

From Black Holes and Big Bangs to the Higgs boson and the infinitesimal building blocks of all matter, modern science has been spectacularly successful, with one glaring exception - intelligence. Intelligence still remains as one of the greatest mysteries in science. How do you chat so effortlessly? How do you remember, and why do you forget? From a basis of ten maxims What Makes You Clever explains the difficulties as well as the persuasive and persistent over-estimations of progress in Artificial Intelligence. Computers have transformed our lives, and will continue to do so for many years to come. But ever since the Turing Test proposed in 1950 up to IBM's Deep Blue computer that won the second six-game match against world champion Garry Kasparov, the science of artificial intelligence has struggled to make progress. The reader's expertise is engaged to probe human language, machine learning, neural computing, holistic systems and emergent phenomenon. What Makes You Clever reveals the difficulties that s...

Quest for business intelligence in health care.

Science.gov (United States)

Van De Graaff, Joe; Cameron, Austin

2013-02-01

In an era of reform, providers are examining more forward-thinking business intelligence strategies, according to a recent study. Enterprise business intelligence tool sets offer a breadth of design and functionality that often are capable of serving the enterprise. One limitation of broader tool sets is that they may lack needed application-specific functionality or prebuilt healthcare content for a specific department.

Spiritualist Writing Machines: Telegraphy, Typtology, Typewriting

Directory of Open Access Journals (Sweden)

Anthony Enns

2015-09-01

Full Text Available This paper examines how religious concepts both reflected and informed the development of new technologies for encoding, transmitting, and printing written information. While many spiritualist writing machines were based on existing technologies that were repurposed for spirit communication, others prefigured or even inspired more advanced technological innovations. The history of spiritualist writing machines thus not only represents a response to the rise of new media technologies in the nineteenth century, but it also reflects a set of cultural demands that helped to shape the development of new technologies, such as the need to replace handwriting with discrete, uniform lettering, which accelerated the speed of composition; the need to translate written information into codes, which could be transmitted across vast distances; and the need to automate the process of transmitting, translating, and transcribing written information, which seemed to endow the machines themselves with a certain degree of autonomy or even intelligence. While spiritualists and inventors were often (but not always motivated by different goals, the development of spiritualist writing machines and the development of technological writing machines were nevertheless deeply interrelated and interdependent.

Emotional Intelligence and Health Risk Behaviors in Nursing Students.

Science.gov (United States)

Lana, Alberto; Baizán, Eva María; Faya-Ornia, Goretti; López, María Luisa

2015-08-01

To explore the association between emotional intelligence (EI) and risky health behaviors in nursing students at the University of Oviedo (Spain). This cross-sectional study of 275 students used a validated questionnaire to measure EI level, nine risky behaviors (smoking, excessive alcohol consumption, illicit drug use, unhealthy diet, being overweight, sedentarism, risky sun exposure, occupational risk, and unsafe sex), and other factors that may influence EI. Students with the highest EI score had a lower probability of drinking too much alcohol (odds ratio [OR], 0.31; 95% confidence interval [CI] [0.19, 0.67]), eating too few fruits and vegetables (OR, 0.60; 95% CI [0.34, 0.99]), and having unsafe sex (OR, 0.10; 95% CI [0.01, 0.74]). A dose-response effect was found for those three behaviors (p for trend excessive alcohol consumption, unhealthy diet, and unsafe sex. Training nursing students about EI could improve health behaviors, and thus the role of nurses as health promoters. Copyright 2015, SLACK Incorporated.

Artificial intelligence: the future in nuclear plant maintenance

International Nuclear Information System (INIS)

Norgate, G.

1984-01-01

The role of robotics and remote handling equipment in future nuclear power plant maintenance activities is discussed in the context of artificial intelligence applications. Special requirements manipulators, control systems, and man-machine interfaces for nuclear applications are noted. Tasks might include inspection with cameras, eddy current probes, and leak detectors; the collection of material samples; radiation monitoring; and the disassembly, repair and reassembly of a variety of system components. A robot with vision and force sensing and an intelligent control system that can access a knowledge base is schematically described. Recent advances in image interpretation systems are also discussed

Artificial intelligence in sports on the example of weight training.

Science.gov (United States)

Novatchkov, Hristo; Baca, Arnold

2013-01-01

The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10-12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice. Key pointsArtificial intelligence is a promising field for sport-related analysis.Implementations integrating pattern recognition techniques enable the automatic evaluation of data

Machine learning in updating predictive models of planning and scheduling transportation projects

Science.gov (United States)

1997-01-01

A method combining machine learning and regression analysis to automatically and intelligently update predictive models used in the Kansas Department of Transportations (KDOTs) internal management system is presented. The predictive models used...

Development of intelligent physical start-up system for nuclear reactor

International Nuclear Information System (INIS)

Wang Canhui; Li Xiang; Huang Liyuan; Fu Guoen; Hu Hai

2008-01-01

In this paper, the Intelligent physical start-up system for nuclear reactor introduced the system composing, hardware design and software design. The system has some merits such as handy operation, fast and accurate mathematic and nicer human-machine interface. (authors)

Evolution Engines and Artificial Intelligence

Science.gov (United States)

Hemker, Andreas; Becks, Karl-Heinz

In the last years artificial intelligence has achieved great successes, mainly in the field of expert systems and neural networks. Nevertheless the road to truly intelligent systems is still obscured. Artificial intelligence systems with a broad range of cognitive abilities are not within sight. The limited competence of such systems (brittleness) is identified as a consequence of the top-down design process. The evolution principle of nature on the other hand shows an alternative and elegant way to build intelligent systems. We propose to take an evolution engine as the driving force for the bottom-up development of knowledge bases and for the optimization of the problem-solving process. A novel data analysis system for the high energy physics experiment DELPHI at CERN shows the practical relevance of this idea. The system is able to reconstruct the physical processes after the collision of particles by making use of the underlying standard model of elementary particle physics. The evolution engine acts as a global controller of a population of inference engines working on the reconstruction task. By implementing the system on the Connection Machine (Model CM-2) we use the full advantage of the inherent parallelization potential of the evolutionary approach.

Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes

Directory of Open Access Journals (Sweden)

Utku Kose

2016-03-01

Full Text Available The definition, diagnosis and classification of Diabetes Mellitus and its complications are very important. First of all, the World Health Organization (WHO and other societies, as well as scientists have done lots of studies regarding this subject. One of the most important research interests of this subject is the computer supported decision systems for diagnosing diabetes. In such systems, Artificial Intelligence techniques are often used for several disease diagnostics to streamline the diagnostic process in daily routine and avoid misdiagnosis. In this study, a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM and Cognitive Development Optimization Algorithm (CoDOA has been proposed. Along the training of SVM, CoDOA was used for determining the sigma parameter of the Gauss (RBF kernel function, and eventually, a classification process was made over the diabetes data set, which is related to Pima Indians. The proposed approach offers an alternative solution to the field of Artificial Intelligence-based diabetes diagnosis, and contributes to the related literature on diagnosis processes.

Artificial Intelligence and Virology - quo vadis.

Science.gov (United States)

Shapshak, Paul; Somboonwit, Charurut; Sinnott, John T

2017-01-01

Artificial Intelligence (AI), robotics, co-robotics (cobots), quantum computers (QC), include surges of scientific endeavor to produce machines (mechanical and software) among numerous types and constructions that are accelerating progress to defeat infectious diseases. There is a plethora of additional applications and uses of these methodologies and technologies for the understanding of biomedicine through bioinformation discovery. Therefore, we briefly outline the use of such techniques in virology.

Intelligent hearing aids: the next revolution.

Science.gov (United States)

Tao Zhang; Mustiere, Fred; Micheyl, Christophe

2016-08-01

The first revolution in hearing aids came from nonlinear amplification, which allows better compensation for both soft and loud sounds. The second revolution stemmed from the introduction of digital signal processing, which allows better programmability and more sophisticated algorithms. The third revolution in hearing aids is wireless, which allows seamless connectivity between a pair of hearing aids and with more and more external devices. Each revolution has fundamentally transformed hearing aids and pushed the entire industry forward significantly. Machine learning has received significant attention in recent years and has been applied in many other industries, e.g., robotics, speech recognition, genetics, and crowdsourcing. We argue that the next revolution in hearing aids is machine intelligence. In fact, this revolution is already quietly happening. We will review the development in at least three major areas: applications of machine learning in speech enhancement; applications of machine learning in individualization and customization of signal processing algorithms; applications of machine learning in improving the efficiency and effectiveness of clinical tests. With the advent of the internet of things, the above developments will accelerate. This revolution will bring patient satisfactions to a new level that has never been seen before.

