WorldWideScience

Sample records for networking artificial intelligence

  1. Artificial organic networks artificial intelligence based on carbon networks

    CERN Document Server

    Ponce-Espinosa, Hiram; Molina, Arturo

    2014-01-01

    This monograph describes the synthesis and use of biologically-inspired artificial hydrocarbon networks (AHNs) for approximation models associated with machine learning and a novel computational algorithm with which to exploit them. The reader is first introduced to various kinds of algorithms designed to deal with approximation problems and then, via some conventional ideas of organic chemistry, to the creation and characterization of artificial organic networks and AHNs in particular. The advantages of using organic networks are discussed with the rules to be followed to adapt the network to its objectives. Graph theory is used as the basis of the necessary formalism. Simulated and experimental examples of the use of fuzzy logic and genetic algorithms with organic neural networks are presented and a number of modeling problems suitable for treatment by AHNs are described: ·        approximation; ·        inference; ·        clustering; ·        control; ·        class...

  2. 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

  3. Artificial neural network intelligent method for prediction

    Science.gov (United States)

    Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

    2017-09-01

    Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

  4. An artificial neural network controller for intelligent transportation systems applications

    Energy Technology Data Exchange (ETDEWEB)

    Vitela, J.E.; Hanebutte, U.R.; Reifman, J. [Argonne National Lab., IL (United States). Reactor Analysis Div.

    1996-04-01

    An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems applications. The AICC is based on a simple nonlinear model of the vehicle dynamics. A Neural Network Controller (NNC) code developed at Argonne National Laboratory to control discrete dynamical systems was used for this purpose. In order to test the NNC, an AICC-simulator containing graphical displays was developed for a system of two vehicles driving in a single lane. Two simulation cases are shown, one involving a lead vehicle with constant velocity and the other a lead vehicle with varying acceleration. More realistic vehicle dynamic models will be considered in future work.

  5. Artificial Intelligence,

    Science.gov (United States)

    PATTERN RECOGNITION, * ARTIFICIAL INTELLIGENCE, *TEXTBOOKS, COMPUTER PROGRAMMING, MATHEMATICAL LOGIC, ROBOTS, PROBLEM SOLVING, STATISTICAL ANALYSIS, GAME THEORY, NATURAL LANGUAGE, SELF ORGANIZING SYSTEMS.

  6. Evaluating neural networks and artificial intelligence systems

    Science.gov (United States)

    Alberts, David S.

    1994-02-01

    Systems have no intrinsic value in and of themselves, but rather derive value from the contributions they make to the missions, decisions, and tasks they are intended to support. The estimation of the cost-effectiveness of systems is a prerequisite for rational planning, budgeting, and investment documents. Neural network and expert system applications, although similar in their incorporation of a significant amount of decision-making capability, differ from each other in ways that affect the manner in which they can be evaluated. Both these types of systems are, by definition, evolutionary systems, which also impacts their evaluation. This paper discusses key aspects of neural network and expert system applications and their impact on the evaluation process. A practical approach or methodology for evaluating a certain class of expert systems that are particularly difficult to measure using traditional evaluation approaches is presented.

  7. 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

  8. Instrumentation for Scientific Computing in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics.

    Science.gov (United States)

    1987-10-01

    include Security Classification) Instrumentation for scientific computing in neural networks, information science, artificial intelligence, and...instrumentation grant to purchase equipment for support of research in neural networks, information science, artificail intellignece , and applied mathematics...in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics Contract AFOSR 86-0282 Principal Investigator: Stephen

  9. Intelligent reservoir operation system based on evolving artificial neural networks

    Science.gov (United States)

    Chaves, Paulo; Chang, Fi-John

    2008-06-01

    We propose a novel intelligent reservoir operation system based on an evolving artificial neural network (ANN). Evolving means the parameters of the ANN model are identified by the GA evolutionary optimization technique. Accordingly, the ANN model should represent the operational strategies of reservoir operation. The main advantages of the Evolving ANN Intelligent System (ENNIS) are as follows: (i) only a small number of parameters to be optimized even for long optimization horizons, (ii) easy to handle multiple decision variables, and (iii) the straightforward combination of the operation model with other prediction models. The developed intelligent system was applied to the operation of the Shihmen Reservoir in North Taiwan, to investigate its applicability and practicability. The proposed method is first built to a simple formulation for the operation of the Shihmen Reservoir, with single objective and single decision. Its results were compared to those obtained by dynamic programming. The constructed network proved to be a good operational strategy. The method was then built and applied to the reservoir with multiple (five) decision variables. The results demonstrated that the developed evolving neural networks improved the operation performance of the reservoir when compared to its current operational strategy. The system was capable of successfully simultaneously handling various decision variables and provided reasonable and suitable decisions.

  10. Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks

    OpenAIRE

    Chen, Mingzhe; Challita, Ursula; Saad, Walid; Yin, Changchuan; Debbah, Mérouane

    2017-01-01

    Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user d...

  11. Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks

    OpenAIRE

    Nabil Ali Alrajeh; Lloret, J.

    2013-01-01

    Intrusion detection system (IDS) is regarded as the second line of defense against network anomalies and threats. IDS plays an important role in network security. There are many techniques which are used to design IDSs for specific scenario and applications. Artificial intelligence techniques are widely used for threats detection. This paper presents a critical study on genetic algorithm, artificial immune, and artificial neural network (ANN) based IDSs techniques used in wireless sensor netw...

  12. Research on architecture of intelligent design platform for artificial neural network expert system

    Science.gov (United States)

    Gu, Honghong

    2017-09-01

    Based on the review of the development and current situation of CAD technology, the necessity of combination of artificial neural network and expert system, and then present an intelligent design system based on artificial neural network. Moreover, it discussed the feasibility of realization of a design-oriented expert system development tools on the basis of above combination. In addition, knowledge representation strategy and method and the solving process are given in this paper.

  13. DATA MAYHEM VERSUS NIMBLE INFORMATION: TRANSFORMING HECTIC IMAGERY INTELLIGENCE DATA INTO ACTIONABLE INFORMATION USING ARTIFICIAL NEURAL NETWORKS

    Science.gov (United States)

    2017-10-01

    sensitive information. For tactical purposes, intelligence value is proportional to time, where the operational and strategic value is not as diminished...HECTIC IMAGERY INTELLIGENCE DATA INTO ACTIONABLE INFORMATION USING ARTIFICIAL NEURAL NETWORKS by Luis A. Morales, Major, USAF A Research...PED) process does not satisfy the demands for intelligence to warfighters. Data production rate from Imagery Intelligence (IMINT) sensors far

  14. Soft computing in artificial intelligence

    CERN Document Server

    Matson, Eric

    2014-01-01

    This book explores the concept of artificial intelligence based on knowledge-based algorithms. Given the current hardware and software technologies and artificial intelligence theories, we can think of how efficient to provide a solution, how best to implement a model and how successful to achieve it. This edition provides readers with the most recent progress and novel solutions in artificial intelligence. This book aims at presenting the research results and solutions of applications in relevance with artificial intelligence technologies. We propose to researchers and practitioners some methods to advance the intelligent systems and apply artificial intelligence to specific or general purpose. This book consists of 13 contributions that feature fuzzy (r, s)-minimal pre- and β-open sets, handling big coocurrence matrices, Xie-Beni-type fuzzy cluster validation, fuzzy c-regression models, combination of genetic algorithm and ant colony optimization, building expert system, fuzzy logic and neural network, ind...

  15. Classification of intelligence quotient via brainwave sub-band power ratio features and artificial neural network.

    Science.gov (United States)

    Jahidin, A H; Megat Ali, M S A; Taib, M N; Tahir, N Md; Yassin, I M; Lias, S

    2014-04-01

    This paper elaborates on the novel intelligence assessment method using the brainwave sub-band power ratio features. The study focuses only on the left hemisphere brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the brainwave sub-band power ratios with 100% training and 88.89% testing accuracies. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  16. Artificial intelligence

    OpenAIRE

    Duda, Antonín

    2009-01-01

    Abstract : Issue of this work is to acquaint the reader with the history of artificial inteligence, esspecialy branch of chess computing. Main attention is given to progress from fifties to the present. The work also deals with fighting chess programs against each other, and against human opponents. The greatest attention is focused on 1997 and duel Garry Kasparov against chess program Deep Blue. The work is divided into chapters according to chronological order.

  17. Intelligence: Real or artificial?

    OpenAIRE

    Schlinger, Henry D.

    1992-01-01

    Throughout the history of the artificial intelligence movement, researchers have strived to create computers that could simulate general human intelligence. This paper argues that workers in artificial intelligence have failed to achieve this goal because they adopted the wrong model of human behavior and intelligence, namely a cognitive essentialist model with origins in the traditional philosophies of natural intelligence. An analysis of the word “intelligence” suggests that it originally r...

  18. Artificial intelligence based event detection in wireless sensor networks

    NARCIS (Netherlands)

    Bahrepour, M.

    2013-01-01

    Wireless sensor networks (WSNs) are composed of large number of small, inexpensive devices, called sensor nodes, which are equipped with sensing, processing, and communication capabilities. While traditional applications of wireless sensor networks focused on periodic monitoring, the focus of more

  19. Artificial intelligence based event detection in wireless sensor networks

    OpenAIRE

    Bahrepour, M.

    2013-01-01

    Wireless sensor networks (WSNs) are composed of large number of small, inexpensive devices, called sensor nodes, which are equipped with sensing, processing, and communication capabilities. While traditional applications of wireless sensor networks focused on periodic monitoring, the focus of more recent applications is on fast and reliable identification of out-of-ordinary situations and events. This new functionality of wireless sensor networks is known as event detection. Due to the fact t...

  20. 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.

  1. Software implementation of artificial neural networks in automated intelligent systems

    Directory of Open Access Journals (Sweden)

    В.П. Харченко

    2009-02-01

    Full Text Available  Application of neural networks technologies effectively decides the task of synthesis of origin of accident risk and gives out the vector of managing signals of network on incomplete and distorted information about the phenomena, events and processes which influence on safety flights.

  2. Artificial Intelligence Education

    National Research Council Canada - National Science Library

    Michael Wollowski; Todd Neller; James Boerkoel

    2017-01-01

    .... The article Ethical Considerations in Artificial Intelligence Courses by Emanuelle Burton, Judy Goldsmith, Sven Koenig, Benjamin Kuipers, Nicholas Mattei, and Toby Walsh is concerned with providing...

  3. Natural and artificial intelligence misconceptions about brains and neural networks

    CERN Document Server

    de Callataÿ, A

    1992-01-01

    How does the mind work? How is data stored in the brain? How does the mental world connect with the physical world? The hybrid system developed in this book shows a radically new view on the brain. Briefly, in this model memory remains permanent by changing the homeostasis rebuilding the neuronal organelles. These transformations are approximately abstracted as all-or-none operations. Thus the computer-like neural systems become plausible biological models. This illustrated book shows how artificial animals with such brains learn invariant methods of behavior control from their repeated action

  4. The role of networks and artificial intelligence in nanotechnology design and analysis.

    Science.gov (United States)

    Hudson, D L; Cohen, M E

    2004-05-01

    Techniques with their origins in artificial intelligence have had a great impact on many areas of biomedicine. Expert-based systems have been used to develop computer-assisted decision aids. Neural networks have been used extensively in disease classification and more recently in many bioinformatics applications including genomics and drug design. Network theory in general has proved useful in modeling all aspects of biomedicine from healthcare organizational structure to biochemical pathways. These methods show promise in applications involving nanotechnology both in the design phase and in interpretation of system functioning.

  5. Recent advances on artificial intelligence and learning techniques in cognitive radio networks

    National Research Council Canada - National Science Library

    Abbas, Nadine; Nasser, Youssef; Ahmad, Karim El

    2015-01-01

    ... of the radio spectrum. For efficient real-time process, the cognitive radio is usually combined with artificial intelligence and machine-learning techniques so that an adaptive and intelligent allocation is achieved...

  6. 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

  7. Improved Space Surveillance Network (SSN) Scheduling using Artificial Intelligence Techniques

    Science.gov (United States)

    Stottler, D.

    There are close to 20,000 cataloged manmade objects in space, the large majority of which are not active, functioning satellites. These are tracked by phased array and mechanical radars and ground and space-based optical telescopes, collectively known as the Space Surveillance Network (SSN). A better SSN schedule of observations could, using exactly the same legacy sensor resources, improve space catalog accuracy through more complementary tracking, provide better responsiveness to real-time changes, better track small debris in low earth orbit (LEO) through efficient use of applicable sensors, efficiently track deep space (DS) frequent revisit objects, handle increased numbers of objects and new types of sensors, and take advantage of future improved communication and control to globally optimize the SSN schedule. We have developed a scheduling algorithm that takes as input the space catalog and the associated covariance matrices and produces a globally optimized schedule for each sensor site as to what objects to observe and when. This algorithm is able to schedule more observations with the same sensor resources and have those observations be more complementary, in terms of the precision with which each orbit metric is known, to produce a satellite observation schedule that, when executed, minimizes the covariances across the entire space object catalog. If used operationally, the results would be significantly increased accuracy of the space catalog with fewer lost objects with the same set of sensor resources. This approach inherently can also trade-off fewer high priority tasks against more lower-priority tasks, when there is benefit in doing so. Currently the project has completed a prototyping and feasibility study, using open source data on the SSN's sensors, that showed significant reduction in orbit metric covariances. The algorithm techniques and results will be discussed along with future directions for the research.

  8. Artificial Intelligence Study (AIS).

    Science.gov (United States)

    1987-02-01

    ARTIFICIAL INTELLIGNECE HARDWARE ....... 2-50 AI Architecture ................................... 2-49 AI Hardware ....................................... 2...ftf1 829 ARTIFICIAL INTELLIGENCE STUDY (RIS)(U) MAY CONCEPTS 1/3 A~NLYSIS AGENCY BETHESA RD R B NOJESKI FED 6? CM-RP-97-1 NCASIFIED /01/6 M |K 1.0...p/ - - ., e -- CAA- RP- 87-1 SAOFŔ)11 I ARTIFICIAL INTELLIGENCE STUDY (AIS) tNo DTICFEBRUARY 1987 LECT 00 I PREPARED BY RESEARCH AND ANALYSIS

  9. Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Gilberto Bojorquez

    2007-08-01

    Full Text Available The development of smart sensors involves the design of reconfigurable systemscapable of working with different input sensors. Reconfigurable systems ideally shouldspend the least possible amount of time in their calibration. An autocalibration algorithmfor intelligent sensors should be able to fix major problems such as offset, variation of gainand lack of linearity, as accurately as possible. This paper describes a new autocalibrationmethodology for nonlinear intelligent sensors based on artificial neural networks, ANN.The methodology involves analysis of several network topologies and training algorithms.The proposed method was compared against the piecewise and polynomial linearizationmethods. Method comparison was achieved using different number of calibration points,and several nonlinear levels of the input signal. This paper also shows that the proposedmethod turned out to have a better overall accuracy than the other two methods. Besides,experimentation results and analysis of the complete study, the paper describes theimplementation of the ANN in a microcontroller unit, MCU. In order to illustrate themethod capability to build autocalibration and reconfigurable systems, a temperaturemeasurement system was designed and tested. The proposed method is an improvement over the classic autocalibration methodologies, because it impacts on the design process of intelligent sensors, autocalibration methodologies and their associated factors, like time and cost.

  10. Intelligent Monitoring System on Prediction of Building Damage Index using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Reni Suryanita

    2012-03-01

    Full Text Available An earthquake potentially destroys a tall building. The building damage can be indexed by FEMA into three categories namely Immediate Occupancy (IO, Life Safety (LS, and Collapse Prevention (CP. To determine the damage index, the building model has been simulated into structure analysis software. Acceleration data has been analyzed using non linear method in structure analysis program. The earthquake load is time history at surface, PGA=0105g. This work proposes an intelligent monitoring system utilizing Artificial Neural Network to predict the building damage index. The system also provides an alert system and notification to inform the status of the damage. Data learning is trained on ANN utilizing feed forward and back propagation algorithm. The alert system is designed to be able to activate the alarm sound, view the alert bar or text, and send notification via email to the security or management. The system is tested using sample data represented in three conditions involving IO, LS, and CP. The results show that the proposed intelligent monitoring system could provide prediction of up to 92% rate of accuracy and activate the alert. Implementation of the system in building monitoring would allow for rapid, intelligent and accurate prediction of the building damage index due to earthquake.

  11. Fuzzy Logic, Neural Networks, Genetic Algorithms: Views of Three Artificial Intelligence Concepts Used in Modeling Scientific Systems

    Science.gov (United States)

    Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.

    2003-01-01

    Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…

  12. Artificial intelligence in nanotechnology

    Science.gov (United States)

    Sacha, G. M.; Varona, P.

    2013-11-01

    During the last decade there has been increasing use of artificial intelligence tools in nanotechnology research. In this paper we review some of these efforts in the context of interpreting scanning probe microscopy, the study of biological nanosystems, the classification of material properties at the nanoscale, theoretical approaches and simulations in nanoscience, and generally in the design of nanodevices. Current trends and future perspectives in the development of nanocomputing hardware that can boost artificial-intelligence-based applications are also discussed. Convergence between artificial intelligence and nanotechnology can shape the path for many technological developments in the field of information sciences that will rely on new computer architectures and data representations, hybrid technologies that use biological entities and nanotechnological devices, bioengineering, neuroscience and a large variety of related disciplines.

  13. Artificial intelligence in nanotechnology.

    Science.gov (United States)

    Sacha, G M; Varona, P

    2013-11-15

    During the last decade there has been increasing use of artificial intelligence tools in nanotechnology research. In this paper we review some of these efforts in the context of interpreting scanning probe microscopy, the study of biological nanosystems, the classification of material properties at the nanoscale, theoretical approaches and simulations in nanoscience, and generally in the design of nanodevices. Current trends and future perspectives in the development of nanocomputing hardware that can boost artificial-intelligence-based applications are also discussed. Convergence between artificial intelligence and nanotechnology can shape the path for many technological developments in the field of information sciences that will rely on new computer architectures and data representations, hybrid technologies that use biological entities and nanotechnological devices, bioengineering, neuroscience and a large variety of related disciplines.

  14. Applications Of Artificial Intelligence

    Science.gov (United States)

    Trivedi, Mohan M.; Gilmore, John F.

    1986-03-01

    Intelligence evolves out of matter, so said the Sankhya philosophers of ancient India. The discipline of artificial intelligence (Al), which was established some 30 years ago, has confirmed the validity of the above assertion. Recently, a number of AI applications have been successfully demonstrated, generating a great deal of excitement and interest in scientific and technical circles. In this special issue of Optical Engineering a representative set of applications that incorporate Al principles is presented.

  15. Bayesian artificial intelligence

    CERN Document Server

    Korb, Kevin B

    2003-01-01

    As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors' website.

  16. Artificial intelligence in cardiology.

    Science.gov (United States)

    Bonderman, Diana

    2017-12-01

    Decision-making is complex in modern medicine and should ideally be based on available data, structured knowledge and proper interpretation in the context of an individual patient. Automated algorithms, also termed artificial intelligence that are able to extract meaningful patterns from data collections and build decisions upon identified patterns may be useful assistants in clinical decision-making processes. In this article, artificial intelligence-based studies in clinical cardiology are reviewed. The text also touches on the ethical issues and speculates on the future roles of automated algorithms versus clinicians in cardiology and medicine in general.

  17. Web Intelligence and Artificial Intelligence in Education

    Science.gov (United States)

    Devedzic, Vladan

    2004-01-01

    This paper surveys important aspects of Web Intelligence (WI) in the context of Artificial Intelligence in Education (AIED) research. WI explores the fundamental roles as well as practical impacts of Artificial Intelligence (AI) and advanced Information Technology (IT) on the next generation of Web-related products, systems, services, and…

  18. Generality in Artificial Intelligence

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 19; Issue 3. Generality in Artificial Intelligence. John McCarthy. Classics Volume 19 Issue 3 March 2014 pp 283-296. Fulltext. Click here to view fulltext PDF. Permanent link: http://www.ias.ac.in/article/fulltext/reso/019/03/0283-0296. Author Affiliations.

  19. Artificial intelligence within AFSC

    Science.gov (United States)

    Gersh, Mark A.

    1990-01-01

    Information on artificial intelligence research in the Air Force Systems Command is given in viewgraph form. Specific research that is being conducted at the Rome Air Development Center, the Space Technology Center, the Human Resources Laboratory, the Armstrong Aerospace Medical Research Laboratory, the Armamant Laboratory, and the Wright Research and Development Center is noted.

  20. Artificial Intelligence and Economic Theories

    OpenAIRE

    Marwala, Tshilidzi; Hurwitz, Evan

    2017-01-01

    The advent of artificial intelligence has changed many disciplines such as engineering, social science and economics. Artificial intelligence is a computational technique which is inspired by natural intelligence such as the swarming of birds, the working of the brain and the pathfinding of the ants. These techniques have impact on economic theories. This book studies the impact of artificial intelligence on economic theories, a subject that has not been extensively studied. The theories that...

  1. Integrating artificial intelligence into organizational intelligence

    OpenAIRE

    Leon, Florin; Gabriela M. Atanasiu

    2008-01-01

    Organizational intelligence is the capability of an organization to create knowledge and to use it in order to strategically adapt to its environment. Intelligence of an organization is more than the aggregated intelligence of its members – it is an emergent property of the complex interactions of its subsystems and the way they are aggregated. Processes analyse related to organizational intelligence can be achieved by means of agent-based simulations. Distributed artificial intelligence addr...

  2. Essentials of artificial intelligence

    CERN Document Server

    Ginsberg, Matt

    1993-01-01

    Since its publication, Essentials of Artificial Intelligence has beenadopted at numerous universities and colleges offering introductory AIcourses at the graduate and undergraduate levels. Based on the author'scourse at Stanford University, the book is an integrated, cohesiveintroduction to the field. The author has a fresh, entertaining writingstyle that combines clear presentations with humor and AI anecdotes. At thesame time, as an active AI researcher, he presents the materialauthoritatively and with insight that reflects a contemporary, first hand

  3. Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models

    Directory of Open Access Journals (Sweden)

    Juhwan Kim

    2018-01-01

    Full Text Available Recent developments in artificial intelligence (AI have led to a significant increase in the use of AI technologies. Many experts are researching and developing AI technologies in their respective fields, often submitting papers and patent applications as a result. In particular, owing to the characteristics of the patent system that is used to protect the exclusive rights to registered technology, patent documents contain detailed information on the developed technology. Therefore, in this study, we propose a statistical method for analyzing patent data on AI technology to improve our understanding of sustainable technology in the field of AI. We collect patent documents that are related to AI technology, and then analyze the patent data to identify sustainable AI technology. In our analysis, we develop a statistical method that combines social network analysis and Bayesian modeling. Based on the results of the proposed method, we provide a technological structure that can be applied to understand the sustainability of AI technology. To show how the proposed method can be applied to a practical problem, we apply the technological structure to a case study in order to analyze sustainable AI technology.

  4. Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

    Directory of Open Access Journals (Sweden)

    Montri Inthachot

    2016-01-01

    Full Text Available This study investigated the use of Artificial Neural Network (ANN and Genetic Algorithm (GA for prediction of Thailand’s SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid’s prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.

  5. Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend.

    Science.gov (United States)

    Inthachot, Montri; Boonjing, Veera; Intakosum, Sarun

    2016-01-01

    This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.

  6. Uncertainty in artificial intelligence

    CERN Document Server

    Kanal, LN

    1986-01-01

    How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.

  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. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.

    Science.gov (United States)

    Hirasawa, Toshiaki; Aoyama, Kazuharu; Tanimoto, Tetsuya; Ishihara, Soichiro; Shichijo, Satoki; Ozawa, Tsuyoshi; Ohnishi, Tatsuya; Fujishiro, Mitsuhiro; Matsuo, Keigo; Fujisaki, Junko; Tada, Tomohiro

    2018-01-15

    Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images. A CNN-based diagnostic system was constructed based on Single Shot MultiBox Detector architecture and trained using 13,584 endoscopic images of gastric cancer. To evaluate the diagnostic accuracy, an independent test set of 2296 stomach images collected from 69 consecutive patients with 77 gastric cancer lesions was applied to the constructed CNN. The CNN required 47 s to analyze 2296 test images. The CNN correctly diagnosed 71 of 77 gastric cancer lesions with an overall sensitivity of 92.2%, and 161 non-cancerous lesions were detected as gastric cancer, resulting in a positive predictive value of 30.6%. Seventy of the 71 lesions (98.6%) with a diameter of 6 mm or more as well as all invasive cancers were correctly detected. All missed lesions were superficially depressed and differentiated-type intramucosal cancers that were difficult to distinguish from gastritis even for experienced endoscopists. Nearly half of the false-positive lesions were gastritis with changes in color tone or an irregular mucosal surface. The constructed CNN system for detecting gastric cancer could process numerous stored endoscopic images in a very short time with a clinically relevant diagnostic ability. It may be well applicable to daily clinical practice to reduce the burden of endoscopists.

