WorldWideScience

Sample records for intelligent machine health

  1. Machine listening intelligence

    Science.gov (United States)

    Cella, C. E.

    2017-05-01

    This manifesto paper will introduce machine listening intelligence, an integrated research framework for acoustic and musical signals modelling, based on signal processing, deep learning and computational musicology.

  2. Quo vadis, Intelligent Machine?

    Directory of Open Access Journals (Sweden)

    Rosemarie Velik

    2010-09-01

    Full Text Available Artificial Intelligence (AI is a branch of computer science concerned with making computers behave like humans. At least this was the original idea. However, it turned out that this is no task easy to be solved. This article aims to give a comprehensible review on the last 60 years of artificial intelligence taking a philosophical viewpoint. It is outlined what happened so far in AI, what is currently going on in this research area, and what can be expected in future. The goal is to mediate an understanding for the developments and changes in thinking in course of time about how to achieve machine intelligence. The clear message is that AI has to join forces with neuroscience and other brain disciplines in order to make a step towards the development of truly intelligent machines.

  3. Are there intelligent Turing machines?

    OpenAIRE

    Bátfai, Norbert

    2015-01-01

    This paper introduces a new computing model based on the cooperation among Turing machines called orchestrated machines. Like universal Turing machines, orchestrated machines are also designed to simulate Turing machines but they can also modify the original operation of the included Turing machines to create a new layer of some kind of collective behavior. Using this new model we can define some interested notions related to cooperation ability of Turing machines such as the intelligence quo...

  4. Elementary epistemological features of machine intelligence

    OpenAIRE

    Horvat, Marko

    2008-01-01

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

  5. 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.

  6. Reliability analysis in intelligent machines

    Science.gov (United States)

    Mcinroy, John E.; Saridis, George N.

    1990-01-01

    Given an explicit task to be executed, an intelligent machine must be able to find the probability of success, or reliability, of alternative control and sensing strategies. By using concepts for information theory and reliability theory, new techniques for finding the reliability corresponding to alternative subsets of control and sensing strategies are proposed such that a desired set of specifications can be satisfied. The analysis is straightforward, provided that a set of Gaussian random state variables is available. An example problem illustrates the technique, and general reliability results are presented for visual servoing with a computed torque-control algorithm. Moreover, the example illustrates the principle of increasing precision with decreasing intelligence at the execution level of an intelligent machine.

  7. Machine intelligence and signal processing

    CERN Document Server

    Vatsa, Mayank; Majumdar, Angshul; Kumar, Ajay

    2016-01-01

    This book comprises chapters on key problems in machine learning and signal processing arenas. The contents of the book are a result of a 2014 Workshop on Machine Intelligence and Signal Processing held at the Indraprastha Institute of Information Technology. Traditionally, signal processing and machine learning were considered to be separate areas of research. However in recent times the two communities are getting closer. In a very abstract fashion, signal processing is the study of operator design. The contributions of signal processing had been to device operators for restoration, compression, etc. Applied Mathematicians were more interested in operator analysis. Nowadays signal processing research is gravitating towards operator learning – instead of designing operators based on heuristics (for example wavelets), the trend is to learn these operators (for example dictionary learning). And thus, the gap between signal processing and machine learning is fast converging. The 2014 Workshop on Machine Intel...

  8. The machine intelligence Hex project

    Science.gov (United States)

    Chalup, Stephan K.; Mellor, Drew; Rosamond, Fran

    2005-12-01

    Hex is a challenging strategy board game for two players. To enhance students’ progress in acquiring understanding and practical experience with complex machine intelligence and programming concepts we developed the Machine Intelligence Hex (MIHex) project. The associated undergraduate student assignment is about designing and implementing Hex players and evaluating them in an automated tournament of all programs developed by the class. This article surveys educational aspects of the MIHex project. Additionally, fundamental techniques for game programming as well as specific concepts for Hex board evaluation are reviewed. The MIHex game server and possibilities of tournament organisation are described. We summarise and discuss our experiences from running the MIHex project assignment over four consecutive years. The impact on student motivation and learning benefits are evaluated using questionnaires and interviews.

  9. Machine learning an artificial intelligence approach

    CERN Document Server

    Banerjee, R; Bradshaw, Gary; Carbonell, Jaime Guillermo; Mitchell, Tom Michael; Michalski, Ryszard Spencer

    1983-01-01

    Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs-particularly programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV a

  10. Art in the Age of Machine Intelligence

    Directory of Open Access Journals (Sweden)

    Blaise Agüera y Arcas

    2017-09-01

    Full Text Available In this wide‐ranging essay, the leader of Google’s Seattle AI group and founder of the Artists and Machine Intelligence program discusses the long‐standing and complex relationship between art and technology. The transformation of artistic practice and theory that attended the 19th century photographic revolution is explored as a parallel for the current revolution in machine intelligence, which promises not only to mechanize (or democratize the means of reproduction, but also of production.

  11. A computer architecture for intelligent machines

    Science.gov (United States)

    Lefebvre, D. R.; Saridis, G. N.

    1992-01-01

    The theory of intelligent machines proposes a hierarchical organization for the functions of an autonomous robot based on the principle of increasing precision with decreasing intelligence. An analytic formulation of this theory using information-theoretic measures of uncertainty for each level of the intelligent machine has been developed. The authors present a computer architecture that implements the lower two levels of the intelligent machine. The architecture supports an event-driven programming paradigm that is independent of the underlying computer architecture and operating system. Execution-level controllers for motion and vision systems are briefly addressed, as well as the Petri net transducer software used to implement coordination-level functions. A case study illustrates how this computer architecture integrates real-time and higher-level control of manipulator and vision systems.

  12. 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

  13. Probabilistic machine learning and artificial intelligence.

    Science.gov (United States)

    Ghahramani, Zoubin

    2015-05-28

    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.

  14. 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.

  15. The role of soft computing in intelligent machines.

    Science.gov (United States)

    de Silva, Clarence W

    2003-08-15

    An intelligent machine relies on computational intelligence in generating its intelligent behaviour. This requires a knowledge system in which representation and processing of knowledge are central functions. Approximation is a 'soft' concept, and the capability to approximate for the purposes of comparison, pattern recognition, reasoning, and decision making is a manifestation of intelligence. This paper examines the use of soft computing in intelligent machines. Soft computing is an important branch of computational intelligence, where fuzzy logic, probability theory, neural networks, and genetic algorithms are synergistically used to mimic the reasoning and decision making of a human. This paper explores several important characteristics and capabilities of machines that exhibit intelligent behaviour. Approaches that are useful in the development of an intelligent machine are introduced. The paper presents a general structure for an intelligent machine, giving particular emphasis to its primary components, such as sensors, actuators, controllers, and the communication backbone, and their interaction. The role of soft computing within the overall system is discussed. Common techniques and approaches that will be useful in the development of an intelligent machine are introduced, and the main steps in the development of an intelligent machine for practical use are given. An industrial machine, which employs the concepts of soft computing in its operation, is presented, and one aspect of intelligent tuning, which is incorporated into the machine, is illustrated.

  16. Complementary Machine Intelligence and Human Intelligence in Virtual Teaching Assistant for Tutoring Program Tracing

    Science.gov (United States)

    Chou, Chih-Yueh; Huang, Bau-Hung; Lin, Chi-Jen

    2011-01-01

    This study proposes a virtual teaching assistant (VTA) to share teacher tutoring tasks in helping students practice program tracing and proposes two mechanisms of complementing machine intelligence and human intelligence to develop the VTA. The first mechanism applies machine intelligence to extend human intelligence (teacher answers) to evaluate…

  17. [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.

  18. Tool path strategy and cutting process monitoring in intelligent machining

    Science.gov (United States)

    Chen, Ming; Wang, Chengdong; An, Qinglong; Ming, Weiwei

    2018-06-01

    Intelligent machining is a current focus in advanced manufacturing technology, and is characterized by high accuracy and efficiency. A central technology of intelligent machining—the cutting process online monitoring and optimization—is urgently needed for mass production. In this research, the cutting process online monitoring and optimization in jet engine impeller machining, cranio-maxillofacial surgery, and hydraulic servo valve deburring are introduced as examples of intelligent machining. Results show that intelligent tool path optimization and cutting process online monitoring are efficient techniques for improving the efficiency, quality, and reliability of machining.

  19. 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

  20. 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.

  1. 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.

  2. 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 ...

  3. 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

  4. 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 ...

  5. Integrating Oil Debris and Vibration Measurements for Intelligent Machine Health Monitoring. Degree awarded by Toledo Univ., May 2002

    Science.gov (United States)

    Dempsey, Paula J.

    2003-01-01

    A diagnostic tool for detecting damage to gears was developed. Two different measurement technologies, oil debris analysis and vibration were integrated into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual measurement technologies. This diagnostic tool was developed and evaluated experimentally by collecting vibration and oil debris data from fatigue tests performed in the NASA Glenn Spur Gear Fatigue Rig. An oil debris sensor and the two vibration algorithms were adapted as the diagnostic tools. An inductance type oil debris sensor was selected for the oil analysis measurement technology. Gear damage data for this type of sensor was limited to data collected in the NASA Glenn test rigs. For this reason, this analysis included development of a parameter for detecting gear pitting damage using this type of sensor. The vibration data was used to calculate two previously available gear vibration diagnostic algorithms. The two vibration algorithms were selected based on their maturity and published success in detecting damage to gears. Oil debris and vibration features were then developed using fuzzy logic analysis techniques, then input into a multi sensor data fusion process. Results show combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spur gears. As a result of this research, this new diagnostic tool has significantly improved detection of gear damage in the NASA Glenn Spur Gear Fatigue Rigs. This research also resulted in several other findings that will improve the development of future health monitoring systems. Oil debris analysis was found to be more reliable than vibration analysis for detecting pitting fatigue failure of gears and is capable of indicating damage progression. Also, some vibration algorithms are as sensitive to operational effects as they

  6. 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

  7. Integrated human-machine intelligence in space systems

    Science.gov (United States)

    Boy, Guy A.

    1992-01-01

    The integration of human and machine intelligence in space systems is outlined with respect to the contributions of artificial intelligence. The current state-of-the-art in intelligent assistant systems (IASs) is reviewed, and the requirements of some real-world applications of the technologies are discussed. A concept of integrated human-machine intelligence is examined in the contexts of: (1) interactive systems that tolerate human errors; (2) systems for the relief of workloads; and (3) interactive systems for solving problems in abnormal situations. Key issues in the development of IASs include the compatibility of the systems with astronauts in terms of inputs/outputs, processing, real-time AI, and knowledge-based system validation. Real-world applications are suggested such as the diagnosis, planning, and control of enginnered systems.

  8. 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...

  9. 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

  10. 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.

  11. The Properties of Intelligent Human-Machine Interface

    Directory of Open Access Journals (Sweden)

    Alexander Alfimtsev

    2012-04-01

    Full Text Available Intelligent human-machine interfaces based on multimodal interaction are developed separately in different application areas. No unified opinion exists about the issue of what properties should these interfaces have to provide an intuitive and natural interaction. Having carried out an analytical survey of the papers that deal with intelligent interfaces a set of properties are presented, which are necessary for intelligent interface between an information system and a human: absolute response, justification, training, personification, adaptiveness, collectivity, security, hidden persistence, portability, filtering.

  12. From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

    Science.gov (United States)

    Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao

    2017-11-01

    Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Design of intelligent proximity detection zones to prevent striking and pinning fatalities around continuous mining machines.

    Science.gov (United States)

    Bissert, P T; Carr, J L; DuCarme, J P; Smith, A K

    2016-01-01

    The continuous mining machine is a key piece of equipment used in underground coal mining operations. Over the past several decades these machines have been involved in a number of mine worker fatalities. Proximity detection systems have been developed to avert hazards associated with operating continuous mining machines. Incorporating intelligent design into proximity detection systems allows workers greater freedom to position themselves to see visual cues or avoid other hazards such as haulage equipment or unsupported roof or ribs. However, intelligent systems must be as safe as conventional proximity detection systems. An evaluation of the 39 fatal accidents for which the Mine Safety and Health Administration has published fatality investigation reports was conducted to determine whether the accident may have been prevented by conventional or intelligent proximity. Multiple zone configurations for the intelligent systems were studied to determine how system performance might be affected by the zone configuration. Researchers found that 32 of the 39 fatalities, or 82 percent, may have been prevented by both conventional and intelligent proximity systems. These results indicate that, by properly configuring the zones of an intelligent proximity detection system, equivalent protection to a conventional system is possible.

  14. 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

  15. 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...

  16. 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.

  17. 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.

  18. 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...

  19. 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

  20. 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.

  1. 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.

  2. 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...

  3. 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.

  4. 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.

  5. Intelligent sensor networks the integration of sensor networks, signal processing and machine learning

    CERN Document Server

    Hu, Fei

    2012-01-01

    Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, incl

  6. 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.

  7. 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

  8. 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.

  9. 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

  10. Research on intelligent machine self-perception method based on LSTM

    Science.gov (United States)

    Wang, Qiang; Cheng, Tao

    2018-05-01

    In this paper, we use the advantages of LSTM in feature extraction and processing high-dimensional and complex nonlinear data, and apply it to the autonomous perception of intelligent machines. Compared with the traditional multi-layer neural network, this model has memory, can handle time series information of any length. Since the multi-physical domain signals of processing machines have a certain timing relationship, and there is a contextual relationship between states and states, using this deep learning method to realize the self-perception of intelligent processing machines has strong versatility and adaptability. The experiment results show that the method proposed in this paper can obviously improve the sensing accuracy under various working conditions of the intelligent machine, and also shows that the algorithm can well support the intelligent processing machine to realize self-perception.

  11. 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.

  12. 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.

  13. 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...

  14. 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...

  15. 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…

  16. 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

  17. 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.

  18. 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

  19. 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

  20. 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.…

  1. 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.

  2. 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.

  3. 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...

  4. 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.

  5. 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...

  6. 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.

  7. 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…

  8. 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

  9. 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

  10. 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.

  11. 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.

  12. 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...

  13. 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)

  14. 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.

  15. The internet and intelligent machines: search engines, agents and robots

    International Nuclear Information System (INIS)

    Achenbach, S.; Alfke, H.

    2000-01-01

    The internet plays an important role in a growing number of medical applications. Finding relevant information is not always easy as the amount of available information on the Web is rising quickly. Even the best Search Engines can only collect links to a fraction of all existing Web pages. In addition, many of these indexed documents have been changed or deleted. The vast majority of information on the Web is not searchable with conventional methods. New search strategies, technologies and standards are combined in Intelligent Search Agents (ISA) an Robots, which can retrieve desired information in a specific approach. Conclusion: The article describes differences between ISAs and conventional Search Engines and how communication between Agents improves their ability to find information. Examples of existing ISAs are given and the possible influences on the current and future work in radiology is discussed. (orig.) [de

  16. 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.

  17. 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.

  18. 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.))

  19. 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.

  20. 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...

  1. Integrating Human and Machine Intelligence in Galaxy Morphology Classification Tasks

    Science.gov (United States)

    Beck, Melanie Renee

    thus solved both the visual classification problem of time efficiency and improved accuracy by producing a distribution of independent classifications for each galaxy. While crowd-sourced galaxy classifications have proven their worth, challenges remain before establishing this method as a critical and standard component of the data processing pipelines for the next generation of surveys. In particular, though innovative, crowd-sourcing techniques do not have the capacity to handle the data volume and rates expected in the next generation of surveys. These algorithms will be delegated to handle the majority of the classification tasks, freeing citizen scientists to contribute their efforts on subtler and more complex assignments. This thesis presents 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 days of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7% 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 a factor of 11.4 increase in the classification rate, classifying 210,803 galaxies in just 32 days of GZ2 project time with 93.1% accuracy. As the Random Forest algorithm requires a minimal amount of computational

  2. 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.

  3. 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...

  4. 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

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. Machine Intelligence

    Science.gov (United States)

    2013-03-01

    angular velocity of the pole. There are three available actions; accelerate to the left, to the right, and to coast. We use a problem set of 20 different...the angular velocities of the beams, θ ′ 1 and θ ′ 2. Once again, time is discretized into small intervals, and during any such interval the learner...with a maximum of 200 generations of learning. Neuroevolution is provided by Another NEAT Java Implementation (ANJI) 2. We used the Sarsa(λ) learning

  17. 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.

  18. 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%.

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. 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

  4. 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

  5. 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...

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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

  17. 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.

  18. 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

  19. 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...

  20. 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

  1. 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.

  2. 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

  3. 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.

  4. [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.

  5. 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.

  6. 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.

  7. 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.

  8. Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Kun He

    2018-04-01

    Full Text Available Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM. An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44% was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers.

  9. Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines

    Science.gov (United States)

    He, Kun; Yang, Zhijun; Bai, Yun; Long, Jianyu; Li, Chuan

    2018-01-01

    Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers. PMID:29690641

  10. Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines.

    Science.gov (United States)

    He, Kun; Yang, Zhijun; Bai, Yun; Long, Jianyu; Li, Chuan

    2018-04-23

    Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers.

  11. 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...

  12. Intelligent Control and Health Monitoring. Chapter 3

    Science.gov (United States)

    Garg, Sanjay; Kumar, Aditya; Mathews, H. Kirk; Rosenfeld, Taylor; Rybarik, Pavol; Viassolo, Daniel E.

    2009-01-01

    Advanced model-based control architecture overcomes the limitations state-of-the-art engine control and provides the potential of virtual sensors, for example for thrust and stall margin. "Tracking filters" are used to adapt the control parameters to actual conditions and to individual engines. For health monitoring standalone monitoring units will be used for on-board analysis to determine the general engine health and detect and isolate sudden faults. Adaptive models open up the possibility of adapting the control logic to maintain desired performance in the presence of engine degradation or to accommodate any faults. Improved and new sensors are required to allow sensing at stations within the engine gas path that are currently not instrumented due in part to the harsh conditions including high operating temperatures and to allow additional monitoring of vibration, mass flows and energy properties, exhaust gas composition, and gas path debris. The environmental and performance requirements for these sensors are summarized.

  13. Intelligence

    Science.gov (United States)

    Sternberg, Robert J.

    2012-01-01

    Intelligence is the ability to learn from experience and to adapt to, shape, and select environments. Intelligence as measured by (raw scores on) conventional standardized tests varies across the lifespan, and also across generations. Intelligence can be understood in part in terms of the biology of the brain—especially with regard to the functioning in the prefrontal cortex—and also correlates with brain size, at least within humans. Studies of the effects of genes and environment suggest that the heritability coefficient (ratio of genetic to phenotypic variation) is between .4 and .8, although heritability varies as a function of socioeconomic status and other factors. Racial differences in measured intelligence have been observed, but race is a socially constructed rather than biological variable, so such differences are difficult to interpret. PMID:22577301

  14. Intelligence.

    Science.gov (United States)

    Sternberg, Robert J

    2012-03-01

    Intelligence is the ability to learn from experience and to adapt to, shape, and select environments. Intelligence as measured by (raw scores on) conventional standardized tests varies across the lifespan, and also across generations. Intelligence can be understood in part in terms of the biology of the brain-especially with regard to the functioning in the prefrontal cortex-and also correlates with brain size, at least within humans. Studies of the effects of genes and environment suggest that the heritability coefficient (ratio of genetic to phenotypic variation) is between .4 and .8, although heritability varies as a function of socioeconomic status and other factors. Racial differences in measured intelligence have been observed, but race is a socially constructed rather than biological variable, so such differences are difficult to interpret.

  15. 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.

  16. 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.

  17. 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.

  18. Toward Augmented Radiologists: Changes in Radiology Education in the Era of Machine Learning and Artificial Intelligence.

    Science.gov (United States)

    Tajmir, Shahein H; Alkasab, Tarik K

    2018-06-01

    Radiology practice will be altered by the coming of artificial intelligence, and the process of learning in radiology will be similarly affected. In the short term, radiologists will need to understand the first wave of artificially intelligent tools, how they can help them improve their practice, and be able to effectively supervise their use. Radiology training programs will need to develop curricula to help trainees acquire the knowledge to carry out this new supervisory duty of radiologists. In the longer term, artificially intelligent software assistants could have a transformative effect on the training of residents and fellows, and offer new opportunities to bring learning into the ongoing practice of attending radiologists. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

  19. 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...

  20. 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)

  1. 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.

  2. 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.

  3. 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

  4. Intelligent coverage path planning for agricultural robots and autonomous machines on three-dimensional terrain

    DEFF Research Database (Denmark)

    Hameed, Ibahim

    2014-01-01

    Field operations should be done in a manner that minimizes time and travels over the field surface. Automated and intelligent path planning can help to find the best coverage path so that costs of various field operations can be minimized. The algorithms for generating an optimized field coverage...

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

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

    Science.gov (United States)

    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

  12. 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

  13. An intelligent condition monitoring system for on-line classification of machine tool wear

    Energy Technology Data Exchange (ETDEWEB)

    Pan, Fu; Hope, A D; Javed, M [Systems Engineering Faculty, Southampton Institute (United Kingdom)

    1998-12-31

    The development of intelligent tool condition monitoring systems is a necessary requirement for successful automation of manufacturing processes. This presentation introduces a tool wear monitoring system for milling operations. The system utilizes power, force, acoustic emission and vibration sensors to monitor tool condition comprehensively. Features relevant to tool wear are drawn from time and frequency domain signals and a fuzzy pattern recognition technique is applied to combine the multisensor information and provide reliable classification results of tool wear states. (orig.) 10 refs.

  14. An intelligent condition monitoring system for on-line classification of machine tool wear

    Energy Technology Data Exchange (ETDEWEB)

    Fu Pan; Hope, A.D.; Javed, M. [Systems Engineering Faculty, Southampton Institute (United Kingdom)

    1997-12-31

    The development of intelligent tool condition monitoring systems is a necessary requirement for successful automation of manufacturing processes. This presentation introduces a tool wear monitoring system for milling operations. The system utilizes power, force, acoustic emission and vibration sensors to monitor tool condition comprehensively. Features relevant to tool wear are drawn from time and frequency domain signals and a fuzzy pattern recognition technique is applied to combine the multisensor information and provide reliable classification results of tool wear states. (orig.) 10 refs.

  15. 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.