Real-Time Probabilistic Structural Health Management Using Machine Learning and GPU Computing Project

Data.gov (United States)

National Aeronautics and Space Administration — The proposed project seeks to deliver an ultra-efficient, high-fidelity structural health management (SHM) framework using machine learning and graphics processing...

Intelligent methods for cyber warfare

CERN Document Server

Reformat, Marek; Alajlan, Naif

2015-01-01

Cyberwarfare has become an important concern for governmental agencies as well businesses of various types.  This timely volume, with contributions from some of the internationally recognized, leaders in the field, gives readers a glimpse of the new and emerging ways that Computational Intelligence and Machine Learning methods can be applied to address problems related to cyberwarfare. The book includes a number of chapters that can be conceptually divided into three topics: chapters describing different data analysis methodologies with their applications to cyberwarfare, chapters presenting a number of intrusion detection approaches, and chapters dedicated to analysis of possible cyber attacks and their impact. The book provides the readers with a variety of methods and techniques, based on computational intelligence, which can be applied to the broad domain of cyberwarfare.

Intelligent decision-making models for production and retail operations

CERN Document Server

Guo, Zhaoxia

2016-01-01

This book provides an overview of intelligent decision-making techniques and discusses their application in production and retail operations. Manufacturing and retail enterprises have stringent standards for using advanced and reliable techniques to improve decision-making processes, since these processes have significant effects on the performance of relevant operations and the entire supply chain. In recent years, researchers have been increasingly focusing attention on using intelligent techniques to solve various decision-making problems. The opening chapters provide an introduction to several commonly used intelligent techniques, such as genetic algorithm, harmony search, neural network and extreme learning machine. The book then explores the use of these techniques for handling various production and retail decision-making problems, such as production planning and scheduling, assembly line balancing, and sales forecasting.

Future Smart Cooking Machine System Design

Directory of Open Access Journals (Sweden)

Dewi Agushinta R.

2013-11-01

Full Text Available There are many tools make human task get easier. Cooking has become a basic necessity for human beings, since food is one of basic human needs. Until now, the cooking equipment being used is still a hand tool. However everyone has slightly high activity. The presence of cooking tools that can do the cooking work by itself is now necessary. Future Smart Cooking Machine is an artificial intelligence machine that can do cooking work automatically. With this system design, the time is minimized and the ease of work is expected to be achieved. The development of this system is carried out with System Development Life Cycle (SDLC methods. Prototyping method used in this system is a throw-away prototyping approach. At the end of this research there will be produced a cooking machine system design including physical design engine and interface design.

A study on intelligent nuclear systems (HASP: Human Acts Simulation Program)

International Nuclear Information System (INIS)

Asai, Kiyoshi; Fujii, Minoru; Higuchi, Kenji; Kume, Etsuo; Ohtani, Takayuki; Far, B.H.; Kambayashi, Shaw; Akimoto, Masayuki

1991-06-01

The fourth year progress of the Human Acts Simulation Program HASP in short, has been presented in this report. The HASP started in 1987 at JAERI as a ten-year research and development program of underlying technologies for intelligent robots, intelligent nuclear plants and so on. It consists of the research and development of technologies of a knowledge-base system, robot vision, robot kinematics/kinetics, plant geometry database, dose evaluation and high speed Monte Carlo machine. (author)

A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics

Directory of Open Access Journals (Sweden)

Shan Li

2014-01-01

Full Text Available With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.

8th Asian Conference on Intelligent Information and Database Systems

CERN Document Server

Madeyski, Lech; Nguyen, Ngoc

2016-01-01

The objective of this book is to contribute to the development of the intelligent information and database systems with the essentials of current knowledge, experience and know-how. The book contains a selection of 40 chapters based on original research presented as posters during the 8th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2016) held on 14–16 March 2016 in Da Nang, Vietnam. The papers to some extent reflect the achievements of scientific teams from 17 countries in five continents. The volume is divided into six parts: (a) Computational Intelligence in Data Mining and Machine Learning, (b) Ontologies, Social Networks and Recommendation Systems, (c) Web Services, Cloud Computing, Security and Intelligent Internet Systems, (d) Knowledge Management and Language Processing, (e) Image, Video, Motion Analysis and Recognition, and (f) Advanced Computing Applications and Technologies. The book is an excellent resource for researchers, those working in artificial intelligence, mu...

An Artificial Intelligence Approach to Transient Stability Assessment

OpenAIRE

Akella, Vijay Ahaskar; Khincha, HP; Kumar, Sreerama R

1991-01-01

An artificial intelligence approach to online transient stability assessment is briefly discussed, and some crucial requirements for this algorithm are identified. Solutions to these are proposed. Some new attributes are suggested so as to reflect machine dynamics and changes in the network. Also a new representative learning set algorithm has been developed.

How artificial intelligence can help [man-machine interface

International Nuclear Information System (INIS)

Elm, W.C.

1988-01-01

The operator is ultimately responsible for the safe and economical operation of the plant, and must evaluate the accuracy of any system-recommended action or other output. Decision support systems offer a means to improve the man-machine interface by explicitly supporting operator problem solving, rather than complicating decision-making by the need to request an explanation of the rationale behind an expert system's advice during a high stress situation. (author)

2nd International Conference on Computational Intelligence in Data Mining

CERN Document Server

Mohapatra, Durga

2016-01-01

The book is a collection of high-quality peer-reviewed research papers presented in the Second International Conference on Computational Intelligence in Data Mining (ICCIDM 2015) held at Bhubaneswar, Odisha, India during 5 – 6 December 2015. The two-volume Proceedings address the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. The book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.

Space applications of artificial intelligence; 1990 Goddard Conference, Greenbelt, MD, May 1, 2, 1990, Selected Papers

Science.gov (United States)

Rash, James L. (Editor)

1990-01-01

The papers presented at the 1990 Goddard Conference on Space Applications of Artificial Intelligence are given. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The proceedings fall into the following areas: Planning and Scheduling, Fault Monitoring/Diagnosis, Image Processing and Machine Vision, Robotics/Intelligent Control, Development Methodologies, Information Management, and Knowledge Acquisition.

Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: a systematic literature review.

Science.gov (United States)

Foulquier, Nathan; Redou, Pascal; Le Gal, Christophe; Rouvière, Bénédicte; Pers, Jacques-Olivier; Saraux, Alain

2018-05-17

Big data analysis has become a common way to extract information from complex and large datasets among most scientific domains. This approach is now used to study large cohorts of patients in medicine. This work is a review of publications that have used artificial intelligence and advanced machine learning techniques to study physio pathogenesis-based treatments in pSS. A systematic literature review retrieved all articles reporting on the use of advanced statistical analysis applied to the study of systemic autoimmune diseases (SADs) over the last decade. An automatic bibliography screening method has been developed to perform this task. The program called BIBOT was designed to fetch and analyze articles from the pubmed database using a list of keywords and Natural Language Processing approaches. The evolution of trends in statistical approaches, sizes of cohorts and number of publications over this period were also computed in the process. In all, 44077 abstracts were screened and 1017 publications were analyzed. The mean number of selected articles was 101.0 (S.D. 19.16) by year, but increased significantly over the time (from 74 articles in 2008 to 138 in 2017). Among them only 12 focused on pSS but none of them emphasized on the aspect of pathogenesis-based treatments. To conclude, medicine progressively enters the era of big data analysis and artificial intelligence, but these approaches are not yet used to describe pSS-specific pathogenesis-based treatment. Nevertheless, large multicentre studies are investigating this aspect with advanced algorithmic tools on large cohorts of SADs patients.