  9. 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.

  10. Artificial intelligence-based 5G network capacity planning and operation

    OpenAIRE

    Pérez Romero, Jordi; Sallent Roig, José Oriol; Ferrús Ferré, Ramón Antonio; Agustí Comes, Ramon

    2015-01-01

    The highly demanding requirements envisaged for future 5G networks together with the required support of new customers from vertical industries (e.g. e-health, automotive, energy) pose a big challenge for operators in 5G on how to balance investments, user experience and profitability. There will be the need to revisit the actual methodologies of network planning and operation, fully exploiting cognitive capabilities that embrace knowledge and intelligence to achie...

  11. The handbook of artificial intelligence

    CERN Document Server

    Barr, Avron

    1982-01-01

    The Handbook of Artificial Intelligence, Volume II focuses on the improvements in artificial intelligence (AI) and its increasing applications, including programming languages, intelligent CAI systems, and the employment of AI in medicine, science, and education. The book first elaborates on programming languages for AI research and applications-oriented AI research. Discussions cover scientific applications, teiresias, applications in chemistry, dependencies and assumptions, AI programming-language features, and LISP. The manuscript then examines applications-oriented AI research in medicine

  12. 15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing

    CERN Document Server

    2015-01-01

    This edited book presents scientific results of 15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2014) held on June 30 – July 2, 2014 in Las Vegas Nevada, USA. The aim of this conference was to bring together scientists, engineers, computer users, and students to share their experiences and exchange new ideas, research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The conference organizers selected the 13 outstanding papers from those papers accepted for presentation at the conference.

  13. 14th ACIS/IEEE International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing

    CERN Document Server

    Studies in Computational Intelligence : Volume 492

    2013-01-01

    This edited book presents scientific results of the 14th ACIS/IEEE International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2013), held in Honolulu, Hawaii, USA on July 1-3, 2013. The aim of this conference was to bring together scientists, engineers, computer users, and students to share their experiences and exchange new ideas, research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The conference organizers selected the 17 outstanding papers from those papers accepted for presentation at the conference.  

  14. Intelligent quotient estimation of mental retarded people from different psychometric instruments using artificial neural networks.

    Science.gov (United States)

    Di Nuovo, Alessandro G; Di Nuovo, Santo; Buono, Serafino

    2012-02-01

    The estimation of a person's intelligence quotient (IQ) by means of psychometric tests is indispensable in the application of psychological assessment to several fields. When complex tests as the Wechsler scales, which are the most commonly used and universally recognized parameter for the diagnosis of degrees of retardation, are not applicable, it is necessary to use other psycho-diagnostic tools more suited for the subject's specific condition. But to ensure a homogeneous diagnosis it is necessary to reach a common metric, thus, the aim of our work is to build models able to estimate accurately and reliably the Wechsler IQ, starting from different psycho-diagnostic tools. Four different psychometric tests (Leiter international performance scale; coloured progressive matrices test; the mental development scale; psycho educational profile), along with the Wechsler scale, were administered to a group of 40 mentally retarded subjects, with various pathologies, and control persons. The obtained database is used to evaluate Wechsler IQ estimation models starting from the scores obtained in the other tests. Five modelling methods, two statistical and three from machine learning, that belong to the family of artificial neural networks (ANNs) are employed to build the estimator. Several error metrics for estimated IQ and for retardation level classification are defined to compare the performance of the various models with univariate and multivariate analyses. Eight empirical studies show that, after ten-fold cross-validation, best average estimation error is of 3.37 IQ points and mental retardation level classification error of 7.5%. Furthermore our experiments prove the superior performance of ANN methods over statistical regression ones, because in all cases considered ANN models show the lowest estimation error (from 0.12 to 0.9 IQ points) and the lowest classification error (from 2.5% to 10%). Since the estimation performance is better than the confidence interval of

  15. Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes

    Directory of Open Access Journals (Sweden)

    Igor Vyacheslavovich Buzaev

    2016-09-01

    Full Text Available Objective: The continuous uninterrupted feedback system is the essential part of any well-organized system. We propose aLYNX concept that is a possibility to use an artificial intelligence algorithm or a neural network model in decision-making system so as to avoid possible mistakes and to remind the doctors to review tactics once more in selected cases. Method: aLYNX system includes: registry with significant factors, decisions and results; machine learning process based on this registry data; the use of the machine learning results as the adviser. We show a possibility to build a computer adviser with a neural network model for making a choice between coronary aortic bypass surgery (CABG and percutaneous coronary intervention (PCI in order to achieve a higher 5-year survival rate in patients with angina based on the experience of 5107 patients. Results: The neural network was trained by 4679 patients who achieved 5-year survival. Among them, 2390 patients underwent PCI and 2289 CABG. After training, the correlation coefficient (r of the network was 0.74 for training, 0.67 for validation, 0.71 for test and 0.73 for total. Simulation of the neural network function has been performed after training in the two groups of patients with known 5-year outcome. The disagreement rate was significantly higher in the dead patient group than that in the survivor group between neural network model and heart team [16.8% (787/4679 vs. 20.3% (87/428, P = 0.065]. Conclusion: The study shows the possibility to build a computer adviser with a neural network model for making a choice between CABG and PCI in order to achieve a higher 5-year survival rate in patients with angina. Keywords: Coronary artery bypass grafting, Percutaneous coronary intervention, Artificial intelligence, Decision making

  16. Artificial Intelligence and Expert Systems.

    Science.gov (United States)

    Wilson, Harold O.; Burford, Anna Marie

    1990-01-01

    Delineates artificial intelligence/expert systems (AI/ES) concepts; provides an exposition of some business application areas; relates progress; and creates an awareness of the benefits, limitations, and reservations of AI/ES. (Author)

  17. The handbook of artificial intelligence

    CERN Document Server

    Barr, Avron J

    1981-01-01

    The Handbook of Artificial Intelligence, Volume I focuses on the progress in artificial intelligence (AI) and its increasing applications, including parsing, grammars, and search methods.The book first elaborates on AI, AI handbook and literature, problem representation, search methods, and sample search programs. The text then ponders on representation of knowledge, including survey of representation techniques and representation schemes. The manuscript explores understanding natural languages, as well as machine translation, grammars, parsing, test generation, and natural language processing

  18. Medical applications of artificial intelligence

    CERN Document Server

    Agah, Arvin

    2013-01-01

    Enhanced, more reliable, and better understood than in the past, artificial intelligence (AI) systems can make providing healthcare more accurate, affordable, accessible, consistent, and efficient. However, AI technologies have not been as well integrated into medicine as predicted. In order to succeed, medical and computational scientists must develop hybrid systems that can effectively and efficiently integrate the experience of medical care professionals with capabilities of AI systems. After providing a general overview of artificial intelligence concepts, tools, and techniques, Medical Ap

  19. USE OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN QUALITY IMPROVING PROCESS

    Directory of Open Access Journals (Sweden)

    KALİTE İYİLEŞTİRME SÜRECİNDE YAPAY ZEKÃ KAYA

    2005-01-01

    Full Text Available Today, changing of competition conditions and customer preferences caused to happen many differences in the viewpoint of firms' quality studies. At the same time, improvements in computer technologies accelerated use of artificial intelligence. Artificial intelligence technologies are being used to solve many industry problems. In this paper, we investigated the use of artificial intelligence techniques to solve quality problems. The artificial intelligence techniques, which are used in quality improving process in the recent years, are artificial neural networks, expert systems, genetic algorithms and fuzzy logic.

  20. Fuzzy logic and neural networks in artificial intelligence and pattern recognition

    Science.gov (United States)

    Sanchez, Elie

    1991-10-01

    With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

  1. Artificial Intelligence -- Research and Applications.

    Science.gov (United States)

    1976-06-01

    1975 through 11 April 1976 Stanford Research Institute Projects 3805 and 4763 ARIFICIAL INTELLIGENCE -- RESEARCH AND APPLICATIONS Edited by Bertram...of " vision by distinguishing features." Object recognition via distinguishing features is performed by looking only for those features that...August 1974). 11. T. D. Garvey, "Perceptual Strategies for Purposive Vision , Technical Note 127, Artificial Intelligence Center, Stanford Research

  2. Portable AI Lab for Teaching Artificial Intelligence.

    Science.gov (United States)

    Rosner, Michael; Baj, Fabio.

    1993-01-01

    Describes the Portable AI Lab, a computing environment containing artificial intelligence (AI) tools, examples, and documentation for use with university AI courses. Two modules of the lab are highlighted: the automated theorem proving module and the natural language processing module, which includes augmented transition networks. (23 references)…

  3. Artificial Neural Networks and Instructional Technology.

    Science.gov (United States)

    Carlson, Patricia A.

    1991-01-01

    Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

  4. Artificial intelligence and intelligent tutoring systems

    Energy Technology Data Exchange (ETDEWEB)

    Livergood, N.D.

    1989-01-01

    As a species we have evolved by increasing our mental and physical powers through the deliberate development and use of instruments that amplify our inherent capabilities. Whereas hereditarily given instincts predetermine the actions of lower animal forms, human existence begins with freedom. As humans we can choose what actions we will perform. We have invented a technology called education to prepare ourselves for life. At present, our educational structures and procedures are failing to prepare us efficiently for the demands of modern life. One of the most important new technologies, in relation to human development, is the digital computer. This dissertation proposes that artificial intelligence maintain a highly critical technological awareness. Artificial intelligence, because of its origin as a politically sponsored field of investigation, must strive for constant awareness of its place within the larger political-economic world and its possible misuse by factions intent on manipulation and control. Computerized models of the human mind could be used in developing progressively more sophisticated brainwashing systems. Intelligent tutoring systems comprise an important new technology within the field of artificial intelligence. This dissertation explores specification and design procedures, functions and issues in developing intelligent tutoring systems.

  5. Developing an Intelligent System for Diagnosis of Asthma Based on Artificial Neural Network.

    Science.gov (United States)

    Alizadeh, Behrouz; Safdari, Reza; Zolnoori, Maryam; Bashiri, Azadeh

    2015-08-01

    Lack of proper diagnosis and inadequate treatment of asthma, leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different modes was made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. So considering the data mining approaches due to the nature of medical data is necessary.

  6. Artificial Intelligence in planetary spectroscopy

    Science.gov (United States)

    Waldmann, Ingo

    2017-10-01

    The field of exoplanetary spectroscopy is as fast moving as it is new. Analysing currently available observations of exoplanetary atmospheres often invoke large and correlated parameter spaces that can be difficult to map or constrain. This is true for both: the data analysis of observations as well as the theoretical modelling of their atmospheres.Issues of low signal-to-noise data and large, non-linear parameter spaces are nothing new and commonly found in many fields of engineering and the physical sciences. Recent years have seen vast improvements in statistical data analysis and machine learning that have revolutionised fields as diverse as telecommunication, pattern recognition, medical physics and cosmology.In many aspects, data mining and non-linearity challenges encountered in other data intensive fields are directly transferable to the field of extrasolar planets. In this conference, I will discuss how deep neural networks can be designed to facilitate solving said issues both in exoplanet atmospheres as well as for atmospheres in our own solar system. I will present a deep belief network, RobERt (Robotic Exoplanet Recognition), able to learn to recognise exoplanetary spectra and provide artificial intelligences to state-of-the-art atmospheric retrieval algorithms. Furthermore, I will present a new deep convolutional network that is able to map planetary surface compositions using hyper-spectral imaging and demonstrate its uses on Cassini-VIMS data of Saturn.

  7. Hybrid intelligence systems and artificial neural network (ANN approach for modeling of surface roughness in drilling

    Directory of Open Access Journals (Sweden)

    Ch. Sanjay

    2014-12-01

    Full Text Available In machining processes, drilling operation is material removal process that has been widely used in manufacturing since industrial revolution. The useful life of cutting tool and its operating conditions largely controls the economics of machining operations. Drilling is most frequently performed material removing process and is used as a preliminary step for many operations, such as reaming, tapping, and boring. Drill wear has a bad effect on the surface finish and dimensional accuracy of the work piece. The surface finish of a machined part is one of the most important quality characteristics in manufacturing industries. The primary objective of this research is the prediction of suitable parameters for surface roughness in drilling. Cutting speed, cutting force, and machining time were given as inputs to the adaptive fuzzy neural network and neuro-fuzzy analysis for estimating the values of surface roughness by using 2, 3, 4, and 5 membership functions. The best structures were selected based on minimum of summation of square with the actual values with the estimated values by artificial neural fuzzy inference system (ANFIS and neuro-fuzzy systems. For artificial neural network (ANN analysis, the number of neurons was selected from 1, 2, 3, … , 20. The learning rate was selected as .5 and .5 smoothing factor was used. The inputs were selected as cutting speed, feed, machining time, and thrust force. The best structures of neural networks were selected based on the criteria as the minimum of summation of square with the actual value of surface roughness. Drilling experiments with 10 mm size were performed at two cutting speeds and feeds. Comparative analysis has been done between the actual values and the estimated values obtained by ANFIS, neuro-fuzzy, and ANN analysis.

  8. HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    AbdoulAhad Validi

    2012-01-01

    Full Text Available A Dynamic model of Homogeneous Charge Compression Ignition (HCCI, based on chemical kinetics principles and artificial intelligence, is developed. The model can rapidly predict the combustion probability, thermochemistry properties, and exact timing of the Start of Combustion (SOC. A realization function is developed on the basis of the Sandia National Laboratory chemical kinetics model, and GRI3.0 methane chemical mechanism. The inlet conditions are optimized by Genetic Algorithm (GA, so that combustion initiates and SOC timing posits in the desired crank angle. The best SOC timing to achieve higher performance and efficiency in HCCI engines is between 5 and 15 degrees crank angle (CAD after top dead center (TDC. To achieve this SOC timing, in the first case, the inlet temperature and equivalence ratio are optimized simultaneously and in the second case, compression ratio is optimized by GA. The model’s results are validated with previous works. The SOC timing can be predicted in less than 0.01 second and the CPU time savings are encouraging. This model can successfully be used for real engine control applications.

  9. 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing

    CERN Document Server

    SNPD 2016

    2016-01-01

    This edited book presents scientific results of the 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2016) which was held on May 30 - June 1, 2016 in Shanghai, China. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them.

  10. 6th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing

    CERN Document Server

    2016-01-01

    This edited book presents scientific results of the 16th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2015) which was held on June 1 – 3, 2015 in Takamatsu, Japan. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them.

  11. Computational aerodynamics and artificial intelligence

    Science.gov (United States)

    Mehta, U. B.; Kutler, P.

    1984-01-01

    The general principles of artificial intelligence are reviewed and speculations are made concerning how knowledge based systems can accelerate the process of acquiring new knowledge in aerodynamics, how computational fluid dynamics may use expert systems, and how expert systems may speed the design and development process. In addition, the anatomy of an idealized expert system called AERODYNAMICIST is discussed. Resource requirements for using artificial intelligence in computational fluid dynamics and aerodynamics are examined. Three main conclusions are presented. First, there are two related aspects of computational aerodynamics: reasoning and calculating. Second, a substantial portion of reasoning can be achieved with artificial intelligence. It offers the opportunity of using computers as reasoning machines to set the stage for efficient calculating. Third, expert systems are likely to be new assets of institutions involved in aeronautics for various tasks of computational aerodynamics.

  12. 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

  13. ARTIFICIAL INTELLIGENCE APPLICATIONS IN THE FINANCIAL SECTOR

    OpenAIRE

    Adrian Cozgarea; Gabriel Cozgarea; Andrei Stanciu

    2008-01-01

    The present paper exposes some of artificial intelligence specific technologies regarding financial sector. Through non-deterministic solutions and simple algorithms, artificial intelligence could become a base alternative for solving financial problems which require complex mathematic calculations or complex optimization.

  14. Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes.

    Science.gov (United States)

    Buzaev, Igor Vyacheslavovich; Plechev, Vladimir Vyacheslavovich; Nikolaeva, Irina Evgenievna; Galimova, Rezida Maratovna

    2016-09-01

    The continuous uninterrupted feedback system is the essential part of any well-organized system. We propose aLYNX concept that is a possibility to use an artificial intelligence algorithm or a neural network model in decision-making system so as to avoid possible mistakes and to remind the doctors to review tactics once more in selected cases. aLYNX system includes: registry with significant factors, decisions and results; machine learning process based on this registry data; the use of the machine learning results as the adviser. We show a possibility to build a computer adviser with a neural network model for making a choice between coronary aortic bypass surgery (CABG) and percutaneous coronary intervention (PCI) in order to achieve a higher 5-year survival rate in patients with angina based on the experience of 5107 patients. The neural network was trained by 4679 patients who achieved 5-year survival. Among them, 2390 patients underwent PCI and 2289 CABG. After training, the correlation coefficient ( r ) of the network was 0.74 for training, 0.67 for validation, 0.71 for test and 0.73 for total. Simulation of the neural network function has been performed after training in the two groups of patients with known 5-year outcome. The disagreement rate was significantly higher in the dead patient group than that in the survivor group between neural network model and heart team [16.8% (787/4679) vs. 20.3% (87/428), P  = 0.065)]. The study shows the possibility to build a computer adviser with a neural network model for making a choice between CABG and PCI in order to achieve a higher 5-year survival rate in patients with angina.

  15. Impact of Artificial Intelligence on Economic Theory

    OpenAIRE

    Tshilidzi Marwala

    2015-01-01

    Artificial intelligence has impacted many aspects of human life. This paper studies the impact of artificial intelligence on economic theory. In particular we study the impact of artificial intelligence on the theory of bounded rationality, efficient market hypothesis and prospect theory.

  16. Thinking, Creativity, and Artificial Intelligence.

    Science.gov (United States)

    DeSiano, Michael; DeSiano, Salvatore

    This document provides an introduction to the relationship between the current knowledge of focused and creative thinking and artificial intelligence. A model for stages of focused and creative thinking gives: problem encounter/setting, preparation, concentration/incubation, clarification/generation and evaluation/judgment. While a computer can…

  17. Artificial Intelligence: Applications in Education.

    Science.gov (United States)

    Thorkildsen, Ron J.; And Others

    1986-01-01

    Artificial intelligence techniques are used in computer programs to search out rapidly and retrieve information from very large databases. Programing advances have also led to the development of systems that provide expert consultation (expert systems). These systems, as applied to education, are the primary emphasis of this article. (LMO)

  18. Artificial Intelligence Assists Ultrasonic Inspection

    Science.gov (United States)

    Schaefer, Lloyd A.; Willenberg, James D.

    1992-01-01

    Subtle indications of flaws extracted from ultrasonic waveforms. Ultrasonic-inspection system uses artificial intelligence to help in identification of hidden flaws in electron-beam-welded castings. System involves application of flaw-classification logic to analysis of ultrasonic waveforms.

  19. Application of artificial intelligence in process control

    CERN Document Server

    Krijgsman, A

    1993-01-01

    This book is the result of a united effort of six European universities to create an overall course on the appplication of artificial intelligence (AI) in process control. The book includes an introduction to key areas including; knowledge representation, expert, logic, fuzzy logic, neural network, and object oriented-based approaches in AI. Part two covers the application to control engineering, part three: Real-Time Issues, part four: CAD Systems and Expert Systems, part five: Intelligent Control and part six: Supervisory Control, Monitoring and Optimization.

  20. Parallel processing for artificial intelligence 1

    CERN Document Server

    Kanal, LN; Kumar, V; Suttner, CB

    1994-01-01

    Parallel processing for AI problems is of great current interest because of its potential for alleviating the computational demands of AI procedures. The articles in this book consider parallel processing for problems in several areas of artificial intelligence: image processing, knowledge representation in semantic networks, production rules, mechanization of logic, constraint satisfaction, parsing of natural language, data filtering and data mining. The publication is divided into six sections. The first addresses parallel computing for processing and understanding images. The second discus

  1. The National Artificial Intelligence Research And Development Strategic Plan

    Data.gov (United States)

    Networking and Information Technology Research and Development, Executive Office of the President — Executive Summary: Artificial intelligence (AI) is a transformative technology that holds promise for tremendous societal and economic benefit. AI has the potential...

  2. Artificial intelligence techniques in power systems

    Energy Technology Data Exchange (ETDEWEB)

    Laughton, M.A.

    1997-12-31

    Since the early to mid 1980s much of the effort in power systems analysis has turned away from the methodology of formal mathematical modelling which came from the fields of operations research, control theory and numerical analysis to the less rigorous techniques of artificial intelligence (AI). Today the main AI techniques found in power systems applications are those utilising the logic and knowledge representations of expert systems, fuzzy systems, artificial neural networks (ANN) and, more recently, evolutionary computing. These techniques will be outlined in this chapter and the power system applications indicated. (Author)

  3. Uncertainty in artificial intelligence

    CERN Document Server

    Levitt, TS; Lemmer, JF; Shachter, RD

    1990-01-01

    Clearly illustrated in this volume is the current relationship between Uncertainty and AI.It has been said that research in AI revolves around five basic questions asked relative to some particular domain: What knowledge is required? How can this knowledge be acquired? How can it be represented in a system? How should this knowledge be manipulated in order to provide intelligent behavior? How can the behavior be explained? In this volume, all of these questions are addressed. From the perspective of the relationship of uncertainty to the basic questions of AI, the book divides naturally i

  4. Artificial intelligence for analyzing orthopedic trauma radiographs.

    Science.gov (United States)

    Olczak, Jakub; Fahlberg, Niklas; Maki, Atsuto; Razavian, Ali Sharif; Jilert, Anthony; Stark, André; Sköldenberg, Olof; Gordon, Max

    2017-12-01

    Background and purpose - Recent advances in artificial intelligence (deep learning) have shown remarkable performance in classifying non-medical images, and the technology is believed to be the next technological revolution. So far it has never been applied in an orthopedic setting, and in this study we sought to determine the feasibility of using deep learning for skeletal radiographs. Methods - We extracted 256,000 wrist, hand, and ankle radiographs from Danderyd's Hospital and identified 4 classes: fracture, laterality, body part, and exam view. We then selected 5 openly available deep learning networks that were adapted for these images. The most accurate network was benchmarked against a gold standard for fractures. We furthermore compared the network's performance with 2 senior orthopedic surgeons who reviewed images at the same resolution as the network. Results - All networks exhibited an accuracy of at least 90% when identifying laterality, body part, and exam view. The final accuracy for fractures was estimated at 83% for the best performing network. The network performed similarly to senior orthopedic surgeons when presented with images at the same resolution as the network. The 2 reviewer Cohen's kappa under these conditions was 0.76. Interpretation - This study supports the use for orthopedic radiographs of artificial intelligence, which can perform at a human level. While current implementation lacks important features that surgeons require, e.g. risk of dislocation, classifications, measurements, and combining multiple exam views, these problems have technical solutions that are waiting to be implemented for orthopedics.

  5. Intelligence Quotient and Intelligence Grade of Artificial Intelligence

    OpenAIRE

    LIU, Feng; Shi, Yong; Liu, Ying

    2017-01-01

    Although artificial intelligence is currently one of the most interesting areas in scientific research, the potential threats posed by emerging AI systems remain a source of persistent controversy. To address the issue of AI threat, this study proposes a standard intelligence model that unifies AI and human characteristics in terms of four aspects of knowledge, i.e., input, output, mastery, and creation. Using this model, we observe three challenges, namely, expanding of the von Neumann archi...

  6. 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.

  7. Artificial intelligence and computer vision

    CERN Document Server

    Li, Yujie

    2017-01-01

    This edited book presents essential findings in the research fields of artificial intelligence and computer vision, with a primary focus on new research ideas and results for mathematical problems involved in computer vision systems. The book provides an international forum for researchers to summarize the most recent developments and ideas in the field, with a special emphasis on the technical and observational results obtained in the past few years.

  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. Ascribing Consciousness to Artificial Intelligence

    OpenAIRE

    Shanahan, Murray

    2015-01-01

    This paper critically assesses the anti-functionalist stance on consciousness adopted by certain advocates of integrated information theory (IIT), a corollary of which is that human-level artificial intelligence implemented on conventional computing hardware is necessarily not conscious. The critique draws on variations of a well-known gradual neuronal replacement thought experiment, as well as bringing out tensions in IIT's treatment of self-knowledge. The aim, though, is neither to reject I...

  10. Automated Scheduling Via Artificial Intelligence

    Science.gov (United States)

    Biefeld, Eric W.; Cooper, Lynne P.