  16. 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

  17. IDOCS: intelligent distributed ontology consensus system--the use of machine learning in retinal drusen phenotyping.

    Science.gov (United States)

    Thomas, George; Grassi, Michael A; Lee, John R; Edwards, Albert O; Gorin, Michael B; Klein, Ronald; Casavant, Thomas L; Scheetz, Todd E; Stone, Edwin M; Williams, Andrew B

    2007-05-01

    To use the power of knowledge acquisition and machine learning in the development of a collaborative computer classification system based on the features of age-related macular degeneration (AMD). A vocabulary was acquired from four AMD experts who examined 100 ophthalmoscopic images. The vocabulary was analyzed, hierarchically structured, and incorporated into a collaborative computer classification system called IDOCS. Using this system, three of the experts examined images from a second set of digital images compiled from more than 1000 patients with AMD. Images were annotated, and features were identified and defined. Decision trees, a machine learning method, were trained on the data collected and used to extract patterns. Interrelationships between the data from the different clinicians were investigated. Six drusen classes in the structured vocabulary were largely sufficient to describe all the identified features. The decision trees classified the data with 76.86% to 88.5% accuracy and distilled patterns in the form of hierarchical trees composed of 5 to 15 nodes. Experts were largely consistent in their characterization of soft, and to a lesser extent, hard drusen, but diverge in definition of other drusen. Size and crystalline morphology were the main determinants of drusen type across all experts. Machine learning is a powerful tool for the characterization of disease phenotypes. The creation of a defined feature set for AMD will facilitate the development of an IDOCS-based classification system.

  18. 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.

  19. Cellular computational generalized neuron network for frequency situational intelligence in a multi-machine power system.

    Science.gov (United States)

    Wei, Yawei; Venayagamoorthy, Ganesh Kumar

    2017-09-01

    To prevent large interconnected power system from a cascading failure, brownout or even blackout, grid operators require access to faster than real-time information to make appropriate just-in-time control decisions. However, the communication and computational system limitations of currently used supervisory control and data acquisition (SCADA) system can only deliver delayed information. However, the deployment of synchrophasor measurement devices makes it possible to capture and visualize, in near-real-time, grid operational data with extra granularity. In this paper, a cellular computational network (CCN) approach for frequency situational intelligence (FSI) in a power system is presented. The distributed and scalable computing unit of the CCN framework makes it particularly flexible for customization for a particular set of prediction requirements. Two soft-computing algorithms have been implemented in the CCN framework: a cellular generalized neuron network (CCGNN) and a cellular multi-layer perceptron network (CCMLPN), for purposes of providing multi-timescale frequency predictions, ranging from 16.67 ms to 2 s. These two developed CCGNN and CCMLPN systems were then implemented on two different scales of power systems, one of which installed a large photovoltaic plant. A real-time power system simulator at weather station within the Real-Time Power and Intelligent Systems (RTPIS) laboratory at Clemson, SC, was then used to derive typical FSI results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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...

  5. Intelligent Machine Vision Based Modeling and Positioning System in Sand Casting Process

    Directory of Open Access Journals (Sweden)

    Shahid Ikramullah Butt

    2017-01-01

    Full Text Available Advanced vision solutions enable manufacturers in the technology sector to reconcile both competitive and regulatory concerns and address the need for immaculate fault detection and quality assurance. The modern manufacturing has completely shifted from the manual inspections to the machine assisted vision inspection methodology. Furthermore, the research outcomes in industrial automation have revolutionized the whole product development strategy. The purpose of this research paper is to introduce a new scheme of automation in the sand casting process by means of machine vision based technology for mold positioning. Automation has been achieved by developing a novel system in which casting molds of different sizes, having different pouring cup location and radius, position themselves in front of the induction furnace such that the center of pouring cup comes directly beneath the pouring point of furnace. The coordinates of the center of pouring cup are found by using computer vision algorithms. The output is then transferred to a microcontroller which controls the alignment mechanism on which the mold is placed at the optimum location.

  6. 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.

  7. The research of device for measuring film thickness of intelligent coating machine

    Directory of Open Access Journals (Sweden)

    Wang Wanjun

    2015-01-01

    Full Text Available Ion beam sputtering machine uses computer to real time monitor the change of film thickness in the preparation process of soft X ray multilayer element fabrication. It solves the problems of uneven film thickness and too thick film thickness and so on, which exist in the original preparation process. The high-precision quartz crystal converts film thickness measurement into frequency measurement. The equal precision frequency meter based on FPGA measures the frequency. It can reduce the signal delay and interference signal of discrete components, accordingly improving the accuracy of measurement. Then it sents the count value to the host computer through the single chip microcomputer serial port. It calculates and displays the value by the GUI of LabVIEW. The experimental results show that, the relative measurement error can be decreased to 1/10, i.e., the measurement accuracy can be improved by more than ten times.

  8. Adjustment and Prediction of Machine Factors Based on Neural Artificial Intelligence

    International Nuclear Information System (INIS)

    Hussein, A.Z.; Amin, E.S.; Ibrahim, M.S.

    2009-01-01

    Since the discovery of x-ray, it is use in examination has become an integral part of medical diagnostic radiology. The use of X-ray is harmful to human beings but recent technological advances and regulatory constraints have made the medical Xray much safer than they were at the beginning of the 20th century. However, the potential benefits of the engineered safety features can not be fully realized unless the operators are aware of these safety features. The aim of this work is to adjust and predict x-ray machine factors (current and voltage) using neural artificial network in order to obtain effective dose within the range of dose limitation system and assure radiological safety.

  9. Enhanced Flexibility and Reusability through State Machine-Based Architectures for Multisensor Intelligent Robotics

    Directory of Open Access Journals (Sweden)

    Héctor Herrero

    2017-05-01

    Full Text Available This paper presents a state machine-based architecture, which enhances the flexibility and reusability of industrial robots, more concretely dual-arm multisensor robots. The proposed architecture, in addition to allowing absolute control of the execution, eases the programming of new applications by increasing the reusability of the developed modules. Through an easy-to-use graphical user interface, operators are able to create, modify, reuse and maintain industrial processes, increasing the flexibility of the cell. Moreover, the proposed approach is applied in a real use case in order to demonstrate its capabilities and feasibility in industrial environments. A comparative analysis is presented for evaluating the presented approach versus traditional robot programming techniques.

  10. Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success.

    Science.gov (United States)

    Thrall, James H; Li, Xiang; Li, Quanzheng; Cruz, Cinthia; Do, Synho; Dreyer, Keith; Brink, James

    2018-03-01

    Worldwide interest in artificial intelligence (AI) applications, including imaging, is high and growing rapidly, fueled by availability of large datasets ("big data"), substantial advances in computing power, and new deep-learning algorithms. Apart from developing new AI methods per se, there are many opportunities and challenges for the imaging community, including the development of a common nomenclature, better ways to share image data, and standards for validating AI program use across different imaging platforms and patient populations. AI surveillance programs may help radiologists prioritize work lists by identifying suspicious or positive cases for early review. AI programs can be used to extract "radiomic" information from images not discernible by visual inspection, potentially increasing the diagnostic and prognostic value derived from image datasets. Predictions have been made that suggest AI will put radiologists out of business. This issue has been overstated, and it is much more likely that radiologists will beneficially incorporate AI methods into their practices. Current limitations in availability of technical expertise and even computing power will be resolved over time and can also be addressed by remote access solutions. Success for AI in imaging will be measured by value created: increased diagnostic certainty, faster turnaround, better outcomes for patients, and better quality of work life for radiologists. AI offers a new and promising set of methods for analyzing image data. Radiologists will explore these new pathways and are likely to play a leading role in medical applications of AI. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

  11. 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.

  12. 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

  13. 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%.

  14. Fullrmc, a rigid body Reverse Monte Carlo modeling package enabled with machine learning and artificial intelligence.

    Science.gov (United States)

    Aoun, Bachir

    2016-05-05

    A new Reverse Monte Carlo (RMC) package "fullrmc" for atomic or rigid body and molecular, amorphous, or crystalline materials is presented. fullrmc main purpose is to provide a fully modular, fast and flexible software, thoroughly documented, complex molecules enabled, written in a modern programming language (python, cython, C and C++ when performance is needed) and complying to modern programming practices. fullrmc approach in solving an atomic or molecular structure is different from existing RMC algorithms and software. In a nutshell, traditional RMC methods and software randomly adjust atom positions until the whole system has the greatest consistency with a set of experimental data. In contrast, fullrmc applies smart moves endorsed with reinforcement machine learning to groups of atoms. While fullrmc allows running traditional RMC modeling, the uniqueness of this approach resides in its ability to customize grouping atoms in any convenient way with no additional programming efforts and to apply smart and more physically meaningful moves to the defined groups of atoms. In addition, fullrmc provides a unique way with almost no additional computational cost to recur a group's selection, allowing the system to go out of local minimas by refining a group's position or exploring through and beyond not allowed positions and energy barriers the unrestricted three dimensional space around a group. © 2016 Wiley Periodicals, Inc.

  15. Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques.

    Science.gov (United States)

    Cui, De-Mi; Yan, Weizhong; Wang, Xiao-Quan; Lu, Lie-Min

    2017-10-25

    Low strain pile integrity testing (LSPIT), due to its simplicity and low cost, is one of the most popular NDE methods used in pile foundation construction. While performing LSPIT in the field is generally quite simple and quick, determining the integrity of the test piles by analyzing and interpreting the test signals (reflectograms) is still a manual process performed by experienced experts only. For foundation construction sites where the number of piles to be tested is large, it may take days before the expert can complete interpreting all of the piles and delivering the integrity assessment report. Techniques that can automate test signal interpretation, thus shortening the LSPIT's turnaround time, are of great business value and are in great need. Motivated by this need, in this paper, we develop a computer-aided reflectogram interpretation (CARI) methodology that can interpret a large number of LSPIT signals quickly and consistently. The methodology, built on advanced signal processing and machine learning technologies, can be used to assist the experts in performing both qualitative and quantitative interpretation of LSPIT signals. Specifically, the methodology can ease experts' interpretation burden by screening all test piles quickly and identifying a small number of suspected piles for experts to perform manual, in-depth interpretation. We demonstrate the methodology's effectiveness using the LSPIT signals collected from a number of real-world pile construction sites. The proposed methodology can potentially enhance LSPIT and make it even more efficient and effective in quality control of deep foundation construction.

  16. 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.

  17. Big Data in Public Health: Terminology, Machine Learning, and Privacy.

    Science.gov (United States)

    Mooney, Stephen J; Pejaver, Vikas

    2018-04-01

    The digital world is generating data at a staggering and still increasing rate. While these "big data" have unlocked novel opportunities to understand public health, they hold still greater potential for research and practice. This review explores several key issues that have arisen around big data. First, we propose a taxonomy of sources of big data to clarify terminology and identify threads common across some subtypes of big data. Next, we consider common public health research and practice uses for big data, including surveillance, hypothesis-generating research, and causal inference, while exploring the role that machine learning may play in each use. We then consider the ethical implications of the big data revolution with particular emphasis on maintaining appropriate care for privacy in a world in which technology is rapidly changing social norms regarding the need for (and even the meaning of) privacy. Finally, we make suggestions regarding structuring teams and training to succeed in working with big data in research and practice.

  18. 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.

  19. Automated Machinery Health Monitoring Using Stress Wave Analysis & Artificial Intelligence

    National Research Council Canada - National Science Library

    Board, David

    1998-01-01

    .... Army, for application to helicopter drive train components. The system will detect structure borne, high frequency acoustic data, and process it with feature extraction and polynomial network artificial intelligence software...

  20. 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

  1. 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.

  2. 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

  3. 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.

  4. 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…

  5. 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.

  6. 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.

  7. 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.

  8. Bringing Business Intelligence to Health Information Technology Curriculum

    Science.gov (United States)

    Zheng, Guangzhi; Zhang, Chi; Li, Lei

    2015-01-01

    Business intelligence (BI) and healthcare analytics are the emerging technologies that provide analytical capability to help healthcare industry improve service quality, reduce cost, and manage risks. However, such component on analytical healthcare data processing is largely missed from current healthcare information technology (HIT) or health…

  9. 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...

  10. 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...

  11. 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…

  12. 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…

  13. 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.

  14. 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.

  15. Deep learning architectures for multi-label classification of intelligent health risk prediction.

    Science.gov (United States)

    Maxwell, Andrew; Li, Runzhi; Yang, Bei; Weng, Heng; Ou, Aihua; Hong, Huixiao; Zhou, Zhaoxian; Gong, Ping; Zhang, Chaoyang

    2017-12-28

    Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases. Physical examination records of 110,300 anonymous patients were used to predict diabetes, hypertension, fatty liver, a combination of these three chronic diseases, and the absence of disease (8 classes in total). The dataset was split into training (90%) and testing (10%) sub-datasets. Ten-fold cross validation was used to evaluate prediction accuracy with metrics such as precision, recall, and F-score. Deep Learning (DL) architectures were compared with standard and state-of-the-art multi-label classification methods. Preliminary results suggest that Deep Neural Networks (DNN), a DL architecture, when applied to multi-label classification of chronic diseases, produced accuracy that was comparable to that of common methods such as Support Vector Machines. We have implemented DNNs to handle both problem transformation and algorithm adaption type multi-label methods and compare both to see which is preferable. Deep Learning architectures have the potential of inferring more information about the patterns of physical examination data than common classification methods. The advanced techniques of Deep Learning can be used to identify the significance of different features from physical examination data as well as to learn the contributions of each feature that impact a patient's risk for chronic diseases. However, accurate prediction of chronic disease risks remains a challenging

  16. 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

  17. 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.

  18. 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

  19. 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.

  20. 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…

  1. Future uses of machine intelligence and robotics for the Space Station and implications for the U.S. economy

    Science.gov (United States)

    Cohen, A.; Erickson, J. D.

    1985-01-01

    The exciting possibilities for advancing the technologies of artificial intelligence, robotics, and automation on the Space Station is summarized. How these possibilities will be realized and how their realization can benefit the U.S. economy are described. Plans, research programs and preliminary designs that will lead to the realization of many of these possibilities are being formulated.

  2. 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.

  3. 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.

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

    Science.gov (United States)

    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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. A model for Intelligent Random Access Memory architecture (IRAM) cellular automata algorithms on the Associative String Processing machine (ASTRA)

    CERN Document Server

    Rohrbach, F; Vesztergombi, G

    1997-01-01

    In the near future, the computer performance will be completely determined by how long it takes to access memory. There are bottle-necks in memory latency and memory-to processor interface bandwidth. The IRAM initiative could be the answer by putting Processor-In-Memory (PIM). Starting from the massively parallel processing concept, one reached a similar conclusion. The MPPC (Massively Parallel Processing Collaboration) project and the 8K processor ASTRA machine (Associative String Test bench for Research \\& Applications) developed at CERN \\cite{kuala} can be regarded as a forerunner of the IRAM concept. The computing power of the ASTRA machine, regarded as an IRAM with 64 one-bit processors on a 64$\\times$64 bit-matrix memory chip machine, has been demonstrated by running statistical physics algorithms: one-dimensional stochastic cellular automata, as a simple model for dynamical phase transitions. As a relevant result for physics, the damage spreading of this model has been investigated.

  11. 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...

  12. Smart materials on the way to theranostic nanorobots: Molecular machines and nanomotors, advanced biosensors, and intelligent vehicles for drug delivery.

    Science.gov (United States)

    Sokolov, Ilya L; Cherkasov, Vladimir R; Tregubov, Andrey A; Buiucli, Sveatoslav R; Nikitin, Maxim P

    2017-06-01

    Theranostics, a fusion of two key parts of modern medicine - diagnostics and therapy of the organism's disorders, promises to bring the efficacy of medical treatment to a fundamentally new level and to become the basis of personalized medicine. Extrapolating today's progress in the field of smart materials to the long-run prospect, we can imagine future intelligent agents capable of performing complex analysis of different physiological factors inside the living organism and implementing a built-in program thereby triggering a series of therapeutic actions. These agents, by analogy with their macroscopic counterparts, can be called nanorobots. It is quite obscure what these devices are going to look like but they will be more or less based on today's achievements in nanobiotechnology. The present Review is an attempt to systematize highly diverse nanomaterials, which may potentially serve as modules for theranostic nanorobotics, e.g., nanomotors, sensing units, and payload carriers. Biocomputing-based sensing, externally actuated or chemically "fueled" autonomous movement, swarm inter-agent communication behavior are just a few inspiring examples that nanobiotechnology can offer today for construction of truly intelligent drug delivery systems. The progress of smart nanomaterials toward fully autonomous drug delivery nanorobots is an exciting prospect for disease treatment. Synergistic combination of the available approaches and their further development may produce intelligent drugs of unmatched functionality. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Artificial intelligence expert systems with neural network machine learning may assist decision-making for extractions in orthodontic treatment planning.

    Science.gov (United States)

    Takada, Kenji

    2016-09-01

    New approach for the diagnosis of extractions with neural network machine learning. Seok-Ki Jung and Tae-Woo Kim. Am J Orthod Dentofacial Orthop 2016;149:127-33. Not reported. Mathematical modeling. Copyright © 2016 Elsevier Inc. All rights reserved.

  14. 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.

  15. [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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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...

  1. 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.

  2. 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.

  3. 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.

  4. Exploring Machine Learning Techniques Using Patient Interactions in Online Health Forums to Classify Drug Safety

    Science.gov (United States)

    Chee, Brant Wah Kwong

    2011-01-01

    This dissertation explores the use of personal health messages collected from online message forums to predict drug safety using natural language processing and machine learning techniques. Drug safety is defined as any drug with an active safety alert from the US Food and Drug Administration (FDA). It is believed that this is the first…

  5. A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS + ELM

    OpenAIRE

    KAYA, Yılmaz

    2014-01-01

    Breast cancer is one of the leading causes of death among women all around the world. Therefore, true and early diagnosis of breast cancer is an important problem. The rough set (RS) and extreme learning machine (ELM) methods were used collectively in this study for the diagnosis of breast cancer. The unnecessary attributes were discarded from the dataset by means of the RS approach. The classification process by means of ELM was performed using the remaining attributes. The Wisconsin B...

  6. 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.

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

    OpenAIRE

    Huang, Ching Yu; Shen, A.C.T.; Hsieh, Y.P.; Feng, J.Y.; Wei, H.S.; Hwa, H.L.; Feng, J.Y.

    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 path analyses were used to examine the relationships between parenting styles, children's trait EI, and their psychological symptoms, wit...

  8. 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.

  9. 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.

  10. Intelligent simulated annealing algorithm applied to the optimization of the main magnet for magnetic resonance imaging machine

    International Nuclear Information System (INIS)

    Sanchez Lopez, Hector

    2001-01-01

    This work describes an alternative algorithm of Simulated Annealing applied to the design of the main magnet for a Magnetic Resonance Imaging machine. The algorithm uses a probabilistic radial base neuronal network to classify the possible solutions, before the objective function evaluation. This procedure allows reducing up to 50% the number of iterations required by simulated annealing to achieve the global maximum, when compared with the SA algorithm. The algorithm was applied to design a 0.1050 Tesla four coil resistive magnet, which produces a magnetic field 2.13 times more uniform than the solution given by SA. (author)

  11. The biotechnology innovation machine: a source of intelligent biopharmaceuticals for the pharma industry--mapping biotechnology's success.

    Science.gov (United States)

    Evens, R P; Kaitin, K I

    2014-05-01

    The marriage of biotechnology and the pharmaceutical industry (pharma) is predicated on an evolution in technology and product innovation. It has come as a result of advances in both the science and the business practices of the biotechnology sector in the past 30 years. Biotechnology products can be thought of as "intelligent pharmaceuticals," in that they often provide novel mechanisms of action, new approaches to disease control, higher clinical success rates, improved patient care, extended patent protection, and a significant likelihood of reimbursement. Although the first biotechnology product, insulin, was approved just 32 years ago in 1982, today there are more than 200 biotechnology products commercially available. Research has expanded to include more than 900 biotechnology products in clinical trials. Pharma is substantially engaged in both the clinical development of these products and their commercialization.

  12. How do we think machines think? An fMRI study of alleged competition with an artificial intelligence.

    Science.gov (United States)

    Chaminade, Thierry; Rosset, Delphine; Da Fonseca, David; Nazarian, Bruno; Lutcher, Ewald; Cheng, Gordon; Deruelle, Christine

    2012-01-01

    Mentalizing is defined as the inference of mental states of fellow humans, and is a particularly important skill for social interactions. Here we assessed whether activity in brain areas involved in mentalizing is specific to the processing of mental states or can be generalized to the inference of non-mental states by comparing brain responses during the interaction with an intentional and an artificial agent. Participants were scanned using fMRI during interactive rock-paper-scissors games while believing their opponent was a fellow human (Intentional agent, Int), a humanoid robot endowed with an artificial intelligence (Artificial agent, Art), or a computer playing randomly (Random agent, Rnd). Participants' subjective reports indicated that they adopted different stances against the three agents. The contrast of brain activity during interaction with the artificial and the random agents didn't yield any cluster at the threshold used, suggesting the absence of a reproducible stance when interacting with an artificial intelligence. We probed response to the artificial agent in regions of interest corresponding to clusters found in the contrast between the intentional and the random agents. In the precuneus involved in working memory, the posterior intraparietal suclus, in the control of attention and the dorsolateral prefrontal cortex, in executive functions, brain activity for Art was larger than for Rnd but lower than for Int, supporting the intrinsically engaging nature of social interactions. A similar pattern in the left premotor cortex and anterior intraparietal sulcus involved in motor resonance suggested that participants simulated human, and to a lesser extend humanoid robot actions, when playing the game. Finally, mentalizing regions, the medial prefrontal cortex and right temporoparietal junction, responded to the human only, supporting the specificity of mentalizing areas for interactions with intentional agents.