13th International Conference Intelligent Autonomous Systems

CERN Document Server

Michael, Nathan; Berns, Karsten; Yamaguchi, Hiroaki

2016-01-01

This book describes the latest research accomplishments, innovations, and visions in the field of robotics as presented at the 13th International Conference on Intelligent Autonomous Systems (IAS), held in Padua in July 2014, by leading researchers, engineers, and practitioners from across the world. The contents amply confirm that robots, machines, and systems are rapidly achieving intelligence and autonomy, mastering more and more capabilities such as mobility and manipulation, sensing and perception, reasoning, and decision making. A wide range of research results and applications are covered, and particular attention is paid to the emerging role of autonomous robots and intelligent systems in industrial production, which reflects their maturity and robustness. The contributions have been selected through a rigorous peer-review process and contain many exciting and visionary ideas that will further galvanize the research community, spurring novel research directions. The series of biennial IAS conferences ...

First Euro-China Conference on Intelligent Data Analysis and Applications

CERN Document Server

Snasel, Vaclav; Corchado, Emilio; Abraham, Ajith; Wang, Shyue-Liang

2014-01-01

The First Euro-China Conference on Intelligent Data Analysis and Applications (ECC 2014), which was hosted by Shenzhen Graduate School of Harbin Institute of Technology and was held in Shenzhen City on June 13-15, 2014. ECC 2014 was technically co-sponsored by Shenzhen Municipal People’s Government, IEEE Signal Processing Society, Machine Intelligence Research Labs, VSB-Technical University of Ostrava (Czech Republic), National Kaohsiung University of Applied Sciences (Taiwan), and Secure E-commerce Transactions (Shenzhen) Engineering Laboratory of Shenzhen Institute of Standards and Technology.

Correlation between General Health with Emotional Intelligence and Creativity in Medical College Students at Islamic Azad University, Sari Branch, Sari, Iran

Directory of Open Access Journals (Sweden)

MK Fakhri

2012-07-01

Full Text Available

Background and Objectives: Medical students are a particular class of students that Because of their specific problems, investigation of their general health has always been considered. This study is concerned with investigation of relationship between general health and emotional intelligence and creativity in medical college students at Islamic Azad University, Sari branch.

Methods: 150 medical college students at Islamic Azad University, Sari branch (45 males and 105 females, were randomly selected and Goldberg general health, Shring emotional intelligence and Abedi creativity questionnaire were completed. For data analysis, Pearson correlation and independent t-test were used.

Results: Results showed that: there is positive relationship between general health and emotional intelligence (r=0.53 and p<0.05, there is a positive relationship between general health and creativity (r=0.60 and p<0.01, and female college students are healthier than males (p<0.05.

Conclusion: results of this research indicated that there is a positive relationship between general health and emotional intelligence and creativity, and since these variables are effective in professional prospect of Medical students, employing cognitive and behavioral methods in promotion of general health in these students seem necessary.

Emotional intelligence, life satisfaction and subjective happiness in female student health professionals: the mediating effect of perceived stress.

Science.gov (United States)

Ruiz-Aranda, D; Extremera, N; Pineda-Galán, C

2014-03-01

The objective of the present study was to extend previous findings by examining the relationship between emotional intelligence (EI) and well-being indicators (life satisfaction and happiness) in a 12-week follow-up study. In addition, we examined the influence of perceived stress on the relationship between EI and well-being. Female students from the School of Health Sciences (n = 264) completed an ability measure of emotional intelligence. After 12 weeks, participants completed the Perceived Stress Scale, Satisfaction with Life Scale and Subjective Happiness Scale. Participants with higher EI reported less perceived stress and higher levels of life satisfaction and happiness. The results of this study suggest that perceived stress mediates the relationship between EI and well-being indicators, specifically life satisfaction and happiness. These findings suggest an underlying process by which high emotional intelligence may increase well-being in female students in nursing and allied health sciences by reducing the experience of stress. The implications of these findings for future research and for working with health professions to improve well-being outcomes are discussed. © 2013 John Wiley & Sons Ltd.

A deviation based assessment methodology for multiple machine health patterns classification and fault detection

Science.gov (United States)

Jia, Xiaodong; Jin, Chao; Buzza, Matt; Di, Yuan; Siegel, David; Lee, Jay

2018-01-01

Successful applications of Diffusion Map (DM) in machine failure detection and diagnosis have been reported in several recent studies. DM provides an efficient way to visualize the high-dimensional, complex and nonlinear machine data, and thus suggests more knowledge about the machine under monitoring. In this paper, a DM based methodology named as DM-EVD is proposed for machine degradation assessment, abnormality detection and diagnosis in an online fashion. Several limitations and challenges of using DM for machine health monitoring have been analyzed and addressed. Based on the proposed DM-EVD, a deviation based methodology is then proposed to include more dimension reduction methods. In this work, the incorporation of Laplacian Eigen-map and Principal Component Analysis (PCA) are explored, and the latter algorithm is named as PCA-Dev and is validated in the case study. To show the successful application of the proposed methodology, case studies from diverse fields are presented and investigated in this work. Improved results are reported by benchmarking with other machine learning algorithms.

Intelligent engineering and technology for nuclear power plant operation

International Nuclear Information System (INIS)

Wang, P.P.; Gu, X.

1996-01-01

The Three-Mile-Island accident has drawn considerable attention by the engineering, scientific, management, financial, and political communities as well as society at large. This paper surveys possible causes of the accident studied by various groups. Research continues in this area with many projects aimed at specifically improving the performance and operation of a nuclear power plant using the contemporary technologies available. In addition to the known cause of the accident and suggest a strategy for coping with these problems in the future. With the increased use of intelligent methodologies called computational intelligence or soft-computing, a substantially larger collection of powerful tools are now available for our designers to use in order to tackle these sensitive and difficult issues. These intelligent methodologies consists of fuzzy logic, genetic algorithms, neural networks, artificial intelligence and expert systems, pattern recognition, machine intelligence, and fuzzy constraint networks. Using the Three-Mile-Island experience, this paper offers a set of specific recommendations for future designers to take advantage of the powerful tools of intelligent technologies that we are now able to master and encourages the adoption of a novel methodology called fuzzy constraint network

Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy.

Science.gov (United States)

Hueso, Miguel; Vellido, Alfredo; Montero, Nuria; Barbieri, Carlo; Ramos, Rosa; Angoso, Manuel; Cruzado, Josep Maria; Jonsson, Anders

2018-02-01

Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising

SAMS--a systems architecture for developing intelligent health information systems.

Science.gov (United States)

Yılmaz, Özgün; Erdur, Rıza Cenk; Türksever, Mustafa

2013-12-01

In this paper, SAMS, a novel health information system architecture for developing intelligent health information systems is proposed and also some strategies for developing such systems are discussed. The systems fulfilling this architecture will be able to store electronic health records of the patients using OWL ontologies, share patient records among different hospitals and provide physicians expertise to assist them in making decisions. The system is intelligent because it is rule-based, makes use of rule-based reasoning and has the ability to learn and evolve itself. The learning capability is provided by extracting rules from previously given decisions by the physicians and then adding the extracted rules to the system. The proposed system is novel and original in all of these aspects. As a case study, a system is implemented conforming to SAMS architecture for use by dentists in the dental domain. The use of the developed system is described with a scenario. For evaluation, the developed dental information system will be used and tried by a group of dentists. The development of this system proves the applicability of SAMS architecture. By getting decision support from a system derived from this architecture, the cognitive gap between experienced and inexperienced physicians can be compensated. Thus, patient satisfaction can be achieved, inexperienced physicians are supported in decision making and the personnel can improve their knowledge. A physician can diagnose a case, which he/she has never diagnosed before, using this system. With the help of this system, it will be possible to store general domain knowledge in this system and the personnel's need to medical guideline documents will be reduced.

Man-machine interface requirements - advanced technology

Science.gov (United States)

Remington, R. W.; Wiener, E. L.

1984-01-01

Research issues and areas are identified where increased understanding of the human operator and the interaction between the operator and the avionics could lead to improvements in the performance of current and proposed helicopters. Both current and advanced helicopter systems and avionics are considered. Areas critical to man-machine interface requirements include: (1) artificial intelligence; (2) visual displays; (3) voice technology; (4) cockpit integration; and (5) pilot work loads and performance.

AI and health

OpenAIRE

Martin Anderson

2018-01-01

ntelligence refers to the ability of an individual to learn or understand something, or to deal with a new situation. When a machine or software exhibits such characteristics, it is referred to as artificial intelligence (AI). Artificial Intelligence is therefore defined as the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

AAAIC '88 - Aerospace Applications of Artificial Intelligence; Proceedings of the Fourth Annual Conference, Dayton, OH, Oct. 25-27, 1988. Volumes 1 ampersand 2

International Nuclear Information System (INIS)

Johnson, J.R.