    1991-01-01

    Artificial-intelligence software that automates scheduling developed in Operations Mission Planner (OMP) research project. Software used in both generation of new schedules and modification of existing schedules in view of changes in tasks and/or available resources. Approach based on iterative refinement. Although project focused upon scheduling of operations of scientific instruments and other equipment aboard spacecraft, also applicable to such terrestrial problems as scheduling production in factory.

  11. A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method

    Directory of Open Access Journals (Sweden)

    Ali Salmasnia

    2012-01-01

    Full Text Available An important problem encountered in product or process design is the setting of process variables to meet a required specification of quality characteristics (response variables, called a multiple response optimization (MRO problem. Common optimization approaches often begin with estimating the relationship between the response variable with the process variables. Among these methods, response surface methodology (RSM, due to simplicity, has attracted most attention in recent years. However, in many manufacturing cases, on one hand, the relationship between the response variables with respect to the process variables is far too complex to be efficiently estimated; on the other hand, solving such an optimization problem with accurate techniques is associated with problem. Alternative approach presented in this paper is to use artificial neural network to estimate response functions and meet heuristic algorithms in process optimization. In addition, the proposed approach uses the Taguchi robust parameter design to overcome the common limitation of the existing multiple response approaches, which typically ignore the dispersion effect of the responses. The paper presents a case study to illustrate the effectiveness of the proposed intelligent framework for tackling multiple response optimization problems.

  12. Artificial Intelligence--Applications in Education.

    Science.gov (United States)

    Poirot, James L.; Norris, Cathleen A.

    1987-01-01

    This first in a projected series of five articles discusses artificial intelligence and its impact on education. Highlights include the history of artificial intelligence and the impact of microcomputers; learning processes; human factors and interfaces; computer assisted instruction and intelligent tutoring systems; logic programing; and expert…

  13. Artificial Intelligence, Knowledge Extraction and the Study of Human Intelligence.

    Science.gov (United States)

    d'Ydewalle, Gery; Delhaye, Patrick

    1988-01-01

    Describes artificial intelligence (AI) as the study of intelligence with the ideas and methods of computation. States that the goal is to make computers more intelligent and thereby uncover the principles that make intelligent behavior possible. Discusses knowledge representations, production (if-then) systems, and expert systems as forms of AI.…

  14. Artificial Intelligence A New Synthesis

    CERN Document Server

    Nilsson, Nils J

    1998-01-01

    Intelligent agents are employed as the central characters in this new introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. The book provides a refreshing and motivating new synthesis of the field by one of AI's master expositors and leading re

  15. Artificial intelligence analysis of paraspinal power spectra.

    Science.gov (United States)

    Oliver, C W; Atsma, W J

    1996-10-01

    OBJECTIVE: As an aid to discrimination of sufferers with back pain an artificial intelligence neural network was constructed to differentiate paraspinal power spectra. DESIGN: Clinical investigation using surface electromyography. METHOD: The surface electromyogram power spectra from 60 subjects, 33 non-back-pain sufferers and 27 chronic back pain sufferers were used to construct a back propagation neural network that was then tested. Subjects were placed on a test frame in 30 degrees of lumbar forward flexion. An isometric load of two-thirds maximum voluntary contraction was held constant for 30 s whilst surface electromyograms were recorded at the level of the L(4-5). Paraspinal power spectra were calculated and loaded into the input layer of a three-layer back propagation network. The neural network classified the spectra into normal or back pain type. RESULTS: The back propagation neural was shown to have satisfactory convergence with a specificity of 79% and a sensitivity of 80%. CONCLUSIONS: Artificial intelligence neural networks appear to be a useful method of differentiating paraspinal power spectra in back-pain sufferers.

  16. Readings in artificial intelligence and software engineering

    CERN Document Server

    Rich, Charles

    1986-01-01

    Readings in Artificial Intelligence and Software Engineering covers the main techniques and application of artificial intelligence and software engineering. The ultimate goal of artificial intelligence applied to software engineering is automatic programming. Automatic programming would allow a user to simply say what is wanted and have a program produced completely automatically. This book is organized into 11 parts encompassing 34 chapters that specifically tackle the topics of deductive synthesis, program transformations, program verification, and programming tutors. The opening parts p

  17. Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network.

    Science.gov (United States)

    Martínez-Romero, Marcos; Vázquez-Naya, José M; Rabuñal, Juan R; Pita-Fernández, Salvador; Macenlle, Ramiro; Castro-Alvariño, Javier; López-Roses, Leopoldo; Ulla, José L; Martínez-Calvo, Antonio V; Vázquez, Santiago; Pereira, Javier; Porto-Pazos, Ana B; Dorado, Julián; Pazos, Alejandro; Munteanu, Cristian R

    2010-05-01

    Colorectal cancer is one of the most frequent types of cancer in the world and generates important social impact. The understanding of the specific metabolism of this disease and the transformations of the specific drugs will allow finding effective prevention, diagnosis and treatment of the colorectal cancer. All the terms that describe the drug metabolism contribute to the construction of ontology in order to help scientists to link the correlated information and to find the most useful data about this topic. The molecular components involved in this metabolism are included in complex network such as metabolic pathways in order to describe all the molecular interactions in the colorectal cancer. The graphical method of processing biological information such as graphs and complex networks leads to the numerical characterization of the colorectal cancer drug metabolic network by using invariant values named topological indices. Thus, this method can help scientists to study the most important elements in the metabolic pathways and the dynamics of the networks during mutations, denaturation or evolution for any type of disease. This review presents the last studies regarding ontology and complex networks of the colorectal cancer drug metabolism and a basic topology characterization of the drug metabolic process sub-ontology from the Gene Ontology.

  18. Artificial intelligence and science education

    Science.gov (United States)

    Good, Ron

    Artificial intelligence (AI) is defined and related to intelligent computer-assisted instruction (ICAI) and science education. Modeling the student, the teacher, and the natural environment are discussed as important parts of ICAI and the concept of microworlds as a powerful tool for science education is presented. Optimistic predictions about ICAI are tempered with the complex, persistent problems of: 1) teaching and learning as a soft or fuzzy knowledge base, 2) natural language processing, and 3) machine learning. The importance of accurate diagnosis of a student's learning state, including misconceptions and naive theories about nature, is stressed and related to the importance of accurate diagnosis by a physician. Based on the cognitive science/AI paradigm, a revised model of the well-known Karplus/Renner learning cycle is proposed.

  19. Innovative applications of artificial intelligence

    Science.gov (United States)

    Schorr, Herbert; Rappaport, Alain

    Papers concerning applications of artificial intelligence are presented, covering applications in aerospace technology, banking and finance, biotechnology, emergency services, law, media planning, music, the military, operations management, personnel management, retail packaging, and manufacturing assembly and design. Specific topics include Space Shuttle telemetry monitoring, an intelligent training system for Space Shuttle flight controllers, an expert system for the diagnostics of manufacturing equipment, a logistics management system, a cooling systems design assistant, and a knowledge-based integrated circuit design critic. Additional topics include a hydraulic circuit design assistant, the use of a connector assembly specification expert system to harness detailed assembly process knowledge, a mixed initiative approach to airlift planning, naval battle management decision aids, an inventory simulation tool, a peptide synthesis expert system, and a system for planning the discharging and loading of container ships.

  20. An application of artificial intelligence for rainfall–runoff modeling

    Indian Academy of Sciences (India)

    This study proposes an application of two techniques of artificial intelligence (AI) for rainfall–runoff modeling: the artificial neural networks (ANN) and the ... Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square ...

  1. DEALING MEDICAL DATA WITH FUNDAMENTALS OF NEW ARTIFICIAL INTELLIGENCE

    OpenAIRE

    ALI SAKR,; DIANA MOSA

    2010-01-01

    This work applies rough sets and artificial intelligent to analyze and reduce medical data without affecting the information to construct an expert system. The ROSETTA software is applied for analyzing some medical data and deducing rules. This paper discusses fundamentals of Rough Set Theory (RST); uses this theorem to extract decision rules for medical data and drive results by Artificial Neural Network (ANN).

  2. Artificial intelligence a beginner's guide

    CERN Document Server

    Whitby, Blay

    2012-01-01

    Tomorrow begins right here as we embark on an enthralling and jargon-free journey into the world of computers and the inner recesses of the human mind. Readers encounter everything from the nanotechnology used to make insect-like robots, to computers that perform surgery, in addition to discovering the biggest controversies to dog the field of AI. Blay Whitby is a Lecturer on Cognitive Science and Artificial Intelligence at the University of Sussex UK. He is the author of two books and numerous papers.

  3. Improving designer productivity. [artificial intelligence

    Science.gov (United States)

    Hill, Gary C.

    1992-01-01

    Designer and design team productivity improves with skill, experience, and the tools available. The design process involves numerous trials and errors, analyses, refinements, and addition of details. Computerized tools have greatly speeded the analysis, and now new theories and methods, emerging under the label Artificial Intelligence (AI), are being used to automate skill and experience. These tools improve designer productivity by capturing experience, emulating recognized skillful designers, and making the essence of complex programs easier to grasp. This paper outlines the aircraft design process in today's technology and business climate, presenting some of the challenges ahead and some of the promising AI methods for meeting these challenges.

  4. Advanced Artificial Intelligence Technology Testbed

    Science.gov (United States)

    Anken, Craig S.

    1993-01-01

    The Advanced Artificial Intelligence Technology Testbed (AAITT) is a laboratory testbed for the design, analysis, integration, evaluation, and exercising of large-scale, complex, software systems, composed of both knowledge-based and conventional components. The AAITT assists its users in the following ways: configuring various problem-solving application suites; observing and measuring the behavior of these applications and the interactions between their constituent modules; gathering and analyzing statistics about the occurrence of key events; and flexibly and quickly altering the interaction of modules within the applications for further study.

  5. Epistasis analysis using artificial intelligence.

    Science.gov (United States)

    Moore, Jason H; Hill, Doug P

    2015-01-01

    Here we introduce artificial intelligence (AI) methodology for detecting and characterizing epistasis in genetic association studies. The ultimate goal of our AI strategy is to analyze genome-wide genetics data as a human would using sources of expert knowledge as a guide. The methodology presented here is based on computational evolution, which is a type of genetic programming. The ability to generate interesting solutions while at the same time learning how to solve the problem at hand distinguishes computational evolution from other genetic programming approaches. We provide a general overview of this approach and then present a few examples of its application to real data.

  6. Forming of the regional core transport network taking into account the allocation of alternative energy sources based on artificial intelligence methods

    Directory of Open Access Journals (Sweden)

    Marina ZHURAVSKAYA

    2014-12-01

    Full Text Available In the modern world the alternative energy sources, which considerably depend on a region, play more and more significant role. However, the transition of regions to new energy sources lead to the change of transport and logistic network configuration. The formation of optimal core transport network today is a guarantee of the successful economic development of a region tomorrow. The present article studies the issue of advanced core transport network development in a region based on the experience of European and Asian countries and the opportunity to adapt the best foreign experience to Russian conditions. On the basis of artificial intelligence methods for forest industry complex of Sverdlovskaya Oblast the algorithm of problem solution of an optimal logistic infrastructure allocation is offered and some results of a regional transport network are presented. These methods allowed to solve the set task in the conditions of information uncertainty. There are suggestions on the improvement of transport and logistic network in the territory of Sverdlovskaya Oblast. Traditionally the logistics of mineral fuel plays main role in regions development. Actually it is required to develop logistic strategic plans to be able to provide different possibilities of power-supply, flexible enough to change with the population density, transport infrastructure and demographics of different regions. The problem of logistic centers allocation was studied by many authors. The approach, offered by the authors of this paper is to solve the set of tasks by applying artificial intelligence methods, such as fuzzy set theory and genetic algorithms.

  7. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks.

    Science.gov (United States)

    Kim, D H; MacKinnon, T

    2017-12-18

    To identify the extent to which transfer learning from deep convolutional neural networks (CNNs), pre-trained on non-medical images, can be used for automated fracture detection on plain radiographs. The top layer of the Inception v3 network was re-trained using lateral wrist radiographs to produce a model for the classification of new studies as either "fracture" or "no fracture". The model was trained on a total of 11,112 images, after an eightfold data augmentation technique, from an initial set of 1,389 radiographs (695 "fracture" and 694 "no fracture"). The training data set was split 80:10:10 into training, validation, and test groups, respectively. An additional 100 wrist radiographs, comprising 50 "fracture" and 50 "no fracture" images, were used for final testing and statistical analysis. The area under the receiver operator characteristic curve (AUC) for this test was 0.954. Setting the diagnostic cut-off at a threshold designed to maximise both sensitivity and specificity resulted in values of 0.9 and 0.88, respectively. The AUC scores for this test were comparable to state-of-the-art providing proof of concept for transfer learning from CNNs in fracture detection on plain radiographs. This was achieved using only a moderate sample size. This technique is largely transferable, and therefore, has many potential applications in medical imaging, which may lead to significant improvements in workflow productivity and in clinical risk reduction. Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

  8. Marine litter prediction by artificial intelligence.

    Science.gov (United States)

    Balas, Can Elmar; Ergin, Aysen; Williams, Allan T; Koc, Levent

    2004-03-01

    Artificial intelligence techniques of neural network and fuzzy systems were applied as alternative methods to determine beach litter grading, based on litter surveys of the Antalya coastline (the Turkish Riviera). Litter measurements were categorized and assessed by artificial intelligence techniques, which lead to a new litter categorization system. The constructed neural network satisfactorily predicted the grading of the Antalya beaches and litter categories based on the number of litter items in the general litter category. It has been concluded that, neural networks could be used for high-speed predictions of litter items and beach grading, when the characteristics of the main litter category was determined by field studies. This can save on field effort when fast and reliable estimations of litter categories are required for management or research studies of beaches--especially those concerned with health and safety, and it has economic implications. The main advantages in using fuzzy systems are that they consider linguistic adjectival definitions, e.g. many/few, etc. As a result, additional information inherent in linguistic comments/refinements and judgments made during field studies can be incorporated in grading systems.

  9. APPLICATION OF ARTIFICIAL INTELLIGENCE IN MECHATRONIC SYSTEMS

    Directory of Open Access Journals (Sweden)

    A.P. Sobchak

    2013-06-01

    Full Text Available This article discusses the main issues of artificial intelligence and its implementation in daily life in the form of control systems for mechatronic systems. Due to intensive application of the latest scientific and technological achievements and a new element base, there spring up new technologies of artificial intelligence creation principles and laws realization, examples of which given in the article

  10. Artificial Intelligence in Education: An Exploration.

    Science.gov (United States)

    Cumming, Geoff

    1998-01-01

    Gives a brief outline of the development of Artificial Intelligence in Education (AIED) which includes psychology, education, cognitive science, computer science, and artificial intelligence. Highlights include learning environments; learner modeling; a situated approach to learning; and current examples of AIED research. (LRW)

  11. A DISTRIBUTED SMART HOME ARTIFICIAL INTELLIGENCE SYSTEM

    DEFF Research Database (Denmark)

    Lynggaard, Per

    2013-01-01

    A majority of the research performed today explore artificial intelligence in smart homes by using a centralized approach where a smart home server performs the necessary calculations. This approach has some disadvantages that can be overcome by shifting focus to a distributed approach where...... the artificial intelligence system is implemented as distributed as agents running parts of the artificial intelligence system. This paper presents a distributed smart home architecture that distributes artificial intelligence in smart homes and discusses the pros and cons of such a concept. The presented...... distributed model is a layered model. Each layer offers a different complexity level of the embedded distributed artificial intelligence. At the lowest layer smart objects exists, they are small cheap embedded microcontroller based smart devices that are powered by batteries. The next layer contains a more...

  12. Artificial astrocytes improve neural network performance.

    Science.gov (United States)

    Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-04-19

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  13. Accelerating artificial intelligence with reconfigurable computing

    Science.gov (United States)

    Cieszewski, Radoslaw

    Reconfigurable computing is emerging as an important area of research in computer architectures and software systems. Many algorithms can be greatly accelerated by placing the computationally intense portions of an algorithm into reconfigurable hardware. Reconfigurable computing combines many benefits of both software and ASIC implementations. Like software, the mapped circuit is flexible, and can be changed over the lifetime of the system. Similar to an ASIC, reconfigurable systems provide a method to map circuits into hardware. Reconfigurable systems therefore have the potential to achieve far greater performance than software as a result of bypassing the fetch-decode-execute operations of traditional processors, and possibly exploiting a greater level of parallelism. Such a field, where there is many different algorithms which can be accelerated, is an artificial intelligence. This paper presents example hardware implementations of Artificial Neural Networks, Genetic Algorithms and Expert Systems.

  14. An Intelligent Approach to Educational Data: Performance Comparison of the Multilayer Perceptron and the Radial Basis Function Artificial Neural Networks

    Science.gov (United States)

    Kayri, Murat

    2015-01-01

    The objective of this study is twofold: (1) to investigate the factors that affect the success of university students by employing two artificial neural network methods (i.e., multilayer perceptron [MLP] and radial basis function [RBF]); and (2) to compare the effects of these methods on educational data in terms of predictive ability. The…

  15. Research and applications: Artificial intelligence

    Science.gov (United States)

    Raphael, B.; Fikes, R. E.; Chaitin, L. J.; Hart, P. E.; Duda, R. O.; Nilsson, N. J.

    1971-01-01

    A program of research in the field of artificial intelligence is presented. The research areas discussed include automatic theorem proving, representations of real-world environments, problem-solving methods, the design of a programming system for problem-solving research, techniques for general scene analysis based upon television data, and the problems of assembling an integrated robot system. Major accomplishments include the development of a new problem-solving system that uses both formal logical inference and informal heuristic methods, the development of a method of automatic learning by generalization, and the design of the overall structure of a new complete robot system. Eight appendices to the report contain extensive technical details of the work described.

  16. Development and evaluation of a novel lossless image compression method (AIC: artificial intelligence compression method) using neural networks as artificial intelligence.

    Science.gov (United States)

    Fukatsu, Hiroshi; Naganawa, Shinji; Yumura, Shinnichiro

    2008-04-01

    This study was aimed to validate the performance of a novel image compression method using a neural network to achieve a lossless compression. The encoding consists of the following blocks: a prediction block; a residual data calculation block; a transformation and quantization block; an organization and modification block; and an entropy encoding block. The predicted image is divided into four macro-blocks using the original image for teaching; and then redivided into sixteen sub-blocks. The predicted image is compared to the original image to create the residual image. The spatial and frequency data of the residual image are compared and transformed. Chest radiography, computed tomography (CT), magnetic resonance imaging, positron emission tomography, radioisotope mammography, ultrasonography, and digital subtraction angiography images were compressed using the AIC lossless compression method; and the compression rates were calculated. The compression rates were around 15:1 for chest radiography and mammography, 12:1 for CT, and around 6:1 for other images. This method thus enables greater lossless compression than the conventional methods. This novel method should improve the efficiency of handling of the increasing volume of medical imaging data.

  17. Artificial Intelligence and Information Management

    Science.gov (United States)

    Fukumura, Teruo

    After reviewing the recent popularization of the information transmission and processing technologies, which are supported by the progress of electronics, the authors describe that by the introduction of the opto-electronics into the information technology, the possibility of applying the artificial intelligence (AI) technique to the mechanization of the information management has emerged. It is pointed out that althuogh AI deals with problems in the mental world, its basic methodology relies upon the verification by evidence, so the experiment on computers become indispensable for the study of AI. The authors also describe that as computers operate by the program, the basic intelligence which is concerned in AI is that expressed by languages. This results in the fact that the main tool of AI is the logical proof and it involves an intrinsic limitation. To answer a question “Why do you employ AI in your problem solving”, one must have ill-structured problems and intend to conduct deep studies on the thinking and the inference, and the memory and the knowledge-representation. Finally the authors discuss the application of AI technique to the information management. The possibility of the expert-system, processing of the query, and the necessity of document knowledge-base are stated.

  18. Economic reasoning and artificial intelligence.

    Science.gov (United States)

    Parkes, David C; Wellman, Michael P

    2015-07-17

    The field of artificial intelligence (AI) strives to build rational agents capable of perceiving the world around them and taking actions to advance specified goals. Put another way, AI researchers aim to construct a synthetic homo economicus, the mythical perfectly rational agent of neoclassical economics. We review progress toward creating this new species of machine, machina economicus, and discuss some challenges in designing AIs that can reason effectively in economic contexts. Supposing that AI succeeds in this quest, or at least comes close enough that it is useful to think about AIs in rationalistic terms, we ask how to design the rules of interaction in multi-agent systems that come to represent an economy of AIs. Theories of normative design from economics may prove more relevant for artificial agents than human agents, with AIs that better respect idealized assumptions of rationality than people, interacting through novel rules and incentive systems quite distinct from those tailored for people. Copyright © 2015, American Association for the Advancement of Science.

  19. Projective simulation for artificial intelligence

    Science.gov (United States)

    Briegel, Hans J.; de Las Cuevas, Gemma

    2012-05-01

    We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation.

  20. Artificial intelligence in power system optimization

    CERN Document Server

    Ongsakul, Weerakorn

    2013-01-01

    With the considerable increase of AI applications, AI is being increasingly used to solve optimization problems in engineering. In the past two decades, the applications of artificial intelligence in power systems have attracted much research. This book covers the current level of applications of artificial intelligence to the optimization problems in power systems. This book serves as a textbook for graduate students in electric power system management and is also be useful for those who are interested in using artificial intelligence in power system optimization.

  1. Economic modeling using artificial intelligence methods

    CERN Document Server

    Marwala, Tshilidzi

    2013-01-01

    This book examines the application of artificial intelligence methods to model economic data. It addresses causality and proposes new frameworks for dealing with this issue. It also applies evolutionary computing to model evolving economic environments.

  2. Artificial Intelligence and Vocational Education: An Impending Confluence.

    Science.gov (United States)

    Roth, Gene L.; McEwing, Richard A.

    1986-01-01

    Reports on the relatively new field of artificial intelligence and its relationship to vocational education. Compares human intelligence with artificial intelligence. Discusses expert systems, natural language technology, and current trends. Lists potential applications for vocational education. (CH)

  3. The 2002 Starting Artificial Intelligence Researchers Symposium

    OpenAIRE

    Vidal, Thierry

    2003-01-01

    During the 2002 European Conference on Artificial Intelligence (ECAI-02) was introduced the Starting Artificial Intelligence Researchers Symposium STAIRS), the first-ever international symposium specifically aimed at Ph.D. students in AI. The outcome was a thorough, high-quality, and successful event, with all the features one usually finds in the best international conferences: large international committees, comprehensive coverage, published proceedings, renowned speakers and panelists, sub...

  4. Artificial intelligence approaches to astronomical observation scheduling

    Science.gov (United States)

    Johnston, Mark D.; Miller, Glenn

    1988-01-01

    Automated scheduling will play an increasing role in future ground- and space-based observatory operations. Due to the complexity of the problem, artificial intelligence technology currently offers the greatest potential for the development of scheduling tools with sufficient power and flexibility to handle realistic scheduling situations. Summarized here are the main features of the observatory scheduling problem, how artificial intelligence (AI) techniques can be applied, and recent progress in AI scheduling for Hubble Space Telescope.

  5. Artificial intelligence techniques for rational decision making

    CERN Document Server

    Marwala, Tshilidzi

    2014-01-01

    Develops insights into solving complex problems in engineering, biomedical sciences, social science and economics based on artificial intelligence. Some of the problems studied are in interstate conflict, credit scoring, breast cancer diagnosis, condition monitoring, wine testing, image processing and optical character recognition. The author discusses and applies the concept of flexibly-bounded rationality which prescribes that the bounds in Nobel Laureate Herbert Simon's bounded rationality theory are flexible due to advanced signal processing techniques, Moore's Law and artificial intellig

  6. Exploring Artificial Intelligence Utilizing BioArt

    OpenAIRE

    Simou, Panagiota; Tiligadis, Konstantinos; Alexiou, Athanasios

    2013-01-01

    Part 15: First Workshop on Ethics and Philosophy in Artificial Intelligence (EPAI 2013); International audience; While artificial intelligence combined with Bioinformatics and Nanotechnology offers a variety of improvements and a technological and healthcare revolution, Bioartists attempt to replace the traditional artistic medium with biological materials, bio-imaging techniques, bioreactors and several times to treat their own body as an alive canvas. BioArt seems to play the role of a new ...

  7. Artificial Intelligence and Spacecraft Power Systems

    Science.gov (United States)

    Dugel-Whitehead, Norma R.

    1997-01-01

    This talk will present the work which has been done at NASA Marshall Space Flight Center involving the use of Artificial Intelligence to control the power system in a spacecraft. The presentation will include a brief history of power system automation, and some basic definitions of the types of artificial intelligence which have been investigated at MSFC for power system automation. A video tape of one of our autonomous power systems using co-operating expert systems, and advanced hardware will be presented.