  13. The internet and intelligent machines: search engines, agents and robots; Radiologische Informationssuche im Internet: Datenbanken, Suchmaschinen und intelligente Agenten

    Energy Technology Data Exchange (ETDEWEB)

    Achenbach, S; Alfke, H [Marburg Univ. (Germany). Abt. fuer Strahlendiagnostik

    2000-04-01

    The internet plays an important role in a growing number of medical applications. Finding relevant information is not always easy as the amount of available information on the Web is rising quickly. Even the best Search Engines can only collect links to a fraction of all existing Web pages. In addition, many of these indexed documents have been changed or deleted. The vast majority of information on the Web is not searchable with conventional methods. New search strategies, technologies and standards are combined in Intelligent Search Agents (ISA) an Robots, which can retrieve desired information in a specific approach. Conclusion: The article describes differences between ISAs and conventional Search Engines and how communication between Agents improves their ability to find information. Examples of existing ISAs are given and the possible influences on the current and future work in radiology is discussed. (orig.) [German] Das Internet findet zunehmend in medizinischen Anwendungen Verbreitung, jedoch ist das Auffinden relevanter Informationen nicht immer leicht. Die Anzahl der verfuegbaren Dokumente im World wide web nimmt so schnell zu, dass die Suche zunehmend Probleme bereitet: Auch gute Suchmaschinen erfassen nur einige Prozent der vorhandenen Seiten in Ihren Datenbanken. Zusaetzlich sorgen staendige Veraenderungen dafuer, dass nur ein Teil dieser durchsuchbaren Dokumente ueberhaupt noch existiert. Der Grossteil des Internets ist daher mit konventionellen Methoden nicht zu erschliessen. Neue Standards, Suchstrategien und Technologien vereinen sich in den Suchagenten und Robots, die gezielter und intelligenter Inhalte ermitteln koennen. Schlussfolgerung: Der Artikel stellt dar, wie sich ein Intelligent search agent (ISA) von einer Suchmaschine unterscheidet und durch Kooperation mit anderen Agenten die Anforderungen der Benutzer besser erfuellen kann. Neben den Grundlagen werden exemplarische Anwendungen gezeigt, die heute im Netz existieren, und ein Ausblick

  14. How do we think machines think? An fMRI study of alleged competition with an artificial intelligence

    Science.gov (United States)

    Chaminade, Thierry; Rosset, Delphine; Da Fonseca, David; Nazarian, Bruno; Lutcher, Ewald; Cheng, Gordon; Deruelle, Christine

    2012-01-01

    Mentalizing is defined as the inference of mental states of fellow humans, and is a particularly important skill for social interactions. Here we assessed whether activity in brain areas involved in mentalizing is specific to the processing of mental states or can be generalized to the inference of non-mental states by comparing brain responses during the interaction with an intentional and an artificial agent. Participants were scanned using fMRI during interactive rock-paper-scissors games while believing their opponent was a fellow human (Intentional agent, Int), a humanoid robot endowed with an artificial intelligence (Artificial agent, Art), or a computer playing randomly (Random agent, Rnd). Participants' subjective reports indicated that they adopted different stances against the three agents. The contrast of brain activity during interaction with the artificial and the random agents didn't yield any cluster at the threshold used, suggesting the absence of a reproducible stance when interacting with an artificial intelligence. We probed response to the artificial agent in regions of interest corresponding to clusters found in the contrast between the intentional and the random agents. In the precuneus involved in working memory, the posterior intraparietal suclus, in the control of attention and the dorsolateral prefrontal cortex, in executive functions, brain activity for Art was larger than for Rnd but lower than for Int, supporting the intrinsically engaging nature of social interactions. A similar pattern in the left premotor cortex and anterior intraparietal sulcus involved in motor resonance suggested that participants simulated human, and to a lesser extend humanoid robot actions, when playing the game. Finally, mentalizing regions, the medial prefrontal cortex and right temporoparietal junction, responded to the human only, supporting the specificity of mentalizing areas for interactions with intentional agents. PMID:22586381

  15. 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.

  16. Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions.

    Science.gov (United States)

    Kassahun, Yohannes; Yu, Bingbin; Tibebu, Abraham Temesgen; Stoyanov, Danail; Giannarou, Stamatia; Metzen, Jan Hendrik; Vander Poorten, Emmanuel

    2016-04-01

    Advances in technology and computing play an increasingly important role in the evolution of modern surgical techniques and paradigms. This article reviews the current role of machine learning (ML) techniques in the context of surgery with a focus on surgical robotics (SR). Also, we provide a perspective on the future possibilities for enhancing the effectiveness of procedures by integrating ML in the operating room. The review is focused on ML techniques directly applied to surgery, surgical robotics, surgical training and assessment. The widespread use of ML methods in diagnosis and medical image computing is beyond the scope of the review. Searches were performed on PubMed and IEEE Explore using combinations of keywords: ML, surgery, robotics, surgical and medical robotics, skill learning, skill analysis and learning to perceive. Studies making use of ML methods in the context of surgery are increasingly being reported. In particular, there is an increasing interest in using ML for developing tools to understand and model surgical skill and competence or to extract surgical workflow. Many researchers begin to integrate this understanding into the control of recent surgical robots and devices. ML is an expanding field. It is popular as it allows efficient processing of vast amounts of data for interpreting and real-time decision making. Already widely used in imaging and diagnosis, it is believed that ML will also play an important role in surgery and interventional treatments. In particular, ML could become a game changer into the conception of cognitive surgical robots. Such robots endowed with cognitive skills would assist the surgical team also on a cognitive level, such as possibly lowering the mental load of the team. For example, ML could help extracting surgical skill, learned through demonstration by human experts, and could transfer this to robotic skills. Such intelligent surgical assistance would significantly surpass the state of the art in surgical

  17. 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.

  18. 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.

  19. Savana: Re-using Electronic Health Records with Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Ignacio Hernández Medrano

    2018-03-01

    Full Text Available Health information grows exponentially (doubling every 5 years, thus generating a sort of inflation of science, i.e. the generation of more knowledge than we can leverage. In an unprecedented data-driven shift, today doctors have no longer time to keep updated. This fact explains why only one in every five medical decisions is based strictly on evidence, which inevitably leads to variability. A good solution lies on clinical decision support systems, based on big data analysis. As the processing of large amounts of information gains relevance, automatic approaches become increasingly capable to see and correlate information further and better than the human mind can. In this context, healthcare professionals are increasingly counting on a new set of tools in order to deal with the growing information that becomes available to them on a daily basis. By allowing the grouping of collective knowledge and prioritizing “mindlines” against “guidelines”, these support systems are among the most promising applications of big data in health. In this demo paper we introduce Savana, an AI-enabled system based on Natural Language Processing (NLP and Neural Networks, capable of, for instance, the automatic expansion of medical terminologies, thus enabling the re-use of information expressed in natural language in clinical reports. This automatized and precise digital extraction allows the generation of a real time information engine, which is currently being deployed in healthcare institutions, as well as clinical research and management.

  20. Creating a 21st-century intelligent health system.

    Science.gov (United States)

    Newt, Gingrich; Nancy, Desmond

    2008-02-01

    In most areas of life, Americans enjoy the ease and convenience offered by advances in technology, communications, and transportation. Every day we experience the 21st-century model of America, which is one of effectiveness, accuracy, speed, flexibility, efficiency, lower cost, more choices, and greater achievement. We can shop online, compare prices for goods and services, and when decisions need to be made, we have access to a wide array of information sources to assist in making those decisions. In short, Americans enjoy great latitude in our power to determine what is best for us. This is not, however, the case when it comes to health and healthcare. In our current healthcare system, individuals are dependent on a structure that has resisted the natural progress and modernization achieved by market-oriented, 21st-century industries. The information age has been leaving health behind. Although it is the nature of a science- and technology-based entrepreneurial free market to provide more choices of higher quality at lower cost, in the healthcare sector, prices continue to rise, quality is inconsistent, and individuals lack the information, incentives, and power to make choices.

  1. Using business intelligence to analyze and share health system infrastructure data in a rural health authority.

    Science.gov (United States)

    Haque, Waqar; Urquhart, Bonnie; Berg, Emery; Dhanoa, Ramandeep

    2014-08-06

    Health care organizations gather large volumes of data, which has been traditionally stored in legacy formats making it difficult to analyze or use effectively. Though recent government-funded initiatives have improved the situation, the quality of most existing data is poor, suffers from inconsistencies, and lacks integrity. Generating reports from such data is generally not considered feasible due to extensive labor, lack of reliability, and time constraints. Advanced data analytics is one way of extracting useful information from such data. The intent of this study was to propose how Business Intelligence (BI) techniques can be applied to health system infrastructure data in order to make this information more accessible and comprehensible for a broader group of people. An integration process was developed to cleanse and integrate data from disparate sources into a data warehouse. An Online Analytical Processing (OLAP) cube was then built to allow slicing along multiple dimensions determined by various key performance indicators (KPIs), representing population and patient profiles, case mix groups, and healthy community indicators. The use of mapping tools, customized shape files, and embedded objects further augment the navigation. Finally, Web forms provide a mechanism for remote uploading of data and transparent processing of the cube. For privileged information, access controls were implemented. Data visualization has eliminated tedious analysis through legacy reports and provided a mechanism for optimally aligning resources with needs. Stakeholders are able to visualize KPIs on a main dashboard, slice-and-dice data, generate ad hoc reports, and quickly find the desired information. In addition, comparison, availability, and service level reports can also be generated on demand. All reports can be drilled down for navigation at a finer granularity. We have demonstrated how BI techniques and tools can be used in the health care environment to make informed

  2. Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics

    International Nuclear Information System (INIS)

    Chen, Zhicong; Wu, Lijun; Cheng, Shuying; Lin, Peijie; Wu, Yue; Lin, Wencheng

    2017-01-01

    Highlights: •An improved Simulink based modeling method is proposed for PV modules and arrays. •Key points of I-V curves and PV model parameters are used as the feature variables. •Kernel extreme learning machine (KELM) is explored for PV arrays fault diagnosis. •The parameters of KELM algorithm are optimized by the Nelder-Mead simplex method. •The optimized KELM fault diagnosis model achieves high accuracy and reliability. -- Abstract: Fault diagnosis of photovoltaic (PV) arrays is important for improving the reliability, efficiency and safety of PV power stations, because the PV arrays usually operate in harsh outdoor environment and tend to suffer various faults. Due to the nonlinear output characteristics and varying operating environment of PV arrays, many machine learning based fault diagnosis methods have been proposed. However, there still exist some issues: fault diagnosis performance is still limited due to insufficient monitored information; fault diagnosis models are not efficient to be trained and updated; labeled fault data samples are hard to obtain by field experiments. To address these issues, this paper makes contribution in the following three aspects: (1) based on the key points and model parameters extracted from monitored I-V characteristic curves and environment condition, an effective and efficient feature vector of seven dimensions is proposed as the input of the fault diagnosis model; (2) the emerging kernel based extreme learning machine (KELM), which features extremely fast learning speed and good generalization performance, is utilized to automatically establish the fault diagnosis model. Moreover, the Nelder-Mead Simplex (NMS) optimization method is employed to optimize the KELM parameters which affect the classification performance; (3) an improved accurate Simulink based PV modeling approach is proposed for a laboratory PV array to facilitate the fault simulation and data sample acquisition. Intensive fault experiments are

  3. Local health department translation processes: potential of machine translation technologies to help meet needs.

    Science.gov (United States)

    Turner, Anne M; Mandel, Hannah; Capurro, Daniel

    2013-01-01

    Limited English proficiency (LEP), defined as a limited ability to read, speak, write, or understand English, is associated with health disparities. Despite federal and state requirements to translate health information, the vast majority of health materials are solely available in English. This project investigates barriers to translation of health information and explores new technologies to improve access to multilingual public health materials. We surveyed all 77 local health departments (LHDs) in the Northwest about translation needs, practices, barriers and attitudes towards machine translation (MT). We received 67 responses from 45 LHDs. Translation of health materials is the principle strategy used by LHDs to reach LEP populations. Cost and access to qualified translators are principle barriers to producing multilingual materials. Thirteen LHDs have used online MT tools. Many respondents expressed concerns about the accuracy of MT. Overall, respondents were positive about its potential use, if low costs and quality could be assured.

  4. 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.

  5. Predicting Complexation Thermodynamic Parameters of β-Cyclodextrin with Chiral Guests by Using Swarm Intelligence and Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Luckhana Lawtrakul

    2009-05-01

    Full Text Available The Particle Swarm Optimization (PSO and Support Vector Machines (SVMs approaches are used for predicting the thermodynamic parameters for the 1:1 inclusion complexation of chiral guests with β-cyclodextrin. A PSO is adopted for descriptor selection in the quantitative structure-property relationships (QSPR of a dataset of 74 chiral guests due to its simplicity, speed, and consistency. The modified PSO is then combined with SVMs for its good approximating properties, to generate a QSPR model with the selected features. Linear, polynomial, and Gaussian radial basis functions are used as kernels in SVMs. All models have demonstrated an impressive performance with R2 higher than 0.8.

  6. 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.

  7. 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

  8. Cells, Agents, and Support Vectors in Interaction - Modeling Urban Sprawl based on Machine Learning and Artificial Intelligence Techniques in a Post-Industrial Region

    Science.gov (United States)

    Rienow, A.; Menz, G.

    2015-12-01

    Since the beginning of the millennium, artificial intelligence techniques as cellular automata (CA) and multi-agent systems (MAS) have been incorporated into land-system simulations to address the complex challenges of transitions in urban areas as open, dynamic systems. The study presents a hybrid modeling approach for modeling the two antagonistic processes of urban sprawl and urban decline at once. The simulation power of support vector machines (SVM), cellular automata (CA) and multi-agent systems (MAS) are integrated into one modeling framework and applied to the largest agglomeration of Central Europe: the Ruhr. A modified version of SLEUTH (short for Slope, Land-use, Exclusion, Urban, Transport, and Hillshade) functions as the CA component. SLEUTH makes use of historic urban land-use data sets and growth coefficients for the purpose of modeling physical urban expansion. The machine learning algorithm of SVM is applied in order to enhance SLEUTH. Thus, the stochastic variability of the CA is reduced and information about the human and ecological forces driving the local suitability of urban sprawl is incorporated. Subsequently, the supported CA is coupled with the MAS ReHoSh (Residential Mobility and the Housing Market of Shrinking City Systems). The MAS models population patterns, housing prices, and housing demand in shrinking regions based on interactions between household and city agents. Semi-explicit urban weights are introduced as a possibility of modeling from and to the pixel simultaneously. Three scenarios of changing housing preferences reveal the urban development of the region in terms of quantity and location. They reflect the dissemination of sustainable thinking among stakeholders versus the steady dream of owning a house in sub- and exurban areas. Additionally, the outcomes are transferred into a digital petri dish reflecting a synthetic environment with perfect conditions of growth. Hence, the generic growth elements affecting the future

  9. Using design science and artificial intelligence to improve health communication: ChronologyMD case example.

    Science.gov (United States)

    Neuhauser, Linda; Kreps, Gary L; Morrison, Kathleen; Athanasoulis, Marcos; Kirienko, Nikolai; Van Brunt, Deryk

    2013-08-01

    This paper describes how design science theory and methods and use of artificial intelligence (AI) components can improve the effectiveness of health communication. We identified key weaknesses of traditional health communication and features of more successful eHealth/AI communication. We examined characteristics of the design science paradigm and the value of its user-centered methods to develop eHealth/AI communication. We analyzed a case example of the participatory design of AI components in the ChronologyMD project intended to improve management of Crohn's disease. eHealth/AI communication created with user-centered design shows improved relevance to users' needs for personalized, timely and interactive communication and is associated with better health outcomes than traditional approaches. Participatory design was essential to develop ChronologyMD system architecture and software applications that benefitted patients. AI components can greatly improve eHealth/AI communication, if designed with the intended audiences. Design science theory and its iterative, participatory methods linked with traditional health communication theory and methods can create effective AI health communication. eHealth/AI communication researchers, developers and practitioners can benefit from a holistic approach that draws from theory and methods in both design sciences and also human and social sciences to create successful AI health communication. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  10. Interactive machine learning for health informatics: when do we need the human-in-the-loop?

    Science.gov (United States)

    Holzinger, Andreas

    2016-06-01

    Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as "algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human." This "human-in-the-loop" can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

  11. Intelligence: is it the epidemiologists' elusive "fundamental cause" of social class inequalities in health?

    Science.gov (United States)

    Gottfredson, Linda S

    2004-01-01

    Virtually all indicators of physical health and mental competence favor persons of higher socioeconomic status (SES). Conventional theories in the social sciences assume that the material disadvantages of lower SES are primarily responsible for these inequalities, either directly or by inducing psychosocial harm. These theories cannot explain, however, why the relation between SES and health outcomes (knowledge, behavior, morbidity, and mortality) is not only remarkably general across time, place, disease, and kind of health system but also so finely graded up the entire SES continuum. Epidemiologists have therefore posited, but not yet identified, a more general "fundamental cause" of health inequalities. This article concatenates various bodies of evidence to demonstrate that differences in general intelligence (g) may be that fundamental cause.

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

    Science.gov (United States)

    Lane, India F

    2010-03-01

    Nontechnical competencies identified as essential to the health professional's success include ethical behavior, interpersonal, self-management, leadership, business, and thinking competencies. The literature regarding such diverse topics, and the literature regarding "professional success" is extensive and wide-ranging, crossing educational, psychological, business, medical and vocational fields of study. This review is designed to introduce ways of viewing nontechnical competence from the psychology of human capacity to current perspectives, initiatives and needs in practice. After an introduction to the tensions inherent in educating individuals for both biomedical competency and "bedside" or "cageside" manner, the paper presents a brief overview of the major lines of inquiry into intelligence theory and how theories of multiple intelligences can build a foundation for conceptualizing professional and life skills. The discussion then moves from broad concepts of intelligence to more specific workplace skill sets, with an emphasis on professional medical education. This section introduces the research on noncognitive variables in various disciplines, the growing emphasis on competency based education, and the SKA movement in veterinary education. The next section presents the evidence that nontechnical, noncognitive or humanistic skills influence achievement in academic settings, medical education and clinical performance, as well as the challenges faced when educational priorities must be made.

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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)

  19. Machine Learning Methods to Extract Documentation of Breast Cancer Symptoms From Electronic Health Records.

    Science.gov (United States)

    Forsyth, Alexander W; Barzilay, Regina; Hughes, Kevin S; Lui, Dickson; Lorenz, Karl A; Enzinger, Andrea; Tulsky, James A; Lindvall, Charlotta

    2018-02-27

    Clinicians document cancer patients' symptoms in free-text format within electronic health record visit notes. Although symptoms are critically important to quality of life and often herald clinical status changes, computational methods to assess the trajectory of symptoms over time are woefully underdeveloped. To create machine learning algorithms capable of extracting patient-reported symptoms from free-text electronic health record notes. The data set included 103,564 sentences obtained from the electronic clinical notes of 2695 breast cancer patients receiving paclitaxel-containing chemotherapy at two academic cancer centers between May 1996 and May 2015. We manually annotated 10,000 sentences and trained a conditional random field model to predict words indicating an active symptom (positive label), absence of a symptom (negative label), or no symptom at all (neutral label). Sentences labeled by human coder were divided into training, validation, and test data sets. Final model performance was determined on 20% test data unused in model development or tuning. The final model achieved precision of 0.82, 0.86, and 0.99 and recall of 0.56, 0.69, and 1.00 for positive, negative, and neutral symptom labels, respectively. The most common positive symptoms were pain, fatigue, and nausea. Machine-based labeling of 103,564 sentences took two minutes. We demonstrate the potential of machine learning to gather, track, and analyze symptoms experienced by cancer patients during chemotherapy. Although our initial model requires further optimization to improve the performance, further model building may yield machine learning methods suitable to be deployed in routine clinical care, quality improvement, and research applications. Copyright © 2018 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

  20. 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.

  1. 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.

  2. Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept.

    Science.gov (United States)

    Abajian, Aaron; Murali, Nikitha; Savic, Lynn Jeanette; Laage-Gaupp, Fabian Max; Nezami, Nariman; Duncan, James S; Schlachter, Todd; Lin, MingDe; Geschwind, Jean-François; Chapiro, Julius

    2018-06-01

    To use magnetic resonance (MR) imaging and clinical patient data to create an artificial intelligence (AI) framework for the prediction of therapeutic outcomes of transarterial chemoembolization by applying machine learning (ML) techniques. This study included 36 patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization. The cohort (age 62 ± 8.9 years; 31 men; 13 white; 24 Eastern Cooperative Oncology Group performance status 0, 10 status 1, 2 status 2; 31 Child-Pugh stage A, 4 stage B, 1 stage C; 1 Barcelona Clinic Liver Cancer stage 0, 12 stage A, 10 stage B, 13 stage C; tumor size 5.2 ± 3.0 cm; number of tumors 2.6 ± 1.1; and 30 conventional transarterial chemoembolization, 6 with drug-eluting embolic agents). MR imaging was obtained before and 1 month after transarterial chemoembolization. Image-based tumor response to transarterial chemoembolization was assessed with the use of the 3D quantitative European Association for the Study of the Liver (qEASL) criterion. Clinical information, baseline imaging, and therapeutic features were used to train logistic regression (LR) and random forest (RF) models to predict patients as treatment responders or nonresponders under the qEASL response criterion. The performance of each model was validated using leave-one-out cross-validation. Both LR and RF models predicted transarterial chemoembolization treatment response with an overall accuracy of 78% (sensitivity 62.5%, specificity 82.1%, positive predictive value 50.0%, negative predictive value 88.5%). The strongest predictors of treatment response included a clinical variable (presence of cirrhosis) and an imaging variable (relative tumor signal intensity >27.0). Transarterial chemoembolization outcomes in patients with HCC may be predicted before procedures by combining clinical patient data and baseline MR imaging with the use of AI and ML techniques. Copyright © 2018 SIR. Published by Elsevier Inc. All rights reserved.

  3. [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.

  4. A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems

    Science.gov (United States)

    Litt, Jonathan S.; Simon, Donald L.; Garg, Sanjay; Guo, Ten-Heui; Mercer, Carolyn; Behbahani, Alireza; Bajwa, Anupa; Jensen, Daniel T.

    2005-01-01

    Intelligent Control and Health Management technology for aircraft propulsion systems is much more developed in the laboratory than in practice. With a renewed emphasis on reducing engine life cycle costs, improving fuel efficiency, increasing durability and life, etc., driven by various government programs, there is a strong push to move these technologies out of the laboratory and onto the engine. This paper describes the existing state of engine control and on-board health management, and surveys some specific technologies under development that will enable an aircraft propulsion system to operate in an intelligent way--defined as self-diagnostic, self-prognostic, self-optimizing, and mission adaptable. These technologies offer the potential for creating extremely safe, highly reliable systems. The technologies will help to enable a level of performance that far exceeds that of today s propulsion systems in terms of reduction of harmful emissions, maximization of fuel efficiency, and minimization of noise, while improving system affordability and safety. Technologies that are discussed include various aspects of propulsion control, diagnostics, prognostics, and their integration. The paper focuses on the improvements that can be achieved through innovative software and algorithms. It concentrates on those areas that do not require significant advances in sensors and actuators to make them achievable, while acknowledging the additional benefit that can be realized when those technologies become available. The paper also discusses issues associated with the introduction of some of the technologies.

  5. Emotional Intelligence and resilience in mental health professionals caring for patients with serious mental illness.

    Science.gov (United States)

    Frajo-Apor, Beatrice; Pardeller, Silvia; Kemmler, Georg; Hofer, Alex

    2016-09-01

    Emotional Intelligence (EI) and resilience may be considered as prerequisites for mental health professionals caring for patients with serious mental illness (SMI), since they are often exposed to severe emotional stress during daily work. Accordingly, this cross-sectional study assessed both EI and resilience and their interrelationship in 61 individuals belonging to an assertive outreach team for patients suffering from SMI compared 61 control subjects without healthcare-related working conditions. EI was assessed by means of the German version of the Mayer-Salovey-Caruso-Emotional-Intelligence Test (MSCEIT), resilience was assessed using the German version of the Resilience Scale. Both groups showed an average level of EI in all categories of the MSCEIT and indicated high levels of resilience. They did not differ significantly from each other, neither in terms of EI nor resilience. Correlation analysis revealed a positive association between EI and resilience, albeit small in magnitude. Our results suggest that mental health professionals are not more resilient and therefore not more 'protected' from stressors than the general population. Though this finding warrants cautious interpretation, the positive correlation between EI and resilience suggests that EI may be a potential target for education and training in order to strengthen resilience even in healthy individuals and vice versa.