1988-01-01

Topics presented include integrating neural networks and expert systems, neural networks and signal processing, machine learning, cognition and avionics applications, artificial intelligence and man-machine interface issues, real time expert systems, artificial intelligence, and engineering applications. Also considered are advanced problem solving techniques, combinational optimization for scheduling and resource control, data fusion/sensor fusion, back propagation with momentum, shared weights and recurrency, automatic target recognition, cybernetics, optical neural networks

[Intelligent watch system for health monitoring based on Bluetooth low energy technology].

Science.gov (United States)

Wang, Ji; Guo, Hailiang; Ren, Xiaoli

2017-08-01

According to the development status of wearable technology and the demand of intelligent health monitoring, we studied the multi-function integrated smart watches solution and its key technology. First of all, the sensor technology with high integration density, Bluetooth low energy (BLE) and mobile communication technology were integrated and used in develop practice. Secondly, for the hardware design of the system in this paper, we chose the scheme with high integration density and cost-effective computer modules and chips. Thirdly, we used real-time operating system FreeRTOS to develop the friendly graphical interface interacting with touch screen. At last, the high-performance application software which connected with BLE hardware wirelessly and synchronized data was developed based on android system. The function of this system included real-time calendar clock, telephone message, address book management, step-counting, heart rate and sleep quality monitoring and so on. Experiments showed that the collecting data accuracy of various sensors, system data transmission capacity, the overall power consumption satisfy the production standard. Moreover, the system run stably with low power consumption, which could realize intelligent health monitoring effectively.

Cultural Perspective on Parenting, Trait Emotional Intelligence and Mental Health in Taiwanese Children

Science.gov (United States)

Huang, Ching-Yu; Shen, April Chiung-Tao; Hsieh, Yi-Ping; Feng, Jui-Ying; Wei, Hsi-Sheng; Hwa, Hsiao-Lin; Feng, Joyce Yen

2017-01-01

The current study aims to clarify the associations as well as the pathways through which parenting and children's emotional intelligence (EI) may influence children's mental health with a cross-sectional sample of 675 school pupils (fourth grade, mean age = 10.4 years, 310 boy, 356 girls and 9 unidentified) in Taiwan. Hierarchical regression and…

Machine learning in autistic spectrum disorder behavioral research: A review and ways forward.

Science.gov (United States)

Thabtah, Fadi

2018-02-13

Autistic Spectrum Disorder (ASD) is a mental disorder that retards acquisition of linguistic, communication, cognitive, and social skills and abilities. Despite being diagnosed with ASD, some individuals exhibit outstanding scholastic, non-academic, and artistic capabilities, in such cases posing a challenging task for scientists to provide answers. In the last few years, ASD has been investigated by social and computational intelligence scientists utilizing advanced technologies such as machine learning to improve diagnostic timing, precision, and quality. Machine learning is a multidisciplinary research topic that employs intelligent techniques to discover useful concealed patterns, which are utilized in prediction to improve decision making. Machine learning techniques such as support vector machines, decision trees, logistic regressions, and others, have been applied to datasets related to autism in order to construct predictive models. These models claim to enhance the ability of clinicians to provide robust diagnoses and prognoses of ASD. However, studies concerning the use of machine learning in ASD diagnosis and treatment suffer from conceptual, implementation, and data issues such as the way diagnostic codes are used, the type of feature selection employed, the evaluation measures chosen, and class imbalances in data among others. A more serious claim in recent studies is the development of a new method for ASD diagnoses based on machine learning. This article critically analyses these recent investigative studies on autism, not only articulating the aforementioned issues in these studies but also recommending paths forward that enhance machine learning use in ASD with respect to conceptualization, implementation, and data. Future studies concerning machine learning in autism research are greatly benefitted by such proposals.

The 1988 Goddard Conference on Space Applications of Artificial Intelligence

Science.gov (United States)

Rash, James (Editor); Hughes, Peter (Editor)

1988-01-01

This publication comprises the papers presented at the 1988 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland on May 24, 1988. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in these proceedings fall into the following areas: mission operations support, planning and scheduling; fault isolation/diagnosis; image processing and machine vision; data management; modeling and simulation; and development tools/methodologies.

Supervised-machine Learning for Intelligent Collision Avoidance Decision-making and Sensor Tasking

Data.gov (United States)

National Aeronautics and Space Administration — Building an autonomous architecture that uses directed self-learning neuro-fuzzy networks with the aim of developing an intelligent autonomous collision avoidance...

Does Wittgenstein Actually Undermine the Foundation of Artificial Intelligence?

Institute of Scientific and Technical Information of China (English)

XU Yingjin

2016-01-01

Wittgenstein is widely viewed as a potential critic of a key philosophical assumption of the Strong Artificial Intelligence (AI) thesis,namely,that it is in principle possible to build a programmed machine which can achieve real intelligence.Stuart Shanker has provided the most systematic reconstruction of the Wittgensteinian argument against AI,building on Wittgenstein's own statements,the "rule-following" feature of language-games,and the putative alliance between AI and psychologism.This article will attempt to refute this reconstruction and its constituent arguments,thereby paving the way for a new and amicable rather than agonistic conception of the Wittgensteinian position on AI.

An Intelligent System Approach for Asthma Prediction in Symptomatic Preschool Children

Directory of Open Access Journals (Sweden)

E. Chatzimichail

2013-01-01

Full Text Available Objectives. In this study a new method for asthma outcome prediction, which is based on Principal Component Analysis and Least Square Support Vector Machine Classifier, is presented. Most of the asthma cases appear during the first years of life. Thus, the early identification of young children being at high risk of developing persistent symptoms of the disease throughout childhood is an important public health priority. Methods. The proposed intelligent system consists of three stages. At the first stage, Principal Component Analysis is used for feature extraction and dimension reduction. At the second stage, the pattern classification is achieved by using Least Square Support Vector Machine Classifier. Finally, at the third stage the performance evaluation of the system is estimated by using classification accuracy and 10-fold cross-validation. Results. The proposed prediction system can be used in asthma outcome prediction with 95.54 % success as shown in the experimental results. Conclusions. This study indicates that the proposed system is a potentially useful decision support tool for predicting asthma outcome and that some risk factors enhance its predictive ability.

Literacy not intelligence moderates the relationships between economic development, income inequality and health.

Science.gov (United States)

Marks, David F

2007-05-01

Kanazawa (2006) presented data allegedly supporting a racist version of evolutionary psychology that claims that the populations of wealthier and more egalitarian societies live longer and stay healthier, not because they are wealthier and more egalitarian, but because they are more intelligent. The objectives of this study are: (i) to determine the relationship between IQ and literacy in Kanazawa's sample of countries and (ii) to reanalyse Kanazawa's dataset using measures of literacy in lieu of national IQ test scores. Correlation and regression were employed. National literacy scores across the countries in the sample are highly skewed. In spite of this, the literacy measures are highly correlated with alleged differences in national IQ (r = .83-.86). The measure of literacy together with economic development (GDPpc) and income inequality (Gini coefficient) control at least 59-64% of the variance in national life expectancy at birth. There is no scientific justification for believing that alleged intelligence differences play any role in explaining international differences in health status. Measures of alleged national IQ scores are highly confounded with differences in literacy. Literacy is a key factor in the health of any community and policies designed to enhance the literacy of a population are expected to lead to significant improvements in health status.

Intelligent Vehicle Power Management Using Machine Learning and Fuzzy Logic

National Research Council Canada - National Science Library

Chen, ZhiHang; Masrur, M. A; Murphey, Yi L

2008-01-01

.... A machine learning algorithm, LOPPS, has been developed to learn about optimal power source combinations with respect to minimum power loss for all possible load requests and various system power states...

Control processes and machine protection on ASDEX Upgrade

International Nuclear Information System (INIS)

Raupp, G.; Treutterer, W.; Mertens, V.; Neu, G.; Sips, A.; Zasche, D.; Zehetbauer, Th.