  8. Artificial Intelligence for Controlling Robotic Aircraft

    Science.gov (United States)

    Krishnakumar, Kalmanje

    2005-01-01

    A document consisting mostly of lecture slides presents overviews of artificial-intelligence-based control methods now under development for application to robotic aircraft [called Unmanned Aerial Vehicles (UAVs) in the paper] and spacecraft and to the next generation of flight controllers for piloted aircraft. Following brief introductory remarks, the paper presents background information on intelligent control, including basic characteristics defining intelligent systems and intelligent control and the concept of levels of intelligent control. Next, the paper addresses several concepts in intelligent flight control. The document ends with some concluding remarks, including statements to the effect that (1) intelligent control architectures can guarantee stability of inner control loops and (2) for UAVs, intelligent control provides a robust way to accommodate an outer-loop control architecture for planning and/or related purposes.

  9. Improving Tools in Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Angel Garrido

    2010-01-01

    Full Text Available The historical origin of the Artificial Intelligence (AI is usually established in the Dartmouth Conference, of 1956. But we can find many more arcane origins [1]. Also, we can consider, in more recent times, very great thinkers, as Janos Neumann (then, John von Neumann, arrived in USA, Norbert Wiener, Alan Mathison Turing, or Lofti Zadeh, for instance [12, 14]. Frequently AI requires Logic. But its Classical version shows too many insufficiencies. So, it was necessary to introduce more sophisticated tools, as Fuzzy Logic, Modal Logic, Non-Monotonic Logic and so on [1, 2]. Among the things that AI needs to represent are categories, objects, properties, relations between objects, situations, states, time, events, causes and effects, knowledge about knowledge, and so on. The problems in AI can be classified in two general types [3, 5], search problems and representation problems. On this last "peak", there exist different ways to reach their summit. So, we have [4] Logics, Rules, Frames, Associative Nets, Scripts, and so on, many times connected among them. We attempt, in this paper, a panoramic vision of the scope of application of such representation methods in AI. The two more disputable questions of both modern philosophy of mind and AI will be perhaps the Turing Test and the Chinese Room Argument. To elucidate these very difficult questions, see our final note.

  10. Artificial intelligence what everyone needs to know

    CERN Document Server

    Kaplan, Jerry

    2016-01-01

    Over the coming decades, Artificial Intelligence will profoundly impact the way we work and live. Whose interests should such systems serve? What limits should we place on their use? This book is a succinct introduction to the complex social, ethical, legal, and economic issues raised by the emergence of intelligent machines.

  11. SOME PARADIGMS OF ARTIFICIAL INTELLIGENCE IN FINANCIAL COMPUTER SYSTEMS

    Directory of Open Access Journals (Sweden)

    Jerzy Balicki

    2015-12-01

    Full Text Available The article discusses some paradigms of artificial intelligence in the context of their applications in computer financial systems. The proposed approach has a significant po-tential to increase the competitiveness of enterprises, including financial institutions. However, it requires the effective use of supercomputers, grids and cloud computing. A reference is made to the computing environment for Bitcoin. In addition, we characterized genetic programming and artificial neural networks to prepare investment strategies on the stock exchange market.

  12. Artificial intelligence in the diagnosis of low back pain.

    Science.gov (United States)

    Mann, N H; Brown, M D

    1991-04-01

    Computerized methods are used to recognize the characteristics of patient pain drawings. Artificial neural network (ANN) models are compared with expert predictions and traditional statistical classification methods when placing the pain drawings of low back pain patients into one of five clinically significant categories. A discussion is undertaken outlining the differences in these classifiers and the potential benefits of the ANN model as an artificial intelligence technique.

  13. Artificial Intelligence and Public Healthcare Service Innovation

    DEFF Research Database (Denmark)

    Sun, Tara Qian; Medaglia, Rony

    Public healthcare ecosystems are complex networks of diverse actors that are subject to pressures to innovate, also a result of technological advancements. Artificial Intelligence (AI), in particular, has the potential to transform the way hospitals, doctors, patients, government agencies......, and IT providers cocreate value in a healthcare ecosystem. Adopting a ServiceDominant (S-D) logic perspective, we present part of an ongoing doctoral project that focuses on the role of shared worldview of technology in service cocreation. Using the lens of Technology Frames of Reference (TFR) theory, we...... investigate how different actors frame the cognitive assistant IBM Watson used in a healthcare service ecosystem in China for decision-making in the treatment of cancer patients. Drawing on preliminary findings, we present a tentative research model that links technology framing to value cocreation...

  14. Artificial Intelligence in Surgery: Promises and Perils.

    Science.gov (United States)

    Hashimoto, Daniel A; Rosman, Guy; Rus, Daniela; Meireles, Ozanan R

    2018-01-31

    The aim of this review was to summarize major topics in artificial intelligence (AI), including their applications and limitations in surgery. This paper reviews the key capabilities of AI to help surgeons understand and critically evaluate new AI applications and to contribute to new developments. AI is composed of various subfields that each provide potential solutions to clinical problems. Each of the core subfields of AI reviewed in this piece has also been used in other industries such as the autonomous car, social networks, and deep learning computers. A review of AI papers across computer science, statistics, and medical sources was conducted to identify key concepts and techniques within AI that are driving innovation across industries, including surgery. Limitations and challenges of working with AI were also reviewed. Four main subfields of AI were defined: (1) machine learning, (2) artificial neural networks, (3) natural language processing, and (4) computer vision. Their current and future applications to surgical practice were introduced, including big data analytics and clinical decision support systems. The implications of AI for surgeons and the role of surgeons in advancing the technology to optimize clinical effectiveness were discussed. Surgeons are well positioned to help integrate AI into modern practice. Surgeons should partner with data scientists to capture data across phases of care and to provide clinical context, for AI has the potential to revolutionize the way surgery is taught and practiced with the promise of a future optimized for the highest quality patient care.

  15. Artificial intelligence technologies for power system operations. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Talukdar, S.N.; Cardozo, E.

    1986-01-01

    Researchers in this study examined the potential of artificial intelligence (AI) technologies for improving problem-solving strategies in 16 power system operations. To demonstrate the use of AI in the area they considered most promising, contingency selection-security assessment, they also developed two programs - one to simulate network protection schemes, the other to diagnose faults.

  16. Artificial intelligence systems in new media art and design

    OpenAIRE

    DiPaola, Steve

    2006-01-01

    Science and art are merging and with artificial intelligence system like neural networks, genetic programs and rule based systems, artists or designers are using smart systems that allow them to better immerse themselves in the creative process. Artist/Scientist Steve DiPaola uses AI techniques in his self created programs and artwork.

  17. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  18. Artificial Intelligence Research at the Artificial Intelligence Laboratory, Massachusetts Institute of Technology

    OpenAIRE

    Winston, Patrick H.

    1983-01-01

    The primary goal of the Artificial Intelligence Laboratory is to understand how computers can be made to exhibit intelligence. Two corollary goals are to make computers more useful and to understand certain aspects of human intelligence. Current research includes work on computer robotics and vision, expert systems, learning and commonsense reasoning, natural language understanding, and computer architecture.

  19. Amplify scientific discovery with artificial intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Gil, Yolanda; Greaves, Mark T.; Hendler, James; Hirsch, Hyam

    2014-10-10

    Computing innovations have fundamentally changed many aspects of scientific inquiry. For example, advances in robotics, high-end computing, networking, and databases now underlie much of what we do in science such as gene sequencing, general number crunching, sharing information between scientists, and analyzing large amounts of data. As computing has evolved at a rapid pace, so too has its impact in science, with the most recent computing innovations repeatedly being brought to bear to facilitate new forms of inquiry. Recently, advances in Artificial Intelligence (AI) have deeply penetrated many consumer sectors, including for example Apple’s Siri™ speech recognition system, real-time automated language translation services, and a new generation of self-driving cars and self-navigating drones. However, AI has yet to achieve comparable levels of penetration in scientific inquiry, despite its tremendous potential in aiding computers to help scientists tackle tasks that require scientific reasoning. We contend that advances in AI will transform the practice of science as we are increasingly able to effectively and jointly harness human and machine intelligence in the pursuit of major scientific challenges.

  20. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  1. drinking water treatment using artificial neural network

    African Journals Online (AJOL)

    ogwueleka

    synaptic weights are used to store the knowledge.” The neural network approach is a branch of artificial intelligence. The ANN is based on a model of the human neurological system that consists of basic computing elements (called neurons) interconnected together (Figure 1). The model used for all classification attempts.

  2. How to Improve Artificial Intelligence through Web

    Directory of Open Access Journals (Sweden)

    Adrian LUPASC

    2005-10-01

    Full Text Available Intelligent agents, intelligent software applications and artificial intelligent applications from artificial intelligence service providers maymake their way onto the Web in greater number as adaptive software, dynamic programming languages and Learning Algorithms are introduced intoWeb Services. The evolution of Web architecture may allow intelligent applications to run directly on the Web by introducing XML, RDF and logiclayer. The Intelligent Wireless Web’s significant potential for rapidly completing information transactions may take an important contribution toglobal worker productivity. Artificial intelligence can be defined as the study of the ways in which computers can be made to perform cognitivetasks. Examples of such tasks include understanding natural language statements, recognizing visual patterns or scenes, diagnosing diseases orillnesses, solving mathematical problems, performing financial analyses, learning new procedures for solving problems. The term expert system canbe considered to be a particular type of knowledge-based system. An expert system is a system in which the knowledge is deliberately represented“as it is”. Expert systems are applications that make decisions in real-life situations that would otherwise be performed by a human expert. They areprograms designed to mimic human performance at specialized, constrained problem-solving tasks. They are constructed as a collection of IF-THENproduction rules combined with a reasoning engine that applies those rules, either in a forward or backward direction, to specific problems.

  3. Artificial intelligence: the clinician of the future.

    Science.gov (United States)

    Gallagher, S M

    2001-09-01

    Human beings have long been fascinated with the idea of artificial intelligence. This fascination is fueled by popular films such as Stanley Kubrick's 2001: A Space Odyssey and Stephen Spielberg's recent film, AI. However intriguing artificial intelligence may be, Hubert and Spencer Dreyfus contend that qualities exist that are uniquely human--the qualities thought to be inaccessible to the computer "mind." Patricia Benner further investigated the qualities that guide clinicians in making decisions and assessments that are not entirely evidence-based or grounded in scientific data. Perhaps it is the intuitive nature of the human being that separates us from the machine. The state of artificial intelligence is described herein, along with a discussion of computerized clinical decision-making and the role of the human being in these decisions.

  4. 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...

  5. Solving Complex Logistics Problems with Multi-Artificial Intelligent System

    Directory of Open Access Journals (Sweden)

    Y.K. Tse

    2009-10-01

    Full Text Available The economy, which has become more information intensive, more global and more technologically dependent, is undergoing dramatic changes. The role of logistics is also becoming more and more important. In logistics, the objective of service providers is to fulfill all customers? demands while adapting to the dynamic changes of logistics networks so as to achieve a higher degree of customer satisfaction and therefore a higher return on investment. In order to provide high quality service, knowledge and information sharing among departments becomes a must in this fast changing market environment. In particular, artificial intelligence (AI technologies have achieved significant attention for enhancing the agility of supply chain management, as well as logistics operations. In this research, a multi-artificial intelligence system, named Integrated Intelligent Logistics System (IILS is proposed. The objective of IILS is to provide quality logistics solutions to achieve high levels of service performance in the logistics industry. The new feature of this agile intelligence system is characterized by the incorporation of intelligence modules through the capabilities of the case-based reasoning, multi-agent, fuzzy logic and artificial neural networks, achieving the optimization of the performance of organizations.

  6. Algorithms and architectures of artificial intelligence

    CERN Document Server

    Tyugu, E

    2007-01-01

    This book gives an overview of methods developed in artificial intelligence for search, learning, problem solving and decision-making. It gives an overview of algorithms and architectures of artificial intelligence that have reached the degree of maturity when a method can be presented as an algorithm, or when a well-defined architecture is known, e.g. in neural nets and intelligent agents. It can be used as a handbook for a wide audience of application developers who are interested in using artificial intelligence methods in their software products. Parts of the text are rather independent, so that one can look into the index and go directly to a description of a method presented in the form of an abstract algorithm or an architectural solution. The book can be used also as a textbook for a course in applied artificial intelligence. Exercises on the subject are added at the end of each chapter. Neither programming skills nor specific knowledge in computer science are expected from the reader. However, some p...

  7. 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

  8. Knowledge representation an approach to artificial intelligence

    CERN Document Server

    Bench-Capon, TJM

    1990-01-01

    Although many texts exist offering an introduction to artificial intelligence (AI), this book is unique in that it places an emphasis on knowledge representation (KR) concepts. It includes small-scale implementations in PROLOG to illustrate the major KR paradigms and their developments.****back cover copy:**Knowledge representation is at the heart of the artificial intelligence enterprise: anyone writing a program which seeks to work by encoding and manipulating knowledge needs to pay attention to the scheme whereby he will represent the knowledge, and to be aware of the consequences of the ch

  9. Abstraction in artificial intelligence and complex systems

    CERN Document Server

    Saitta, Lorenza

    2013-01-01

    Abstraction is a fundamental mechanism underlying both human and artificial perception, representation of knowledge, reasoning and learning. This mechanism plays a crucial role in many disciplines, notably Computer Programming, Natural and Artificial Vision, Complex Systems, Artificial Intelligence and Machine Learning, Art, and Cognitive Sciences. This book first provides the reader with an overview of the notions of abstraction proposed in various disciplines by comparing both commonalities and differences.  After discussing the characterizing properties of abstraction, a formal model, the K

  10. Artificial Intelligence, Counseling, and Cognitive Psychology.

    Science.gov (United States)

    Brack, Greg; And Others

    With the exception of a few key writers, counselors largely ignore the benefits that Artificial Intelligence (AI) and Cognitive Psychology (CP) can bring to counseling. It is demonstrated that AI and CP can be integrated into the counseling literature. How AI and CP can offer new perspectives on information processing, cognition, and helping is…

  11. The Nexus between Artificial Intelligence and Economics

    NARCIS (Netherlands)

    van de Gevel, A.J.W.; Noussair, C.N.

    2012-01-01

    This book is organized as follows. Section 2 introduces the notion of the Singularity, a stage in development in which technological progress and economic growth increase at a near-infinite rate. Section 3 describes what artificial intelligence is and how it has been applied. Section 4 considers

  12. Artificial Intelligence Techniques: Applications for Courseware Development.

    Science.gov (United States)

    Dear, Brian L.

    1986-01-01

    Introduces some general concepts and techniques of artificial intelligence (natural language interfaces, expert systems, knowledge bases and knowledge representation, heuristics, user-interface metaphors, and object-based environments) and investigates ways these techniques might be applied to analysis, design, development, implementation, and…

  13. Applications of artificial intelligence and expert systems

    Energy Technology Data Exchange (ETDEWEB)

    1987-01-01

    This book contains over 30 papers. Some of the titles are: operating systems for CD/ROM; the impact of optical storage technology on education; the future of expert systems in the financial services industry; the future of compact disk/DC-1 explosive ordinance disposal rendered safe information system; and will artificial intelligence improve computer based training (CBT) development process.

  14. Artificial Intelligence Applications to Fire Management

    Science.gov (United States)

    Don J. Latham

    1987-01-01

    Artificial intelligence could be used in Forest Service fire management and land-use planning to a larger degree than is now done. Robots, for example, could be programmed to monitor for fire and insect activity, to keep track of wildlife, and to do elementary thinking about the environment. Catching up with the fast-changing technology is imperative.

  15. Artificial Intelligence: Underlying Assumptions and Basic Objectives.

    Science.gov (United States)

    Cercone, Nick; McCalla, Gordon

    1984-01-01

    Presents perspectives on methodological assumptions underlying research efforts in artificial intelligence (AI) and charts activities, motivations, methods, and current status of research in each of the major AI subareas: natural language understanding; computer vision; expert systems; search, problem solving, planning; theorem proving and logic…

  16. Dynamic Restructuring Of Problems In Artificial Intelligence

    Science.gov (United States)

    Schwuttke, Ursula M.

    1992-01-01

    "Dynamic tradeoff evaluation" (DTE) denotes proposed method and procedure for restructuring problem-solving strategies in artificial intelligence to satisfy need for timely responses to changing conditions. Detects situations in which optimal problem-solving strategies cannot be pursued because of real-time constraints, and effects tradeoffs among nonoptimal strategies in such way to minimize adverse effects upon performance of system.

  17. Artificial Intelligence, Computational Thinking, and Mathematics Education

    Science.gov (United States)

    Gadanidis, George

    2017-01-01

    Purpose: The purpose of this paper is to examine the intersection of artificial intelligence (AI), computational thinking (CT), and mathematics education (ME) for young students (K-8). Specifically, it focuses on three key elements that are common to AI, CT and ME: agency, modeling of phenomena and abstracting concepts beyond specific instances.…

  18. Artificial Intelligence: A Revolution Waiting to Happen

    Science.gov (United States)

    2017-06-09

    9 CHAPTER 2 LITERARY REVIEW...the programming and expertise in programming which the author does not possess. 10 CHAPTER 2 LITERARY REVIEW The literary review is designed to...characteristics are then used to assessed by case studies of Artificial Intelligence through qualitative analysis of books, journals, and articles

  19. Employing Artificial Intelligence To Minimise Internet Fraud

    Directory of Open Access Journals (Sweden)

    Edward Wong Sek Khin

    2009-12-01

    Full Text Available Internet fraud is increasing on a daily basis with new methods for extracting funds from government, corporations, businesses in general, and persons appearing almost hourly. The increases in on-line purchasing and the constant vigilance of both seller and buyer have meant that the criminal seems to be one-step ahead at all times. To pre-empt or to stop fraud before it can happen occurs in the non-computer based daily transactions of today because of the natural intelligence of the players, both seller and buyer. Currently, even with advances in computing techniques, intelligence is not the current strength of any computing system of today, yet techniques are available which may reduce the occurrences of fraud, and are usually referred to as artificial intelligence systems.This paper provides an overview of the use of current artificial intelligence (AI techniques as a means of combating fraud.Initially the paper describes how artificial intelligence techniques are employed in systems for detecting credit card fraud (online and offline fraud and insider trading.Following this, an attempt is made to propose the using of MonITARS (Monitoring Insider Trading and Regulatory Surveillance Systems framework which use a combination of genetic algorithms, neural nets and statistical analysis in detecting insider dealing. Finally, the paper discusses future research agenda to the role of using MonITARS system.

  20. Artificial intelligence and bladder cancer arrays.

    Science.gov (United States)

    Wild, P J; Catto, J W F; Abbod, M F; Linkens, D A; Herr, A; Pilarsky, C; Wissmann, C; Stoehr, R; Denzinger, S; Knuechel, R; Hamdy, F C; Hartmann, A

    2007-01-01

    Non-muscle invasive bladder cancer is a heterogenous disease whose management is dependent upon the risk of progression to muscle invasion. Although the recurrence rate is high, the majority of tumors are indolent and can be managed by endoscopic means alone. The prognosis of muscle invasion is poor and radical treatment is required if cure is to be obtained. Progression risk in non-invasive tumors is hard to determine at tumor diagnosis using current clinicopathological means. To improve the accuracy of progression prediction various biomarkers have been evaluated. To discover novel biomarkers several authors have used gene expression microarrays. Various statistical methods have been described to interpret array data, but to date no biomarkers have entered clinical practice. Here, we describe a new method of microarray analysis using neurofuzzy modeling (NFM), a form of artificial intelligence, and integrate it with artificial neural networks (ANN) to investigate non-muscle invasive bladder cancer array data (n=66 tumors). We develop a predictive panel of 11 genes, from 2800 expressed genes, that can significantly identify tumor progression (average Logrank p = 0.0288) in the analyzed cancers. In comparison, this panel appears superior to those genes chosen using traditional analyses (average Logrank p = 0.3455) and tumor grade (Logrank, p = 0.2475) in this non-muscle invasive cohort. We then analyze panel members in a new non-muscle invasive bladder cancer cohort (n=199) using immunohistochemistry with six commercially available antibodies. The combination of 6 genes (LIG3, TNFRSF6, KRT18, ICAM1, DSG2 and BRCA2) significantly stratifies tumor progression (Logrank p = 0.0096) in the new cohort. We discuss the benefits of the transparent NFM approach with respect to other reported methods.

  1. Emotions and a Prior Knowledge Representation in Artificial General Intelligence

    OpenAIRE

    Gavrilov, Andrey

    2008-01-01

    In this paper a prior knowledge representation for Artificial General Intelligence is proposed based on fuzzy rules using linguistic variables. These linguistic variables may be produced by neural network. Rules may be used for generation of basic emotions – positive and negative, which influence on planning and execution of behavior. The representation of Three Laws of Robotics as such prior knowledge is suggested as highest level of motivation in AGI.

  2. The potential of artificial intelligence toys

    DEFF Research Database (Denmark)

    Dai, Zheng

    2008-01-01

    value for existing resources. It combines AI programs and common sensors to realize the function of intelligence input and output. Designers can use technology-based criteria to design and need to consider the possible issues in this new field. All of these aspects can be referenced from electronic game......Artificial intelligence is moving to a next step of development and application areas. From electronic games to human-like robots, AI toy is a good choice for next step during this process. Technology-based design is fit to the development of AI toy. It can exert the advantages and explore more...

  3. Seventh Scandinavian Conference on Artificial Intelligence

    DEFF Research Database (Denmark)

    Lund, Henrik Hautop; Mayoh, Brian Henry; Perram, John

    2001-01-01

    The book covers the seventh Scandinavian Conference on Artificial Intelligence, held at the Maersk Mc-Kinney Moller Institute for Production Technology at the University of Southern Denmark during the period 20-21 February, 2001. It continues the tradition established by SCAI of being one...... of the most important regional AI conferences in Europe, attracting high quality submissions from Scandinavia and the rest of the world, including the Baltic countries. The contents include robotics, sensor/motor intelligence, evolutionary robotics, behaviour-based systems, multi-agent systems, applications...

  4. [Artificial neural networks in Neurosciences].

    Science.gov (United States)

    Porras Chavarino, Carmen; Salinas Martínez de Lecea, José María

    2011-11-01

    This article shows that artificial neural networks are used for confirming the relationships between physiological and cognitive changes. Specifically, we explore the influence of a decrease of neurotransmitters on the behaviour of old people in recognition tasks. This artificial neural network recognizes learned patterns. When we change the threshold of activation in some units, the artificial neural network simulates the experimental results of old people in recognition tasks. However, the main contributions of this paper are the design of an artificial neural network and its operation inspired by the nervous system and the way the inputs are coded and the process of orthogonalization of patterns.

  5. Artificial intelligence - NASA. [robotics for Space Station

    Science.gov (United States)

    Erickson, J. D.

    1985-01-01

    Artificial Intelligence (AI) represents a vital common space support element needed to enable the civil space program and commercial space program to perform their missions successfully. It is pointed out that advances in AI stimulated by the Space Station Program could benefit the U.S. in many ways. A fundamental challenge for the civil space program is to meet the needs of the customers and users of space with facilities enabling maximum productivity and having low start-up costs, and low annual operating costs. An effective way to meet this challenge may involve a man-machine system in which artificial intelligence, robotics, and advanced automation are integrated into high reliability organizations. Attention is given to the benefits, NASA strategy for AI, candidate space station systems, the Space Station as a stepping stone, and the commercialization of space.

  6. Liquefaction Microzonation of Babol City Using Artificial Neural Network

    DEFF Research Database (Denmark)

    Farrokhzad, F.; Choobbasti, A.J.; Barari, Amin

    2012-01-01

    that will be less susceptible to damage during earthquakes. The scope of present study is to prepare the liquefaction microzonation map for the Babol city based on Seed and Idriss (1983) method using artificial neural network. Artificial neural network (ANN) is one of the artificial intelligence (AI) approaches...... is proposed in this paper. To meet this objective, an effort is made to introduce a total of 30 boreholes data in an area of 7 km2 which includes the results of field tests into the neural network model and the prediction of artificial neural network is checked in some test boreholes, finally the liquefaction...

  7. Self Driving Car: Artificial Intelligence Approach

    OpenAIRE

    Chandra, Ronal; Agani, Nazori; - University of Budi Luhur Jakarta, Yoga Prihastomo

    2012-01-01

    - Artificial Intelligence also known as (AI) is the capability of a machine to function as if the machine has the capability to think like a human. In automotive industry, AI plays an important role in developing vehicle technology. Vehicular automation involves the use of mechatronics and in particular, AI to assist in the control of the vehicle, thereby relieving responsibilities from the driver or making a responsibility more manageable. Autonomous vehicles sense the world with such techni...