  6. Development and implementation of a clinical and business intelligence system for the Florida health data warehouse.

    Science.gov (United States)

    AlHazme, Raed H; Rana, Arif M; De Lucca, Michael

    2014-01-01

    To develop and implement a Clinical and Business Intelligence (CBI) system for the Florida Health Data Warehouse (FHDW) in order to bridge the gap between Florida's healthcare stakeholders and the health data archived in FHWD. A gap analysis study has been conducted to evaluate the technological divide between the relevant users and FHWD health data, which is maintained by the Broward Regional Health Planning Council (BRHPC). The study revealed a gap between the health care data and the decision makers that utilize the FHDW data. To bridge the gap, a CBI system was proposed, developed and implemented by BRHPC as a viable solution to address this issue, using the System Development Life Cycle methodology. The CBI system was successfully implemented and yielded a number of positive outcomes. In addition to significantly shortening the time required to analyze the health data for decision-making processes, the solution also provided end-users with the ability to automatically track public health parameters. A large amount of data is collected and stored by various health care organizations at the local, state, and national levels. If utilized properly, such data can go a long way in optimizing health care services. CBI systems provide health care organizations with valuable insights for improving patient care, tracking trends for medical research, and for controlling costs. The CBI system has been found quite effective in bridging the gap between Florida's healthcare stake holders and FHDW health data. Consequently, the solution has improved in the planning and coordination of health care services for the state of Florida.

  7. Emotional intelligence as predictor of mental, social, and physical health in university students.

    Science.gov (United States)

    Extremera, Natalio; Fernández-Berrocal, Pablo

    2006-05-01

    This study examined the association between emotional intelligence (EI), anxiety, depression, and mental, social, and physical health in university students. The sample was made up of 184 university students (38 men and 146 women). El was evaluated by the Trait Meta-Mood Scale (Salovey, Mayer, Goldman, Turvey, and Palfai, 1995), which evaluates the three dimensions (Attention, Clarity, and Mood Repair). Anxiety was evaluated with the Trait Anxiety Questionnaire (Spielberger, Gorsuch, Lushene, Vagg, and Jacobs, 1983) and depression with the Beck Depression Inventory (Beck, Rush, Shaw, and Emery, 1979). Mental, social, and physical health were evaluated with the SF-12 Health Survey (Ware, Kosinski, and Keller, 1996). Results showed that high Emotional Attention was positively and significantly related to high anxiety, depression, and to low levels of Role Emotional, Social Functioning, and Mental Health. However, high levels of emotional Clarity and Mood Repair were related to low levels of anxiety and depression, high Role Physical, Social Functioning, Mental Health, Vitality, and General Health. This study confirmed the predictive value of Attention, Clarity and Mood Repair regarding the levels of anxiety, depression, and areas related to mental, social, and physical health in university students.

  8. 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.

  9. A machine learning-based framework to identify type 2 diabetes through electronic health records.

    Science.gov (United States)

    Zheng, Tao; Xie, Wei; Xu, Liling; He, Xiaoying; Zhang, Ya; You, Mingrong; Yang, Gong; Chen, You

    2017-01-01

    To discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide association study (PheWAS), more cases (T2DM subjects) and controls (subjects without T2DM) are required to be identified (e.g., via Electronic Health Records (EHR)). However, existing expert based identification algorithms often suffer in a low recall rate and could miss a large number of valuable samples under conservative filtering standards. The goal of this work is to develop a semi-automated framework based on machine learning as a pilot study to liberalize filtering criteria to improve recall rate with a keeping of low false positive rate. We propose a data informed framework for identifying subjects with and without T2DM from EHR via feature engineering and machine learning. We evaluate and contrast the identification performance of widely-used machine learning models within our framework, including k-Nearest-Neighbors, Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression. Our framework was conducted on 300 patient samples (161 cases, 60 controls and 79 unconfirmed subjects), randomly selected from 23,281 diabetes related cohort retrieved from a regional distributed EHR repository ranging from 2012 to 2014. We apply top-performing machine learning algorithms on the engineered features. We benchmark and contrast the accuracy, precision, AUC, sensitivity and specificity of classification models against the state-of-the-art expert algorithm for identification of T2DM subjects. Our results indicate that the framework achieved high identification performances (∼0.98 in average AUC), which are much higher than the state-of-the-art algorithm (0.71 in AUC). Expert algorithm-based identification of T2DM subjects from EHR is often hampered by the high missing rates due to their conservative selection criteria. Our framework leverages machine learning and feature

  10. Emotional intelligence and health-related quality of life in institutionalised Spanish older adults.

    Science.gov (United States)

    Luque-Reca, Octavio; Pulido-Martos, Manuel; Lopez-Zafra, Esther; Augusto-Landa, José María

    2015-06-01

    This study explores the relationship between emotional intelligence (EI) and health-related quality of life (HRQoL) in a sample of Spanish older adults who are institutionalised in long-term care (LTC) facilities. One hundred fifteen institutionalised individuals (47.82% women; 88.3 ± 7.9 years) from southern Spain completed a set of questionnaires that included measures of EI, health and personality. Data were analysed via hierarchical regression. After controlling for personality and sociodemographic variables, the EI dimensions, emotional comprehension and emotional facilitation, accounted for part of the variance in several HRQoL facets. These dimensions could have an important role in the HRQoL of residents in LTC. Moreover, the use of a performance measure addresses the limitations of previous studies that have relied on self-report measures. These aspects underscore the importance of the results of this study. © 2014 International Union of Psychological Science.

  11. 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.

  12. Robust Machine Learning Variable Importance Analyses of Medical Conditions for Health Care Spending.

    Science.gov (United States)

    Rose, Sherri

    2018-03-11

    To propose nonparametric double robust machine learning in variable importance analyses of medical conditions for health spending. 2011-2012 Truven MarketScan database. I evaluate how much more, on average, commercially insured enrollees with each of 26 of the most prevalent medical conditions cost per year after controlling for demographics and other medical conditions. This is accomplished within the nonparametric targeted learning framework, which incorporates ensemble machine learning. Previous literature studying the impact of medical conditions on health care spending has almost exclusively focused on parametric risk adjustment; thus, I compare my approach to parametric regression. My results demonstrate that multiple sclerosis, congestive heart failure, severe cancers, major depression and bipolar disorders, and chronic hepatitis are the most costly medical conditions on average per individual. These findings differed from those obtained using parametric regression. The literature may be underestimating the spending contributions of several medical conditions, which is a potentially critical oversight. If current methods are not capturing the true incremental effect of medical conditions, undesirable incentives related to care may remain. Further work is needed to directly study these issues in the context of federal formulas. © Health Research and Educational Trust.

  13. Impact of Health Care Employees’ Job Satisfaction on Organizational Performance Support Vector Machine Approach

    Directory of Open Access Journals (Sweden)

    CEMIL KUZEY

    2018-01-01

    Full Text Available This study is undertaken to search for key factors that contribute to job satisfaction among health care workers, and also to determine the impact of these underlying dimensions of employee satisfaction on organizational performance. Exploratory Factor Analysis (EFA is applied to initially uncover the key factors, and then, in the next stage of analysis, a popular data mining technique, Support Vector Machine (SVM is employed on a sample of 249 to determine the impact of job satisfaction factors on organizational performance. According to the proposed model, the main factors are revealed to be management’s attitude, pay/reward, job security and colleagues.

  14. HClass: Automatic classification tool for health pathologies using artificial intelligence techniques.

    Science.gov (United States)

    Garcia-Chimeno, Yolanda; Garcia-Zapirain, Begonya

    2015-01-01

    The classification of subjects' pathologies enables a rigorousness to be applied to the treatment of certain pathologies, as doctors on occasions play with so many variables that they can end up confusing some illnesses with others. Thanks to Machine Learning techniques applied to a health-record database, it is possible to make using our algorithm. hClass contains a non-linear classification of either a supervised, non-supervised or semi-supervised type. The machine is configured using other techniques such as validation of the set to be classified (cross-validation), reduction in features (PCA) and committees for assessing the various classifiers. The tool is easy to use, and the sample matrix and features that one wishes to classify, the number of iterations and the subjects who are going to be used to train the machine all need to be introduced as inputs. As a result, the success rate is shown either via a classifier or via a committee if one has been formed. A 90% success rate is obtained in the ADABoost classifier and 89.7% in the case of a committee (comprising three classifiers) when PCA is applied. This tool can be expanded to allow the user to totally characterise the classifiers by adjusting them to each classification use.

  15. 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.

  16. 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.

  17. 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.

  18. Health monitoring and rehabilitation of a concrete structure using intelligent materials

    Science.gov (United States)

    Song, G.; Mo, Y. L.; Otero, K.; Gu, H.

    2006-04-01

    This paper presents the concept of an intelligent reinforced concrete structure (IRCS) and its application in structural health monitoring and rehabilitation. The IRCS has multiple functions which include self-rehabilitation, self-vibration damping, and self-structural health monitoring. These functions are enabled by two types of intelligent (smart) materials: shape memory alloys (SMAs) and piezoceramics. In this research, Nitinol type SMA and PZT (lead zirconate titanate) type piezoceramics are used. The proposed concrete structure is reinforced by martensite Nitinol cables using the method of post-tensioning. The martensite SMA significantly increases the concrete's damping property and its ability to handle large impact. In the presence of cracks due to explosions or earthquakes, by electrically heating the SMA cables, the SMA cables contract and close up the cracks. In this research, PZT patches are embedded in the concrete structure to detect possible cracks inside the concrete structure. The wavelet packet analysis method is then applied as a signal-processing tool to analyze the sensor signals. A damage index is defined to describe the damage severity for health monitoring purposes. In addition, by monitoring the electric resistance change of the SMA cables, the crack width can be estimated. To demonstrate this concept, a concrete beam specimen with reinforced SMA cables and with embedded PZT patches is fabricated. Experiments demonstrate that the IRC has the ability of self-sensing and self-rehabilitation. Three-point bending tests were conducted. During the loading process, a crack opens up to 0.47 inches. Upon removal of the load and heating the SMA cables, the crack closes up. The damage index formed by wavelet packet analysis of the PZT sensor data predicts and confirms the onset and severity of the crack during the loading. Also during the loading, the electrical resistance value of the SMA cable changes by up to 27% and this phenomenon is used to

  19. Big Data Analysis for Personalized Health Activities: Machine Learning Processing for Automatic Keyword Extraction Approach

    Directory of Open Access Journals (Sweden)

    Jun-Ho Huh

    2018-04-01

    Full Text Available The obese population is increasing rapidly due to the change of lifestyle and diet habits. Obesity can cause various complications and is becoming a social disease. Nonetheless, many obese patients are unaware of the medical treatments that are right for them. Although a variety of online and offline obesity management services have been introduced, they are still not enough to attract the attention of users and are not much of help to solve the problem. Obesity healthcare and personalized health activities are the important factors. Since obesity is related to lifestyle habits, eating habits, and interests, I concluded that the big data analysis of these factors could deduce the problem. Therefore, I collected big data by applying the machine learning and crawling method to the unstructured citizen health data in Korea and the search data of Naver, which is a Korean portal company, and Google for keyword analysis for personalized health activities. It visualized the big data using text mining and word cloud. This study collected and analyzed the data concerning the interests related to obesity, change of interest on obesity, and treatment articles. The analysis showed a wide range of seasonal factors according to spring, summer, fall, and winter. It also visualized and completed the process of extracting the keywords appropriate for treatment of abdominal obesity and lower body obesity. The keyword big data analysis technique for personalized health activities proposed in this paper is based on individual’s interests, level of interest, and body type. Also, the user interface (UI that visualizes the big data compatible with Android and Apple iOS. The users can see the data on the app screen. Many graphs and pictures can be seen via menu, and the significant data values are visualized through machine learning. Therefore, I expect that the big data analysis using various keywords specific to a person will result in measures for personalized

  20. Artificial intelligence and immediacy: designing health communication to personally engage consumers and providers.

    Science.gov (United States)

    Kreps, Gary L; Neuhauser, Linda

    2013-08-01

    We describe how ehealth communication programs can be improved by using artificial intelligence (AI) to increase immediacy. We analyzed major deficiencies in ehealth communication programs, illustrating how programs often fail to fully engage audiences and can even have negative consequences by undermining the effective delivery of information intended to guide health decision-making and influence adoption of health-promoting behaviors. We examined the use of AI in ehealth practices to promote immediacy and provided examples from the ChronologyMD project. Strategic use of AI is shown to help enhance immediacy in ehealth programs by making health communication more engaging, relevant, exciting, and actionable. AI can enhance the "immediacy" of ehealth by humanizing health promotion efforts, promoting physical and emotional closeness, increasing authenticity and enthusiasm in health promotion efforts, supporting personal involvement in communication interactions, increasing exposure to relevant messages, reducing demands on healthcare staff, improving program efficiency, and minimizing costs. User-centered AI approaches, such as the use of personally involving verbal and nonverbal cues, natural language translation, virtual coaches, and comfortable human-computer interfaces can promote active information processing and adoption of new ideas. Immediacy can improve information access, trust, sharing, motivation, and behavior changes. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  1. Intelligence in Artificial Intelligence

    OpenAIRE

    Datta, Shoumen Palit Austin

    2016-01-01

    The elusive quest for intelligence in artificial intelligence prompts us to consider that instituting human-level intelligence in systems may be (still) in the realm of utopia. In about a quarter century, we have witnessed the winter of AI (1990) being transformed and transported to the zenith of tabloid fodder about AI (2015). The discussion at hand is about the elements that constitute the canonical idea of intelligence. The delivery of intelligence as a pay-per-use-service, popping out of ...

  2. Modeling workflow to design machine translation applications for public health practice.

    Science.gov (United States)

    Turner, Anne M; Brownstein, Megumu K; Cole, Kate; Karasz, Hilary; Kirchhoff, Katrin

    2015-02-01

    Provide a detailed understanding of the information workflow processes related to translating health promotion materials for limited English proficiency individuals in order to inform the design of context-driven machine translation (MT) tools for public health (PH). We applied a cognitive work analysis framework to investigate the translation information workflow processes of two large health departments in Washington State. Researchers conducted interviews, performed a task analysis, and validated results with PH professionals to model translation workflow and identify functional requirements for a translation system for PH. The study resulted in a detailed description of work related to translation of PH materials, an information workflow diagram, and a description of attitudes towards MT technology. We identified a number of themes that hold design implications for incorporating MT in PH translation practice. A PH translation tool prototype was designed based on these findings. This study underscores the importance of understanding the work context and information workflow for which systems will be designed. Based on themes and translation information workflow processes, we identified key design guidelines for incorporating MT into PH translation work. Primary amongst these is that MT should be followed by human review for translations to be of high quality and for the technology to be adopted into practice. The time and costs of creating multilingual health promotion materials are barriers to translation. PH personnel were interested in MT's potential to improve access to low-cost translated PH materials, but expressed concerns about ensuring quality. We outline design considerations and a potential machine translation tool to best fit MT systems into PH practice. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. The Influence of Emotional Intelligence (EI) on Coping and Mental Health in Adolescence: Divergent Roles for Trait and Ability EI

    Science.gov (United States)

    Davis, Sarah K.; Humphrey, Neil

    2012-01-01

    Theoretically, trait and ability emotional intelligence (EI) should mobilise coping processes to promote adaptation, plausibly operating as personal resources determining choice and/or implementation of coping style. However, there is a dearth of research deconstructing if/how EI impacts mental health via multiple coping strategies in adolescence.…

  4. Economist intelligence unit democracy index in relation to health services accessibility: a regression analysis.

    Science.gov (United States)

    Walker, Mary Ellen; Anonson, June; Szafron, Michael

    2015-01-01

    The relationship between political environment and health services accessibility (HSA) has not been the focus of any specific studies. The purpose of this study was to address this gap in the literature by examining the relationship between political environment and HSA. This relationship that HSA indicators (physicians, nurses and hospital beds per 10 000 people) has with political environment was analyzed with multiple least-squares regression using the components of democracy (electoral processes and pluralism, functioning of government, political participation, political culture, and civil liberties). The components of democracy were represented by the 2011 Economist Intelligence Unit Democracy Index (EIUDI) sub-scores. The EIUDI sub-scores and the HSA indicators were evaluated for significant relationships with multiple least-squares regression. While controlling for a country's geographic location and level of democracy, we found that two components of a nation's political environment: functioning of government and political participation, and their interaction had significant relationships with the three HSA indicators. These study findings are of significance to health professionals because they examine the political contexts in which citizens access health services, they come from research that is the first of its kind, and they help explain the effect political environment has on health. © The Author 2014. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  5. Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards.

    Science.gov (United States)

    da Costa, Cristiano André; Pasluosta, Cristian F; Eskofier, Björn; da Silva, Denise Bandeira; da Rosa Righi, Rodrigo

    2018-06-02

    Large amounts of patient data are routinely manually collected in hospitals by using standalone medical devices, including vital signs. Such data is sometimes stored in spreadsheets, not forming part of patients' electronic health records, and is therefore difficult for caregivers to combine and analyze. One possible solution to overcome these limitations is the interconnection of medical devices via the Internet using a distributed platform, namely the Internet of Things. This approach allows data from different sources to be combined in order to better diagnose patient health status and identify possible anticipatory actions. This work introduces the concept of the Internet of Health Things (IoHT), focusing on surveying the different approaches that could be applied to gather and combine data on vital signs in hospitals. Common heuristic approaches are considered, such as weighted early warning scoring systems, and the possibility of employing intelligent algorithms is analyzed. As a result, this article proposes possible directions for combining patient data in hospital wards to improve efficiency, allow the optimization of resources, and minimize patient health deterioration. It is concluded that a patient-centered approach is critical, and that the IoHT paradigm will continue to provide more optimal solutions for patient management in hospital wards. Copyright © 2018 Elsevier B.V. All rights reserved.

  6. [Emotional Intelligence Index: a tool for the routine assessment of mental health promotion programs in schools].

    Science.gov (United States)

    Veltro, Franco; Ialenti, Valentina; Morales García, Manuel Alejandro; Gigantesco, Antonella

    2016-01-01

    After critical examination of several aspects relating to the evaluation of some dimensions of emotional intelligence through self-assessment tools, is described the procedure of construction and validation of an Index for its measurement, conceived only for the routine assessment of health promotion programs mental in schools that include among their objectives the improvement of emotional intelligence specifically "outcome-oriented". On the basis of the two most common international tools, are listed 27 items plus 6 of control, illustrated two Focus Group (FG) of students (face validity). The scale obtained by FG was administered to 300 students, and the results were submitted to factorial analysis (construct validity). It was also evaluated the internal consistency with Cronbach's Alpha and studied concurrent validity with the emotional quotient inventory, a scale of perceived self-efficacy and a stress test rating. From the analysis of FG all the original items were modified, deleted 4, and reduced the encoding system from 6 to 4 levels of Likert scale. Of the 23 items included in the analysis have emerged five factors (intra-psychic dimension, interpersonal, impulsivity, adaptive coping, sense of self-efficacy) for a total of 15 items. Very satisfactory were the results of the validation process of internal consistency (0.72) and the concurrent validity. The results are positive. It is obtained in fact the shortest routine assessment tool currently available in Italy which constitutes a real Index, for which compilation are required on average 3 minutes. Is emphasized the characteristic of an Index, and not of questionnaire or interview for clinical use, highlighting the only specific use for mental health promotion programs in schools.

  7. The Global Public Health Intelligence Network and early warning outbreak detection: a Canadian contribution to global public health.

    Science.gov (United States)

    Mykhalovskiy, Eric; Weir, Lorna

    2006-01-01

    The recent SARS epidemic has renewed widespread concerns about the global transmission of infectious diseases. In this commentary, we explore novel approaches to global infectious disease surveillance through a focus on an important Canadian contribution to the area--the Global Public Health Intelligence Network (GPHIN). GPHIN is a cutting-edge initiative that draws on the capacity of the Internet and newly available 24/7 global news coverage of health events to create a unique form of early warning outbreak detection. This commentary outlines the operation and development of GPHIN and compares it to ProMED-mail, another Internet-based approach to global health surveillance. We argue that GPHIN has created an important shift in the relationship of public health and news information. By exiting the pyramid of official reporting, GPHIN has created a new monitoring technique that has disrupted national boundaries of outbreak notification, while creating new possibilities for global outbreak response. By incorporating news within the emerging apparatus of global infectious disease surveillance, GPHIN has effectively responded to the global media's challenge to official country reporting of outbreak and enhanced the effectiveness and credibility of international public health.

  8. 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

  9. 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.

  10. The effect of social demographic factors, snack consumption and vending machine use on oral health of children living in London.

    Science.gov (United States)

    Maliderou, M; Reeves, S; Noble, C

    2006-10-07

    To investigate the effect of socio-economic status, sugar, snack consumption and vending machine use on the prevalence and severity of caries (DMF) in children. An observational study was carried out in a dental practice in inner city London. Sixty children were asked to complete a questionnaire and a three day food and drink diary. After a dental examination the number of decayed (D), missing (M) or filled (F) teeth provided a DMF score. Anova and Pearsons correlations were used to analyse the data statistically. Children from social groups I and II consumed significantly less (P vending machine less often than children from other social groups. Children from Social groups I, II and III had significantly lower DMF scores. The average DMF from social group I children was 0.5 +/- 0.6, whilst group IV children had the greatest incidence and a DMF of 4.6 +/- 0.8. Significant correlations were identified between DMF and sugar, confectionery and crisp consumption and vending machine use, and a negative correlation between DMF and vegetable consumption. Socio-economic status and access to vending machines were found to have a significant effect on sugar intakes, foods choices, and dental health. The removal of vending machines from schools or at least installing 'healthy' vending machines is recommended. Health promotion programmes that account for social groups and snacking habits that are cost effective are required.

  11. The relationship of general health, hardiness and spiritual intelligence relationship in Iranian nurses.

    Directory of Open Access Journals (Sweden)

    Fatemeh Akbarizadeh

    2013-12-01

    Full Text Available Nursing is one of the stressful jobs that affect nurse's well-being. The aim of this study was to assess the relationship between spiritual intelligence, hardiness and well-being among Iranian nurses.Samples of this cross- sectional study selected by Randomized stratified sampling, 125 nurses who have been working in different wards of Bushehr university hospitals. Data were collected using spiritual intelligence, hardiness, well-being and demographic characteristics questionnaires. Correlation, t-test, ANOVA, Tukey and regression analysis were applied.The results revealed a significant relationship between spiritual intelligence and hardiness, spiritual intelligence and well-being, Hardiness and well-being. It also showed that among the demographic characteristics (age, gender, working ward, marital status, job experiences, and education working ward significantly correlated with spiritual intelligence.Improvement of spiritual intelligence and reinforcement of hardiness could help increase the well-being of nurses.