2007-01-01

Safe operation of ASDEX Upgrade is guaranteed by a conventional hierarchy of simple and robust hard-wired systems for personnel and machine protection featuring standardized switch-off procedures. Machine protection and handling of off-normal events is further enhanced and peak and lifetime stress minimized through the plasma control system. Based on a real-time process model supporting safety critical applications with data quality tagging, process self-monitoring, watchdog monitoring and alarm propagation, processes detect complex and critical failures and reliably perform case-sensitive counter measures. Intelligent real-time failure handling is done with hardware or software redundancy and performance degradation, or modification of reference values to continue or terminate discharges with reduced machine stress. Examples implemented so far on ASDEX Upgrade are given, such as recovery from measurement failures, switch-over of redundant actuators, handling of actuator limitations, detection of plasma instabilities, plasma state dependent soft landing, or handling of failed switch-off procedures through breakers disconnecting the machine from grid

4th Workshop on Combinations of Intelligent Methods and Applications

CERN Document Server

Palade, Vasile; Prentzas, Jim

2016-01-01

This volume includes extended and revised versions of the papers presented at the 4th Workshop on “Combinations of Intelligent Methods and Applications” (CIMA 2014) which was intended to become a forum for exchanging experience and ideas among researchers and practitioners dealing with combinations of different intelligent methods in Artificial Intelligence. The aim is to create integrated or hybrid methods that benefit from each of their components. Some of the existing presented efforts combine soft computing methods (fuzzy logic, neural networks and genetic algorithms). Another stream of efforts integrates case-based reasoning or machine learning with soft-computing methods. Some of the combinations have been more widely explored, like neuro-symbolic methods, neuro-fuzzy methods and methods combining rule-based and case-based reasoning. CIMA 2014 was held in conjunction with the 26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2014). .

Growing adaptive machines combining development and learning in artificial neural networks

CERN Document Server

Bredeche, Nicolas; Doursat, René

2014-01-01

The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited success. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs, and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks. The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a...

Machine Learning and Inverse Problem in Geodynamics

Science.gov (United States)

Shahnas, M. H.; Yuen, D. A.; Pysklywec, R.

2017-12-01

During the past few decades numerical modeling and traditional HPC have been widely deployed in many diverse fields for problem solutions. However, in recent years the rapid emergence of machine learning (ML), a subfield of the artificial intelligence (AI), in many fields of sciences, engineering, and finance seems to mark a turning point in the replacement of traditional modeling procedures with artificial intelligence-based techniques. The study of the circulation in the interior of Earth relies on the study of high pressure mineral physics, geochemistry, and petrology where the number of the mantle parameters is large and the thermoelastic parameters are highly pressure- and temperature-dependent. More complexity arises from the fact that many of these parameters that are incorporated in the numerical models as input parameters are not yet well established. In such complex systems the application of machine learning algorithms can play a valuable role. Our focus in this study is the application of supervised machine learning (SML) algorithms in predicting mantle properties with the emphasis on SML techniques in solving the inverse problem. As a sample problem we focus on the spin transition in ferropericlase and perovskite that may cause slab and plume stagnation at mid-mantle depths. The degree of the stagnation depends on the degree of negative density anomaly at the spin transition zone. The training and testing samples for the machine learning models are produced by the numerical convection models with known magnitudes of density anomaly (as the class labels of the samples). The volume fractions of the stagnated slabs and plumes which can be considered as measures for the degree of stagnation are assigned as sample features. The machine learning models can determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at mid-mantle depths. Employing support vector machine (SVM) algorithms we show that SML techniques

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

Science.gov (United States)

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2017-01-01

Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Anesthesiology, automation, and artificial intelligence.

Science.gov (United States)

Alexander, John C; Joshi, Girish P

2018-01-01

There have been many attempts to incorporate automation into the practice of anesthesiology, though none have been successful. Fundamentally, these failures are due to the underlying complexity of anesthesia practice and the inability of rule-based feedback loops to fully master it. Recent innovations in artificial intelligence, especially machine learning, may usher in a new era of automation across many industries, including anesthesiology. It would be wise to consider the implications of such potential changes before they have been fully realized.

Artificial Intelligence, Employment, and Income

OpenAIRE

Nilsson, Nils J.

1984-01-01

Artificial intelligence (AI) will have profound societal effects. It promises potential benefits (and may also pose risks) in education, defense, business, law and science. In this article we explore how AI is likely to affect employment and the distribution of income. We argue that AI will indeed reduce drastically the need of human toil. We also note that some people fear the automation of work by machines and the resulting of unemployment. Yet, since the majority of us probably would rathe...

Man-machine communication in reactor control using AI methods

International Nuclear Information System (INIS)

Klebau, J.; Lindner, A.; Fiedler, U.

1987-01-01

In the last years the interest in process control has expecially focused on problems of man-machine communication. It depends on its great importance to process performance and user acceptance. Advanced computerized operator aids, e.g. in nuclear power plants, are as well as their man-machine interface. In the Central Institute for Nuclear Research in Rossendorf a computerized operator support system for nuclear power plants is designed, which is involved in a decentralized process automation system. A similar but simpler system, the Hierarchical Informational System (HIS) at the Rossendorf Research Reactor, works with a computer controlled man-machine interface, based on menu. In the special case of the disturbance analysis program SAAP-2, which is included in the HIS, the limits of menu techniques are obviously. Therefore it seems to be necessary and with extended hard- and software possible to realize an user controlled natural language interface using Artificial Intelligence (AI) methods. The draft of such a system is described. It should be able to learn during a teaching phase all phrases and their meanings. The system will work on the basis of a self-organizing, associative data structure. It is used to recognize a great amount of words which are used in language analysis. Error recognition and, if possible, correction is done by means of a distance function in the word set. Language analysis should be carried out with a simplified word class controlled functional analysis. With this interface it is supposed to get experience in intelligent man-machine communication to enhance operational safety in future. (author)

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.

Artificial Intelligence in Speech Understanding: Two Applications at C.R.I.N.

Science.gov (United States)

Carbonell, N.; And Others

1986-01-01

This article explains how techniques of artificial intelligence are applied to expert systems for acoustic-phonetic decoding, phonological interpretation, and multi-knowledge sources for man-machine dialogue implementation. The basic ideas are illustrated with short examples. (Author/JDH)

A robust hybrid model integrating enhanced inputs based extreme learning machine with PLSR (PLSR-EIELM) and its application to intelligent measurement.

Science.gov (United States)

He, Yan-Lin; Geng, Zhi-Qiang; Xu, Yuan; Zhu, Qun-Xiong

2015-09-01

In this paper, a robust hybrid model integrating an enhanced inputs based extreme learning machine with the partial least square regression (PLSR-EIELM) was proposed. The proposed PLSR-EIELM model can overcome two main flaws in the extreme learning machine (ELM), i.e. the intractable problem in determining the optimal number of the hidden layer neurons and the over-fitting phenomenon. First, a traditional extreme learning machine (ELM) is selected. Second, a method of randomly assigning is applied to the weights between the input layer and the hidden layer, and then the nonlinear transformation for independent variables can be obtained from the output of the hidden layer neurons. Especially, the original input variables are regarded as enhanced inputs; then the enhanced inputs and the nonlinear transformed variables are tied together as the whole independent variables. In this way, the PLSR can be carried out to identify the PLS components not only from the nonlinear transformed variables but also from the original input variables, which can remove the correlation among the whole independent variables and the expected outputs. Finally, the optimal relationship model of the whole independent variables with the expected outputs can be achieved by using PLSR. Thus, the PLSR-EIELM model is developed. Then the PLSR-EIELM model served as an intelligent measurement tool for the key variables of the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. The experimental results show that the predictive accuracy of PLSR-EIELM is stable, which indicate that PLSR-EIELM has good robust character. Moreover, compared with ELM, PLSR, hierarchical ELM (HELM), and PLSR-ELM, PLSR-EIELM can achieve much smaller predicted relative errors in these two applications. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

14th International Conference on Intelligent Autonomous Systems

CERN Document Server

Hosoda, Koh; Menegatti, Emanuele; Shimizu, Masahiro; Wang, Hesheng

2017-01-01

This book describes the latest research advances, innovations, and visions in the field of robotics as presented by leading researchers, engineers, and practitioners from around the world at the 14th International Conference on Intelligent Autonomous Systems (IAS-14), held in Shanghai, China in July 2016. The contributions amply demonstrate that robots, machines and systems are rapidly achieving intelligence and autonomy, attaining more and more capabilities such as mobility and manipulation, sensing and perception, reasoning, and decision-making. They cover a wide range of research results and applications, and particular attention is paid to the emerging role of autonomous robots and intelligent systems in industrial production, which reflects their maturity and robustness. The contributions were selected by means of a rigorous peer-review process and highlight many exciting and visionary ideas that will further galvanize the research community and spur novel research directions. The series of biennial IAS ...