  8. Encyclopedia of artificial intelligence: 2 Vol. set

    Energy Technology Data Exchange (ETDEWEB)

    Shapiro, S.C.

    1987-01-01

    Drawing on the fields of computer science, electrical engineering, linguistics, mathematics, philosophy, psychology, and physiology, this one-volume encyclopedia brings together the core of knowledge on artificial intelligence. It provides an overview of how to program computers to emulate human behavior, offering a wide range of techniques for speech and visual generation, problem-solving and more. Over 250 entries are organized alphabetically, cross-referenced and indexed.

  9. Artificial Intelligence and Virology - quo vadis.

    Science.gov (United States)

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

    2017-01-01

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

  10. Using Artificial Intelligence to Retrieve the Optimal Parameters and Structures of Adaptive Network-Based Fuzzy Inference System for Typhoon Precipitation Forecast Modeling

    Directory of Open Access Journals (Sweden)

    Chien-Lin Huang

    2015-01-01

    Full Text Available This study aims to construct a typhoon precipitation forecast model providing forecasts one to six hours in advance using optimal model parameters and structures retrieved from a combination of the adaptive network-based fuzzy inference system (ANFIS and artificial intelligence. To enhance the accuracy of the precipitation forecast, two structures were then used to establish the precipitation forecast model for a specific lead-time: a single-model structure and a dual-model hybrid structure where the forecast models of higher and lower precipitation were integrated. In order to rapidly, automatically, and accurately retrieve the optimal parameters and structures of the ANFIS-based precipitation forecast model, a tabu search was applied to identify the adjacent radius in subtractive clustering when constructing the ANFIS structure. The coupled structure was also employed to establish a precipitation forecast model across short and long lead-times in order to improve the accuracy of long-term precipitation forecasts. The study area is the Shimen Reservoir, and the analyzed period is from 2001 to 2009. Results showed that the optimal initial ANFIS parameters selected by the tabu search, combined with the dual-model hybrid method and the coupled structure, provided the favors in computation efficiency and high-reliability predictions in typhoon precipitation forecasts regarding short to long lead-time forecasting horizons.

  11. Teachers and artificial intelligence. The Logo connection.

    Science.gov (United States)

    Merbler, J B

    1990-12-01

    This article describes a three-phase program for training special education teachers to teach Logo and artificial intelligence. Logo is derived from the LISP computer language and is relatively simple to learn and use, and it is argued that these factors make it an ideal tool for classroom experimentation in basic artificial intelligence concepts. The program trains teachers to develop simple demonstrations of artificial intelligence using Logo. The material that the teachers learn to teach is suitable as an advanced level topic for intermediate- through secondary-level students enrolled in computer competency or similar courses. The material emphasizes problem-solving and thinking skills using a nonverbal expressive medium (Logo), thus it is deemed especially appropriate for hearing-impaired children. It is also sufficiently challenging for academically talented children, whether hearing or deaf. Although the notion of teachers as programmers is controversial, Logo is relatively easy to learn, has direct implications for education, and has been found to be an excellent tool for empowerment-for both teachers and children.

  12. Artificial Intelligence Application in Power Generation Industry: Initial considerations

    Science.gov (United States)

    Ismail, Rahmat Izaizi B.; Ismail Alnaimi, Firas B.; AL-Qrimli, Haidar F.

    2016-03-01

    With increased competitiveness in power generation industries, more resources are directed in optimizing plant operation, including fault detection and diagnosis. One of the most powerful tools in faults detection and diagnosis is artificial intelligence (AI). Faults should be detected early so correct mitigation measures can be taken, whilst false alarms should be eschewed to avoid unnecessary interruption and downtime. For the last few decades there has been major interest towards intelligent condition monitoring system (ICMS) application in power plant especially with AI development particularly in artificial neural network (ANN). ANN is based on quite simple principles, but takes advantage of their mathematical nature, non-linear iteration to demonstrate powerful problem solving ability. With massive possibility and room for improvement in AI, the inspiration for researching them are apparent, and literally, hundreds of papers have been published, discussing the findings of hybrid AI for condition monitoring purposes. In this paper, the studies of ANN and genetic algorithm (GA) application will be presented.

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

    African Journals Online (AJOL)

    Artificial versus Natural Intelligence: An Adendum to the Philosophy of Mind. ... Is it possible for artificial intelligence to replace the human brain? These and similar ... prospects and limitations of “intelligent” machines, that is, what they can or cannot do as compared with the functioning of human or natural intelligence.

  14. Artificial intelligence in wireless communications

    CERN Document Server

    Rondeau, Thomas W

    2009-01-01

    This cutting-edge resource offers practical overview of cognitive radio, a paradigm for wireless communications in which a network or a wireless node changes its transmission or reception parameters. The alteration of parameters is based on the active monitoring of several factors in the external and internal radio environment. This book offers a detailed description of cognitive radio and its individual parts. Practitioners learn how the basic processing elements and their capabilities are implemented as modular components. Moreover, the book explains how each component can be developed and t

  15. A Multidisciplinary Artificial Intelligence Model of an Affective Robot

    Directory of Open Access Journals (Sweden)

    Hooman Aghaebrahimi Samani

    2012-03-01

    Full Text Available A multidisciplinary approach to a novel artificial intelligence system for an affective robot is presented in this paper. The general objective of the system is to develop a robotic system which strives to achieve a high level of emotional bond between humans and robot by exploring human love. Such a relationship is a contingent process of attraction, affection and attachment from humans towards robots, and the belief of the vice versa from robots to humans. The advanced artificial intelligence of the system includes three modules, namely Probabilistic Love Assembly (PLA, based on the psychology of love, Artificial Endocrine System (AES, based on the physiology of love, and Affective State Transition (AST, based on emotions. The PLA module employs a Bayesian network to incorporate psychological parameters of affection in the robot. The AES module employs artificial emotional and biological hormones via a Dynamic Bayesian Network (DBN. The AST module uses a novel transition method for handling affective states of the robot. These three modules work together to manage emotional behaviours of the robot.

  16. Does Artificial Neural Network Support Connectivism's Assumptions?

    Science.gov (United States)

    AlDahdouh, Alaa A.

    2017-01-01

    Connectivism was presented as a learning theory for the digital age and connectivists claim that recent developments in Artificial Intelligence (AI) and, more specifically, Artificial Neural Network (ANN) support their assumptions of knowledge connectivity. Yet, very little has been done to investigate this brave allegation. Does the advancement…

  17. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  18. Artificial neural networks in forecasting tourists’ flow, an intelligent technique to help the economic development of tourism in Albania.

    Directory of Open Access Journals (Sweden)

    Dezdemona Gjylapi

    2014-07-01

    The aim of this paper is to present the neural network usage in the tourists’ number forecasting and to determine the trends of the future tourist inflow, thus helping tourism management agencies in making scientific based financial decisions.

  19. Evolutionary computation: the next major transition of artificial intelligence?

    National Research Council Canada - National Science Library

    Moshe Sipper; Randal S Olson; Jason H Moore

    2017-01-01

    Artificial intelligence (AI), a broad field that deals with the ongoing pursuit to render machines capable of performing intelligent tasks, has taken the academic and industrial worlds by storm in a breathtakingly short time span...

  20. International Symposium on Distributed Computing and Artificial Intelligence

    OpenAIRE

    Abraham, Ajith P.; Corchado Rodríguez, Juan M.; Rodríguez González, Sara; De Paz Santana, Juan Francisco

    2017-01-01

    The International Symposium on Distributed Computing and Artificial Intelligence 2011 (DCAI 2011) is a stimulating and productive forum where the scientific community can work towards future cooperation on Distributed Computing and Artificial Intelligence areas. This conference is the forum in which to present application of innovative techniques to complex problems. Artificial intelligence is changing our society. Its application in distributed environments, such as internet, electronic comm...

  1. Color Regeneration from Reflective Color Sensor Using an Artificial Intelligent Technique

    OpenAIRE

    Hayriye Altural; Ömer Galip Saracoglu

    2010-01-01

    A low-cost optical sensor based on reflective color sensing is presented. Artificial neural network models are used to improve the color regeneration from the sensor signals. Analog voltages of the sensor are successfully converted to RGB colors. The artificial intelligent models presented in this work enable color regeneration from analog outputs of the color sensor. Besides, inverse modeling supported by an intelligent technique enables the sensor probe for use of a colorimetric sensor that...

  2. Color regeneration from reflective color sensor using an artificial intelligent technique.

    Science.gov (United States)

    Saracoglu, Ömer Galip; Altural, Hayriye

    2010-01-01

    A low-cost optical sensor based on reflective color sensing is presented. Artificial neural network models are used to improve the color regeneration from the sensor signals. Analog voltages of the sensor are successfully converted to RGB colors. The artificial intelligent models presented in this work enable color regeneration from analog outputs of the color sensor. Besides, inverse modeling supported by an intelligent technique enables the sensor probe for use of a colorimetric sensor that relates color changes to analog voltages.

  3. Modelling fuel cell performance using artificial intelligence

    Science.gov (United States)

    Ogaji, S. O. T.; Singh, R.; Pilidis, P.; Diacakis, M.

    Over the last few years, fuel cell technology has been increasing promisingly its share in the generation of stationary power. Numerous pilot projects are operating worldwide, continuously increasing the amount of operating hours either as stand-alone devices or as part of gas turbine combined cycles. An essential tool for the adequate and dynamic analysis of such systems is a software model that enables the user to assess a large number of alternative options in the least possible time. On the other hand, the sphere of application of artificial neural networks has widened covering such endeavours of life such as medicine, finance and unsurprisingly engineering (diagnostics of faults in machines). Artificial neural networks have been described as diagrammatic representation of a mathematical equation that receives values (inputs) and gives out results (outputs). Artificial neural networks systems have the capacity to recognise and associate patterns and because of their inherent design features, they can be applied to linear and non-linear problem domains. In this paper, the performance of the fuel cell is modelled using artificial neural networks. The inputs to the network are variables that are critical to the performance of the fuel cell while the outputs are the result of changes in any one or all of the fuel cell design variables, on its performance. Critical parameters for the cell include the geometrical configuration as well as the operating conditions. For the neural network, various network design parameters such as the network size, training algorithm, activation functions and their causes on the effectiveness of the performance modelling are discussed. Results from the analysis as well as the limitations of the approach are presented and discussed.

  4. Quantum neuromorphic hardware for quantum artificial intelligence

    Science.gov (United States)

    Prati, Enrico

    2017-08-01

    The development of machine learning methods based on deep learning boosted the field of artificial intelligence towards unprecedented achievements and application in several fields. Such prominent results were made in parallel with the first successful demonstrations of fault tolerant hardware for quantum information processing. To which extent deep learning can take advantage of the existence of a hardware based on qubits behaving as a universal quantum computer is an open question under investigation. Here I review the convergence between the two fields towards implementation of advanced quantum algorithms, including quantum deep learning.

  5. 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.

  6. 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.

  7. Artificial intelligence applications at the ICPP

    Energy Technology Data Exchange (ETDEWEB)

    Johnson, C.E.

    1989-08-02

    Westinghouse Idaho Nuclear Company (WINCO) initiated an aggressive program for artificial intelligence (AI) expert system implementations in 1985. The first expert system, Safety Analysis Methods Advisor (SAMA) was completed in 1986 to help operational safety analysts select analysis methodologies for safety analysis reports. The SAMA expert system was implemented as a rule-based system using a commercial expert system shell. The major benefit of the system is for training new safety analysts. The first successful implementation launched three other expert system projects: a process alarm filtering system, a process control advisor, and a mass spectrometer trouble-shooting advisor. This paper describes the current status of these projects. (GHH)

  8. Markov decision processes in artificial intelligence

    CERN Document Server

    Sigaud, Olivier

    2013-01-01

    Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as Reinforcement Learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in Artificial Intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, Reinforcement Learning, Partially Observable MDPs, Markov games and the use of non-classical criteria). Then it presents more advanced research trends in the domain and gives some concrete examples using illustr

  9. Parallel processing for artificial intelligence 2

    CERN Document Server

    Kumar, V; Suttner, CB

    1994-01-01

    With the increasing availability of parallel machines and the raising of interest in large scale and real world applications, research on parallel processing for Artificial Intelligence (AI) is gaining greater importance in the computer science environment. Many applications have been implemented and delivered but the field is still considered to be in its infancy. This book assembles diverse aspects of research in the area, providing an overview of the current state of technology. It also aims to promote further growth across the discipline. Contributions have been grouped according to their

  10. Human-Level Artificial Intelligence? Be Serious!

    OpenAIRE

    Nilsson, Nils J.

    2005-01-01

    I claim that achieving real human-level artificial intelligence would necessarily imply that most of the tasks that humans perform for pay could be automated. Rather than work toward this goal of automation by building special-purpose systems, I argue for the development of general-purpose, educable systems that can learn and be taught to perform any of the thousands of jobs that humans can perform. Joining others who have made similar proposals, I advocate beginning with a system that has mi...

  11. Artificial intelligence applied to process signal analysis

    Science.gov (United States)

    Corsberg, Dan

    1988-01-01

    Many space station processes are highly complex systems subject to sudden, major transients. In any complex process control system, a critical aspect of the human/machine interface is the analysis and display of process information. Human operators can be overwhelmed by large clusters of alarms that inhibit their ability to diagnose and respond to a disturbance. Using artificial intelligence techniques and a knowledge base approach to this problem, the power of the computer can be used to filter and analyze plant sensor data. This will provide operators with a better description of the process state. Once a process state is recognized, automatic action could be initiated and proper system response monitored.

  12. A Characterization of the Utility of Using Artificial Intelligence to Test Two Artificial Intelligence Systems

    Directory of Open Access Journals (Sweden)

    Jeremy Straub

    2013-05-01

    Full Text Available An artificial intelligence system, designed for operations in a real-world environment faces a nearly infinite set of possible performance scenarios. Designers and developers, thus, face the challenge of validating proper performance across both foreseen and unforeseen conditions, particularly when the artificial intelligence is controlling a robot that will be operating in close proximity, or may represent a danger, to humans. While the manual creation of test cases allows limited testing (perhaps ensuring that a set of foreseeable conditions trigger an appropriate response, this may be insufficient to fully characterize and validate safe system performance. An approach to validating the performance of an artificial intelligence system using a simple artificial intelligence test case producer (AITCP is presented. The AITCP allows the creation and simulation of prospective operating scenarios at a rate far exceeding that possible by human testers. Four scenarios for testing an autonomous navigation control system are presented: single actor in two-dimensional space, multiple actors in two-dimensional space, single actor in three-dimensional space, and multiple actors in three-dimensional space. The utility of using the AITCP is compared to that of human testers in each of these scenarios.

  13. An overview of artificial intelligence and robotics. Volume 1: Artificial intelligence. Part A: The core ingredients

    Science.gov (United States)

    Gevarter, W. B.

    1983-01-01

    Artificial Intelligence (AI) is an emerging technology that has recently attracted considerable attention. Many applications are now under development. The goal of Artificial Intelligence is focused on developing computational approaches to intelligent behavior. This goal is so broad - covering virtually all aspects of human cognitive activity - that substantial confusion has arisen as to the actual nature of AI, its current status and its future capability. This volume, the first in a series of NBS/NASA reports on the subject, attempts to address these concerns. Thus, this report endeavors to clarify what AI is, the foundations on which it rests, the techniques utilized, applications, the participants and, finally, AI's state-of-the-art and future trends. It is anticipated that this report will prove useful to government and private engineering and research managers, potential users, and others who will be affected by this field as it unfolds.

  14. Artificial intelligence for the CTA Observatory scheduler

    Science.gov (United States)

    Colomé, Josep; Colomer, Pau; Campreciós, Jordi; Coiffard, Thierry; de Oña, Emma; Pedaletti, Giovanna; Torres, Diego F.; Garcia-Piquer, Alvaro

    2014-08-01

    The Cherenkov Telescope Array (CTA) project will be the next generation ground-based very high energy gamma-ray instrument. The success of the precursor projects (i.e., HESS, MAGIC, VERITAS) motivated the construction of this large infrastructure that is included in the roadmap of the ESFRI projects since 2008. CTA is planned to start the construction phase in 2015 and will consist of two arrays of Cherenkov telescopes operated as a proposal-driven open observatory. Two sites are foreseen at the southern and northern hemispheres. The CTA observatory will handle several observation modes and will have to operate tens of telescopes with a highly efficient and reliable control. Thus, the CTA planning tool is a key element in the control layer for the optimization of the observatory time. The main purpose of the scheduler for CTA is the allocation of multiple tasks to one single array or to multiple sub-arrays of telescopes, while maximizing the scientific return of the facility and minimizing the operational costs. The scheduler considers long- and short-term varying conditions to optimize the prioritization of tasks. A short-term scheduler provides the system with the capability to adapt, in almost real-time, the selected task to the varying execution constraints (i.e., Targets of Opportunity, health or status of the system components, environment conditions). The scheduling procedure ensures that long-term planning decisions are correctly transferred to the short-term prioritization process for a suitable selection of the next task to execute on the array. In this contribution we present the constraints to CTA task scheduling that helped classifying it as a Flexible Job-Shop Problem case and finding its optimal solution based on Artificial Intelligence techniques. We describe the scheduler prototype that uses a Guarded Discrete Stochastic Neural Network (GDSN), for an easy representation of the possible long- and short-term planning solutions, and Constraint

  15. Teaching artificial intelligence to read electropherograms.

    Science.gov (United States)

    Taylor, Duncan; Powers, David

    2016-11-01

    Electropherograms are produced in great numbers in forensic DNA laboratories as part of everyday criminal casework. Before the results of these electropherograms can be used they must be scrutinised by analysts to determine what the identified data tells us about the underlying DNA sequences and what is purely an artefact of the DNA profiling process. A technique that lends itself well to such a task of classification in the face of vast amounts of data is the use of artificial neural networks. These networks, inspired by the workings of the human brain, have been increasingly successful in analysing large datasets, performing medical diagnoses, identifying handwriting, playing games, or recognising images. In this work we demonstrate the use of an artificial neural network which we train to 'read' electropherograms and show that it can generalise to unseen profiles. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  16. What are artificial neural networks?

    DEFF Research Database (Denmark)

    Krogh, Anders

    2008-01-01

    Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb......Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb...

  17. Smart Collections: Can Artificial Intelligence Tools and Techniques Assist with Discovering, Evaluating and Tagging Digital Learning Resources?

    Science.gov (United States)

    Leibbrandt, Richard; Yang, Dongqiang; Pfitzner, Darius; Powers, David; Mitchell, Pru; Hayman, Sarah; Eddy, Helen

    2010-01-01

    This paper reports on a joint proof of concept project undertaken by researchers from the Flinders University Artificial Intelligence Laboratory in partnership with information managers from the Education Network Australia (edna) team at Education Services Australia to address the question of whether artificial intelligence techniques could be…

  18. Artificial intelligence approaches to software engineering

    Science.gov (United States)

    Johannes, James D.; Macdonald, James R.

    1988-01-01

    Artificial intelligence approaches to software engineering are examined. The software development life cycle is a sequence of not so well-defined phases. Improved techniques for developing systems have been formulated over the past 15 years, but pressure continues to attempt to reduce current costs. Software development technology seems to be standing still. The primary objective of the knowledge-based approach to software development presented in this paper is to avoid problem areas that lead to schedule slippages, cost overruns, or software products that fall short of their desired goals. Identifying and resolving software problems early, often in the phase in which they first occur, has been shown to contribute significantly to reducing risks in software development. Software development is not a mechanical process but a basic human activity. It requires clear thinking, work, and rework to be successful. The artificial intelligence approaches to software engineering presented support the software development life cycle through the use of software development techniques and methodologies in terms of changing current practices and methods. These should be replaced by better techniques that that improve the process of of software development and the quality of the resulting products. The software development process can be structured into well-defined steps, of which the interfaces are standardized, supported and checked by automated procedures that provide error detection, production of the documentation and ultimately support the actual design of complex programs.

  19. Artificial intelligence. Fears of an AI pioneer.

    Science.gov (United States)

    Russell, Stuart; Bohannon, John

    2015-07-17

    From the enraged robots in the 1920 play R.U.R. to the homicidal computer H.A.L. in 2001: A Space Odyssey, science fiction writers have embraced the dark side of artificial intelligence (AI) ever since the concept entered our collective imagination. Sluggish progress in AI research, especially during the “AI winter” of the 1970s and 1980s, made such worries seem far-fetched. But recent breakthroughs in machine learning and vast improvements in computational power have brought a flood of research funding— and fresh concerns about where AI may lead us. One researcher now speaking up is Stuart Russell, a computer scientist at the University of California, Berkeley, who with Peter Norvig, director of research at Google, wrote the premier AI textbook, Artificial Intelligence: A Modern Approach, now in its third edition. Last year, Russell joined the Centre for the Study of Existential Risk at Cambridge University in the United Kingdom as an AI expert focusing on “risks that could lead to human extinction.” Among his chief concerns, which he aired at an April meeting in Geneva, Switzerland, run by the United Nations, is the danger of putting military drones and weaponry under the full control of AI systems. This interview has been edited for clarity and brevity.

  20. Artificial Intelligence Research Branch future plans

    Science.gov (United States)

    Stewart, Helen (Editor)

    1992-01-01

    This report contains information on the activities of the Artificial Intelligence Research Branch (FIA) at NASA Ames Research Center (ARC) in 1992, as well as planned work in 1993. These activities span a range from basic scientific research through engineering development to fielded NASA applications, particularly those applications that are enabled by basic research carried out in FIA. Work is conducted in-house and through collaborative partners in academia and industry. All of our work has research themes with a dual commitment to technical excellence and applicability to NASA short, medium, and long-term problems. FIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at the Jet Propulsion Laboratory (JPL) and AI applications groups throughout all NASA centers. This report is organized along three major research themes: (1) Planning and Scheduling: deciding on a sequence of actions to achieve a set of complex goals and determining when to execute those actions and how to allocate resources to carry them out; (2) Machine Learning: techniques for forming theories about natural and man-made phenomena; and for improving the problem-solving performance of computational systems over time; and (3) Research on the acquisition, representation, and utilization of knowledge in support of diagnosis design of engineered systems and analysis of actual systems.

  1. Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J. M. [Escuela Politecnica Superior, Departamento de Electrotecnia y Electronica, Avda. Menendez Pidal s/n, Cordoba (Spain); Martinez B, M. R.; Vega C, H. R. [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Calle Cipres No. 10, Fracc. La Penuela, 98068 Zacatecas (Mexico); Gallego D, E.; Lorente F, A. [Universidad Politecnica de Madrid, Departamento de Ingenieria Nuclear, ETSI Industriales, C. Jose Gutierrez Abascal 2, 28006 Madrid (Spain); Mendez V, R.; Los Arcos M, J. M.; Guerrero A, J. E., E-mail: morvymm@yahoo.com.m [CIEMAT, Laboratorio de Metrologia de Radiaciones Ionizantes, Avda. Complutense 22, 28040 Madrid (Spain)

    2011-02-15

    With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks (Ann) have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Ann still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning Ann parameters. In recent years the use of hybrid technologies, combining Ann and genetic algorithms, has been utilized to. In this work, several Ann topologies were trained and tested using Ann and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out. (Author)

  2. PLANNING IN ARTIFICIAL INTELLIGENCE AND ROBOTICS (PAIR

    Directory of Open Access Journals (Sweden)

    Editorial, Foreword

    2016-11-01

    Full Text Available September 18th, 2016Deggendorf, Germanyhttp://robotics.fel.cvut.cz/pair16/Organized by: Artificial Intelligence Center Department of Computer Science Faculty of Electrical Engineering Czech Technical University in PragueTechnicka 2, Prague 6, 166 27, Czech RepublicGuest editors:Jan Faigl (Artificial Intelligence Center, Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in PragueJiří Vokřínek (Artificial Intelligence Center, Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in PragueScientific comittee:D. Belter (Poznań University of Technology, PolandW. Dorner (Technische Hochschule Deggendorf, GermanyJ. Faigl (Czech Technical University in PragueT. Krajník (University of Lincoln, United KingdomA. Komenda (Czech Technical University in PragueG. Kupris (Technische Hochschule Deggendorf, GermanyM. Rollo (Czech Technical University in PragueM. Saska (Czech Technical University in PragueJ. Vokřínek (Czech Technical University in PragueV. Vonásek (Czech Technical University in PragueK. Walas (Poznań University of Technology, Poland Foreword:The third year of the student conference on “Planning in Artificial Intelligence and Robotics” (PAIR continues in joining young researchers and students interested in robotics and artificial intelligence. In 2016, we follow the schema of the last year as a joint event with the RoboTour competition in Deggendorf, Germany. Thanks to the great collaboration with Gerald Kupris and Wolfgang Donner from Technische Hochschule Deggendorf and support from Czech Technical University under project No. SVK 26/16/F3 and Bayerisches Staatsministerium der Finanzen, für Landesentwicklung und Heimat, we have been able to provide accommodations and travel support to participants and an invited speaker. Fourteen papers have accepted and listed in the conference program. The papers have been authored by students from Central Europe

  3. The Potential of Artificial Intelligence in Aids for the Disabled.

    Science.gov (United States)

    Boyer, John J.