  12. Computer Aided Diagnosis for mental health care: On the Clinical Validation of Sensitive Machines

    NARCIS (Netherlands)

    van der Sluis, Frans; Dijkstra, Ton; van den Broek, Egon; Conchon, E.; Correia, C.; Fred, A.; Gamboa, H.

    2012-01-01

    This study explores the feasibility of sensitive machines; that is, machines with empathic abilities, at least to some extent. A signal processing and machine learning pipeline is presented that is used to analyze data from two studies in which 25 Post-Traumatic Stress Disorder (PTSD) patients

  13. Computer aided diagnosis for mental health care : On the clinical validation of sensitive machines

    NARCIS (Netherlands)

    Sluis, F. van der; Dijkstra, T.; Broek, E.L. van den

    2012-01-01

    This study explores the feasibility of sensitive machines; that is, machines with empathic abilities, at least to some extent. A signal processing and machine learning pipeline is presented that is used to analyze data from two studies in which 25 Post-Traumatic Stress Disorder (PTSD) patients

  14. Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning

    OpenAIRE

    Roysden, Nathaniel; Wright, Adam

    2015-01-01

    Mental health problems are an independent predictor of increased healthcare utilization. We created random forest classifiers for predicting two outcomes following a patient’s first behavioral health encounter: decreased utilization by any amount (AUROC 0.74) and ultra-high absolute utilization (AUROC 0.88). These models may be used for clinical decision support by referring providers, to automatically detect patients who may benefit from referral, for cost management, or for risk/protection ...

  15. Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning.

    Science.gov (United States)

    Roysden, Nathaniel; Wright, Adam

    2015-01-01

    Mental health problems are an independent predictor of increased healthcare utilization. We created random forest classifiers for predicting two outcomes following a patient's first behavioral health encounter: decreased utilization by any amount (AUROC 0.74) and ultra-high absolute utilization (AUROC 0.88). These models may be used for clinical decision support by referring providers, to automatically detect patients who may benefit from referral, for cost management, or for risk/protection factor analysis.

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

    Science.gov (United States)

    Zhao, Rui; Yan, Ruqiang; Wang, Jinjiang; Mao, Kezhi

    2017-01-30

    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 models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.

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

    Directory of Open Access Journals (Sweden)

    Rui Zhao

    2017-01-01

    Full Text Available 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 models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.

  18. Relationship between Emotional Intelligence and Mental Health in School Counselors (Relación entre Inteligencia Emocional y salud mental en Orientadores Educativos)

    Science.gov (United States)

    Cejudo, Javier

    2016-01-01

    Introduction: The purpose of the present research is aimed at studying the relationship between emotional intelligence as an ability and emotional intelligence as a trait and mental health of a sample of school counsellors. Method: The sample has been made up of 203 school counsellors. The instruments used have been: Mayer-Salovey-Caruso Emotional…

  19. Artificial Intelligence-Assisted Online Social Therapy for Youth Mental Health

    Directory of Open Access Journals (Sweden)

    Simon D'Alfonso

    2017-06-01

    Full Text Available Introduction: Benefits from mental health early interventions may not be sustained over time, and longer-term intervention programs may be required to maintain early clinical gains. However, due to the high intensity of face-to-face early intervention treatments, this may not be feasible. Adjunctive internet-based interventions specifically designed for youth may provide a cost-effective and engaging alternative to prevent loss of intervention benefits. However, until now online interventions have relied on human moderators to deliver therapeutic content. More sophisticated models responsive to user data are critical to inform tailored online therapy. Thus, integration of user experience with a sophisticated and cutting-edge technology to deliver content is necessary to redefine online interventions in youth mental health. This paper discusses the development of the moderated online social therapy (MOST web application, which provides an interactive social media-based platform for recovery in mental health. We provide an overview of the system's main features and discus our current work regarding the incorporation of advanced computational and artificial intelligence methods to enhance user engagement and improve the discovery and delivery of therapy content.Methods: Our case study is the ongoing Horyzons site (5-year randomized controlled trial for youth recovering from early psychosis, which is powered by MOST. We outline the motivation underlying the project and the web application's foundational features and interface. We discuss system innovations, including the incorporation of pertinent usage patterns as well as identifying certain limitations of the system. This leads to our current motivations and focus on using computational and artificial intelligence methods to enhance user engagement, and to further improve the system with novel mechanisms for the delivery of therapy content to users. In particular, we cover our usage of natural

  20. Artificial Intelligence-Assisted Online Social Therapy for Youth Mental Health.

    Science.gov (United States)

    D'Alfonso, Simon; Santesteban-Echarri, Olga; Rice, Simon; Wadley, Greg; Lederman, Reeva; Miles, Christopher; Gleeson, John; Alvarez-Jimenez, Mario

    2017-01-01

    Introduction: Benefits from mental health early interventions may not be sustained over time, and longer-term intervention programs may be required to maintain early clinical gains. However, due to the high intensity of face-to-face early intervention treatments, this may not be feasible. Adjunctive internet-based interventions specifically designed for youth may provide a cost-effective and engaging alternative to prevent loss of intervention benefits. However, until now online interventions have relied on human moderators to deliver therapeutic content. More sophisticated models responsive to user data are critical to inform tailored online therapy. Thus, integration of user experience with a sophisticated and cutting-edge technology to deliver content is necessary to redefine online interventions in youth mental health. This paper discusses the development of the moderated online social therapy (MOST) web application, which provides an interactive social media-based platform for recovery in mental health. We provide an overview of the system's main features and discus our current work regarding the incorporation of advanced computational and artificial intelligence methods to enhance user engagement and improve the discovery and delivery of therapy content. Methods: Our case study is the ongoing Horyzons site (5-year randomized controlled trial for youth recovering from early psychosis), which is powered by MOST. We outline the motivation underlying the project and the web application's foundational features and interface. We discuss system innovations, including the incorporation of pertinent usage patterns as well as identifying certain limitations of the system. This leads to our current motivations and focus on using computational and artificial intelligence methods to enhance user engagement, and to further improve the system with novel mechanisms for the delivery of therapy content to users. In particular, we cover our usage of natural language analysis

  1. Integrated system of structural health monitoring and intelligent management for a cable-stayed bridge.

    Science.gov (United States)

    Chen, Bin; Wang, Xu; Sun, Dezhang; Xie, Xu

    2014-01-01

    It is essential to construct structural health monitoring systems for large important bridges. Zhijiang Bridge is a cable-stayed bridge that was built recently over the Hangzhou Qiantang River (the largest river in Zhejiang Province). The length of Zhijiang Bridge is 478 m, which comprises an arched twin-tower space and a twin-cable plane structure. As an example, the present study describes the integrated system of structural health monitoring and intelligent management for Zhijiang Bridge, which comprises an information acquisition system, data management system, evaluation and decision-making system, and application service system. The monitoring components include the working environment of the bridge and various factors that affect bridge safety, such as the stress and strain of the main bridge structure, vibration, cable force, temperature, and wind speed. In addition, the integrated system includes a forecasting and decision-making module for real-time online evaluation, which provides warnings and makes decisions based on the monitoring information. From this, the monitoring information, evaluation results, maintenance decisions, and warning information can be input simultaneously into the bridge monitoring center and traffic emergency center to share the monitoring data, thereby facilitating evaluations and decision making using the system.

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

    Directory of Open Access Journals (Sweden)

    Ching-Yu Huang

    2017-11-01

    Full Text Available 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 path analyses were used to examine the relationships between parenting styles, children's trait EI, and their psychological symptoms, with children's psychological symptoms as the dependent variable. The results showed that authoritative parenting was positively associated with children’s trait EI, which in turn had a negative effect on children’s psychological symptoms, whereas authoritarian and Chinese-specific parenting styles had direct negative effect on children’s psychological symptoms. These findings shed light on the pathways of the interrelations between different parenting styles, children's trait EI, and psychological symptoms, providing theoretical as well as practical implications for children's emotional development and mental health.

  3. Integrated System of Structural Health Monitoring and Intelligent Management for a Cable-Stayed Bridge

    Directory of Open Access Journals (Sweden)

    Bin Chen

    2014-01-01

    Full Text Available It is essential to construct structural health monitoring systems for large important bridges. Zhijiang Bridge is a cable-stayed bridge that was built recently over the Hangzhou Qiantang River (the largest river in Zhejiang Province. The length of Zhijiang Bridge is 478 m, which comprises an arched twin-tower space and a twin-cable plane structure. As an example, the present study describes the integrated system of structural health monitoring and intelligent management for Zhijiang Bridge, which comprises an information acquisition system, data management system, evaluation and decision-making system, and application service system. The monitoring components include the working environment of the bridge and various factors that affect bridge safety, such as the stress and strain of the main bridge structure, vibration, cable force, temperature, and wind speed. In addition, the integrated system includes a forecasting and decision-making module for real-time online evaluation, which provides warnings and makes decisions based on the monitoring information. From this, the monitoring information, evaluation results, maintenance decisions, and warning information can be input simultaneously into the bridge monitoring center and traffic emergency center to share the monitoring data, thereby facilitating evaluations and decision making using the system.

  4. 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...

  5. 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.

  6. 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...

  7. 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

  8. Humanitarian health computing using artificial intelligence and social media: A narrative literature review.

    Science.gov (United States)

    Fernandez-Luque, Luis; Imran, Muhammad

    2018-06-01

    According to the World Health Organization (WHO), over 130 million people are in constant need of humanitarian assistance due to natural disasters, disease outbreaks, and conflicts, among other factors. These health crises can compromise the resilience of healthcare systems, which are essential for achieving the health objectives of the sustainable development goals (SDGs) of the United Nations (UN). During a humanitarian health crisis, rapid and informed decision making is required. This is often challenging due to information scarcity, limited resources, and strict time constraints. Moreover, the traditional approach to digital health development, which involves a substantial requirement analysis, a feasibility study, and deployment of technology, is ill-suited for many crisis contexts. The emergence of Web 2.0 technologies and social media platforms in the past decade, such as Twitter, has created a new paradigm of massive information and misinformation, in which new technologies need to be developed to aid rapid decision making during humanitarian health crises. Humanitarian health crises increasingly require the analysis of massive amounts of information produced by different sources, such as social media content, and, hence, they are a prime case for the use of artificial intelligence (AI) techniques to help identify relevant information and make it actionable. To identify challenges and opportunities for using AI in humanitarian health crises, we reviewed the literature on the use of AI techniques to process social media. We performed a narrative literature review aimed at identifying examples of the use of AI in humanitarian health crises. Our search strategy was designed to get a broad overview of the different applications of AI in a humanitarian health crisis and their challenges. A total of 1459 articles were screened, and 24 articles were included in the final analysis. Successful case studies of AI applications in a humanitarian health crisis have

  9. Information Design for “Weak Signal” detection and processing in Economic Intelligence: A case study on Health resources

    Directory of Open Access Journals (Sweden)

    Sahbi Sidhom

    2011-12-01

    Full Text Available The topics of this research cover all phases of “Information Design” applied to detect and profit from weak signals in economic intelligence (EI or business intelligence (BI. The field of the information design (ID applies to the process of translating complex, unorganized or unstructured data into valuable and meaningful information. ID practice requires an interdisciplinary approach, which combines skills in graphic design (writing, analysis processing and editing, human performances technology and human factors. Applied in the context of information system, it allows end-users to easily detect implicit topics known as “weak signals” (WS. In our approach to implement the ID, the processes cover the development of a knowledge management (KM process in the context of EI. A case study concerning information monitoring health resources is presented using ID processes to outline weak signals. Both French and American bibliographic databases were applied to make the connection to multilingual concepts in the health watch process.

  10. Investigating the effect of work stress, general health quality, organizational intelligence and job satisfaction on employee performance

    Directory of Open Access Journals (Sweden)

    Masoud Samadzadeh

    2013-12-01

    Full Text Available During the past few years, there have been tremendous efforts on measuring the effects of different factors such as work stress, general heath quality, etc. on performance of employees. In this paper, we present an empirical investigation to study the effects of work stress, general health, organizational intelligence and job satisfaction on employee performance. The proposed study of this paper uses two questionnaires where one is associated with general heath quality (GHQ with 20 questions and the other one consists of 12 questions, which is associated with work stress. The study chooses a sample of 144 employees from 222 people who worked for one of Islamic Azad University in Iran. Cronbach alphas for work stress, general health, organizational intelligence, job satisfaction and organizational performance are 0.911, 0.895, 0.795, 0.863 and, 0.864, respectively. The results indicate that job satisfaction has the highest influence on organizational performance followed by other factors.

  11. Use of Local Intelligence to Reduce Energy Consumption of Wireless Sensor Nodes in Elderly Health Monitoring Systems

    Directory of Open Access Journals (Sweden)

    Thomas J. Lampoltshammer

    2014-03-01

    Full Text Available The percentage of elderly people in European countries is increasing. Such conjuncture affects socio-economic structures and creates demands for resourceful solutions, such as Ambient Assisted Living (AAL, which is a possible methodology to foster health care for elderly people. In this context, sensor-based devices play a leading role in surveying, e.g., health conditions of elderly people, to alert care personnel in case of an incident. However, the adoption of such devices strongly depends on the comfort of wearing the devices. In most cases, the bottleneck is the battery lifetime, which impacts the effectiveness of the system. In this paper we propose an approach to reduce the energy consumption of sensors’ by use of local sensors’ intelligence. By increasing the intelligence of the sensor node, a substantial decrease in the necessary communication payload can be achieved. The results show a significant potential to preserve energy and decrease the actual size of the sensor device units.

  12. Use of Local Intelligence to Reduce Energy Consumption of Wireless Sensor Nodes in Elderly Health Monitoring Systems

    Science.gov (United States)

    Lampoltshammer, Thomas J.; de Freitas, Edison Pignaton; Nowotny, Thomas; Plank, Stefan; da Costa, João Paulo Carvalho Lustosa; Larsson, Tony; Heistracher, Thomas

    2014-01-01

    The percentage of elderly people in European countries is increasing. Such conjuncture affects socio-economic structures and creates demands for resourceful solutions, such as Ambient Assisted Living (AAL), which is a possible methodology to foster health care for elderly people. In this context, sensor-based devices play a leading role in surveying, e.g., health conditions of elderly people, to alert care personnel in case of an incident. However, the adoption of such devices strongly depends on the comfort of wearing the devices. In most cases, the bottleneck is the battery lifetime, which impacts the effectiveness of the system. In this paper we propose an approach to reduce the energy consumption of sensors' by use of local sensors' intelligence. By increasing the intelligence of the sensor node, a substantial decrease in the necessary communication payload can be achieved. The results show a significant potential to preserve energy and decrease the actual size of the sensor device units. PMID:24618777

  13. 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.

  14. 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.

  15. 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

  16. An Intelligent and Secure Health Monitoring Scheme Using IoT Sensor Based on Cloud Computing

    Directory of Open Access Journals (Sweden)

    Jin-Xin Hu

    2017-01-01

    Full Text Available Internet of Things (IoT is the network of physical objects where information and communication technology connect multiple embedded devices to the Internet for collecting and exchanging data. An important advancement is the ability to connect such devices to large resource pools such as cloud. The integration of embedded devices and cloud servers offers wide applicability of IoT to many areas of our life. With the aging population increasing every day, embedded devices with cloud server can provide the elderly with more flexible service without the need to visit hospitals. Despite the advantages of the sensor-cloud model, it still has various security threats. Therefore, the design and integration of security issues, like authentication and data confidentiality for ensuring the elderly’s privacy, need to be taken into consideration. In this paper, an intelligent and secure health monitoring scheme using IoT sensor based on cloud computing and cryptography is proposed. The proposed scheme achieves authentication and provides essential security requirements.

  17. Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation

    Directory of Open Access Journals (Sweden)

    Hongzhi Hu

    2015-01-01

    Full Text Available Due to the extensive social influence, public health emergency has attracted great attention in today’s society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event’s social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback based on ACP simulation system which was successfully applied to the analysis of A (H1N1 Flu emergency.

  18. Artificial Intelligence in Public Health Prevention of Legionelosis in Drinking Water Systems

    Directory of Open Access Journals (Sweden)

    Peter Sinčak

    2014-08-01

    Full Text Available Good quality water supplies and safe sanitation in urban areas are a big challenge for governments throughout the world. Providing adequate water quality is a basic requirement for our lives. The colony forming units of the bacterium Legionella pneumophila in potable water represent a big problem which cannot be overlooked for health protection reasons. We analysed several methods to program a virtual hot water tank with AI (artificial intelligence tools including neuro-fuzzy systems as a precaution against legionelosis. The main goal of this paper is to present research which simulates the temperature profile in the water tank. This research presents a tool for a water management system to simulate conditions which are able to prevent legionelosis outbreaks in a water system. The challenge is to create a virtual water tank simulator including the water environment which can simulate a situation which is common in building water distribution systems. The key feature of the presented system is its adaptation to any hot water tank. While respecting the basic parameters of hot water, a water supplier and building maintainer are required to ensure the predefined quality and water temperature at each sampling site and avoid the growth of Legionella. The presented system is one small contribution how to overcome a situation when legionelosis could find good conditions to spread and jeopardize human lives.

  19. Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation.

    Science.gov (United States)

    Hu, Hongzhi; Mao, Huajuan; Hu, Xiaohua; Hu, Feng; Sun, Xuemin; Jing, Zaiping; Duan, Yunsuo

    2015-01-01

    Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency.

  20. Artificial intelligence in public health prevention of legionelosis in drinking water systems.

    Science.gov (United States)

    Sinčak, Peter; Ondo, Jaroslav; Kaposztasova, Daniela; Virčikova, Maria; Vranayova, Zuzana; Sabol, Jakub

    2014-08-21

    Good quality water supplies and safe sanitation in urban areas are a big challenge for governments throughout the world. Providing adequate water quality is a basic requirement for our lives. The colony forming units of the bacterium Legionella pneumophila in potable water represent a big problem which cannot be overlooked for health protection reasons. We analysed several methods to program a virtual hot water tank with AI (artificial intelligence) tools including neuro-fuzzy systems as a precaution against legionelosis. The main goal of this paper is to present research which simulates the temperature profile in the water tank. This research presents a tool for a water management system to simulate conditions which are able to prevent legionelosis outbreaks in a water system. The challenge is to create a virtual water tank simulator including the water environment which can simulate a situation which is common in building water distribution systems. The key feature of the presented system is its adaptation to any hot water tank. While respecting the basic parameters of hot water, a water supplier and building maintainer are required to ensure the predefined quality and water temperature at each sampling site and avoid the growth of Legionella. The presented system is one small contribution how to overcome a situation when legionelosis could find good conditions to spread and jeopardize human lives.

  1. Artificial Intelligence in Public Health Prevention of Legionelosis in Drinking Water Systems

    Science.gov (United States)

    Sinčak, Peter; Ondo, Jaroslav; Kaposztasova, Daniela; Virčikova, Maria; Vranayova, Zuzana; Sabol, Jakub

    2014-01-01

    Good quality water supplies and safe sanitation in urban areas are a big challenge for governments throughout the world. Providing adequate water quality is a basic requirement for our lives. The colony forming units of the bacterium Legionella pneumophila in potable water represent a big problem which cannot be overlooked for health protection reasons. We analysed several methods to program a virtual hot water tank with AI (artificial intelligence) tools including neuro-fuzzy systems as a precaution against legionelosis. The main goal of this paper is to present research which simulates the temperature profile in the water tank. This research presents a tool for a water management system to simulate conditions which are able to prevent legionelosis outbreaks in a water system. The challenge is to create a virtual water tank simulator including the water environment which can simulate a situation which is common in building water distribution systems. The key feature of the presented system is its adaptation to any hot water tank. While respecting the basic parameters of hot water, a water supplier and building maintainer are required to ensure the predefined quality and water temperature at each sampling site and avoid the growth of Legionella. The presented system is one small contribution how to overcome a situation when legionelosis could find good conditions to spread and jeopardize human lives. PMID:25153475

  2. 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.

  3. A Mobile Health Application to Predict Postpartum Depression Based on Machine Learning.

    Science.gov (United States)

    Jiménez-Serrano, Santiago; Tortajada, Salvador; García-Gómez, Juan Miguel

    2015-07-01

    Postpartum depression (PPD) is a disorder that often goes undiagnosed. The development of a screening program requires considerable and careful effort, where evidence-based decisions have to be taken in order to obtain an effective test with a high level of sensitivity and an acceptable specificity that is quick to perform, easy to interpret, culturally sensitive, and cost-effective. The purpose of this article is twofold: first, to develop classification models for detecting the risk of PPD during the first week after childbirth, thus enabling early intervention; and second, to develop a mobile health (m-health) application (app) for the Android(®) (Google, Mountain View, CA) platform based on the model with best performance for both mothers who have just given birth and clinicians who want to monitor their patient's test. A set of predictive models for estimating the risk of PPD was trained using machine learning techniques and data about postpartum women collected from seven Spanish hospitals. An internal evaluation was carried out using a hold-out strategy. An easy flowchart and architecture for designing the graphical user interface of the m-health app was followed. Naive Bayes showed the best balance between sensitivity and specificity as a predictive model for PPD during the first week after delivery. It was integrated into the clinical decision support system for Android mobile apps. This approach can enable the early prediction and detection of PPD because it fulfills the conditions of an effective screening test with a high level of sensitivity and specificity that is quick to perform, easy to interpret, culturally sensitive, and cost-effective.

  4. The Moderator Role of Perceived Emotional Intelligence in the Relationship between Sources of Stress and Mental Health in Teachers.

    Science.gov (United States)

    Pulido-Martos, Manuel; Lopez-Zafra, Esther; Estévez-López, Fernando; Augusto-Landa, José María

    2016-03-03

    This study analyzes the role of Perceived Emotional Intelligence (PEI) on sources of job stress and mental health in 250 elementary school teachers from Jaén (Spain). The aim of the study was two-fold: (1) to analyze the associations between Perceived Emotional Intelligence (PEI), sources of occupational stress and mental health; and (2) to determine whether PEI moderates the relationship between sources of occupational stress and mental health. An initial sample of 250 teachers was assessed Three questionnaires, the Trait Meta-Mood Scale, the Sources of Stress Scale in Teachers and the Medical Outcomes Study 36-item Short Form Health Survey, were used to evaluate PEI, sources of occupational stress and mental health, respectively. Teachers with higher levels of emotional attention reported lower levels of mental health (r = -.30; p relationship between sources of occupational stress and emotional role. Specifically, each significant interaction (i.e., deficiencies x attention, adaptation x attention, and adaptation x clarity) made a small and unique contribution in the explanation of emotional role (all p < .05, all sr 2 ∼ .02). Finally, our results imply that PEI is an important moderator of teachers´ occupational stressors on mental health.