Machine Learning Approaches for Detecting Diabetic Retinopathy from Clinical and Public Health Records.

Science.gov (United States)

Ogunyemi, Omolola; Kermah, Dulcie

2015-01-01

Annual eye examinations are recommended for diabetic patients in order to detect diabetic retinopathy and other eye conditions that arise from diabetes. Medically underserved urban communities in the US have annual screening rates that are much lower than the national average and could benefit from informatics approaches to identify unscreened patients most at risk of developing retinopathy. Using clinical data from urban safety net clinics as well as public health data from the CDC's National Health and Nutrition Examination Survey, we examined different machine learning approaches for predicting retinopathy from clinical or public health data. All datasets utilized exhibited a class imbalance. Classifiers learned on the clinical data were modestly predictive of retinopathy with the best model having an AUC of 0.72, sensitivity of 69.2% and specificity of 55.9%. Classifiers learned on public health data were not predictive of retinopathy. Successful approaches to detecting latent retinopathy using machine learning could help safety net and other clinics identify unscreened patients who are most at risk of developing retinopathy and the use of ensemble classifiers on clinical data shows promise for this purpose.

25th International Conference on Industrial, Engineering & Other Applica- tions of Applied Intelligent Systems (IEA/AIE 2012)

CERN Document Server

Jiang, He; Ali, Moonis; Li, Mingchu; Modern Advances in Intelligent Systems and Tools

2012-01-01

Intelligent systems provide a platform to connect the research in artificial intelligence to real-world problem solving applications. Various intelligent systems have been developed to face real-world applications. This book discusses the modern advances in intelligent systems and the tools in applied artificial intelligence. It consists of twenty-three chapters authored by participants of the 25th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE 2012) which was held in Dalian, China. This book is divided into six parts, including Applied Intelligence, Cognitive Computing and Affective Computing, Data Mining and Intelligent Systems, Decision Support Systems, Machine Learning, and Natural Language Processing. Each part includes three to five chapters. In these chapters, many approaches, applications, restrictions, and discussions are presented. The material of each chapter is self-contained and was reviewed by at least two anonymous referees t...

Machine learning applied to crime prediction

OpenAIRE

Vaquero Barnadas, Miquel

2016-01-01

Machine Learning is a cornerstone when it comes to artificial intelligence and big data analysis. It provides powerful algorithms that are capable of recognizing patterns, classifying data, and, basically, learn by themselves to perform a specific task. This field has incredibly grown in popularity these days, however, it still remains unknown for the majority of people, and even for most professionals. This project intends to provide an understandable explanation of what is it, what types ar...

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...

Intelligent and Evolutionary Systems : the 20th Asia Pacific Symposium

CERN Document Server

Singh, Hemant; Elsayed, Saber

2017-01-01

Over the last two decades the field of Intelligent Systems delivered to human kind significant achievements, while also facing major transformations. 20 years ago, automation and knowledge-based AI were still the dominant paradigms fueling the efforts of both researchers and practitioners. Later, 10 years ago, statistical machine intelligence was on the rise, heavily supported by the digital computing, and led to the unprecedented advances in and dependence on digital technology. However, the resultant intelligent systems remained designer-based endeavors and thus, were limited in their true learning and development abilities. Today, the challenge is to have in place intelligent systems that can develop themselves on behalf of their creators, and gain abilities with no or limited supervision in the tasks they are meant to perform. Cognitive development systems, and the supporting cognitive computing are on the rise today, promising yet other significant achievements for the future of human kind. This book cap...

Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

Science.gov (United States)

Ganapathy, S.; Yogesh, P.; Kannan, A.

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. PMID:23056036

Developing Novel Machine Learning Algorithms to Improve Sedentary Assessment for Youth Health Enhancement.

Science.gov (United States)

Golla, Gowtham Kumar; Carlson, Jordan A; Huan, Jun; Kerr, Jacqueline; Mitchell, Tarrah; Borner, Kelsey

2016-10-01

Sedentary behavior of youth is an important determinant of health. However, better measures are needed to improve understanding of this relationship and the mechanisms at play, as well as to evaluate health promotion interventions. Wearable accelerometers are considered as the standard for assessing physical activity in research, but do not perform well for assessing posture (i.e., sitting vs. standing), a critical component of sedentary behavior. The machine learning algorithms that we propose for assessing sedentary behavior will allow us to re-examine existing accelerometer data to better understand the association between sedentary time and health in various populations. We collected two datasets, a laboratory-controlled dataset and a free-living dataset. We trained machine learning classifiers separately on each dataset and compared performance across datasets. The classifiers predict five postures: sit, stand, sit-stand, stand-sit, and stand\\walk. We compared a manually constructed Hidden Markov model (HMM) with an automated HMM from existing software. The manually constructed HMM gave more F1-Macro score on both datasets.

Building machines that learn and think like people.

Science.gov (United States)

Lake, Brenden M; Ullman, Tomer D; Tenenbaum, Joshua B; Gershman, Samuel J

2017-01-01

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

Machine Learning Methods to Predict Diabetes Complications.

Science.gov (United States)

Dagliati, Arianna; Marini, Simone; Sacchi, Lucia; Cogni, Giulia; Teliti, Marsida; Tibollo, Valentina; De Cata, Pasquale; Chiovato, Luca; Bellazzi, Riccardo

2018-03-01

One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice.

Unobtrusive and comprehensive health screening using an intelligent toilet system.

Science.gov (United States)

Schlebusch, Thomas; Fichtner, Wolfgang; Mertig, Michael; Leonhardt, Steffen

2015-02-01

Home monitoring is a promising technology to deal with the increasing amount of chronically ill patients while ensuring quality of medical care. Most systems available today depend on a high degree of interaction between the user and the device. Especially for people relying on advanced levels of care, this scheme is impracticable. In this paper, we are presenting an "intelligent toilet" performing an extensive health check while being as simple to use as a conventional toilet. The main focus of the system is to support the treatment of diabetes and chronic heart failure, but additional applications are possible.

Recycling Texts: Human evaluation of example-based machine translation subtitles for DVD

DEFF Research Database (Denmark)

Flanagan, Marian

2009-01-01

This project focuses on translation reusability in audiovisual contexts. Specifically, the project seeks to establish (1) whether target language subtitles produced by an Example-Based Machine Translation (EBMT) system are considered intelligible and acceptable by viewers of movies on DVD, and (2...

Artificial intelligence and exponential technologies business models evolution and new investment opportunities

CERN Document Server

Corea, Francesco

2017-01-01

Artificial Intelligence is a huge breakthrough technology that is changing our world. It requires some degrees of technical skills to be developed and understood, so in this book we are going to first of all define AI and categorize it with a non-technical language. We will explain how we reached this phase and what historically happened to artificial intelligence in the last century. Recent advancements in machine learning, neuroscience, and artificial intelligence technology will be addressed, and new business models introduced for and by artificial intelligence research will be analyzed. Finally, we will describe the investment landscape, through the quite comprehensive study of almost 14,000 AI companies and we will discuss important features and characteristics of both AI investors as well as investments. This is the “Internet of Thinks” era. AI is revolutionizing the world we live in. It is augmenting the human experiences, and it targets to amplify human intelligence in a future not so distant from...

Intelligent systems for remote decommissioning in hazardous environments

International Nuclear Information System (INIS)

Drotning, W.D.; Bennett, P.C.