    The paper explores the possibilities for applying the knowledge of artificial intelligence (AI) research to aids for the disabled. Following a definition of artificial intelligence, the paper reviews areas of basic AI research, such as computer vision, machine learning, and planning and problem solving. Among application areas relevant to the…

  4. Cotton genotypes selection through artificial neural networks.

    Science.gov (United States)

    Júnior, E G Silva; Cardoso, D B O; Reis, M C; Nascimento, A F O; Bortolin, D I; Martins, M R; Sousa, L B

    2017-09-27

    Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explored in genetic improvement of cotton. Thus, this study was carried out with the objective of presenting the use of artificial neural networks as auxiliary tools in the improvement of the cotton to improve fiber quality. To demonstrate the applicability of this approach, this research was carried out using the evaluation data of 40 genotypes. In order to classify the genotypes for fiber quality, the artificial neural networks were trained with replicate data of 20 genotypes of cotton evaluated in the harvests of 2013/14 and 2014/15, regarding fiber length, uniformity of length, fiber strength, micronaire index, elongation, short fiber index, maturity index, reflectance degree, and fiber quality index. This quality index was estimated by means of a weighted average on the determined score (1 to 5) of each characteristic of the HVI evaluated, according to its industry standards. The artificial neural networks presented a high capacity of correct classification of the 20 selected genotypes based on the fiber quality index, so that when using fiber length associated with the short fiber index, fiber maturation, and micronaire index, the artificial neural networks presented better results than using only fiber length and previous associations. It was also observed that to submit data of means of new genotypes to the neural networks trained with data of repetition, provides better results of classification of the genotypes. When observing the results obtained in the present study, it was verified that the artificial neural networks present great potential to be used in the different stages of a genetic improvement program of the cotton, aiming at the improvement of the fiber quality of the future cultivars.

  5. An Artificial Intelligence Approach for Gears Diagnostics in AUVs.

    Science.gov (United States)

    Marichal, Graciliano Nicolás; Del Castillo, María Lourdes; López, Jesús; Padrón, Isidro; Artés, Mariano

    2016-04-12

    In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles), where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms) have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved.

  6. An Artificial Intelligence Approach for Gears Diagnostics in AUVs

    Directory of Open Access Journals (Sweden)

    Graciliano Nicolás Marichal

    2016-04-01

    Full Text Available In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles, where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved.

  7. An Artificial Intelligence Approach for Gears Diagnostics in AUVs

    Science.gov (United States)

    Marichal, Graciliano Nicolás; Del Castillo, María Lourdes; López, Jesús; Padrón, Isidro; Artés, Mariano

    2016-01-01

    In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles), where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms) have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved. PMID:27077868

  8. Constructing an Intelligent Patent Network Analysis Method

    Directory of Open Access Journals (Sweden)

    Chao-Chan Wu

    2012-11-01

    Full Text Available Patent network analysis, an advanced method of patent analysis, is a useful tool for technology management. This method visually displays all the relationships among the patents and enables the analysts to intuitively comprehend the overview of a set of patents in the field of the technology being studied. Although patent network analysis possesses relative advantages different from traditional methods of patent analysis, it is subject to several crucial limitations. To overcome the drawbacks of the current method, this study proposes a novel patent analysis method, called the intelligent patent network analysis method, to make a visual network with great precision. Based on artificial intelligence techniques, the proposed method provides an automated procedure for searching patent documents, extracting patent keywords, and determining the weight of each patent keyword in order to generate a sophisticated visualization of the patent network. This study proposes a detailed procedure for generating an intelligent patent network that is helpful for improving the efficiency and quality of patent analysis. Furthermore, patents in the field of Carbon Nanotube Backlight Unit (CNT-BLU were analyzed to verify the utility of the proposed method.

  9. Artificial neural network applications in ionospheric studies

    Directory of Open Access Journals (Sweden)

    L. R. Cander

    1998-06-01

    Full Text Available The ionosphere of Earth exhibits considerable spatial changes and has large temporal variability of various timescales related to the mechanisms of creation, decay and transport of space ionospheric plasma. Many techniques for modelling electron density profiles through entire ionosphere have been developed in order to solve the "age-old problem" of ionospheric physics which has not yet been fully solved. A new way to address this problem is by applying artificial intelligence methodologies to current large amounts of solar-terrestrial and ionospheric data. It is the aim of this paper to show by the most recent examples that modern development of numerical models for ionospheric monthly median long-term prediction and daily hourly short-term forecasting may proceed successfully applying the artificial neural networks. The performance of these techniques is illustrated with different artificial neural networks developed to model and predict the temporal and spatial variations of ionospheric critical frequency, f0F2 and Total Electron Content (TEC. Comparisons between results obtained by the proposed approaches and measured f0F2 and TEC data provide prospects for future applications of the artificial neural networks in ionospheric studies.

  10. Artificial Intelligence Applications to High-Technology Training.

    Science.gov (United States)

    Dede, Christopher

    1987-01-01

    Discusses the use of artificial intelligence to improve occupational instruction in complex subjects with high performance goals, such as those required for high-technology jobs. Highlights include intelligent computer assisted instruction, examples in space technology training, intelligent simulation environments, and the need for adult training…

  11. A critique of artificial intelligence | Airoboman | Sophia: An African ...

    African Journals Online (AJOL)

    The question concerning machines‟ intelligence or mindedness has become topical issue in some disciplines. The focus in this research is on philosophic inquiry into the claim widely current in our time that machines are intelligent. The study introduces the debate and presents some definitions of artificial intelligence as a ...

  12. What can the brain teach us about building artificial intelligence?

    Science.gov (United States)

    George, Dileep

    2017-01-01

    Lake et al. offer a timely critique on the recent accomplishments in artificial intelligence from the vantage point of human intelligence and provide insightful suggestions about research directions for building more human-like intelligence. Because we agree with most of the points they raised, here we offer a few points that are complementary.

  13. Artificial intelligence in sports biomechanics: new dawn or false hope?

    Science.gov (United States)

    Bartlett, Roger

    2006-12-15

    This article reviews developments in the use of Artificial Intelligence (AI) in sports biomechanics over the last decade. It outlines possible uses of Expert Systems as diagnostic tools for evaluating faults in sports movements ('techniques') and presents some example knowledge rules for such an expert system. It then compares the analysis of sports techniques, in which Expert Systems have found little place to date, with gait analysis, in which they are routinely used. Consideration is then given to the use of Artificial Neural Networks (ANNs) in sports biomechanics, focusing on Kohonen self-organizing maps, which have been the most widely used in technique analysis, and multi-layer networks, which have been far more widely used in biomechanics in general. Examples of the use of ANNs in sports biomechanics are presented for javelin and discus throwing, shot putting and football kicking. I also present an example of the use of Evolutionary Computation in movement optimization in the soccer throw in, which predicted an optimal technique close to that in the coaching literature. After briefly overviewing the use of AI in both sports science and biomechanics in general, the article concludes with some speculations about future uses of AI in sports biomechanics. Key PointsExpert Systems remain almost unused in sports biomechanics, unlike in the similar discipline of gait analysis.Artificial Neural Networks, particularly Kohonen Maps, have been used, although their full value remains unclear.Other AI applications, including Evolutionary Computation, have received little attention.

  14. Robotics and artificial intelligence: Jewish ethical perspectives.

    Science.gov (United States)

    Rappaport, Z H

    2006-01-01

    In 16th Century Prague, Rabbi Loew created a Golem, a humanoid made of clay, to protect his community. When the Golem became too dangerous to his surroundings, he was dismantled. This Jewish theme illustrates some of the guiding principles in its approach to the moral dilemmas inherent in future technologies, such as artificial intelligence and robotics. Man is viewed as having received the power to improve upon creation and develop technologies to achieve them, with the proviso that appropriate safeguards are taken. Ethically, not-harming is viewed as taking precedence over promoting good. Jewish ethical thinking approaches these novel technological possibilities with a cautious optimism that mankind will derive their benefits without coming to harm.

  15. Issues and challenges in artificial intelligence

    CERN Document Server

    Kulikowski, Juliusz; Mroczek, Teresa; Wtorek, Jerzy

    2014-01-01

    The importance of human-computer system interaction problems is increasing due to the growing expectations of users on general computer systems capabilities in human work and life facilitation. Users expect system which is not only a passive tool in human hands but rather an active partner equipped with a sort of artificial intelligence, having access to large information resources, being able to adapt its behavior to the human requirements and to collaborate with the human users.   This book collects examples of recent human-computer system solutions. The content of the book is divided into three parts. Part I is devoted to detection, recognition and reasoning in different circumstances and applications. Problems associated with data modeling, acquisition and mining are presented by papers collected in part II and part III is devoted to Optimization.

  16. Artificial intelligence in the materials processing laboratory

    Science.gov (United States)

    Workman, Gary L.; Kaukler, William F.

    1990-01-01

    Materials science and engineering provides a vast arena for applications of artificial intelligence. Advanced materials research is an area in which challenging requirements confront the researcher, from the drawing board through production and into service. Advanced techniques results in the development of new materials for specialized applications. Hand-in-hand with these new materials are also requirements for state-of-the-art inspection methods to determine the integrity or fitness for service of structures fabricated from these materials. Two problems of current interest to the Materials Processing Laboratory at UAH are an expert system to assist in eddy current inspection of graphite epoxy components for aerospace and an expert system to assist in the design of superalloys for high temperature applications. Each project requires a different approach to reach the defined goals. Results to date are described for the eddy current analysis, but only the original concepts and approaches considered are given for the expert system to design superalloys.

  17. Artificial Intelligence Software Engineering (AISE) model

    Science.gov (United States)

    Kiss, Peter A.

    1990-01-01

    The American Institute of Aeronautics and Astronautics has initiated a committee on standards for Artificial Intelligence. Presented are the initial efforts of one of the working groups of that committee. A candidate model is presented for the development life cycle of knowledge based systems (KBSs). The intent is for the model to be used by the aerospace community and eventually be evolved into a standard. The model is rooted in the evolutionary model, borrows from the spiral model, and is embedded in the standard Waterfall model for software development. Its intent is to satisfy the development of both stand-alone and embedded KBSs. The phases of the life cycle are shown and detailed as are the review points that constitute the key milestones throughout the development process. The applicability and strengths of the model are discussed along with areas needing further development and refinement by the aerospace community.

  18. Artificial Intelligence In Computational Fluid Dynamics

    Science.gov (United States)

    Vogel, Alison Andrews

    1991-01-01

    Paper compares four first-generation artificial-intelligence (Al) software systems for computational fluid dynamics. Includes: Expert Cooling Fan Design System (EXFAN), PAN AIR Knowledge System (PAKS), grid-adaptation program MITOSIS, and Expert Zonal Grid Generation (EZGrid). Focuses on knowledge-based ("expert") software systems. Analyzes intended tasks, kinds of knowledge possessed, magnitude of effort required to codify knowledge, how quickly constructed, performances, and return on investment. On basis of comparison, concludes Al most successful when applied to well-formulated problems solved by classifying or selecting preenumerated solutions. In contrast, application of Al to poorly understood or poorly formulated problems generally results in long development time and large investment of effort, with no guarantee of success.

  19. Clinical Note Creation, Binning, and Artificial Intelligence.

    Science.gov (United States)

    Deliberato, Rodrigo Octávio; Celi, Leo Anthony; Stone, David J

    2017-08-03

    The creation of medical notes in software applications poses an intrinsic problem in workflow as the technology inherently intervenes in the processes of collecting and assembling information, as well as the production of a data-driven note that meets both individual and healthcare system requirements. In addition, the note writing applications in currently available electronic health records (EHRs) do not function to support decision making to any substantial degree. We suggest that artificial intelligence (AI) could be utilized to facilitate the workflows of the data collection and assembly processes, as well as to support the development of personalized, yet data-driven assessments and plans. ©Rodrigo Octávio Deliberato, Leo Anthony Celi, David J Stone. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 03.08.2017.

  20. Discrete PID Tuning Using Artificial Intelligence Techniques

    Directory of Open Access Journals (Sweden)

    Petr DOLEŽEL

    2009-06-01

    Full Text Available PID controllers are widely used in industry these days due to their useful properties such as simple tuning or robustness. While they are applicable to many control problems, they can perform poorly in some applications. Highly nonlinear system control with constrained manipulated variable can be mentioned as an example. The point of the paper is to string together convenient qualities of conventional PID control and progressive techniques based on Artificial Intelligence. Proposed control method should deal with even highly nonlinear systems. To be more specific, there is described new method of discrete PID controller tuning in this paper. This method tunes discrete PID controller parameters online through the use of genetic algorithm and neural model of controlled system in order to control successfully even highly nonlinear systems. After method description and some discussion, there is performed control simulation and comparison to one chosen conventional control method.

  1. An overview of artificial intelligence and robotics. Volume 1: Artificial intelligence. Part B: Applications

    Science.gov (United States)

    Gevarter, W. B.

    1983-01-01

    Artificial Intelligence (AI) is an emerging technology that has recently attracted considerable attention. Many applications are now under development. This report, Part B of a three part report on AI, presents overviews of the key application areas: Expert Systems, Computer Vision, Natural Language Processing, Speech Interfaces, and Problem Solving and Planning. The basic approaches to such systems, the state-of-the-art, existing systems and future trends and expectations are covered.

  2. Artificial intelligence techniques for embryo and oocyte classification.

    Science.gov (United States)

    Manna, Claudio; Nanni, Loris; Lumini, Alessandra; Pappalardo, Sebastiana

    2013-01-01

    One of the most relevant aspects in assisted reproduction technology is the possibility of characterizing and identifying the most viable oocytes or embryos. In most cases, embryologists select them by visual examination and their evaluation is totally subjective. Recently, due to the rapid growth in the capacity to extract texture descriptors from a given image, a growing interest has been shown in the use of artificial intelligence methods for embryo or oocyte scoring/selection in IVF programmes. This work concentrates the efforts on the possible prediction of the quality of embryos and oocytes in order to improve the performance of assisted reproduction technology, starting from their images. The artificial intelligence system proposed in this work is based on a set of Levenberg-Marquardt neural networks trained using textural descriptors (the local binary patterns). The proposed system was tested on two data sets of 269 oocytes and 269 corresponding embryos from 104 women and compared with other machine learning methods already proposed in the past for similar classification problems. Although the results are only preliminary, they show an interesting classification performance. This technique may be of particular interest in those countries where legislation restricts embryo selection. One of the most relevant aspects in assisted reproduction technology is the possibility of characterizing and identifying the most viable oocytes or embryos. In most cases, embryologists select them by visual examination and their evaluation is totally subjective. Recently, due to the rapid growth in our capacity to extract texture descriptors from a given image, a growing interest has been shown in the use of artificial intelligence methods for embryo or oocyte scoring/selection in IVF programmes. In this work, we concentrate our efforts on the possible prediction of the quality of embryos and oocytes in order to improve the performance of assisted reproduction technology

  3. Virtual Enterprise Risk Management Using Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Hanning Chen

    2010-01-01

    Full Text Available Virtual enterprise (VE has to manage its risk effectively in order to guarantee the profit. However, restricting the risk in a VE to the acceptable level is considered difficult due to the agility and diversity of its distributed characteristics. First, in this paper, an optimization model for VE risk management based on distributed decision making model is introduced. This optimization model has two levels, namely, the top model and the base model, which describe the decision processes of the owner and the partners of the VE, respectively. In order to solve the proposed model effectively, this work then applies two powerful artificial intelligence optimization techniques known as evolutionary algorithms (EA and swarm intelligence (SI. Experiments present comparative studies on the VE risk management problem for one EA and three state-of-the-art SI algorithms. All of the algorithms are evaluated against a test scenario, in which the VE is constructed by one owner and different partners. The simulation results show that the PS2O algorithm, which is a recently developed SI paradigm simulating symbiotic coevolution behavior in nature, obtains the superior solution for VE risk management problem than the other algorithms in terms of optimization accuracy and computation robustness.

  4. Beyond AI: Interdisciplinary Aspects of Artificial Intelligence

    CERN Document Server

    Romportl, Jan; Zackova, Eva; Beyond Artificial Intelligence : Contemplations, Expectations, Applications

    2013-01-01

    Products of modern artificial intelligence (AI) have mostly been formed by the views, opinions and goals of the “insiders”, i.e. people usually with engineering background who are driven by the force that can be metaphorically described as the pursuit of the craft of Hephaestus. However, since the present-day technology allows for tighter and tighter mergence of the “natural” everyday human life with machines of immense complexity, the responsible reaction of the scientific community should be based on cautious reflection of what really lies beyond AI, i.e. on the frontiers where the tumultuous ever-growing and ever-changing cloud of AI touches the rest of the world.   The chapters of this boo are based on the selected subset of the presentations that were delivered by their respective authors at the conference “Beyond AI: Interdisciplinary Aspects of Artificial Intelligence” held in Pilsen in December 2011.   From its very definition, the reflection of the phenomena that lie beyond AI must be i...

  5. Artificial intelligence in sports on the example of weight training.

    Science.gov (United States)

    Novatchkov, Hristo; Baca, Arnold

    2013-01-01

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

  6. Artificial Intelligence in Sports on the Example of Weight Training

    Science.gov (United States)

    Novatchkov, Hristo; Baca, Arnold

    2013-01-01

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

  7. A Novel Artificial Intelligence System for Endotracheal Intubation.

    Science.gov (United States)

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

    2016-01-01

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

  8. 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.

  9. De Novo Design of Bioactive Small Molecules by Artificial Intelligence.

    Science.gov (United States)

    Merk, Daniel; Friedrich, Lukas; Grisoni, Francesca; Schneider, Gisbert

    2018-01-01

    Generative artificial intelligence offers a fresh view on molecular design. We present the first-time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine-tuned on recognizing retinoid X and peroxisome proliferator-activated receptor agonists. We synthesized five top-ranking compounds designed by the generative model. Four of the compounds revealed nanomolar to low-micromolar receptor modulatory activity in cell-based assays. Apparently, the computational model intrinsically captured relevant chemical and biological knowledge without the need for explicit rules. The results of this study advocate generative artificial intelligence for prospective de novo molecular design, and demonstrate the potential of these methods for future medicinal chemistry. © 2018 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

  10. Synthetic biology routes to bio-artificial intelligence.

    Science.gov (United States)

    Nesbeth, Darren N; Zaikin, Alexey; Saka, Yasushi; Romano, M Carmen; Giuraniuc, Claudiu V; Kanakov, Oleg; Laptyeva, Tetyana

    2016-11-30

    The design of synthetic gene networks (SGNs) has advanced to the extent that novel genetic circuits are now being tested for their ability to recapitulate archetypal learning behaviours first defined in the fields of machine and animal learning. Here, we discuss the biological implementation of a perceptron algorithm for linear classification of input data. An expansion of this biological design that encompasses cellular 'teachers' and 'students' is also examined. We also discuss implementation of Pavlovian associative learning using SGNs and present an example of such a scheme and in silico simulation of its performance. In addition to designed SGNs, we also consider the option to establish conditions in which a population of SGNs can evolve diversity in order to better contend with complex input data. Finally, we compare recent ethical concerns in the field of artificial intelligence (AI) and the future challenges raised by bio-artificial intelligence (BI). © 2016 The Author(s). This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).

  11. Artificial Neural Networks·

    Indian Academy of Sciences (India)

    differences between biological neural networks (BNNs) of the brain and ANN s. A thorough understanding of ... neurons. Artificial neural models are loosely based on biology since a complete understanding of the .... A learning scheme for updating a neuron's connections (weights) was proposed by Donald Hebb in 1949.

  12. Distributed artificial intelligence, diversity and information literacy

    Directory of Open Access Journals (Sweden)

    Peter Kåhre

    2010-09-01

    Full Text Available My proposal is based on my doctoral dissertation On the Shoulders of AI-technology : Sociology of Knowledge and Strong Artificial Intelligence which I succesfully defended on May 29th 2009. E-published http://www.lu.se/o.o.i.s?id=12588&postid=1389611 The dissertation is concerned with Sociology’s stance in the debate on Strong Artificial Intelligence,.i.e. AI-systems that is able to shape knowledge on their own. There is a need for sociologists to realize the difference between two approaches to constructing AI systems: Symbolic AI (or Classic AI and Connectionistic AI in a distributed model – DAI. Sociological literature shows a largely critical attitude towards Symbolic AI, an attitude that is justified. The main theme of the dissertation is that DAI is not only compatible with Sociology’s approach to what is social, but also constitutes an apt model of how a social system functions. This is consolidated with help from german sociologist Niklas Luhmann’s social systems theory. A lot of sociologists criticize AI because they think that diversity is important and can only be comprehended in informal circumstances that only humans interacting together can handle. They mean that social intelligence is needed to make something out of diversity and informalism. Luhmann´s systems theory gives the opposite perspective. It tells us that it is social systems that communicate and produce new knowledge structures out of contincency. Psychological systems, i.e. humans, can only think within the circumstances the social system offer. In that way human thoughts are bound by formalism. Diversity is constructed when the social systems interact with complexity in their environments. They reduce the complexity and try to present it as meaningful diversity. Today when most of academic literature is electronically stored and is accessible through the Internet from al over the world, DAI can help social systems to observe and reduce complexity in this

  13. Informatics and Nursing in a Post-Nursing Informatics World: Future Directions for Nurses in an Automated, Artificially Intelligent, Social-Networked Healthcare Environment.

    Science.gov (United States)

    Booth, Richard G

    2016-01-01

    The increased adoption and use of technology within healthcare and society has influenced the nursing informatics specialty in a multitude of fashions. Namely, the nursing informatics specialty currently faces a range of important decisions related to its knowledge base, established values and future directions - all of which are in need of development and future-proofing. In light of the increased use of automation, artificial intelligence and big data in healthcare, the specialty must also reconceptualize the roles of both nurses and informaticians to ensure that the nursing profession is ready to operate within future digitalized healthcare ecosystems. To explore these goals, the author of this manuscript outlines an examination of technological advancements currently taking place within healthcare, and also proposes implications for the nursing role and the nursing informatics specialty. Finally, recommendations and insights towards how the roles of nurses and informaticians might evolve or be shaped in the growing post-nursing informatics era are presented. Copyright © 2016 Longwoods Publishing.

  14. Using Artificial Neural Networks to Predict Stock Prices

    OpenAIRE

    Kozdraj, Tomasz

    2009-01-01

    Artificial neural networks constitute one of the most developed conception of artificial intelligence. They are based on pragmatic mathematical theories adopted to tasks resolution. A wide range of their applications also includes financial investments issues. The reason for NN's popularity is mainly connected with their ability to solve complex or not well recognized computational tasks, efficiency in finding solutions as well as the possibility of learning based on patterns or without them....

  15. Comparison of Artificial Intelligence Techniques for river flow forecasting

    Science.gov (United States)

    Firat, M.

    2008-01-01

    The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN), and Auto-Regressive (AR) models for forecasting of daily river flow is investigated and Seyhan River and Cine River was chosen as case study area. For the Seyhan River, the forecasting models are established using combinations of antecedent daily river flow records. On the other hand, for the Cine River, daily river flow and rainfall records are used in input layer. For both stations, the data sets are divided into three subsets, training, testing and verification data set. The river flow forecasting models having various input structures are trained and tested to investigate the applicability of ANFIS and ANN and AR methods. The results of all models for both training and testing are evaluated and the best fit input structures and methods for both stations are determined according to criteria of performance evaluation. Moreover the best fit forecasting models are also verified by verification set which was not used in training and testing processes and compared according to criteria. The results demonstrate that ANFIS model is superior to the GRNN and FFNN forecasting models, and ANFIS can be successfully applied and provide high accuracy and reliability for daily river flow forecasting.