  5. Intelligent Hardware-Enabled Sensor and Software Safety and Health Management for Autonomous UAS

    Science.gov (United States)

    Rozier, Kristin Y.; Schumann, Johann; Ippolito, Corey

    2015-01-01

    Unmanned Aerial Systems (UAS) can only be deployed if they can effectively complete their mission and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. We propose to design a real-time, onboard system health management (SHM) capability to continuously monitor essential system components such as sensors, software, and hardware systems for detection and diagnosis of failures and violations of safety or performance rules during the ight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the- y temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power hardware realization using Field Programmable Gate Arrays (FPGAs) in order to avoid overburdening limited computing resources or costly re-certi cation of ight software due to instrumentation. No currently available SHM capabilities (or combinations of currently existing SHM capabilities) come anywhere close to satisfying these three criteria yet NASA will require such intelligent, hardwareenabled sensor and software safety and health management for introducing autonomous UAS into the National Airspace System (NAS). We propose a novel approach of creating modular building blocks for combining responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. Our proposed research program includes both developing this novel approach and demonstrating its capabilities using the NASA Swift UAS as a demonstration platform.

  6. Real-time context aware reasoning in on-board intelligent traffic systems: An Architecture for Ontology-based Reasoning using Finite State Machines

    NARCIS (Netherlands)

    Stoter, Arjan; Dalmolen, Simon; Drenth, Eduard; Cornelisse, Erik; Mulder, Wico

    2011-01-01

    In-vehicle information management is vital in intelligent traffic systems. In this paper we motivate an architecture for ontology-based context-aware reasoning for in-vehicle information management. An ontology is essential for system standardization and communication, and ontology-based reasoning

  7. 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...

  8. 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.

  9. 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.

  10. 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.

  11. Intelligent neuroprocessors for in-situ launch vehicle propulsion systems health management

    Science.gov (United States)

    Gulati, S.; Tawel, R.; Thakoor, A. P.

    1993-01-01

    Efficacy of existing on-board propulsion systems health management systems (HMS) are severely impacted by computational limitations (e.g., low sampling rates); paradigmatic limitations (e.g., low-fidelity logic/parameter redlining only, false alarms due to noisy/corrupted sensor signatures, preprogrammed diagnostics only); and telemetry bandwidth limitations on space/ground interactions. Ultra-compact/light, adaptive neural networks with massively parallel, asynchronous, fast reconfigurable and fault-tolerant information processing properties have already demonstrated significant potential for inflight diagnostic analyses and resource allocation with reduced ground dependence. In particular, they can automatically exploit correlation effects across multiple sensor streams (plume analyzer, flow meters, vibration detectors, etc.) so as to detect anomaly signatures that cannot be determined from the exploitation of single sensor. Furthermore, neural networks have already demonstrated the potential for impacting real-time fault recovery in vehicle subsystems by adaptively regulating combustion mixture/power subsystems and optimizing resource utilization under degraded conditions. A class of high-performance neuroprocessors, developed at JPL, that have demonstrated potential for next-generation HMS for a family of space transportation vehicles envisioned for the next few decades, including HLLV, NLS, and space shuttle is presented. Of fundamental interest are intelligent neuroprocessors for real-time plume analysis, optimizing combustion mixture-ratio, and feedback to hydraulic, pneumatic control systems. This class includes concurrently asynchronous reprogrammable, nonvolatile, analog neural processors with high speed, high bandwidth electronic/optical I/O interfaced, with special emphasis on NASA's unique requirements in terms of performance, reliability, ultra-high density ultra-compactness, ultra-light weight devices, radiation hardened devices, power stringency

  12. 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.

  13. 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.  

  14. Mindfulness facets, trait emotional intelligence, emotional distress, and multiple health behaviors: A serial two-mediator model.

    Science.gov (United States)

    Jacobs, Ingo; Wollny, Anna; Sim, Chu-Won; Horsch, Antje

    2016-06-01

    In the present study, we tested a serial mindfulness facets-trait emotional intelligence (TEI)-emotional distress-multiple health behaviors mediation model in a sample of N = 427 German-speaking occupational therapists. The mindfulness facets-TEI-emotional distress section of the mediation model revealed partial mediation for the mindfulness facets Act with awareness (Act/Aware) and Accept without judgment (Accept); inconsistent mediation was found for the Describe facet. The serial two-mediator model included three mediational pathways that may link each of the four mindfulness facets with multiple health behaviors. Eight out of 12 indirect effects reached significance and fully mediated the links between Act/Aware and Describe to multiple health behaviors; partial mediation was found for Accept. The mindfulness facet Observe was most relevant for multiple health behaviors, but its relation was not amenable to mediation. Implications of the findings will be discussed. © 2016 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

  15. MAS Bulletin. Papers Presented at Advisory Group for Aerospace Research and Development (AGARD) Symposium on Machine Intelligence for Aerospace Electronic Systems.

    Science.gov (United States)

    1991-08-01

    neural networks, and machine learning . This list ie not all 9. Future ESM Systems and the Potential for Neural Processing inclusive. This research could...U.S. CAPT James M. Skinner , USAF, Air Force Space Technology 17. Development of Tactical Doecisiont Akid. Center, and Prof. Georg* F. Luger...ntegrat11111ng Macine I~1e900enc Into the Co~pi to Aid t" Pilot 26. Integrated Communications, Navigatlion. Ideintiflocation Avionics Dr. Edward J

  16. Machine Learning Takes on Health Care: Leonard D'Avolio's Cyft Employs Big Data to Benefit Patients and Providers.

    Science.gov (United States)

    Mertz, Leslie

    2018-01-01

    When Leonard D'Avolio (Figure 1) was working on his Ph.D. degree in biomedical informatics, he saw the power of machine learning in transforming multiple industries; health care, however, was not among them. "The reason that Amazon, Netflix, and Google have transformed their industries is because they have embedded learning throughout every aspect of what they do. If we could prove that is possible in health care too, I thought we would have the potential to have a huge impact," he says.

  17. From intelligent buildings to careful buildings : a concept to implement individual health and comfort demands

    NARCIS (Netherlands)

    Zeiler, W.; Wortel, W.; Houten, van M.A.; Hommelberg, M.P.F.; Kamphuis, I.G.; Jelsma, J.; Oliveira Fernandes, de E.; Gameiro da Silva, M.; Rosado Pinto, J.

    2006-01-01

    Buildings are built for users, so the user preferences and their behaviour should become leading in building services control strategy. The paper reviews 3 projects on intelligent process control. The insights of these projects were combined into a new technology; FACT, Forgiving Agent Comfort

  18. What We Know about Emotional Intelligence: How It Affects Learning, Work, Relationships, and Our Mental Health

    Science.gov (United States)

    Zeidner, Moshe; Matthews, Gerald; Roberts, Richard D.

    2009-01-01

    Emotional intelligence (or EI)--the ability to perceive, regulate, and communicate emotions, to understand emotions in ourselves and others--has been the subject of best-selling books, magazine cover stories, and countless media mentions. It has been touted as a solution for problems ranging from relationship issues to the inadequacies of local…

  19. Intelligible Artificial Intelligence

    OpenAIRE

    Weld, Daniel S.; Bansal, Gagan

    2018-01-01

    Since Artificial Intelligence (AI) software uses techniques like deep lookahead search and stochastic optimization of huge neural networks to fit mammoth datasets, it often results in complex behavior that is difficult for people to understand. Yet organizations are deploying AI algorithms in many mission-critical settings. In order to trust their behavior, we must make it intelligible --- either by using inherently interpretable models or by developing methods for explaining otherwise overwh...

  20. Better and more Efficient Treatment: The Individual and Organizational Impacts of Business Intelligence Use in Health Care Organizations

    DEFF Research Database (Denmark)

    Gaardboe, Rikke; Svejvig, Per

    This study investigates the critical success factors for individuals’ use of business intelligence (BI) in health care organizations. We also examine the organizational impact of BI. We develop a model that expands DeLone and McLean’s IS success model to include task characteristics. To analyze....... Second, we investigated the organizational impact through semi-structured interviews. We identified two user types—system users and information users—and we found that BI is used for financial reporting, improving patient progress, and enhancing learning in hospitals. Future research should focus...

  1. Structural health monitoring for bolt loosening via a non-invasive vibro-haptics human-machine cooperative interface

    Science.gov (United States)

    Pekedis, Mahmut; Mascerañas, David; Turan, Gursoy; Ercan, Emre; Farrar, Charles R.; Yildiz, Hasan

    2015-08-01

    For the last two decades, developments in damage detection algorithms have greatly increased the potential for autonomous decisions about structural health. However, we are still struggling to build autonomous tools that can match the ability of a human to detect and localize the quantity of damage in structures. Therefore, there is a growing interest in merging the computational and cognitive concepts to improve the solution of structural health monitoring (SHM). The main object of this research is to apply the human-machine cooperative approach on a tower structure to detect damage. The cooperation approach includes haptic tools to create an appropriate collaboration between SHM sensor networks, statistical compression techniques and humans. Damage simulation in the structure is conducted by releasing some of the bolt loads. Accelerometers are bonded to various locations of the tower members to acquire the dynamic response of the structure. The obtained accelerometer results are encoded in three different ways to represent them as a haptic stimulus for the human subjects. Then, the participants are subjected to each of these stimuli to detect the bolt loosened damage in the tower. Results obtained from the human-machine cooperation demonstrate that the human subjects were able to recognize the damage with an accuracy of 88 ± 20.21% and response time of 5.87 ± 2.33 s. As a result, it is concluded that the currently developed human-machine cooperation SHM may provide a useful framework to interact with abstract entities such as data from a sensor network.

  2. 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...

  3. 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. ...

  4. 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.

     

  5. The relationship between trait emotional intelligence, resiliency, and mental health in older adults: the mediating role of savouring.

    Science.gov (United States)

    Wilson, Claire A; Saklofske, Donald H

    2018-05-01

    The present study explores savouring, defined as the process of attending to positive experiences, as a mediator in the relationships between resiliency, trait emotional intelligence (EI), and subjective mental health in older adults. Following Fredrickson's Broaden and Build Theory of positive emotions, the present study aims to extend our understanding of the underlying processes that link resiliency and trait EI with self-reported mental health in older adulthood. A sample of 149 adults aged 65 and over (M = 73.72) were recruited from retirement homes and community groups. Participants completed measures of resiliency, savouring, trait EI, and subjective mental health either online or in a paper format. Path analysis revealed that savouring fully mediated the relationship between resiliency and mental health. However, trait EI did not significantly predict mental health in this sample. These findings provided partial support for the Broaden and Build Theory of positive emotions. As anticipated, savouring imitated the broadening effect of positive emotions by mediating the relationship between resiliency and mental health. However, savouring failed to reflect the undoing effect of positive emotions and did not mediate the relationship between EI and mental health. These findings have implications for positive psychology exercises and may be a simple, yet effective means of improving the life quality of older adults.

  6. 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.

  7. 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.

  8. Artificial intelligence

    CERN Document Server

    Hunt, Earl B

    1975-01-01

    Artificial Intelligence provides information pertinent to the fundamental aspects of artificial intelligence. This book presents the basic mathematical and computational approaches to problems in the artificial intelligence field.Organized into four parts encompassing 16 chapters, this book begins with an overview of the various fields of artificial intelligence. This text then attempts to connect artificial intelligence problems to some of the notions of computability and abstract computing devices. Other chapters consider the general notion of computability, with focus on the interaction bet

  9. 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.

  10. Development of AI (Artificial Intelligence)-based simulation system for man-machine system behavior in accidental situations of nuclear power plant

    International Nuclear Information System (INIS)

    Yoshida, Kazuo; Yokobayashi, Masao; Tanabe, Fumiya; Kawase, Katumi.

    1996-01-01

    A prototype version of a computer simulation system named JACOS (JAeri COgnitive Simulation system) has been developed at JAERI (Japan Atomic Energy Research Institute) to simulate the man-machine system behavior in which both the cognitive behavior of a human operator and the plant behavior affect each other. The objectives of this system development is to provide man-machine system analysts with detailed information on the cognitive process of an operator and the plant behavior affected by operator's actions in accidental situations of an NPP (nuclear power plant). The simulation system consists of an operator model and a plant model which are coupled dynamically. The operator model simulates an operator's cognitive behavior in accidental situations based on the decision ladder model of Rasmussen, and is implemented using the AI-techniques of the distributed cooperative inference method with the so-called blackboard architecture. Rule-based behavior is simulated using knowledge representation with If-Then type of rules. Knowledge-based behavior is simulated using knowledge representation with MFM (Multilevel Flow Modeling) and qualitative reasoning method. Cognitive characteristics of attentional narrowing, limitation of short-term memory, and knowledge recalling from long-term memory are also modeled. The plant model of a 3-loop PWR is also developed using a best estimate thermal-hydraulic analysis code RELAP5/MOD2. Some simulations of incidents were performed to verify the human model. It was found that AI-techniques used in the human model are suitable to simulate the operator's cognitive behavior in an NPP accident. The models of cognitive characteristics were investigated in the effects on simulated results of cognitive behaviors. (author)

  11. Machine musicianship

    Science.gov (United States)

    Rowe, Robert

    2002-05-01

    The training of musicians begins by teaching basic musical concepts, a collection of knowledge commonly known as musicianship. Computer programs designed to implement musical skills (e.g., to make sense of what they hear, perform music expressively, or compose convincing pieces) can similarly benefit from access to a fundamental level of musicianship. Recent research in music cognition, artificial intelligence, and music theory has produced a repertoire of techniques that can make the behavior of computer programs more musical. Many of these were presented in a recently published book/CD-ROM entitled Machine Musicianship. For use in interactive music systems, we are interested in those which are fast enough to run in real time and that need only make reference to the material as it appears in sequence. This talk will review several applications that are able to identify the tonal center of musical material during performance. Beyond this specific task, the design of real-time algorithmic listening through the concurrent operation of several connected analyzers is examined. The presentation includes discussion of a library of C++ objects that can be combined to perform interactive listening and a demonstration of their capability.

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. From e-Health to i-Health: Prospective Reflexions on the Use of Intelligent Systems in Mental Health Care

    Directory of Open Access Journals (Sweden)

    Xavier Briffault

    2018-05-01

    Full Text Available Depressive disorders cover a set of disabling problems, often chronic or recurrent. They are characterized by a high level of psychiatric and somatic comorbidities and represent an important public health problem. To date, therapeutic solutions remain unsatisfactory. For some researchers, this is a sign of decisive paradigmatic failure due to the way in which disorders are conceptualized. They hypothesize that the symptoms of a categorical disorder, or of different comorbid disorders, can be interwoven in chains of interdependencies on different elements, of which it would be possible to act independently and synergistically to influence the functioning of the symptom system, rather than limiting oneself to targeting a hypothetical single underlying cause. New connected technologies make it possible to invent new observation and intervention tools allowing better phenotypic characterization of disorders and their evolution, that fit particularly well into this new “symptoms network” paradigm. Synergies are possible and desirable between these technological and epistemological innovations and can possibly help to solve some of the difficult problems people with mental disorders face in their everyday life, as we will show through a fictional case study exploring the possibilities of connected technologies in mental disorders in the near future.

  17. Artificial Intelligence and Moral intelligence

    OpenAIRE

    Laura Pana

    2008-01-01

    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,...

  18. Effective e-learning for health professional and medical students: the experience with SIAS-Intelligent Tutoring System.

    Science.gov (United States)

    Muñoz, Diana C; Ortiz, Alexandra; González, Carolina; López, Diego M; Blobel, Bernd

    2010-01-01

    Current e-learning systems are still inadequate to support the level of interaction, personalization and engagement demanded by clinicians, care givers, and the patient themselves. For effective e-learning to be delivered in the health context, collaboration between pedagogy and technology is required. Furthermore, e-learning systems should be flexible enough to be adapted to the students' needs, evaluated regularly, easy to use and maintain and provide students' feedback, guidelines and supporting material in different formats. This paper presents the implementation of an Intelligent Tutoring System (SIAS-ITS), and its evaluation compared to a traditional virtual learning platform (Moodle). The evaluation was carried out as a case study, in which the participants were separated in two groups, each group attending a virtual course on the WHO Integrated Management of Childhood Illness (IMCI) strategy supported by one of the two e-learning platforms. The evaluation demonstrated that the participants' knowledge level, pedagogical strategies used, learning efficiency and systems' usability were improved using the Intelligent Tutoring System.

  19. Rhesus factor modulation of effects of smoking and age on psychomotor performance, intelligence, personality profile, and health in Czech soldiers.

    Directory of Open Access Journals (Sweden)

    Jaroslav Flegr

    Full Text Available BACKGROUND: Rhesus-positive and rhesus-negative persons differ in the presence-absence of highly immunogenic RhD protein on the erythrocyte membrane. This protein is a component of NH(3 or CO(2 pump whose physiological role is unknown. Several recent studies have shown that RhD positivity protects against effects of latent toxoplasmosis on motor performance and personality. It is not known, however, whether the RhD phenotype modifies exclusively the response of the body to toxoplasmosis or whether it also influences effects of other factors. METHODOLOGY/PRINCIPAL FINDINGS: In the present cohort study, we searched for the effects of age and smoking on performance, intelligence, personality and self-estimated health and wellness in about 3800 draftees. We found that the positive effect of age on performance and intelligence was stronger in RhD-positive soldiers, while the negative effect of smoking on performance and intelligence was of similar size regardless of the RhD phenotype. The effect of age on four Cattell's personality factors, i.e., dominance (E, radicalism (Q(1, self-sentiment integration (Q(3, and ergic tension (Q(4, and on Cloninger's factor reward dependency (RD was stronger for RhD-negative than RhD-positive subjects, while the effect of smoking on the number of viral and bacterial diseases was about three times stronger for RhD-negative than RhD-positive subjects. CONCLUSIONS: RhD phenotype modulates the influence not only of latent toxoplasmosis, but also of at least two other potentially detrimental factors, age and smoking, on human behavior and physiology. The negative effect of smoking on health (estimated on the basis of the self-rated number of common viral and bacterial diseases in the past year was much stronger in RhD-negative than RhD-positive subjects. It is critically needed to confirm the differences in health response to smoking between RhD-positive and RhD-negative subjects by objective medical examination in

  20. 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.

  1. SpaceDoc-Intelligent Health Management System for Astronauts, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Crew health and performance are critical to successful space explorations. However, long duration missions present numerous risks to crew health and performance....

  2. 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…

  3. Machine Shop Grinding Machines.

    Science.gov (United States)

    Dunn, James

    This curriculum manual is one in a series of machine shop curriculum manuals intended for use in full-time secondary and postsecondary classes, as well as part-time adult classes. The curriculum can also be adapted to open-entry, open-exit programs. Its purpose is to equip students with basic knowledge and skills that will enable them to enter the…

  4. Does a Fitness Factor Contribute to the Association between Intelligence and Health Outcomes? Evidence from Medical Abnormality Counts among 3654 US Veterans

    Science.gov (United States)

    Arden, Rosalind; Gottfredson, Linda S.; Miller, Geoffrey

    2009-01-01

    We suggest that an over-arching "fitness factor" (an index of general genetic quality that predicts survival and reproductive success) partially explains the observed associations between health outcomes and intelligence. As a proof of concept, we tested this idea in a sample of 3654 US Vietnam veterans aged 31-49 who completed five cognitive…

  5. [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.

  6. 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, ...

  7. Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder.

    Science.gov (United States)

    Bartsch, Georg; Mitra, Anirban P; Mitra, Sheetal A; Almal, Arpit A; Steven, Kenneth E; Skinner, Donald G; Fry, David W; Lenehan, Peter F; Worzel, William P; Cote, Richard J

    2016-02-01

    Due to the high recurrence risk of nonmuscle invasive urothelial carcinoma it is crucial to distinguish patients at high risk from those with indolent disease. In this study we used a machine learning algorithm to identify the genes in patients with nonmuscle invasive urothelial carcinoma at initial presentation that were most predictive of recurrence. We used the genes in a molecular signature to predict recurrence risk within 5 years after transurethral resection of bladder tumor. Whole genome profiling was performed on 112 frozen nonmuscle invasive urothelial carcinoma specimens obtained at first presentation on Human WG-6 BeadChips (Illumina®). A genetic programming algorithm was applied to evolve classifier mathematical models for outcome prediction. Cross-validation based resampling and gene use frequencies were used to identify the most prognostic genes, which were combined into rules used in a voting algorithm to predict the sample target class. Key genes were validated by quantitative polymerase chain reaction. The classifier set included 21 genes that predicted recurrence. Quantitative polymerase chain reaction was done for these genes in a subset of 100 patients. A 5-gene combined rule incorporating a voting algorithm yielded 77% sensitivity and 85% specificity to predict recurrence in the training set, and 69% and 62%, respectively, in the test set. A singular 3-gene rule was constructed that predicted recurrence with 80% sensitivity and 90% specificity in the training set, and 71% and 67%, respectively, in the test set. Using primary nonmuscle invasive urothelial carcinoma from initial occurrences genetic programming identified transcripts in reproducible fashion, which were predictive of recurrence. These findings could potentially impact nonmuscle invasive urothelial carcinoma management. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

  8. Teamwork in Health Care: Maximizing Collective Intelligence via Inclusive Collaboration and Open Communication.

    Science.gov (United States)

    Mayo, Anna T; Woolley, Anita Williams

    2016-09-01

    Teams offer the potential to achieve more than any person could achieve working alone; yet, particularly in teams that span professional boundaries, it is critical to capitalize on the variety of knowledge, skills, and abilities available. This article reviews research from the field of organizational behavior to shed light on what makes for a collectively intelligent team. In doing so, we highlight the importance of moving beyond simply including smart people on a team to thinking about how those people can effectively coordinate and collaborate. In particular, we review the importance of two communication processes: ensuring that team members with relevant knowledge (1) speak up when one's expertise can be helpful and (2) influence the team's work so that the team does its collective best for the patient. © 2016 American Medical Association. All Rights Reserved.

  9. Artificial Intelligence.

    Science.gov (United States)

    Information Technology Quarterly, 1985

    1985-01-01

    This issue of "Information Technology Quarterly" is devoted to the theme of "Artificial Intelligence." It contains two major articles: (1) Artificial Intelligence and Law" (D. Peter O'Neill and George D. Wood); (2) "Artificial Intelligence: A Long and Winding Road" (John J. Simon, Jr.). In addition, it contains two sidebars: (1) "Calculating and…

  10. Competitive Intelligence.

    Science.gov (United States)

    Bergeron, Pierrette; Hiller, Christine A.