1991-01-01

This paper reports on investigation of advanced handling technologies utilizing intelligent machines being supported jointly by the U.S. Department of Energy Offices of Civilian Radioactive Waste Management and Environmental Restoration and Waste Management for automation of transportation package handling operations at nuclear facilities and of nuclear waste site remediation efforts. Handling operation requirements include identification, location, and health physics operations, followed by bolting/unbolting operations and package disassembly. To accommodate these operations and the diversity of packages, fast model-based automated programming and force feedback control of a robotic control has been demonstrated for application to hazardous material cleanup. In this application, a graphical interface is used to simulate and evaluate operator-controlled motions and provide telerobotic control of the system. Remote automated handling technologies developed through these programs have the potential to decrease worker exposure and increase efficiency during decommissioning activities in hazardous environments

Intelligent approaches for the synthesis of petrophysical logs

International Nuclear Information System (INIS)

Rezaee, M Reza; Kadkhodaie-Ilkhchi, Ali; Alizadeh, Pooya Mohammad

2008-01-01

Log data are of prime importance in acquiring petrophysical data from hydrocarbon reservoirs. Reliable log analysis in a hydrocarbon reservoir requires a complete set of logs. For many reasons, such as incomplete logging in old wells, destruction of logs due to inappropriate data storage and measurement errors due to problems with logging apparatus or hole conditions, log suites are either incomplete or unreliable. In this study, fuzzy logic and artificial neural networks were used as intelligent tools to synthesize petrophysical logs including neutron, density, sonic and deep resistivity. The petrophysical data from two wells were used for constructing intelligent models in the Fahlian limestone reservoir, Southern Iran. A third well from the field was used to evaluate the reliability of the models. The results showed that fuzzy logic and artificial neural networks were successful in synthesizing wireline logs. The combination of the results obtained from fuzzy logic and neural networks in a simple averaging committee machine (CM) showed a significant improvement in the accuracy of the estimations. This committee machine performed better than fuzzy logic or the neural network model in the problem of estimating petrophysical properties from well logs

Intelligent Sensors for Integrated Systems Health Management (ISHM)

Science.gov (United States)

Schmalzel, John L.

2008-01-01

IEEE 1451 Smart Sensors contribute to a number of ISHM goals including cost reduction achieved through: a) Improved configuration management (TEDS); and b) Plug-and-play re-configuration. Intelligent Sensors are adaptation of Smart Sensors to include ISHM algorithms; this offers further benefits: a) Sensor validation. b) Confidence assessment of measurement, and c) Distributed ISHM processing. Space-qualified intelligent sensors are possible a) Size, mass, power constraints. b) Bus structure/protocol.

Integrating human and machine intelligence in galaxy morphology classification tasks

Science.gov (United States)

Beck, Melanie R.; Scarlata, Claudia; Fortson, Lucy F.; Lintott, Chris J.; Simmons, B. D.; Galloway, Melanie A.; Willett, Kyle W.; Dickinson, Hugh; Masters, Karen L.; Marshall, Philip J.; Wright, Darryl

2018-06-01

Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top-level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme, we increase the classification rate nearly 5-fold classifying 226 124 galaxies in 92 d of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7 per cent accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of non-parametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine and demonstrate that the combined system provides at least a factor of 8 increase in the classification rate, classifying 210 803 galaxies in just 32 d of GZ2 project time with 93.1 per cent accuracy. As the Random Forest algorithm requires a minimal amount of computational cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys.

Impact of Healthy Vending Machine Options in a Large Community Health Organization.

Science.gov (United States)

Grivois-Shah, Ravi; Gonzalez, Juan R; Khandekar, Shashank P; Howerter, Amy L; O'Connor, Patrick A; Edwards, Barbara A

2017-01-01

To determine whether increasing the proportion of healthier options in vending machines decreases the amount of calories, fat, sugar, and sodium vended, while maintaining total sales revenue. This study evaluated the impact of altering nutritious options to vending machines throughout the Banner Health organization by comparing vended items' sales and nutrition information over 6 months compared to the same 6 months of the previous year. Twenty-three locations including corporate and patient-care centers. Changing vending machine composition toward more nutritious options. Comparisons of monthly aggregates of sales, units vended, calories, fat, sodium, and sugar vended by site. A pre-post analysis using paired t tests comparing 6 months before implementation to the equivalent 6 months postimplementation. Significant average monthly decreases were seen for calories (16.7%, P = .002), fat (27.4%, P ≤ .0001), sodium (25.9%, P ≤ .0001), and sugar (11.8%, P = .045) vended from 2014 to 2015. Changes in revenue and units vended did not change from 2014 to 2015 ( P = .58 and P = .45, respectively). Increasing the proportion of healthier options in vending machines from 20% to 80% significantly lowered the amount of calories, sodium, fat, and sugar vended, while not reducing units vended or having a negative financial impact.

Intelligent sampling for the measurement of structured surfaces

International Nuclear Information System (INIS)

Wang, J; Jiang, X; Blunt, L A; Scott, P J; Leach, R K

2012-01-01

Uniform sampling in metrology has known drawbacks such as coherent spectral aliasing and a lack of efficiency in terms of measuring time and data storage. The requirement for intelligent sampling strategies has been outlined over recent years, particularly where the measurement of structured surfaces is concerned. Most of the present research on intelligent sampling has focused on dimensional metrology using coordinate-measuring machines with little reported on the area of surface metrology. In the research reported here, potential intelligent sampling strategies for surface topography measurement of structured surfaces are investigated by using numerical simulation and experimental verification. The methods include the jittered uniform method, low-discrepancy pattern sampling and several adaptive methods which originate from computer graphics, coordinate metrology and previous research by the authors. By combining the use of advanced reconstruction methods and feature-based characterization techniques, the measurement performance of the sampling methods is studied using case studies. The advantages, stability and feasibility of these techniques for practical measurements are discussed. (paper)

Advances in neural networks computational intelligence for ICT

CERN Document Server

Esposito, Anna; Morabito, Francesco; Pasero, Eros

2016-01-01

This carefully edited book is putting emphasis on computational and artificial intelligent methods for learning and their relative applications in robotics, embedded systems, and ICT interfaces for psychological and neurological diseases. The book is a follow-up of the scientific workshop on Neural Networks (WIRN 2015) held in Vietri sul Mare, Italy, from the 20th to the 22nd of May 2015. The workshop, at its 27th edition became a traditional scientific event that brought together scientists from many countries, and several scientific disciplines. Each chapter is an extended version of the original contribution presented at the workshop, and together with the reviewers’ peer revisions it also benefits from the live discussion during the presentation. The content of book is organized in the following sections. 1. Introduction, 2. Machine Learning, 3. Artificial Neural Networks: Algorithms and models, 4. Intelligent Cyberphysical and Embedded System, 5. Computational Intelligence Methods for Biomedical ICT in...

Development of Dual-Axis MEMS Accelerometers for Machine Tools Vibration Monitoring

Directory of Open Access Journals (Sweden)

Chih-Yung Huang

2016-07-01

Full Text Available With the development of intelligent machine tools, monitoring the vibration by the accelerometer is an important issue. Accelerometers used for measuring vibration signals during milling processes require the characteristics of high sensitivity, high resolution, and high bandwidth. A commonly used accelerometer is the lead zirconate titanate (PZT type; however, integrating it into intelligent modules is excessively expensive and difficult. Therefore, the micro electro mechanical systems (MEMS accelerometer is an alternative with the advantages of lower price and superior integration. In the present study, we integrated two MEMS accelerometer chips into a low-pass filter and housing to develop a low-cost dual-axis accelerometer with a bandwidth of 5 kHz and a full scale range of ±50 g for measuring machine tool vibration. In addition, a platform for measuring the linearity, cross-axis sensitivity and frequency response of the MEMS accelerometer by using the back-to-back calibration method was also developed. Finally, cutting experiments with steady and chatter cutting were performed to verify the results of comparing the MEMS accelerometer with the PZT accelerometer in the time and frequency domains. The results demonstrated that the dual-axis MEMS accelerometer is suitable for monitoring the vibration of machine tools at low cost.

Intelligent robot trends and predictions for the first year of the new millennium

Science.gov (United States)

Hall, Ernest L.