  16. Comparison of Artificial Intelligence Techniques for river flow forecasting

    Directory of Open Access Journals (Sweden)

    M. Firat

    2008-01-01

    Full Text Available The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS and Artificial Neural Network (ANN methods, Generalized Regression Neural Networks (GRNN and Feed Forward Neural Networks (FFNN, and Auto-Regressive (AR models for forecasting of daily river flow is investigated and Seyhan River and Cine River was chosen as case study area. For the Seyhan River, the forecasting models are established using combinations of antecedent daily river flow records. On the other hand, for the Cine River, daily river flow and rainfall records are used in input layer. For both stations, the data sets are divided into three subsets, training, testing and verification data set. The river flow forecasting models having various input structures are trained and tested to investigate the applicability of ANFIS and ANN and AR methods. The results of all models for both training and testing are evaluated and the best fit input structures and methods for both stations are determined according to criteria of performance evaluation. Moreover the best fit forecasting models are also verified by verification set which was not used in training and testing processes and compared according to criteria. The results demonstrate that ANFIS model is superior to the GRNN and FFNN forecasting models, and ANFIS can be successfully applied and provide high accuracy and reliability for daily river flow forecasting.

  17. Predicting chick body mass by artificial intelligence-based models

    Directory of Open Access Journals (Sweden)

    Patricia Ferreira Ponciano Ferraz

    2014-07-01

    Full Text Available The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks - with the variables dry-bulb air temperature, duration of thermal stress (days, chick age (days, and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs and neuro-fuzzy networks (NFNs. The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.

  18. Intelligent networked teleoperation control

    CERN Document Server

    Li, Zhijun; Su, Chun-Yi

    2015-01-01

    This book describes a unified framework for networked teleoperation systems involving multiple research fields: networked control systems for linear and nonlinear forms, bilateral teleoperation, trilateral teleoperation, multilateral teleoperation and cooperative teleoperation. It closely examines networked control as a field at the intersection of systems & control and robotics and presents a number of experimental case studies on testbeds for robotic systems, including networked haptic devices, robotic network systems and sensor network systems. The concepts and results outlined are easy to understand, even for readers fairly new to the subject. As such, the book offers a valuable reference work for researchers and engineers in the fields of systems & control and robotics.

  19. AN ARTIFICIAL INTELLIGENCE-BASED DISTANCE EDUCATION SYSTEM: Artimat

    National Research Council Canada - National Science Library

    Vasif NABIYEV; Hasan KARAL; Selahattin ARSLAN; Ali Kürsat ERUMIT; Ayça CEBI

    2013-01-01

    The purpose of this study is to evaluate the artificial intelligence-based distance education system called as ARTIMAT, which has been prepared in order to improve mathematical problem solving skills...

  20. Artificial intelligence: Learning to play Go from scratch

    Science.gov (United States)

    Singh, Satinder; Okun, Andy; Jackson, Andrew

    2017-10-01

    An artificial-intelligence program called AlphaGo Zero has mastered the game of Go without any human data or guidance. A computer scientist and two members of the American Go Association discuss the implications. See Article p.354

  1. voltage compensation using artificial neural network

    African Journals Online (AJOL)

    Offor Theophilos

    VOLTAGE COMPENSATION USING ARTIFICIAL NEURAL NETWORK: A CASE STUDY OF. RUMUOLA ... using artificial neural network (ANN) controller based dynamic voltage restorer (DVR). ... substation by simulating with sample of average voltage for Omerelu, Waterlines, Rumuola, Shell Industrial and Barracks.

  2. [Artificial neural networks for decision making in urologic oncology].

    Science.gov (United States)

    Remzi, M; Djavan, B

    2007-06-01

    This chapter presents a detailed introduction regarding Artificial Neural Networks (ANNs) and their contribution to modern Urologic Oncology. It includes a description of ANNs methodology and points out the differences between Artifical Intelligence and traditional statistic models in terms of usefulness for patients and clinicians, and its advantages over current statistical analysis.

  3. Learning comunication strategies for distributed artificial intelligence

    Science.gov (United States)

    Kinney, Michael; Tsatsoulis, Costas

    1992-08-01

    We present a methodology that allows collections of intelligent system to automatically learn communication strategies, so that they can exchange information and coordinate their problem solving activity. In our methodology communication between agents is determined by the agents themselves, which consider the progress of their individual problem solving activities compared to the communication needs of their surrounding agents. Through learning, communication lines between agents might be established or disconnected, communication frequencies modified, and the system can also react to dynamic changes in the environment that might force agents to cease to exist or to be added. We have established dynamic, quantitative measures of the usefulness of a fact, the cost of a fact, the work load of an agent, and the selfishness of an agent (a measure indicating an agent's preference between transmitting information versus performing individual problem solving), and use these values to adapt the communication between intelligent agents. In this paper we present the theoretical foundations of our work together with experimental results and performance statistics of networks of agents involved in cooperative problem solving activities.

  4. Interaction between Software Engineering and Artificial Intelligence- A Review

    OpenAIRE

    Prince Jain

    2011-01-01

    Software engineering and artificial intelligence is the two field of the computer science. During the last decades, the disciplines of Artificial Intelligence and Software Engineering have developedseparately without the much exchange of research outcomes. However, both fields of computer science have different characteristics, benefits and limitations. This statement opens many possibilities and ideas for research. One idea is that the researcher applies the available methods, tools and tech...

  5. The Role of Artificial Intelligence Technologies in Crisis Response

    OpenAIRE

    Khalil, Khaled M.; Abdel-Aziz, M.; Nazmy, Taymour T.; Salem, Abdel-Badeeh M.

    2008-01-01

    Crisis response poses many of the most difficult information technology in crisis management. It requires information and communication-intensive efforts, utilized for reducing uncertainty, calculating and comparing costs and benefits, and managing resources in a fashion beyond those regularly available to handle routine problems. In this paper, we explore the benefits of artificial intelligence technologies in crisis response. This paper discusses the role of artificial intelligence technolo...

  6. Artificial Intelligence Information Sources for the Beginner and Expert

    Science.gov (United States)

    1991-05-01

    associations, and a calendar of events for the subjects such as machine vision and artificial intelligence. Annotated bibliography on the foundations of Al...requestlvuse.vanderbilt.edu Al in United Kingdom ikbsbb-request@informatics.rutherford.ac.uk AI Vision research vision -listads.cm Artificial Life alife...ries IV. IFIP World Computer Congress. Annual Review of Computer science. Industrial Robot Applied Arificial Intelligence Inference Engine Applied

  7. ARTIFICIAL INTELLIGENCE PLANNING TECHNIQUES FOR ADAPTIVE VIRTUAL COURSE CONSTRUCTION

    OpenAIRE

    NÉSTOR DARÍO DUQUE; DEMETRIO ARTURO OVALLE

    2011-01-01

    This paper aims at presenting a planning model for adapting the behavior of virtual courses based on artificial intelligence techniques, in particular using not only a multi-agent system approach, but also artificial intelligence planning methods. The design and implementation of the system by means of a pedagogical multi-agent approach and the definition of a framework to specify the adaptation strategy allow us to incorporate several pedagogical and technological approaches that are in acco...

  8. A Python Engine for Teaching Artificial Intelligence in Games

    OpenAIRE

    Riedl, Mark O.

    2015-01-01

    Computer games play an important role in our society and motivate people to learn computer science. Since artificial intelligence is integral to most games, they can also be used to teach artificial intelligence. We introduce the Game AI Game Engine (GAIGE), a Python game engine specifically designed to teach about how AI is used in computer games. A progression of seven assignments builds toward a complete, working Multi-User Battle Arena (MOBA) game. We describe the engine, the assignments,...

  9. 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.

  10. Computational neuroscience for advancing artificial intelligence

    Directory of Open Access Journals (Sweden)

    Fernando P. Ponce

    2011-07-01

    Full Text Available resumen del libro de Alonso, E. y Mondragón, E. (2011. Hershey, NY: Medical Information Science Reference. La neurociencia como disciplinapersigue el entendimiento del cerebro y su relación con el funcionamiento de la mente a través del análisis de la comprensión de la interacción de diversos procesos físicos, químicos y biológicos (Bassett & Gazzaniga, 2011. Por otra parte, numerosas disciplinasprogresivamente han realizado significativas contribuciones en esta empresa tales como la matemática, la psicología o la filosofía, entre otras. Producto de este esfuerzo, es que junto con la neurociencia tradicional han aparecido disciplinas complementarias como la neurociencia cognitiva, la neuropsicología o la neurocienciacomputacional (Bengio, 2007; Dayan & Abbott, 2005. En el contexto de la neurociencia computacional como disciplina complementaria a laneurociencia tradicional. Alonso y Mondragón (2011 editan el libroComputacional Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications.

  11. [Artificial intelligence] AI for protection systems

    Energy Technology Data Exchange (ETDEWEB)

    Aggarwal, R.; Johns, A.

    1997-12-31

    The reliable operation of large power systems with small stability margins is highly dependent on control systems and protection devices. Progress in the field of microprocessor systems and demanding requirements in respect of the performance of protective relays are the reasons for digital device applications to power system protection. The superiority of numeric protection over its analogue alternatives is attributed to such factors as accurate extraction of the fundamental voltage and current components through filtering, functional benefits resulting from multi-processor design and extensive self-monitoring, etc. However, all these reasons have not led to a major impact on speed, sensitivity and selectivity of primary protective relays, and the gains are only marginal; this is so because conventional digital relays still rely on deterministic signal models and a heuristic approach for decision making, so that only a fraction of the information contained within voltage and current signals as well as knowledge about the plant to be protected is used. The performance of digital relays may be substantially improved if the decision making is based on elements of artificial intelligence (AI). (Author)

  12. Artificial intelligence: contemporary applications and future compass.

    Science.gov (United States)

    Khanna, Sunali

    2010-08-01

    The clinical use of information technology in the dental profession has increased substantially in the past 10 to 20 years. In most developing countries an insufficiency of medical and dental specialists has increased the mortality of patients suffering from various diseases. Employing technology, especially artificial intelligence technology, in medical and dental application could reduce cost, time, human expertise and medical error. This approach has the potential to revolutionise the dental public health scenario in developing countries. Clinical decision support systems (CDSS) are computer programs that are designed to provide expert support for health professionals. The applications in dental sciences vary from dental emergencies to differential diagnosis of orofacial pain, radiographic interpretations, analysis of facial growth in orthodontia to prosthetic dentistry. However, despite the recognised need for CDSS, the implementation of these systems has been limited and slow. This can be attributed to lack of formal evaluation of the systems, challenges in developing standard representations, cost and practitioner scepticism about the value and feasibility of CDSS. Increasing public awareness of safety and quality has accelerated the adoption of generic knowledge based CDSS. Information technology applications for dental practice continue to develop rapidly and will hopefully contribute to reduce the morbidity and mortality of oral and maxillofacial diseases and in turn impact patient care.

  13. The Artificial Intelligence Workbench: a retrospective review

    Directory of Open Access Journals (Sweden)

    Hugo LÓPEZ-FERNÁNDEZ

    2016-10-01

    Full Text Available Last decade, biomedical and bioinformatics researchers have been demanding advanced and user-friendly applications for real use in practice. In this context, the Artificial Intelligence Workbench, an open-source Java desktop application framework for scientific software development, emerged with the goal of provid-ing support to both fundamental and applied research in the domain of transla-tional biomedicine and bioinformatics. AIBench automatically provides function-alities that are common to scientific applications, such as user parameter defini-tion, logging facilities, multi-threading execution, experiment repeatability, work-flow management, and fast user interface development, among others. Moreover, AIBench promotes a reusable component based architecture, which also allows assembling new applications by the reuse of libraries from existing projects or third-party software. Ten years have passed since the first release of AIBench, so it is time to look back and check if it has fulfilled the purposes for which it was conceived to and how it evolved over time.

  14. Using artificial intelligence to automate remittance processing.

    Science.gov (United States)

    Adams, W T; Snow, G M; Helmick, P M

    1998-06-01

    The consolidated business office of the Allegheny Health Education Research Foundation (AHERF), a large integrated healthcare system based in Pittsburgh, Pennsylvania, sought to improve its cash-related business office activities by implementing an automated remittance processing system that uses artificial intelligence. The goal was to create a completely automated system whereby all monies it processed would be tracked, automatically posted, analyzed, monitored, controlled, and reconciled through a central database. Using a phased approach, the automated payment system has become the central repository for all of the remittances for seven of the hospitals in the AHERF system and has allowed for the complete integration of these hospitals' existing billing systems, document imaging system, and intranet, as well as the new automated payment posting, and electronic cash tracking and reconciling systems. For such new technology, which is designed to bring about major change, factors contributing to the project's success were adequate planning, clearly articulated objectives, marketing, end-user acceptance, and post-implementation plan revision.

  15. Artificial intelligence in mitral valve analysis.

    Science.gov (United States)

    Jeganathan, Jelliffe; Knio, Ziyad; Amador, Yannis; Hai, Ting; Khamooshian, Arash; Matyal, Robina; Khabbaz, Kamal R; Mahmood, Feroze

    2017-01-01

    Echocardiographic analysis of mitral valve (MV) has become essential for diagnosis and management of patients with MV disease. Currently, the various software used for MV analysis require manual input and are prone to interobserver variability in the measurements. The aim of this study is to determine the interobserver variability in an automated software that uses artificial intelligence for MV analysis. Retrospective analysis of intraoperative three-dimensional transesophageal echocardiography data acquired from four patients with normal MV undergoing coronary artery bypass graft surgery in a tertiary hospital. Echocardiographic data were analyzed using the eSie Valve Software (Siemens Healthcare, Mountain View, CA, USA). Three examiners analyzed three end-systolic (ES) frames from each of the four patients. A total of 36 ES frames were analyzed and included in the study. A multiple mixed-effects ANOVA model was constructed to determine if the examiner, the patient, and the loop had a significant effect on the average value of each parameter. A Bonferroni correction was used to correct for multiple comparisons, and P = 0.0083 was considered to be significant. Examiners did not have an effect on any of the six parameters tested. Patient and loop had an effect on the average parameter value for each of the six parameters as expected (P < 0.0083 for both). We were able to conclude that using automated analysis, it is possible to obtain results with good reproducibility, which only requires minimal user intervention.

  16. [Artificial intelligence applied to radiation oncology].

    Science.gov (United States)

    Bibault, J-E; Burgun, A; Giraud, P

    2017-05-01

    Performing randomised comparative clinical trials in radiation oncology remains a challenge when new treatment modalities become available. One of the most recent examples is the lack of phase III trials demonstrating the superiority of intensity-modulated radiation therapy in most of its current indications. A new paradigm is developing that consists in the mining of large databases to answer clinical or translational issues. Beyond national databases (such as SEER or NCDB), that often lack the necessary level of details on the population studied or the treatments performed, electronic health records can be used to create detailed phenotypic profiles of any patients. In parallel, the Record-and-Verify Systems used in radiation oncology precisely document the planned and performed treatments. Artificial Intelligence and machine learning algorithms can be used to incrementally analyse these data in order to generate hypothesis to better personalize treatments. This review discusses how these methods have already been used in previous studies. Copyright © 2017 Société française de radiothérapie oncologique (SFRO). Published by Elsevier SAS. All rights reserved.

  17. Artificial Intelligence, Physiological Genomics, and Precision Medicine.

    Science.gov (United States)

    Williams, Anna Marie; Liu, Yong; Regner, Kevin R; Jotterand, Fabrice; Liu, Pengyuan; Liang, Mingyu

    2018-01-26

    Big data is a major driver in the development of precision medicine. Efficient analysis methods are needed to transform big data into clinically-actionable knowledge. To accomplish this, many researchers are turning towards machine learning (ML), an approach of artificial intelligence (AI) that utilizes modern algorithms to give computers the ability to learn. Much of the effort to advance ML for precision medicine has been focused on the development and implementation of algorithms and the generation of ever larger quantities of genomic sequence data and electronic health records. However, relevance and accuracy of the data are as important as quantity of data in the advancement of ML for precision medicine. For common diseases, physiological genomic readouts in disease-applicable tissues may be an effective surrogate to measure the effect of genetic and environmental factors and their interactions that underlie disease development and progression. Disease-applicable tissue may be difficult to obtain, but there are important exceptions such as kidney needle biopsy specimens. As AI continues to advance, new analytical approaches, including those that go beyond data correlation, need to be developed and ethical issues of AI need to be addressed. Physiological genomic readouts in disease-relevant tissues, combined with advanced AI, can be a powerful approach for precision medicine for common diseases.

  18. The Potential Role of Artificial Intelligence Technology in Education.

    Science.gov (United States)

    Salem, Abdel-Badeeh M.

    The field of Artificial Intelligence (AI) and Education has traditionally a technology-based focus, looking at the ways in which AI can be used in building intelligent educational software. In addition AI can also provide an excellent methodology for learning and reasoning from the human experiences. This paper presents the potential role of AI in…

  19. Ethico-epistemological implications of artificial intelligence for ...

    African Journals Online (AJOL)

    The celebrated efficiency of these techno-scientific products in outdoing human persons in assignments previously reserved for man such as: translations, warfare and industry raises the level of unemployment and comes with an epistemological challenge of man‟s intelligence. Not only does artificial intelligence threaten ...

  20. APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN PROCESS FAULT DIAGNOSIS

    Directory of Open Access Journals (Sweden)

    M.A. HUSSAIN

    2007-12-01

    Full Text Available Chemical processes are systems that include complicated network of material, energy and process flow. As time passes, the performance of chemical process gradually degrades due to the deterioration of process equipments and components. The early detection and diagnosis of faults in chemical processes is very important both from the viewpoint of plant safety as well as reduced manufacturing costs. The conventional way used in fault detection and diagnosis is through the use of models of the process, which is not easy to be achieved in many cases. In recent years, an artificial intelligence technique such as neural network has been successfully used for pattern recognition and as such it can be suitable for use in fault diagnosis of processes [1]. The application of neural network methods in process fault detection and diagnosis is demonstrated in this work in two case studies using simulated chemical plant systems. Both systems were successfully diagnosed of the faults introduced in them. The neural networks were able to generalise to successfully diagnosed fault combinations it was not explicitly trained upon. Thus, neural network can be fully applied in industries as it has shown several advantages over the conventional way in fault diagnosis.

  1. Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches

    Directory of Open Access Journals (Sweden)

    Atris Suyantohadi

    2010-03-01

    Full Text Available The natural process on plant growth system has a complex system and it has could be developed on characteristic studied using intelligent approaches conducting with artificial life system. The approaches on examining the natural process on soybean (Glycine Max L.Merr plant growth have been analyzed and synthesized in these research through modeling using Artificial Neural Network (ANN and Lindenmayer System (L-System methods. Research aimed to design and to visualize plant growth modeling on the soybean varieties which these could help for studying botany of plant based on fertilizer compositions on plant growth with Nitrogen (N, Phosphor (P and Potassium (K. The soybean plant growth has been analyzed based on the treatments of plant fertilizer compositions in the experimental research to develop plant growth modeling. By using N, P, K fertilizer compositions, its capable result on the highest production 2.074 tons/hectares. Using these models, the simulation on artificial life for describing identification and visualization on the characteristic of soybean plant growth could be demonstrated and applied.

  2. Computerized detection of breast cancer with artificial intelligence and thermograms.

    Science.gov (United States)

    Ng, E Y-K; Fok, S C; Peh, Y C; Ng, F C; Sim, L S J

    2002-01-01

    This paper shows the concurrent use of thermography and artificial neural networks (ANN) for the diagnosis of breast cancer, a disease that is growing in prominence in women all over the world. It has been reported that breast thermography itself could detect breast cancer up to 10 years earlier than the conventional golden methods such as mammography, in particular in the younger patient. However, the accuracy of thermography is dependent on many factors such as the symmetry of the breasts' temperature and temperature stability. A woman's body temperature is known to be stable in certain periods after menstruation and it was found that the accuracy of thermography in women whose thermal images are taken in a suitable period (5th - 12th and 21st day of menstruation) is higher (80%) than the total population of patients (73%). The stability of the body temperature will depend on physiological state. This paper examines the use of ANN to complement the infrared heat radiating from the surface of the body with other physiological data. Four backpropagation neural networks were developed and trained using the results from the Singapore General Hospital patients' physiological data and thermographs. Owing to the inaccuracies found in thermography and the low population size gathered for this project, the networks developed could only accurately diagnose about 61.54% of the breast cancer cases. Nevertheless, the basic neural network framework has been established and it has great potential for future development of an intelligent breast cancer diagnosis system. This would be especially useful to the teenagers and young adults who are unsuitable for mammography at a young age. An intelligent breast thermography-neural network will be able to give an accurate diagnosis of breast cancer and can make a positive impact on breast disease detection.

  3. Computational Narrative Intelligence: A Human-Centered Goal for Artificial Intelligence

    OpenAIRE

    Riedl, Mark O.

    2016-01-01

    Narrative intelligence is the ability to craft, tell, understand, and respond affectively to stories. We argue that instilling artificial intelligences with computational narrative intelligence affords a number of applications beneficial to humans. We lay out some of the machine learning challenges necessary to solve to achieve computational narrative intelligence. Finally, we argue that computational narrative is a practical step towards machine enculturation, the teaching of sociocultural v...

  4. Application of Artificial Intelligence in Prediction of Road Freight Transportation

    Directory of Open Access Journals (Sweden)

    Bogna Mrowczynska

    2017-08-01

    Full Text Available Road freight transport often requires the prediction of volume. Such knowledge is necessary to capture trends in the industry and support decision making by large and small trucking companies. The aim of the presented work is to demonstrate that application of some artificial intelligence methods can improve the accuracy of the forecasts. The first method employed was double exponential smoothing. The modification of this method has been proposed. Not only the parameters but also the initial values were set in order to minimize the mean absolute percentage error (MAPE using the artificial immune system. This change resulted in a marked improvement in the effects of minimization, and suggests that the variability of the initial value of S2 has an impact on this result. Then, the forecasting Bayesian networks method was applied. The Bayesian network approach is able to take into account not only the historical data concerning the volume of freight, but also the data related to the overall state of the national economy. This significantly improves the quality of forecasting. The application of this approach can also help in predicting the trend changes caused by overall state of economy, which is rather impossible when analysing only the historical data.

  5. ARTIFICIAL INTELLIGENCE IN SPORTS BIOMECHANICS: NEW DAWN OR FALSE HOPE?

    Directory of Open Access Journals (Sweden)

    Roger Bartlett

    2006-12-01

    Full Text Available This article reviews developments in the use of Artificial Intelligence (AI in sports biomechanics over the last decade. It outlines possible uses of Expert Systems as diagnostic tools for evaluating faults in sports movements ('techniques' and presents some example knowledge rules for such an expert system. It then compares the analysis of sports techniques, in which Expert Systems have found little place to date, with gait analysis, in which they are routinely used. Consideration is then given to the use of Artificial Neural Networks (ANNs in sports biomechanics, focusing on Kohonen self-organizing maps, which have been the most widely used in technique analysis, and multi-layer networks, which have been far more widely used in biomechanics in general. Examples of the use of ANNs in sports biomechanics are presented for javelin and discus throwing, shot putting and football kicking. I also present an example of the use of Evolutionary Computation in movement optimization in the soccer throw in, which predicted an optimal technique close to that in the coaching literature. After briefly overviewing the use of AI in both sports science and biomechanics in general, the article concludes with some speculations about future uses of AI in sports biomechanics.

  6. Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis

    Directory of Open Access Journals (Sweden)

    Jiqiang Niu

    2016-05-01

    Full Text Available In this article, we conducted the evaluation of artificial intelligence research from 1990–2014 by using bibliometric analysis. We introduced spatial analysis and social network analysis as geographic information retrieval methods for spatially-explicit bibliometric analysis. This study is based on the analysis of data obtained from database of the Science Citation Index Expanded (SCI-Expanded and Conference Proceedings Citation Index-Science (CPCI-S. Our results revealed scientific outputs, subject categories and main journals, author productivity and geographic distribution, international productivity and collaboration, and hot issues and research trends. The growth of article outputs in artificial intelligence research has exploded since the 1990s, along with increasing collaboration, reference, and citations. Computer science and engineering were the most frequently-used subject categories in artificial intelligence studies. The top twenty productive authors are distributed in countries with a high investment of research and development. The United States has the highest number of top research institutions in artificial intelligence, producing most single-country and collaborative articles. Although there is more and more collaboration among institutions, cooperation, especially international ones, are not highly prevalent in artificial intelligence research as expected. The keyword analysis revealed interesting research preferences, confirmed that methods, models, and application are in the central position of artificial intelligence. Further, we found interesting related keywords with high co-occurrence frequencies, which have helped identify new models and application areas in recent years. Bibliometric analysis results from our study will greatly facilitate the understanding of the progress and trends in artificial intelligence, in particular, for those researchers interested in domain-specific AI-driven problem-solving. This will be

  7. Training Software in Artificial-Intelligence Computing Techniques

    Science.gov (United States)

    Howard, Ayanna; Rogstad, Eric; Chalfant, Eugene

    2005-01-01

    The Artificial Intelligence (AI) Toolkit is a computer program for training scientists, engineers, and university students in three soft-computing techniques (fuzzy logic, neural networks, and genetic algorithms) used in artificial-intelligence applications. The program promotes an easily understandable tutorial interface, including an interactive graphical component through which the user can gain hands-on experience in soft-computing techniques applied to realistic example problems. The tutorial provides step-by-step instructions on the workings of soft-computing technology, whereas the hands-on examples allow interaction and reinforcement of the techniques explained throughout the tutorial. In the fuzzy-logic example, a user can interact with a robot and an obstacle course to verify how fuzzy logic is used to command a rover traverse from an arbitrary start to the goal location. For the genetic algorithm example, the problem is to determine the minimum-length path for visiting a user-chosen set of planets in the solar system. For the neural-network example, the problem is to decide, on the basis of input data on physical characteristics, whether a person is a man, woman, or child. The AI Toolkit is compatible with the Windows 95,98, ME, NT 4.0, 2000, and XP operating systems. A computer having a processor speed of at least 300 MHz, and random-access memory of at least 56MB is recommended for optimal performance. The program can be run on a slower computer having less memory, but some functions may not be executed properly.