    2002-01-01

    Reviews the evolution of competitive intelligence since 1994, including terminology and definitions and analytical techniques. Addresses the issue of ethics; explores how information technology supports the competitive intelligence process; and discusses education and training opportunities for competitive intelligence, including core competencies…

  11. Effects of payment method on work control, work risk and work-related musculoskeletal health among sewing machine operators

    Directory of Open Access Journals (Sweden)

    R. Nawawi

    2015-12-01

    Full Text Available Effects of payment method on work control, work risk and work-related musculoskeletal health among sewing machine operators R. Nawawi1, B.M. Deros1*, D.D.I. Daruis2, A. Ramli3, R.M. Zein4 and L.H. Joseph3 1Dept. of Mechanical and Materials Engineering Faculty of Engineering & Built Environment Universiti Kebangsaan Malaysia, Malaysia *Email: hjbaba@ukm.edu.my 2Faculty of Engineering, Universiti Pertahanan Nasional Malaysia, Malaysia 3Department of Physiotherapy Faculty of Science, Lincoln University College, Malaysia 4Department of Consultation, Research & Development, National Institute of Occupational Safety and Health (NIOSH, Malaysia ABSTRACT This study aimed to identify payment method and its effects on work control, work risk and work-related musculoskeletal health among Malaysian sewing machine operators. The study sample comprised 337 sewing machine operators (male, n=122, female, n=215; aged between 18-54 years old; mean 30.74±8.44 from four different garment-making companies in Malaysia. They were being paid via time rate wages (n=246 and piece rate wages (n=91. Data was collected through Nordic Musculoskeletal Questionnaire and pen-and-paper assessment via Rapid Upper Limb Assessment (RULA. From the study, the piece rate wage group was found to take fewer breaks, had high work production demands, worked at a faster pace and experienced more exhaustion and pressure due to increasing work demands as compared to the time rate group. They were also observed working with higher physical exposure such as repetitive tasks, awkward static postures, awkward grips and hand movements, pulling, lifting and pushing as compared to those in the time rate wage group. The final RULA scores was also higher from the piece rate wage group (72.53% RULA score 7 which indicated higher work risks among them. The study found that the type of wage payment was significantly associated with work risks (p=0.036, df=1 and WRMSD at the shoulder, lower back

  12. A conceptual model for worksite intelligent physical exercise training - IPET - intervention for decreasing life style health risk indicators among employees

    DEFF Research Database (Denmark)

    Sjøgaard, Gisela; Justesen, Just Bendix; Murray, Mike

    2014-01-01

    BACKGROUND: Health promotion at the work site in terms of physical activity has proven positive effects but optimization of relevant exercise training protocols and implementation for high adherence are still scanty.Methods/design: The aim of this paper is to present a study protocol with a conce......BACKGROUND: Health promotion at the work site in terms of physical activity has proven positive effects but optimization of relevant exercise training protocols and implementation for high adherence are still scanty.Methods/design: The aim of this paper is to present a study protocol...... parts, and functional training including balance training. The hypotheses of this research are that individually tailored worksite-based intelligent physical exercise training, IPET, among workers with inactive job categories will: 1) Improve cardiorespiratory fitness and/or individual health risk...... indicators, 2) Improve muscle strength and decrease musculoskeletal disorders, 3) Succeed in regular adherence to worksite and leisure physical activity training, and 3) Reduce sickness absence and productivity losses (presenteeism) in office workers. The present RCT study enrolled almost 400 employees...

  13. 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…

  14. 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

  15. 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.

  16. Health Data Entanglement and artificial intelligence-based analysis: a brand new methodology to improve the effectiveness of healthcare services.

    Science.gov (United States)

    Capone, A; Cicchetti, A; Mennini, F S; Marcellusi, A; Baio, G; Favato, G

    2016-01-01

    Healthcare expenses will be the most relevant policy issue for most governments in the EU and in the USA. This expenditure can be associated with two major key categories: demographic and economic drivers. Factors driving healthcare expenditure were rarely recognised, measured and comprehended. An improvement of health data generation and analysis is mandatory, and in order to tackle healthcare spending growth, it may be useful to design and implement an effective, advanced system to generate and analyse these data. A methodological approach relied upon the Health Data Entanglement (HDE) can be a suitable option. By definition, in the HDE a large amount of data sets having several sources are functionally interconnected and computed through learning machines that generate patterns of highly probable future health conditions of a population. Entanglement concept is borrowed from quantum physics and means that multiple particles (information) are linked together in a way such that the measurement of one particle's quantum state (individual health conditions and related economic requirements) determines the possible quantum states of other particles (population health forecasts to predict their impact). The value created by the HDE is based on the combined evaluation of clinical, economic and social effects generated by health interventions. To predict the future health conditions of a population, analyses of data are performed using self-learning AI, in which sequential decisions are based on Bayesian algorithmic probabilities. HDE and AI-based analysis can be adopted to improve the effectiveness of the health governance system in ways that also lead to better quality of care.

  17. [Intelligence and the explanation for socio-economic inequalities in health].

    Science.gov (United States)

    Huisman, M; Mackenbach, J P

    2007-05-12

    Attention is increasingly being paid to the role of cognitive ability to explain socio-economic inequalities in health. The universal socio-economic gradient in health, where each rung lower on the socio-economic ladder implies worse health, has still not been satisfactorily explained scientifically. Because cognitive ability is related to a multitude of social outcomes in a similarly graded manner, hypothesising that cognitive ability plays a major role in health inequalities by socio-economic status is appealing. Recent empirical studies have shown that at least part of socio-economic health inequalities can indeed be explained by differences in cognitive ability. However, this does not imply that we should be pessimistic about future attempts to break the chain that links socio-economic status and cognitive ability with health. During some life stages, environmental factors may be able to influence cognitive ability. Interventions may therefore be targeted in order to optimize these effects. In addition, there is evidence that cognitive ability is correlated with health-related behaviours such as smoking, excessive alcohol consumption and obesity. Therefore, another opportunity for reducing health inequalities related to cognitive ability and socio-economic status would be to develop tailored interventions to improve health-related behaviours in disadvantaged groups. However, the first priority is to further investigate the role of cognitive ability in health inequalities by examining various health outcomes, different age groups and variations across the life course.

  18. Intelligence Ethics:

    DEFF Research Database (Denmark)

    Rønn, Kira Vrist

    2016-01-01

    Questions concerning what constitutes a morally justified conduct of intelligence activities have received increased attention in recent decades. However, intelligence ethics is not yet homogeneous or embedded as a solid research field. The aim of this article is to sketch the state of the art...... of intelligence ethics and point out subjects for further scrutiny in future research. The review clusters the literature on intelligence ethics into two groups: respectively, contributions on external topics (i.e., the accountability of and the public trust in intelligence agencies) and internal topics (i.......e., the search for an ideal ethical framework for intelligence actions). The article concludes that there are many holes to fill for future studies on intelligence ethics both in external and internal discussions. Thus, the article is an invitation – especially, to moral philosophers and political theorists...

  19. 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…

  20. 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.

  1. 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.

  2. 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.

  3. [Relationship between perceived emotional intelligence and professional quality of life with the achievement of occupational objectives in the costa del sol primary health care district].

    Science.gov (United States)

    Macías Fernández, Antonio José; Gutiérrez-Castañeda, Carlos; Carmona González, Francisco Jesús; Crespillo Vílchez, Daniel

    2016-05-01

    To examine the relationship between "Quality of Professional Life" and "Perceived Emotional Intelligence" and the relationship of both of these with the level of achievement of occupational objectives in the Costa del Sol Primary Health Care District. Multicentre descriptive cross-sectional observational study. The Costa del Sol Primary Health Care District in the province of Málaga. Sample of Employees of all categories in fixed and contracted employment in the Management Units of the Costa del Sol District. (N=303). Respondents 247 (81.5%) The data collected was that of the percentage of achievement of objectives in 2010 and the socio-demographic data of the participants, using ad hoc designed self-report questionnaires. The TMMS -24 questionnaire was used to measure the "Perceived Emotional Intelligence", with the following dimensions: Perception, comprehension, and emotional control, and the CVP-35 measuring: management support, work demands, and intrinsic motivation. Significant correlationas were observed between Quality of Professional Life and Emotional Intelligence in the Regulation (p<.01) and Comprehension categories (p<0.05). There were also significant correlations between the profession and the type of contract in the achievement of objectives (p<.005), and quality of professional life and type of contract (p<.05). The perceived quality of professional life is related to perception and regulation dimensions of Emotional Intelligence. Knowledge of emotion management methods should be promoted by management organisations for all employees. Copyright © 2015 Elsevier España, S.L.U. All rights reserved.

  4. Intelligence Naturelle et Intelligence Artificielle

    OpenAIRE

    Dubois, Daniel

    2011-01-01

    Cet article présente une approche systémique du concept d’intelligence naturelle en ayant pour objectif de créer une intelligence artificielle. Ainsi, l’intelligence naturelle, humaine et animale non-humaine, est une fonction composée de facultés permettant de connaître et de comprendre. De plus, l'intelligence naturelle reste indissociable de la structure, à savoir les organes du cerveau et du corps. La tentation est grande de doter les systèmes informatiques d’une intelligence artificielle ...

  5. 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...

  6. 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...

  7. Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset.

    Directory of Open Access Journals (Sweden)

    Wei Luo

    Full Text Available For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD outcomes (four NCDs and two major clinical risk factors, based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88 and those excluded from the development for use as a completely separated validation sample (median correlation 0.85, demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.

  8. Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset.

    Science.gov (United States)

    Luo, Wei; Nguyen, Thin; Nichols, Melanie; Tran, Truyen; Rana, Santu; Gupta, Sunil; Phung, Dinh; Venkatesh, Svetha; Allender, Steve

    2015-01-01

    For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.

  9. Implementing intelligent physical exercise training at the workplace: health effects among office workers-a randomized controlled trial.

    Science.gov (United States)

    Dalager, Tina; Justesen, Just Bendix; Murray, Mike; Boyle, Eleanor; Sjøgaard, Gisela

    2016-07-01

    The aim was to assess 1-year cardiovascular health effects of Intelligent Physical Exercise Training, IPET. Office workers from six companies were randomized 1:1 to a training group, TG (N = 194) or a control group, CG (N = 195). TG received 1-h supervised high intensity IPET every week within working hours for 1 year, and was recommended to perform 30-min of moderate intensity physical activity 6 days a week during leisure. The training program was based on baseline health check measures of cardiorespiratory fitness (CRF), body composition, blood pressure, blood profile, and musculoskeletal health. There were no baseline differences between groups. CRF assessed as VO2max in absolute values and relative to body weight was (mean ± SD): 3.0 ± 0.8 l/min and 35.4 ± 10.9 ml/min/kg for females, 3.9 ± 1.0 l/min and 37.9 ± 11.79 ml/min/kg for males. Intention to treat analysis demonstrated a significant almost 5 % increase in VO2max in TG compared with CG. A per protocol analysis of those with an adherence of ≥70 % demonstrated a significant increase in CRF of more than 10 % compared with CG, and a significant reduction in systolic blood pressure (-5.3 ± 13.7 mm Hg) compared with CG. High intensity IPET combined with the recommendations of moderate intensity physical activity demonstrated significant clinical relevant improvements in CRF and systolic blood pressure. This underlines the effectiveness of health promotion by implementing physical exercise training at the workplace.

  10. 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 ...

  11. 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,...

  12. 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.

  13. 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 ...

  14. 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.

  15. [Levels of emotional intelligence and types of attachment among third year students of the Faculty of Health Science and the Faculty of Medicine--a comparative analysis].

    Science.gov (United States)

    Tyszkiewicz-Bandur, Monika

    2013-01-01

    For the purposes of this research attachment theory was incorporated into the concept of emotional intelligence. The methodological starting point of this study was the assumption that the level of emotional intelligence and social competence is related to a steady feature, namely the type of attachment. Standardized questionnaires available in the Laboratory of Psychological Tests of the Polish Psychological Association were chosen to measure the level of emotional intelligence. However, the type of attachment was studied by Bartholomew's Self Description Test in my own translation. The study involved two groups of students, who were compared: 147 people from the Faculty of Health Sciences/Faculty of Nursing (nursing, midwifery, health promotion, cosmetology, emergency medicine, dietetics), and 181 people from the Faculty of Medicine (medicine), students in their second and third years of studies. A total of 328 people, aged 19-24, were tested. On the basis of the results it was stated that students of the Faculty of Health Sciences/Faculty of Nursing, as compared to students of the Faculty of Medicine, received significantly higher scores on the scale of the social competence scale, which investigated the efficiency of their behaviour in intimate situations. Moreover, statistical analysis proved that students of the Faculty of Health Sciences showed significantly higher scores than those studying at the Faculty of Medicine in the following fields: KKS-I subscale assessing social competencies in--conditioning effective behaviour in intimate situations, emotional intelligence measured with the INTE questionnaire,--awareness of their own emotional states and understanding their causes (DINEMO-I),--ability to recognize emotions in other people and understanding the reasons for the reactions expressed by them (DINEMO-Others)--emotional intelligence measured with the DINEMO questionnaire (DINEMO-general score). Women from both faculties showed higher social competence

  16. Sustainable machining

    CERN Document Server

    2017-01-01

    This book provides an overview on current sustainable machining. Its chapters cover the concept in economic, social and environmental dimensions. It provides the reader with proper ways to handle several pollutants produced during the machining process. The book is useful on both undergraduate and postgraduate levels and it is of interest to all those working with manufacturing and machining technology.

  17. Predicting Health Care Utilization After the First Behavioral Health Visit Using Natural Language Processing and Machine Learning

    OpenAIRE

    Roysden, Nathaniel

    2016-01-01

    Mental health problems are an independent predictor of increased healthcare utilization. We created random forest classifiers for predicting two outcomes following a patient’s first behavioral health encounter: decreased utilization by any amount (AUROC 0.74) and ultra-high absolute utilization (AUROC 0.88). These models may be used for clinical decision support by referring providers, to automatically detect patients who may benefit from referral, for cost management, or for risk/protection ...

  18. 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 ...

  19. Using Social Robots in Health Settings: Implications of Personalization on Human-Machine Communication

    Directory of Open Access Journals (Sweden)

    Lisa Tam and Rajiv Khosla

    2016-09-01

    Full Text Available In view of the shortage of healthcare workers and a growing aging population, it is worthwhile to explore the applicability of new technologies in improving the quality of healthcare and reducing its cost. However, it remains a challenge to deploy such technologies in environments where individuals have limited knowledge about how to use them. Thus, this paper explores how the social robots designed for use in health settings in Australia have sought to overcome some of the limitations through personalization. Deployed in aged care and home-based care facilities, the social robots are person-centered, emphasizing the personalization of care with human-like attributes (e.g., human appearances to engage in reciprocal communication with users. While there have been debates over the advantages and disadvantages of personalization, this paper discusses the implications of personalization on the design of the robots for enhancing engagement, empowerment and enablement in health settings.

  20. Machine learning approach for automatic quality criteria detection of health web pages.

    Science.gov (United States)

    Gaudinat, Arnaud; Grabar, Natalia; Boyer, Célia

    2007-01-01

    The number of medical websites is constantly growing [1]. Owing to the open nature of the Web, the reliability of information available on the Web is uneven. Internet users are overwhelmed by the quantity of information available on the Web. The situation is even more critical in the medical area, as the content proposed by health websites can have a direct impact on the users' well being. One way to control the reliability of health websites is to assess their quality and to make this assessment available to users. The HON Foundation has defined a set of eight ethical principles. HON's experts are working in order to manually define whether a given website complies with s the required principles. As the number of medical websites is constantly growing, manual expertise becomes insufficient and automatic systems should be used in order to help medical experts. In this paper we present the design and the evaluation of an automatic system conceived for the categorisation of medical and health documents according to he HONcode ethical principles. A first evaluation shows promising results. Currently the system shows 0.78 micro precision and 0.73 F-measure, with 0.06 errors.

  1. Toward an ambient empathic health companion for self care in the intelligent home

    NARCIS (Netherlands)

    Evers, V.; Kröse, B.; Brinkman, W.-P.; Neerincx, M.

    2010-01-01

    Motivation--This paper describes our work in progress to develop a personal monitoring system that can monitor the physical and emotional condition of a patient by using contextual information from a sensor network, provide the patient with feedback concerning their health status and motivate the

  2. Changing patterns of migration in Latin America: how can research develop intelligence for public health?

    Directory of Open Access Journals (Sweden)

    Baltica Cabieses

    Full Text Available Migration patterns in Latin America have changed significantly in recent decades, particularly since the onset of global recession in 2007. These recent economic changes have highlighted and exacerbated the weakness of evidence from Latin America regarding migration-a crucial determinant of health. Migration patterns are constantly evolving in Latin America, but research on migration has not developed at the same speed. This article focuses on the need for better understanding of the living conditions and health of migrant populations in Latin America within the context of the recent global recession. The authors explain how new data on migrant well-being could be obtained through improved evidence from censuses and ongoing research surveys to 1 better inform policy-makers about the needs of migrant populations in Latin America and 2 help determine better ways of reaching undocumented immigrants. Longitudinal studies on immigrants in Latin America are essential for generating a better representation of migrant living conditions and health needs during the initial stages of immigration and over time. To help meet this need, the authors support the promotion of sustainable sources of data and evidence on the complex relationship between migration and health.

  3. Professional Competencies in Health Sciences Education: From Multiple Intelligences to the Clinic Floor

    Science.gov (United States)

    Lane, India F.

    2010-01-01

    Nontechnical competencies identified as essential to the health professionals success include ethical behavior, interpersonal, self-management, leadership, business, and thinking competencies. The literature regarding such diverse topics, and the literature regarding "professional success" is extensive and wide-ranging, crossing educational,…

  4. 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.

  5. 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.

  6. Intelligent Engine Systems Work Element 1.3: Sub System Health Management

    Science.gov (United States)

    Ashby, Malcolm; Simpson, Jeffrey; Singh, Anant; Ferguson, Emily; Frontera, mark

    2005-01-01

    The objectives of this program were to develop health monitoring systems and physics-based fault detection models for engine sub-systems including the start, lubrication, and fuel. These models will ultimately be used to provide more effective sub-system fault identification and isolation to reduce engine maintenance costs and engine down-time. Additionally, the bearing sub-system health is addressed in this program through identification of sensing requirements, a review of available technologies and a demonstration of a demonstration of a conceptual monitoring system for a differential roller bearing. This report is divided into four sections; one for each of the subtasks. The start system subtask is documented in section 2.0, the oil system is covered in section 3.0, bearing in section 4.0, and the fuel system is presented in section 5.0.

  7. Artificial Intelligence-Assisted Online Social Therapy for Youth Mental Health

    OpenAIRE

    D'Alfonso, Simon; Santesteban-Echarri, Olga; Rice, Simon; Wadley, Greg; Lederman, Reeva; Miles, Christopher; Gleeson, John; Alvarez-Jimenez, Mario

    2017-01-01

    Introduction: Benefits from mental health early interventions may not be sustained over time, and longer-term intervention programs may be required to maintain early clinical gains. However, due to the high intensity of face-to-face early intervention treatments, this may not be feasible. Adjunctive internet-based interventions specifically designed for youth may provide a cost-effective and engaging alternative to prevent loss of intervention benefits. However, until now online interventions...

  8. Intelligent Design

    DEFF Research Database (Denmark)

    Hjorth, Poul G.

    2005-01-01

    Forestillingen om at naturen er designet af en guddommelig 'intelligens' er et smukt filosofisk princip. Teorier om Intelligent Design som en naturvidenskabeligt baseret teori er derimod helt forfærdelig.......Forestillingen om at naturen er designet af en guddommelig 'intelligens' er et smukt filosofisk princip. Teorier om Intelligent Design som en naturvidenskabeligt baseret teori er derimod helt forfærdelig....

  9. Machine intelligence. Part 2. Biological foundations

    Energy Technology Data Exchange (ETDEWEB)

    Scanlon, R.; Johnson, M.

    1990-08-01

    This report describes the material aspects of how perceptions, existing as codons within the neocortex, are formed through synaptogenesis, synaptic potentiation, depotentiation, and shedding. A simulation of this process on an array of transputers is also discussed.

  10. HYGIENIC AND HEALTH QUALITY OF HOT BEVERAGES DISTRIBUTED BY VENDING MACHINES

    Directory of Open Access Journals (Sweden)

    L. Vallone

    2011-08-01

    Full Text Available The food and beverage vending had in the last 40 years a great development in Italy. From the hygienic and health point of view, the quality of the products distributed by Vending is essentially related to three factors: the quality of raw materials, the quality of tap water and the good working order together with the good cleanliness and hygienic status of equipments. In this work we wanted to test these features. We evaluated microbiological and fungal quality of raw materials (powders, of distributed hot beverages and the used equipments. Despite contamination levels shown by the results of this study, the temperature of the boiler is sufficient to make a significant reduction of bacterial and fungal loads. To obtain satisfactory results on the quality of delivered hot beverages is necessary to apply correct maintenance and cleaning/sanitation procedures of equipments, as well as an appropriate selection of suppliers.

  11. 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...

  12. 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...

  13. 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....

  14. 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...

  15. 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.

  16. 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

  17. Simple machines

    CERN Document Server

    Graybill, George

    2007-01-01

    Just how simple are simple machines? With our ready-to-use resource, they are simple to teach and easy to learn! Chocked full of information and activities, we begin with a look at force, motion and work, and examples of simple machines in daily life are given. With this background, we move on to different kinds of simple machines including: Levers, Inclined Planes, Wedges, Screws, Pulleys, and Wheels and Axles. An exploration of some compound machines follows, such as the can opener. Our resource is a real time-saver as all the reading passages, student activities are provided. Presented in s

  18. 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-

  19. 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

  20. 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...

  1. Intelligent playgrounds

    DEFF Research Database (Denmark)

    Larsen, Lasse Juel

    2009-01-01

    This paper examines play, gaming and learning in regard to intelligent playware developed for outdoor use. The key questions are how does these novel artefacts influence the concept of play, gaming and learning. Up until now play and game have been understood as different activities. This paper...... examines if the sharp differentiation between the two can be uphold in regard to intelligent playware for outdoor use. Play and game activities will be analysed and viewed in conjunction with learning contexts. This paper will stipulate that intelligent playware facilitates rapid shifts in contexts...