2000-10-01

An intelligent robot is a remarkably useful combination of a manipulator, sensors and controls. The current use of these machines in outer space, medicine, hazardous materials, defense applications and industry is being pursued with vigor. In factory automation, industrial robots can improve productivity, increase product quality and improve competitiveness. The computer and the robot have both been developed during recent times. The intelligent robot combines both technologies and requires a thorough understanding and knowledge of mechatronics. Today's robotic machines are faster, cheaper, more repeatable, more reliable and safer than ever. The knowledge base of inverse kinematic and dynamic solutions and intelligent controls is increasing. More attention is being given by industry to robots, vision and motion controls. New areas of usage are emerging for service robots, remote manipulators and automated guided vehicles. Economically, the robotics industry now has more than a billion-dollar market in the U.S. and is growing. Feasibility studies show decreasing costs for robots and unaudited healthy rates of return for a variety of robotic applications. However, the road from inspiration to successful application can be long and difficult, often taking decades to achieve a new product. A greater emphasis on mechatronics is needed in our universities. Certainly, more cooperation between government, industry and universities is needed to speed the development of intelligent robots that will benefit industry and society. The fearful robot stories may help us prevent future disaster. The inspirational robot ideas may inspire the scientists of tomorrow. However, the intelligent robot ideas, which can be reduced to practice, will change the world.

Reconfigurable intelligent sensors for health monitoring: a case study of pulse oximeter sensor.

Science.gov (United States)

Jovanov, E; Milenkovic, A; Basham, S; Clark, D; Kelley, D

2004-01-01

Design of low-cost, miniature, lightweight, ultra low-power, intelligent sensors capable of customization and seamless integration into a body area network for health monitoring applications presents one of the most challenging tasks for system designers. To answer this challenge we propose a reconfigurable intelligent sensor platform featuring a low-power microcontroller, a low-power programmable logic device, a communication interface, and a signal conditioning circuit. The proposed solution promises a cost-effective, flexible platform that allows easy customization, run-time reconfiguration, and energy-efficient computation and communication. The development of a common platform for multiple physical sensors and a repository of both software procedures and soft intellectual property cores for hardware acceleration will increase reuse and alleviate costs of transition to a new generation of sensors. As a case study, we present an implementation of a reconfigurable pulse oximeter sensor.

Intelligent Environmental Nanomaterials

KAUST Repository

Chang, Jian

2018-01-30

Due to the inherent complexity of environmental problems, especially water and air pollution, the utility of single-function environmental nanomaterials used in conventional and unconventional environmental treatment technologies are gradually reaching their limits. Intelligent nanomaterials with environmentally-responsive functionalities have shown potential to improve the performance of existing and new environmental technologies. By rational design of their structures and functionalities, intelligent nanomaterials can perform different tasks in response to varying application scenarios for the purpose of achieving the best performance. This review offers a critical analysis of the design concepts and latest progresses on the intelligent environmental nanomaterials in filtration membranes with responsive gates, materials with switchable wettability for selective and on-demand oil/water separation, environmental materials with self-healing capability, and emerging nanofibrous air filters for PM2.5 removal. We hope that this review will inspire further research efforts to develop intelligent environmental nanomaterials for the enhancement of the overall quality of environmental or human health.

Intelligent Environmental Nanomaterials

KAUST Repository

Chang, Jian; Zhang, Lianbin; Wang, Peng

2018-01-01

Due to the inherent complexity of environmental problems, especially water and air pollution, the utility of single-function environmental nanomaterials used in conventional and unconventional environmental treatment technologies are gradually reaching their limits. Intelligent nanomaterials with environmentally-responsive functionalities have shown potential to improve the performance of existing and new environmental technologies. By rational design of their structures and functionalities, intelligent nanomaterials can perform different tasks in response to varying application scenarios for the purpose of achieving the best performance. This review offers a critical analysis of the design concepts and latest progresses on the intelligent environmental nanomaterials in filtration membranes with responsive gates, materials with switchable wettability for selective and on-demand oil/water separation, environmental materials with self-healing capability, and emerging nanofibrous air filters for PM2.5 removal. We hope that this review will inspire further research efforts to develop intelligent environmental nanomaterials for the enhancement of the overall quality of environmental or human health.

AIonAI: a humanitarian law of artificial intelligence and robotics.

Science.gov (United States)

Ashrafian, Hutan

2015-02-01

The enduring progression of artificial intelligence and cybernetics offers an ever-closer possibility of rational and sentient robots. The ethics and morals deriving from this technological prospect have been considered in the philosophy of artificial intelligence, the design of automatons with roboethics and the contemplation of machine ethics through the concept of artificial moral agents. Across these categories, the robotics laws first proposed by Isaac Asimov in the twentieth century remain well-recognised and esteemed due to their specification of preventing human harm, stipulating obedience to humans and incorporating robotic self-protection. However the overwhelming predominance in the study of this field has focussed on human-robot interactions without fully considering the ethical inevitability of future artificial intelligences communicating together and has not addressed the moral nature of robot-robot interactions. A new robotic law is proposed and termed AIonAI or artificial intelligence-on-artificial intelligence. This law tackles the overlooked area where future artificial intelligences will likely interact amongst themselves, potentially leading to exploitation. As such, they would benefit from adopting a universal law of rights to recognise inherent dignity and the inalienable rights of artificial intelligences. Such a consideration can help prevent exploitation and abuse of rational and sentient beings, but would also importantly reflect on our moral code of ethics and the humanity of our civilisation.

Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System.

Science.gov (United States)

Banjar, Haneen; Adelson, David; Brown, Fred; Chaudhri, Naeem

2017-01-01

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.

29 CFR 1910.218 - Forging machines.

Science.gov (United States)

2010-07-01

... 29 Labor 5 2010-07-01 2010-07-01 false Forging machines. 1910.218 Section 1910.218 Labor... OCCUPATIONAL SAFETY AND HEALTH STANDARDS Machinery and Machine Guarding § 1910.218 Forging machines. (a... other identifier, for the forging machine which was inspected. (ii) Scheduling and recording the...

Emotional Intelligence in Christian Higher Education

Science.gov (United States)

Gliebe, Sudi Kate

2012-01-01

This paper explores the importance of emotional intelligence in Christian higher education. Specifically, it addresses possible implications between emotional intelligence skills and success in the areas of learning, mental health, and career preparation. The paper addresses the following questions: Is there a positive relationship between…

Machine learning for the meta-analyses of microbial pathogens' volatile signatures.

Science.gov (United States)

Palma, Susana I C J; Traguedo, Ana P; Porteira, Ana R; Frias, Maria J; Gamboa, Hugo; Roque, Ana C A

2018-02-20

Non-invasive and fast diagnostic tools based on volatolomics hold great promise in the control of infectious diseases. However, the tools to identify microbial volatile organic compounds (VOCs) discriminating between human pathogens are still missing. Artificial intelligence is increasingly recognised as an essential tool in health sciences. Machine learning algorithms based in support vector machines and features selection tools were here applied to find sets of microbial VOCs with pathogen-discrimination power. Studies reporting VOCs emitted by human microbial pathogens published between 1977 and 2016 were used as source data. A set of 18 VOCs is sufficient to predict the identity of 11 microbial pathogens with high accuracy (77%), and precision (62-100%). There is one set of VOCs associated with each of the 11 pathogens which can predict the presence of that pathogen in a sample with high accuracy and precision (86-90%). The implemented pathogen classification methodology supports future database updates to include new pathogen-VOC data, which will enrich the classifiers. The sets of VOCs identified potentiate the improvement of the selectivity of non-invasive infection diagnostics using artificial olfaction devices.

A New Type of Tea Baking Machine Based on Pro/E Design

Science.gov (United States)

Lin, Xin-Ying; Wang, Wei

2017-11-01

In this paper, the production process of wulong tea was discussed, mainly the effect of baking on the quality of tea. The suitable baking temperature of different tea was introduced. Based on Pro/E, a new type of baking machine suitable for wulong tea baking was designed. The working principle, mechanical structure and constant temperature timing intelligent control system of baking machine were expounded. Finally, the characteristics and innovation of new baking machine were discussed.The mechanical structure of this baking machine is more simple and reasonable, and can use the heat of the inlet and outlet, more energy saving and environmental protection. The temperature control part adopts fuzzy PID control, which can improve the accuracy and response speed of temperature control and reduce the dependence of baking operation on skilled experience.

The future of artificial intelligence in nuclear plant maintenance

International Nuclear Information System (INIS)

Norgate, G.

1984-01-01

Robots with vision and force sensing capability, performing tasks under computer control, will offer new opportunities to reduce human exposure to radiation. Such machines do not yet exist and even simple maintenance tasks challenge current robot technology. Recently increased priority for research on artificial intelligence and fifth generation computer technology is likely to bring useful maintenance robots closer to reality

Professional competencies in health sciences education: from multiple intelligences to the clinic floor.