  2. Artificial intelligence

    CERN Document Server

    Ennals, J R

    1987-01-01

    Artificial Intelligence: State of the Art Report is a two-part report consisting of the invited papers and the analysis. The editor first gives an introduction to the invited papers before presenting each paper and the analysis, and then concludes with the list of references related to the study. The invited papers explore the various aspects of artificial intelligence. The analysis part assesses the major advances in artificial intelligence and provides a balanced analysis of the state of the art in this field. The Bibliography compiles the most important published material on the subject of

  3. A novel approach of battery pack state of health estimation using artificial intelligence optimization algorithm

    Science.gov (United States)

    Zhang, Xu; Wang, Yujie; Liu, Chang; Chen, Zonghai

    2018-02-01

    An accurate battery pack state of health (SOH) estimation is important to characterize the dynamic responses of battery pack and ensure the battery work with safety and reliability. However, the different performances in battery discharge/charge characteristics and working conditions in battery pack make the battery pack SOH estimation difficult. In this paper, the battery pack SOH is defined as the change of battery pack maximum energy storage. It contains all the cells' information including battery capacity, the relationship between state of charge (SOC) and open circuit voltage (OCV), and battery inconsistency. To predict the battery pack SOH, the method of particle swarm optimization-genetic algorithm is applied in battery pack model parameters identification. Based on the results, a particle filter is employed in battery SOC and OCV estimation to avoid the noise influence occurring in battery terminal voltage measurement and current drift. Moreover, a recursive least square method is used to update cells' capacity. Finally, the proposed method is verified by the profiles of New European Driving Cycle and dynamic test profiles. The experimental results indicate that the proposed method can estimate the battery states with high accuracy for actual operation. In addition, the factors affecting the change of SOH is analyzed.

  4. Clinical Process Intelligence

    DEFF Research Database (Denmark)

    Vilstrup Pedersen, Klaus

    2006-01-01

    .e. local guidelines. From a knowledge management point of view, this externalization of generalized processes, gives the opportunity to learn from, evaluate and optimize the processes. "Clinical Process Intelligence" (CPI), will denote the goal of getting generalized insight into patient centered health...

  5. Investigation of the Effectiveness of Emotional Intelligence Training on the Self-esteem and Mental Health in Boy Deaf Students

    Directory of Open Access Journals (Sweden)

    Mohammad A'shouri

    2014-04-01

    Full Text Available Objective: The purpose of the present research was to investigation of the effectiveness of emotional intelligence training on the self-esteem of deaf students in Tehran province. Materials & Methods: The present research was an experimental study by pre-test, post-test design with control group. The study population included of boys deaf students from secondary schools (2ed grade in Tehran province. Subjects were selected randomly by cluster sampling method. In this study were participated 40 students. Subjects were divided into two groups by randomly (experimental and control group, each of which was consisted of 20 students. Experimental group received emotional intelligence training in 12 sessions while control group did not. The instruments of present research were Wechsler intelligence scale for children and Cooper Smith self-esteem questionnaire. The obtained data were statistically analyzed by MANCOVA. Results: The findings of this research showed that there was significant increase in self-esteem scores mean of experimental group in the post intervention in comparison with control group (P<0.05. Also scores mean of experimental group increased significantly in ego self-esteem, social self-esteem, family self-esteem and academic self-esteem (P<0.05. Conclusion: The emotional intelligence training program led to improvement the self-esteem and their subscales of deaf students. Therefore, planning for providing of emotional intelligence training is a particular importance.

  6. 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)

  7. Face machines

    Energy Technology Data Exchange (ETDEWEB)

    Hindle, D.

    1999-06-01

    The article surveys latest equipment available from the world`s manufacturers of a range of machines for tunnelling. These are grouped under headings: excavators; impact hammers; road headers; and shields and tunnel boring machines. Products of thirty manufacturers are referred to. Addresses and fax numbers of companies are supplied. 5 tabs., 13 photos.

  8. Electric machine

    Science.gov (United States)

    El-Refaie, Ayman Mohamed Fawzi [Niskayuna, NY; Reddy, Patel Bhageerath [Madison, WI

    2012-07-17

    An interior permanent magnet electric machine is disclosed. The interior permanent magnet electric machine comprises a rotor comprising a plurality of radially placed magnets each having a proximal end and a distal end, wherein each magnet comprises a plurality of magnetic segments and at least one magnetic segment towards the distal end comprises a high resistivity magnetic material.

  9. Machine Learning.

    Science.gov (United States)

    Kirrane, Diane E.

    1990-01-01

    As scientists seek to develop machines that can "learn," that is, solve problems by imitating the human brain, a gold mine of information on the processes of human learning is being discovered, expert systems are being improved, and human-machine interactions are being enhanced. (SK)

  10. Nonplanar machines

    International Nuclear Information System (INIS)

    Ritson, D.

    1989-05-01

    This talk examines methods available to minimize, but never entirely eliminate, degradation of machine performance caused by terrain following. Breaking of planar machine symmetry for engineering convenience and/or monetary savings must be balanced against small performance degradation, and can only be decided on a case-by-case basis. 5 refs

  11. 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.

  12. Intelligent Advertising

    OpenAIRE

    Díaz Pinedo, Edilfredo Eliot

    2012-01-01

    Intelligent Advertisement diseña e implementa un sistema de publicidad para dispositivos móviles en un centro comercial, donde los clientes reciben publicidad de forma pasiva en sus dispositivos mientras están dentro.

  13. BUSINESS INTELLIGENCE

    OpenAIRE

    Bogdan Mohor Dumitrita

    2011-01-01

    The purpose of this work is to present business intelligence systems. These systems can be extremely complex and important in modern market competition. Its effectiveness also reflects in price, so we have to exlore their financial potential before investment. The systems have 20 years long history and during that time many of such tools have been developed, but they are rarely still in use. Business intelligence system consists of three main areas: Data Warehouse, ETL tools and tools f...

  14. Intelligent indexing

    International Nuclear Information System (INIS)

    Farkas, J.

    1992-01-01

    In this paper we discuss the relevance of artificial intelligence to the automatic indexing of natural language text. We describe the use of domain-specific semantically-based thesauruses and address the problem of creating adequate knowledge bases for intelligent indexing systems. We also discuss the relevance of the Hilbert space ι 2 to the compact representation of documents and to the definition of the similarity of natural language texts. (author). 17 refs., 2 figs

  15. Intelligent indexing

    Energy Technology Data Exchange (ETDEWEB)

    Farkas, J

    1993-12-31

    In this paper we discuss the relevance of artificial intelligence to the automatic indexing of natural language text. We describe the use of domain-specific semantically-based thesauruses and address the problem of creating adequate knowledge bases for intelligent indexing systems. We also discuss the relevance of the Hilbert space {iota}{sup 2} to the compact representation of documents and to the definition of the similarity of natural language texts. (author). 17 refs., 2 figs.

  16. A conceptual model for worksite intelligent physical exercise training - IPET - intervention for decreasing life style health risk indicators among employees: a randomized controlled trial

    Science.gov (United States)

    2014-01-01

    Background Health promotion at the work site in terms of physical activity has proven positive effects but optimization of relevant exercise training protocols and implementation for high adherence are still scanty. Methods/Design The aim of this paper is to present a study protocol with a conceptual model for planning the optimal individually tailored physical exercise training for each worker based on individual health check, existing guidelines and state of the art sports science training recommendations in the broad categories of cardiorespiratory fitness, muscle strength in specific body parts, and functional training including balance training. The hypotheses of this research are that individually tailored worksite-based intelligent physical exercise training, IPET, among workers with inactive job categories will: 1) Improve cardiorespiratory fitness and/or individual health risk indicators, 2) Improve muscle strength and decrease musculoskeletal disorders, 3) Succeed in regular adherence to worksite and leisure physical activity training, and 3) Reduce sickness absence and productivity losses (presenteeism) in office workers. The present RCT study enrolled almost 400 employees with sedentary jobs in the private as well as public sectors. The training interventions last 2 years with measures at baseline as well as one and two years follow-up. Discussion If proven effective, the intelligent physical exercise training scheduled as well as the information for its practical implementation can provide meaningful scientifically based information for public health policy. Trial Registration ClinicalTrials.gov, number: NCT01366950. PMID:24964869

  17. A conceptual model for worksite intelligent physical exercise training--IPET--intervention for decreasing life style health risk indicators among employees: a randomized controlled trial.

    Science.gov (United States)

    Sjøgaard, Gisela; Justesen, Just Bendix; Murray, Mike; Dalager, Tina; Søgaard, Karen

    2014-06-26

    Health promotion at the work site in terms of physical activity has proven positive effects but optimization of relevant exercise training protocols and implementation for high adherence are still scanty. The aim of this paper is to present a study protocol with a conceptual model for planning the optimal individually tailored physical exercise training for each worker based on individual health check, existing guidelines and state of the art sports science training recommendations in the broad categories of cardiorespiratory fitness, muscle strength in specific body parts, and functional training including balance training. The hypotheses of this research are that individually tailored worksite-based intelligent physical exercise training, IPET, among workers with inactive job categories will: 1) Improve cardiorespiratory fitness and/or individual health risk indicators, 2) Improve muscle strength and decrease musculoskeletal disorders, 3) Succeed in regular adherence to worksite and leisure physical activity training, and 3) Reduce sickness absence and productivity losses (presenteeism) in office workers. The present RCT study enrolled almost 400 employees with sedentary jobs in the private as well as public sectors. The training interventions last 2 years with measures at baseline as well as one and two years follow-up. If proven effective, the intelligent physical exercise training scheduled as well as the information for its practical implementation can provide meaningful scientifically based information for public health policy. ClinicalTrials.gov, number: NCT01366950.

  18. 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...

  19. 基于特征选择支持向量机的柱塞泵智能诊断%Intelligent Fault Diagnosis for Plunger Pump Based on Features Selection and Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    崔英; 杜文辽; 孙旺; 李彦明

    2013-01-01

    柱塞泵是工程机械的关键部件,其性能好坏将直接影响整个设备的正常工作。针对柱塞泵提出基于特征选择支持向量机的智能诊断方法。对采集的振动信号基于小波包分解提取能量特征,然后利用Fisher准则函数选择对智能诊断最有利的特征,利用支持向量机进行训练,并将每个二类支持向量机按二叉树的组织形式构成系统的诊断模型。以汽车起重机柱塞泵为研究对象,其6种故障形式,包括正常、轴承内圈故障、滚动体故障、柱塞故障、配流盘故障、斜盘故障,用于检验所提算法的诊断能力,并与传统的BP神经网络和最近的蚁群神经网络方法进行对比。诊断结果表明:所提出的算法优于另外两种方法,具有较好的诊断效果。%In truck crane,the plunger pump is the key equipment,and the quality of the pump affects directly the performance of whole mechanical system. A novel intelligent diagnosis method based on features selection and support vector machine (SVM)was proposed for plunger pump in truck crane. Based on the wavelet packet decompose,the wavelet packet energy was extracted from the original vibration signal to represent the condition of equipment. Then,the Fisher criterion was utilized to select the most suitable fea-tures for diagnosis. Finally,each two-class SVM with binary tree architecture was trained to recognize the condition of mechanism. The proposed method was employed in the diagnosis of plunger pump in truck crane. The six states,including normal state,bearing inner race fault,bearing roller fault,plunger fault,thrust plate wear fault,and swash plate wear fault,were used to test the classification performance of the proposed Fisher-SVMs model,which was compared with the classical and the latest models,such as BP ANN,ANT ANN,respectively. The experimental results show that the Fisher-SVMs is superior to the other two models,and gets a promising re-sult.

  20. 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.

  1. 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...

  2. 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.)

  3. 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 ...

  4. 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...

  5. 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...

  6. The Machine within the Machine

    CERN Multimedia

    Katarina Anthony

    2014-01-01

    Although Virtual Machines are widespread across CERN, you probably won't have heard of them unless you work for an experiment. Virtual machines - known as VMs - allow you to create a separate machine within your own, allowing you to run Linux on your Mac, or Windows on your Linux - whatever combination you need.   Using a CERN Virtual Machine, a Linux analysis software runs on a Macbook. When it comes to LHC data, one of the primary issues collaborations face is the diversity of computing environments among collaborators spread across the world. What if an institute cannot run the analysis software because they use different operating systems? "That's where the CernVM project comes in," says Gerardo Ganis, PH-SFT staff member and leader of the CernVM project. "We were able to respond to experimentalists' concerns by providing a virtual machine package that could be used to run experiment software. This way, no matter what hardware they have ...

  7. 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.

  8. 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...

  9. 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.

  10. Machine translation

    Energy Technology Data Exchange (ETDEWEB)

    Nagao, M

    1982-04-01

    Each language has its own structure. In translating one language into another one, language attributes and grammatical interpretation must be defined in an unambiguous form. In order to parse a sentence, it is necessary to recognize its structure. A so-called context-free grammar can help in this respect for machine translation and machine-aided translation. Problems to be solved in studying machine translation are taken up in the paper, which discusses subjects for semantics and for syntactic analysis and translation software. 14 references.

  11. 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...

  12. 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.

  13. A Dynamic Security Framework for Ambient Intelligent Systems: A Smart-Home Based eHealth Application

    Science.gov (United States)

    Compagna, Luca; El Khoury, Paul; Massacci, Fabio; Saidane, Ayda

    Providing context-dependent security services is an important challenge for ambient intelligent systems. The complexity and the unbounded nature of such systems make it difficult even for the most experienced and knowledgeable security engineers, to foresee all possible situations and interactions when developing the system. In order to solve this problem context based self- diagnosis and reconfiguration at runtime should be provided.

  14. 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...

  15. 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.

  16. 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...

  17. 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.

  18. 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...

  19. 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.

  20. 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.

  1. Machine Learning

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

  2. Machine Translation

    Indian Academy of Sciences (India)

    Research Mt System Example: The 'Janus' Translating Phone Project. The Janus ... based on laptops, and simultaneous translation of two speakers in a dialogue. For more ..... The current focus in MT research is on using machine learning.

  3. 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

  4. 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.

  5. 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...

  6. 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...

  7. 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.

  8. 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.

  9. Intelligent systems

    CERN Document Server

    Irwin, J David

    2011-01-01

    Technology has now progressed to the point that intelligent systems are replacing humans in the decision making processes as well as aiding in the solution of very complex problems. In many cases intelligent systems are already outperforming human activities. Artificial neural networks are not only capable of learning how to classify patterns, such images or sequence of events, but they can also effectively model complex nonlinear systems. Their ability to classify sequences of events is probably more popular in industrial applications where there is an inherent need to model nonlinear system

  10. Intelligent Universe

    Energy Technology Data Exchange (ETDEWEB)

    Hoyle, F

    1983-01-01

    The subject is covered in chapters, entitled: chance and the universe (synthesis of proteins; the primordial soup); the gospel according to Darwin (discussion of Darwin theory of evolution); life did not originate on earth (fossils from space; life in space); the interstellar connection (living dust between the stars; bacteria in space falling to the earth; interplanetary dust); evolution by cosmic control (microorganisms; genetics); why aren't the others here (a cosmic origin of life); after the big bang (big bang and steady state); the information rich universe; what is intelligence up to; the intelligent universe.

  11. Artificial intelligence

    International Nuclear Information System (INIS)

    Perret-Galix, D.

    1992-01-01

    A vivid example of the growing need for frontier physics experiments to make use of frontier technology is in the field of artificial intelligence and related themes. This was reflected in the second international workshop on 'Software Engineering, Artificial Intelligence and Expert Systems in High Energy and Nuclear Physics' which took place from 13-18 January at France Telecom's Agelonde site at La Londe des Maures, Provence. It was the second in a series, the first having been held at Lyon in 1990

  12. Investigation of the Effectiveness of Emotional Intelligence Training on the Self-esteem and Mental Health in Boy Deaf Students

    OpenAIRE

    Mohammad A'shouri; Seyyedeh Somayyeh Jalil-Abkenar; Ma'soumeh Pourmohammadreza-Tajrishi

    2014-01-01

    Objective: The purpose of the present research was to investigation of the effectiveness of emotional intelligence training on the self-esteem of deaf students in Tehran province. Materials & Methods: The present research was an experimental study by pre-test, post-test design with control group. The study population included of boys deaf students from secondary schools (2ed grade) in Tehran province. Subjects were selected randomly by cluster sampling method. In this study were participa...

  13. Plant intelligence

    Science.gov (United States)

    Lipavská, Helena; Žárský, Viktor

    2009-01-01

    The concept of plant intelligence, as proposed by Anthony Trewavas, has raised considerable discussion. However, plant intelligence remains loosely defined; often it is either perceived as practically synonymous to Darwinian fitness, or reduced to a mere decorative metaphor. A more strict view can be taken, emphasizing necessary prerequisites such as memory and learning, which requires clarifying the definition of memory itself. To qualify as memories, traces of past events have to be not only stored, but also actively accessed. We propose a criterion for eliminating false candidates of possible plant intelligence phenomena in this stricter sense: an “intelligent” behavior must involve a component that can be approximated by a plausible algorithmic model involving recourse to stored information about past states of the individual or its environment. Re-evaluation of previously presented examples of plant intelligence shows that only some of them pass our test. “You were hurt?” Kumiko said, looking at the scar. Sally looked down. “Yeah.” “Why didn't you have it removed?” “Sometimes it's good to remember.” “Being hurt?” “Being stupid.”—(W. Gibson: Mona Lisa Overdrive) PMID:19816094

  14. Speech Intelligibility

    Science.gov (United States)

    Brand, Thomas

    Speech intelligibility (SI) is important for different fields of research, engineering and diagnostics in order to quantify very different phenomena like the quality of recordings, communication and playback devices, the reverberation of auditoria, characteristics of hearing impairment, benefit using hearing aids or combinations of these things.

  15. 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.

  16. 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)…

  17. 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

  18. 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.

  19. Machine Protection

    International Nuclear Information System (INIS)

    Zerlauth, Markus; Schmidt, Rüdiger; Wenninger, Jörg

    2012-01-01

    The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012

  20. Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.; Carroll, Thomas E.; Muller, George

    2017-04-21

    The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networks and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.

  1. Machine Protection

    CERN Document Server

    Zerlauth, Markus; Wenninger, Jörg

    2012-01-01

    The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012.

  2. Machine Protection

    Energy Technology Data Exchange (ETDEWEB)

    Zerlauth, Markus; Schmidt, Rüdiger; Wenninger, Jörg [European Organization for Nuclear Research, Geneva (Switzerland)

    2012-07-01

    The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012.

  3. 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.

  4. 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.

  5. 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.

  6. Teletherapy machine

    International Nuclear Information System (INIS)

    Panyam, Vinatha S.; Rakshit, Sougata; Kulkarni, M.S.; Pradeepkumar, K.S.

    2017-01-01

    Radiation Standards Section (RSS), RSSD, BARC is the national metrology institute for ionizing radiation. RSS develops and maintains radiation standards for X-ray, beta, gamma and neutron radiations. In radiation dosimetry, traceability, accuracy and consistency of radiation measurements is very important especially in radiotherapy where the success of patient treatment is dependent on the accuracy of the dose delivered to the tumour. Cobalt teletherapy machines have been used in the treatment of cancer since the early 1950s and India had its first cobalt teletherapy machine installed at the Cancer Institute, Chennai in 1956

  7. 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 ...

  8. 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...

  9. 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

  10. 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.

  11. 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.

  12. Intelligent multivariate process supervision

    International Nuclear Information System (INIS)

    Visuri, Pertti.

    1986-01-01

    This thesis addresses the difficulties encountered in managing large amounts of data in supervisory control of complex systems. Some previous alarm and disturbance analysis concepts are reviewed and a method for improving the supervision of complex systems is presented. The method, called multivariate supervision, is based on adding low level intelligence to the process control system. By using several measured variables linked together by means of deductive logic, the system can take into account the overall state of the supervised system. Thus, it can present to the operators fewer messages with higher information content than the conventional control systems which are based on independent processing of each variable. In addition, the multivariate method contains a special information presentation concept for improving the man-machine interface. (author)

  13. Machine testning

    DEFF Research Database (Denmark)

    De Chiffre, Leonardo

    This document is used in connection with a laboratory exercise of 3 hours duration as a part of the course GEOMETRICAL METROLOGY AND MACHINE TESTING. The exercise includes a series of tests carried out by the student on a conventional and a numerically controled lathe, respectively. This document...

  14. Machine rates for selected forest harvesting machines

    Science.gov (United States)

    R.W. Brinker; J. Kinard; Robert Rummer; B. Lanford

    2002-01-01

    Very little new literature has been published on the subject of machine rates and machine cost analysis since 1989 when the Alabama Agricultural Experiment Station Circular 296, Machine Rates for Selected Forest Harvesting Machines, was originally published. Many machines discussed in the original publication have undergone substantial changes in various aspects, not...

  15. Artificial Intelligence.

    Science.gov (United States)

    Lawrence, David R; Palacios-González, César; Harris, John

    2016-04-01

    It seems natural to think that the same prudential and ethical reasons for mutual respect and tolerance that one has vis-à-vis other human persons would hold toward newly encountered paradigmatic but nonhuman biological persons. One also tends to think that they would have similar reasons for treating we humans as creatures that count morally in our own right. This line of thought transcends biological boundaries-namely, with regard to artificially (super)intelligent persons-but is this a safe assumption? The issue concerns ultimate moral significance: the significance possessed by human persons, persons from other planets, and hypothetical nonorganic persons in the form of artificial intelligence (AI). This article investigates why our possible relations to AI persons could be more complicated than they first might appear, given that they might possess a radically different nature to us, to the point that civilized or peaceful coexistence in a determinate geographical space could be impossible to achieve.

  16. 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...

  17. 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...

  18. The Intelligent Technologies of Electronic Information System

    Science.gov (United States)

    Li, Xianyu

    2017-08-01

    Based upon the synopsis of system intelligence and information services, this paper puts forward the attributes and the logic structure of information service, sets forth intelligent technology framework of electronic information system, and presents a series of measures, such as optimizing business information flow, advancing data decision capability, improving information fusion precision, strengthening deep learning application and enhancing prognostic and health management, and demonstrates system operation effectiveness. This will benefit the enhancement of system intelligence.

  19. [Development and application of a web-based expert system using artificial intelligence for management of mental health by Korean emigrants].

    Science.gov (United States)

    Bae, Jeongyee

    2013-04-01

    The purpose of this project was to develop an international web-based expert system using principals of artificial intelligence and user-centered design for management of mental health by Korean emigrants. Using this system, anyone can access the system via computer access to the web. Our design process utilized principles of user-centered design with 4 phases: needs assessment, analysis, design/development/testing, and application release. A survey was done with 3,235 Korean emigrants. Focus group interviews were also conducted. Survey and analysis results guided the design of the web-based expert system. With this system, anyone can check their mental health status by themselves using a personal computer. The system analyzes facts based on answers to automated questions, and suggests solutions accordingly. A history tracking mechanism enables monitoring and future analysis. In addition, this system will include intervention programs to promote mental health status. This system is interactive and accessible to anyone in the world. It is expected that this management system will contribute to Korean emigrants' mental health promotion and allow researchers and professionals to share information on mental health.

  20. 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.