  8. Artificial intelligence and nuclear power. Report by the Technology Transfer Artificial Intelligence Task Team

    Energy Technology Data Exchange (ETDEWEB)

    1985-06-01

    The Artificial Intelligence Task Team was organized to review the status of Artificial Intelligence (AI) technology, identify guidelines for AI work, and to identify work required to allow the nuclear industry to realize maximum benefit from this technology. The state of the nuclear industry was analyzed to determine where the application of AI technology could be of greatest benefit. Guidelines and criteria were established to focus on those particular problem areas where AI could provide the highest possible payoff to the industry. Information was collected from government, academic, and private organizations. Very little AI work is now being done to specifically support the nuclear industry. The AI Task Team determined that the establishment of a Strategic Automation Initiative (SAI) and the expansion of the DOE Technology Transfer program would ensure that AI technology could be used to develop software for the nuclear industry that would have substantial financial payoff to the industry. The SAI includes both long and short term phases. The short-term phase includes projects which would demonstrate that AI can be applied to the nuclear industry safely, and with substantial financial benefit. The long term phase includes projects which would develop AI technologies with specific applicability to the nuclear industry that would not be developed by people working in any other industry.

  9. Application of artificial intelligence for detecting derived viruses

    CSIR Research Space (South Africa)

    Asiru, OF

    2017-06-01

    Full Text Available of artificial intelligence in detecting computing viruses (Gang & Zhongquan, 2014). However, more research still needs to be conducted in order to fully explore options from the field of artificial intelligence. Furthermore, few research efforts focus... stream_source_info Asiru_19173_2017.pdf.txt stream_content_type text/plain stream_size 35251 Content-Encoding UTF-8 stream_name Asiru_19173_2017.pdf.txt Content-Type text/plain; charset=UTF-8 Application of Artificial...

  10. Electron beam lithographic modeling assisted by artificial intelligence technology

    Science.gov (United States)

    Nakayamada, Noriaki; Nishimura, Rieko; Miura, Satoru; Nomura, Haruyuki; Kamikubo, Takashi

    2017-07-01

    We propose a new concept of tuning a point-spread function (a "kernel" function) in the modeling of electron beam lithography using the machine learning scheme. Normally in the work of artificial intelligence, the researchers focus on the output results from a neural network, such as success ratio in image recognition or improved production yield, etc. In this work, we put more focus on the weights connecting the nodes in a convolutional neural network, which are naturally the fractions of a point-spread function, and take out those weighted fractions after learning to be utilized as a tuned kernel. Proof-of-concept of the kernel tuning has been demonstrated using the examples of proximity effect correction with 2-layer network, and charging effect correction with 3-layer network. This type of new tuning method can be beneficial to give researchers more insights to come up with a better model, yet it might be too early to be deployed to production to give better critical dimension (CD) and positional accuracy almost instantly.

  11. Noncognitive formal approach to knowledge representation in artificial intelligence

    Energy Technology Data Exchange (ETDEWEB)

    McMannama, J.A.

    1986-01-01

    With the entry of artificial intelligence (Al) into real-time applications, a rigorous analysis of Al expert systems is required in order to validate them for operational use. To satisfy this requirement for analysis of the associated knowledge representations, the techniques of formal language theory are used. A combination of theorems, proofs and problem-solving techniques from formal language theory are employed to analyze language equivalents of the more commonly used Al knowledge representations of production rules (excluding working memory or situation data) and semantic networks. Using formal language characteristics, it is shown that no single support-tool or automatic-programming tool can ever be constructed that can handle all possible production-rule or semantic-network variations. Additionally, it is shown that the entire set of finite production-rule languages is able to be stored in and retrieved from finite semantic-network languages. In effect, the semantic-network structure is shown to be a viable candidate for a centralized database of knowledge.

  12. Artificial intelligence in mitral valve analysis

    Directory of Open Access Journals (Sweden)

    Jelliffe Jeganathan

    2017-01-01

    Full Text Available Background: Echocardiographic analysis of mitral valve (MV has become essential for diagnosis and management of patients with MV disease. Currently, the various software used for MV analysis require manual input and are prone to interobserver variability in the measurements. Aim: The aim of this study is to determine the interobserver variability in an automated software that uses artificial intelligence for MV analysis. Settings and Design: Retrospective analysis of intraoperative three-dimensional transesophageal echocardiography data acquired from four patients with normal MV undergoing coronary artery bypass graft surgery in a tertiary hospital. Materials and Methods: Echocardiographic data were analyzed using the eSie Valve Software (Siemens Healthcare, Mountain View, CA, USA. Three examiners analyzed three end-systolic (ES frames from each of the four patients. A total of 36 ES frames were analyzed and included in the study. Statistical Analysis: A multiple mixed-effects ANOVA model was constructed to determine if the examiner, the patient, and the loop had a significant effect on the average value of each parameter. A Bonferroni correction was used to correct for multiple comparisons, and P = 0.0083 was considered to be significant. Results: Examiners did not have an effect on any of the six parameters tested. Patient and loop had an effect on the average parameter value for each of the six parameters as expected (P < 0.0083 for both. Conclusion: We were able to conclude that using automated analysis, it is possible to obtain results with good reproducibility, which only requires minimal user intervention.

  13. Artificial intelligence in drug combination therapy.

    Science.gov (United States)

    Tsigelny, Igor F

    2018-02-09

    Currently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many cases, one drug cannot target all necessary points of intervention. For example, in cancer therapy, a physician often meets a patient having a genomic profile including more than five molecular aberrations. Drug combination therapy has been an area of interest for a while, for example the classical work of Loewe devoted to the synergism of drugs was published in 1928-and it is still used in calculations for optimal drug combinations. More recently, over the past several years, there has been an explosion in the available information related to the properties of drugs and the biomedical parameters of patients. For the drugs, hundreds of 2D and 3D molecular descriptors for medicines are now available, while for patients, large data sets related to genetic/proteomic and metabolomics profiles of the patients are now available, as well as the more traditional data relating to the histology, history of treatments, pretreatment state of the organism, etc. Moreover, during disease progression, the genetic profile can change. Thus, the ability to optimize drug combinations for each patient is rapidly moving beyond the comprehension and capabilities of an individual physician. This is the reason, that biomedical informatics methods have been developed and one of the more promising directions in this field is the application of artificial intelligence (AI). In this review, we discuss several AI methods that have been successfully implemented in several instances of combination drug therapy from HIV, hypertension, infectious diseases to cancer. The data clearly show that the combination of rule-based expert systems with machine learning algorithms may be promising direction in this field. © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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

    CERN Document Server

    2013-01-01

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

  15. Development of hybrid artificial intelligent based handover decision algorithm

    Directory of Open Access Journals (Sweden)

    A.M. Aibinu

    2017-04-01

    Full Text Available The possibility of seamless handover remains a mirage despite the plethora of existing handover algorithms. The underlying factor responsible for this has been traced to the Handover decision module in the Handover process. Hence, in this paper, the development of novel hybrid artificial intelligent handover decision algorithm has been developed. The developed model is made up of hybrid of Artificial Neural Network (ANN based prediction model and Fuzzy Logic. On accessing the network, the Received Signal Strength (RSS was acquired over a period of time to form a time series data. The data was then fed to the newly proposed k-step ahead ANN-based RSS prediction system for estimation of prediction model coefficients. The synaptic weights and adaptive coefficients of the trained ANN was then used to compute the k-step ahead ANN based RSS prediction model coefficients. The predicted RSS value was later codified as Fuzzy sets and in conjunction with other measured network parameters were fed into the Fuzzy logic controller in order to finalize handover decision process. The performance of the newly developed k-step ahead ANN based RSS prediction algorithm was evaluated using simulated and real data acquired from available mobile communication networks. Results obtained in both cases shows that the proposed algorithm is capable of predicting ahead the RSS value to about ±0.0002 dB. Also, the cascaded effect of the complete handover decision module was also evaluated. Results obtained show that the newly proposed hybrid approach was able to reduce ping-pong effect associated with other handover techniques.

  16. Evolvable mathematical models: A new artificial Intelligence paradigm

    Science.gov (United States)

    Grouchy, Paul

    We develop a novel Artificial Intelligence paradigm to generate autonomously artificial agents as mathematical models of behaviour. Agent/environment inputs are mapped to agent outputs via equation trees which are evolved in a manner similar to Symbolic Regression in Genetic Programming. Equations are comprised of only the four basic mathematical operators, addition, subtraction, multiplication and division, as well as input and output variables and constants. From these operations, equations can be constructed that approximate any analytic function. These Evolvable Mathematical Models (EMMs) are tested and compared to their Artificial Neural Network (ANN) counterparts on two benchmarking tasks: the double-pole balancing without velocity information benchmark and the challenging discrete Double-T Maze experiments with homing. The results from these experiments show that EMMs are capable of solving tasks typically solved by ANNs, and that they have the ability to produce agents that demonstrate learning behaviours. To further explore the capabilities of EMMs, as well as to investigate the evolutionary origins of communication, we develop NoiseWorld, an Artificial Life simulation in which interagent communication emerges and evolves from initially noncommunicating EMM-based agents. Agents develop the capability to transmit their x and y position information over a one-dimensional channel via a complex, dialogue-based communication scheme. These evolved communication schemes are analyzed and their evolutionary trajectories examined, yielding significant insight into the emergence and subsequent evolution of cooperative communication. Evolved agents from NoiseWorld are successfully transferred onto physical robots, demonstrating the transferability of EMM-based AIs from simulation into physical reality.

  17. Decade Review (1999-2009): Artificial Intelligence Techniques in Student Modeling

    Science.gov (United States)

    Drigas, Athanasios S.; Argyri, Katerina; Vrettaros, John

    Artificial Intelligence applications in educational field are getting more and more popular during the last decade (1999-2009) and that is why much relevant research has been conducted. In this paper, we present the most interesting attempts to apply artificial intelligence methods such as fuzzy logic, neural networks, genetic programming and hybrid approaches such as neuro - fuzzy systems and genetic programming neural networks (GPNN) in student modeling. This latest research trend is a part of every Intelligent Tutoring System and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student's answers. In this paper, we make a brief presentation of methods used to point out their qualities and then we attempt a navigation to the most representative studies sought in the decade of our interest after classifying them according to the principal aim they attempted to serve.

  18. Advances in neural networks computational intelligence for ICT

    CERN Document Server

    Esposito, Anna; Morabito, Francesco; Pasero, Eros

    2016-01-01

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

  19. Artificial Intelligence Techniques for Predicting and Mapping Daily Pan Evaporation

    Science.gov (United States)

    Arunkumar, R.; Jothiprakash, V.; Sharma, Kirty

    2017-08-01

    In this study, Artificial Intelligence techniques such as Artificial Neural Network (ANN), Model Tree (MT) and Genetic Programming (GP) are used to develop daily pan evaporation time-series (TS) prediction and cause-effect (CE) mapping models. Ten years of observed daily meteorological data such as maximum temperature, minimum temperature, relative humidity, sunshine hours, dew point temperature and pan evaporation are used for developing the models. For each technique, several models are developed by changing the number of inputs and other model parameters. The performance of each model is evaluated using standard statistical measures such as Mean Square Error, Mean Absolute Error, Normalized Mean Square Error and correlation coefficient (R). The results showed that daily TS-GP (4) model predicted better with a correlation coefficient of 0.959 than other TS models. Among various CE models, CE-ANN (6-10-1) resulted better than MT and GP models with a correlation coefficient of 0.881. Because of the complex non-linear inter-relationship among various meteorological variables, CE mapping models could not achieve the performance of TS models. From this study, it was found that GP performs better for recognizing single pattern (time series modelling), whereas ANN is better for modelling multiple patterns (cause-effect modelling) in the data.

  20. Artificial Intelligence Techniques for Predicting and Mapping Daily Pan Evaporation

    Science.gov (United States)

    Arunkumar, R.; Jothiprakash, V.; Sharma, Kirty

    2017-09-01

    In this study, Artificial Intelligence techniques such as Artificial Neural Network (ANN), Model Tree (MT) and Genetic Programming (GP) are used to develop daily pan evaporation time-series (TS) prediction and cause-effect (CE) mapping models. Ten years of observed daily meteorological data such as maximum temperature, minimum temperature, relative humidity, sunshine hours, dew point temperature and pan evaporation are used for developing the models. For each technique, several models are developed by changing the number of inputs and other model parameters. The performance of each model is evaluated using standard statistical measures such as Mean Square Error, Mean Absolute Error, Normalized Mean Square Error and correlation coefficient (R). The results showed that daily TS-GP (4) model predicted better with a correlation coefficient of 0.959 than other TS models. Among various CE models, CE-ANN (6-10-1) resulted better than MT and GP models with a correlation coefficient of 0.881. Because of the complex non-linear inter-relationship among various meteorological variables, CE mapping models could not achieve the performance of TS models. From this study, it was found that GP performs better for recognizing single pattern (time series modelling), whereas ANN is better for modelling multiple patterns (cause-effect modelling) in the data.

  1. [Artificial intelligence to assist clinical diagnosis in medicine].

    Science.gov (United States)

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

    2014-01-01

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

  2. Inteligência artificial aplicada à Zootecnia Artificial intelligence in Animal Science

    Directory of Open Access Journals (Sweden)

    Ernane José Xavier Costa

    2009-07-01

    Full Text Available Os sistemas biológicos são surpreendentemente flexíveis pra processar informação proveniente do mundo real. Alguns organismos biológicos possuem uma unidade central de processamento denominada de cérebro. O cérebro humano consiste de 10(11 neurônios e realiza processamento inteligente de forma exata e subjetiva. A Inteligência Artificial (IA tenta trazer para o mundo da computação digital a heurística dos sistemas biológicos de várias maneiras, mas, ainda resta muito para que isso seja concretizado. No entanto, algumas técnicas como Redes neurais artificiais e lógica fuzzy tem mostrado efetivas para resolver problemas complexos usando a heurística dos sistemas biológicos. Recentemente o numero de aplicação dos métodos da IA em sistemas zootécnicos tem aumentado significativamente. O objetivo deste artigo é explicar os princípios básicos da resolução de problemas usando heurística e demonstrar como a IA pode ser aplicada para construir um sistema especialista para resolver problemas na área de zootecnia.Biological systems are surprising flexible in processing information in the real world. Some biological organisms have a central unit processing named brain. The human's brain, consisting of 10(11 neurons, realizes intelligent information processing based on exact and commonsense reasoning. Artificial intelligence (AI has been trying to implement biological intelligence in computers in various ways, but is still far from real one. Therefore, there are approaches like Symbolic AI, Artificial Neural Network and Fuzzy system that partially successful in implementing heuristic from biological intelligence. Many recent applications of these approaches show an increased interest in animal science research. The main goal of this article is to explain the principles of heuristic problem-solving approach and to demonstrate how they can be applied to building knowledge-based systems for animal science problem solving.

  3. [Artificial intelligence--the knowledge base applied to nephrology].

    Science.gov (United States)

    Sancipriano, G P

    2005-01-01

    The idea that efficacy efficiency, and quality in medicine could not be reached without sorting the huge knowledge of medical and nursing science is very common. Engineers and computer scientists have developed medical software with great prospects for success, but currently these software applications are not so useful in clinical practice. The medical doctor and the trained nurse live the 'information age' in many daily activities, but the main benefits are not so widespread in working activities. Artificial intelligence and, particularly, export systems charm health staff because of their potential. The first part of this paper summarizes the characteristics of 'weak artificial intelligence' and of expert systems important in clinical practice. The second part discusses medical doctors' requirements and the current nephrologic knowledge bases available for artificial intelligence development.

  4. 9th International conference on distributed computing and artificial intelligence

    CERN Document Server

    Santana, Juan; González, Sara; Molina, Jose; Bernardos, Ana; Rodríguez, Juan; DCAI 2012; International Symposium on Distributed Computing and Artificial Intelligence 2012

    2012-01-01

    The International Symposium on Distributed Computing and Artificial Intelligence 2012 (DCAI 2012) is a stimulating and productive forum where the scientific community can work towards future cooperation in Distributed Computing and Artificial Intelligence areas. This conference is a forum in which  applications of innovative techniques for solving complex problems will be presented. Artificial intelligence is changing our society. Its application in distributed environments, such as the internet, electronic commerce, environment monitoring, mobile communications, wireless devices, distributed computing, to mention only a few, is continuously increasing, becoming an element of high added value with social and economic potential, in industry, quality of life, and research. These technologies are changing constantly as a result of the large research and technical effort being undertaken in both universities and businesses. The exchange of ideas between scientists and technicians from both the academic and indus...

  5. 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.

  6. A PHILOSOPHICAL APPROACH TO ARTIFICIAL INTELLIGENCE AND ISLAMIC VALUES

    Directory of Open Access Journals (Sweden)

    Ali Akbar Ziaee

    2012-02-01

    Full Text Available Artificial Intelligence has the potential to empower humans through enhanced learning and performance. But if this potential is to be realized and accepted, the ethical aspects as well as the technical must be addressed. Many engineers claim that AI will be smarter than human brains, including scientific creativity, general wisdom and social skills, so we must consider it an important factor for making decisions in our social life and especially in our Islamic societies. The most important challenges will be the quality of representing the Islamic values like piety, obedience, Halal and Haram, and etc in the form of semantics. In this paper, I want to emphasize on the role of Divine Islamic values in the application of AI and discuss it according to philosophy of AI and Islamic perspective.Keywords- Value, expert, Community Development, Artificial Intelligence, Superintelligence, Friendly Artificial Intelligence

  7. Distributed computing and artificial intelligence : 10th International Conference

    CERN Document Server

    Neves, José; Rodriguez, Juan; Santana, Juan; Gonzalez, Sara

    2013-01-01

    The International Symposium on Distributed Computing and Artificial Intelligence 2013 (DCAI 2013) is a forum in which applications of innovative techniques for solving complex problems are presented. Artificial intelligence is changing our society. Its application in distributed environments, such as the internet, electronic commerce, environment monitoring, mobile communications, wireless devices, distributed computing, to mention only a few, is continuously increasing, becoming an element of high added value with social and economic potential, in industry, quality of life, and research. This conference is a stimulating and productive forum where the scientific community can work towards future cooperation in Distributed Computing and Artificial Intelligence areas. These technologies are changing constantly as a result of the large research and technical effort being undertaken in both universities and businesses. The exchange of ideas between scientists and technicians from both the academic and industry se...

  8. Artificial intelligence and robot responsibilities: innovating beyond rights.

    Science.gov (United States)

    Ashrafian, Hutan

    2015-04-01

    The enduring innovations in artificial intelligence and robotics offer the promised capacity of computer consciousness, sentience and rationality. The development of these advanced technologies have been considered to merit rights, however these can only be ascribed in the context of commensurate responsibilities and duties. This represents the discernable next-step for evolution in this field. Addressing these needs requires attention to the philosophical perspectives of moral responsibility for artificial intelligence and robotics. A contrast to the moral status of animals may be considered. At a practical level, the attainment of responsibilities by artificial intelligence and robots can benefit from the established responsibilities and duties of human society, as their subsistence exists within this domain. These responsibilities can be further interpreted and crystalized through legal principles, many of which have been conserved from ancient Roman law. The ultimate and unified goal of stipulating these responsibilities resides through the advancement of mankind and the enduring preservation of the core tenets of humanity.

  9. 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.

  10. The Coming of Age of Artificial Intelligence in Medicine*

    Science.gov (United States)

    Patel, Vimla L.; Shortliffe, Edward H.; Stefanelli, Mario; Szolovits, Peter; Berthold, Michael R.; Bellazzi, Riccardo; Abu-Hanna, Ameen

    2009-01-01

    Summary This paper is based on a panel discussion held at the Artificial Intelligence in Medicine Europe (AIME) conference in Amsterdam, The Netherlands, in July 2007. It had been more than 15 years since Edward Shortliffe gave a talk at AIME in which he characterized artificial intelligence (AI) in medicine as being in its “adolescence” (Shortliffe EH. The adolescence of AI in medicine: Will the field come of age in the ‘90s? Artificial Intelligence in Medicine 1993; 5:93–106). In this article, the discussants reflect on medical AI research during the subsequent years and attempt to characterize the maturity and influence that has been achieved to date. Participants focus on their personal areas of expertise, ranging from clinical decision making, reasoning under uncertainty, and knowledge representation to systems integration, translational bioinformatics, and cognitive issues in both the modeling of expertise and the creation of acceptable systems. PMID:18790621

  11. Artificial and Computational Intelligence for Games on Mobile Platforms

    OpenAIRE

    Congdon, Clare Bates; Hingston, Philip; Kendall, Graham

    2013-01-01

    In this chapter, we consider the possibilities of creating new and innovative games that are targeted for mobile devices, such as smart phones and tablets, and that showcase AI (Artificial Intelligence) and CI (Computational Intelligence) approaches. Such games might take advantage of the sensors and facilities that are not available on other platforms, or might simply rely on the "app culture" to facilitate getting the games into users' hands. While these games might be profitable in themsel...

  12. Algorithmic Randomness as Foundation of Inductive Reasoning and Artificial Intelligence

    CERN Document Server

    Hutter, Marcus

    2011-01-01

    This article is a brief personal account of the past, present, and future of algorithmic randomness, emphasizing its role in inductive inference and artificial intelligence. It is written for a general audience interested in science and philosophy. Intuitively, randomness is a lack of order or predictability. If randomness is the opposite of determinism, then algorithmic randomness is the opposite of computability. Besides many other things, these concepts have been used to quantify Ockham's razor, solve the induction problem, and define intelligence.

  13. Fifth Conference on Artificial Intelligence for Space Applications

    Science.gov (United States)

    Odell, Steve L. (Compiler)

    1990-01-01

    The Fifth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: automation for Space Station; intelligent control, testing, and fault diagnosis; robotics and vision; planning and scheduling; simulation, modeling, and tutoring; development tools and automatic programming; knowledge representation and acquisition; and knowledge base/data base integration.

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

    Science.gov (United States)

    Altman, R B

    2017-05-01

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

  15. Artificial Intelligence as a Means to Moral Enhancement

    Directory of Open Access Journals (Sweden)

    Klincewicz Michał

    2016-12-01

    Full Text Available This paper critically assesses the possibility of moral enhancement with ambient intelligence technologies and artificial intelligence presented in Savulescu and Maslen (2015. The main problem with their proposal is that it is not robust enough to play a normative role in users’ behavior. A more promising approach, and the one presented in the paper, relies on an artificial moral reasoning engine, which is designed to present its users with moral arguments grounded in first-order normative theories, such as Kantianism or utilitarianism, that reason-responsive people can be persuaded by. This proposal can play a normative role and it is also a more promising avenue towards moral enhancement. It is more promising because such a system can be designed to take advantage of the sometimes undue trust that people put in automated technologies. We could therefore expect a well-designed moral reasoner system to be able to persuade people that may not be persuaded by similar arguments from other people. So, all things considered, there is hope in artificial intelligence for moral enhancement, but not in artificial intelligence that relies solely on ambient intelligence technologies.

  16. Solving Systems of Equations with Techniques from Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Irina Maria Terfaloaga

    2015-07-01

    Full Text Available A frequent problem in numerical analysis is solving the systems of equations. That problem has generated in time a great interest among mathematicians and computer scientists, as evidenced by the large number of numerical methods developed. Besides the classical numerical methods, in the last years were proposed methods inspired by techniques from artificial intelligence. Hybrid methods have been also proposed along the time [15, 19]. The goal of this study is to make a survey of methods inspired from artificial intelligence for solving systems of equations

  17. Artificial intelligence and expert systems in the steel industry

    Science.gov (United States)

    Carayannis, Gregory

    1993-10-01

    This article was prepared in an effort to determine the state of the art with respect to the use of artificial intelligence and expert system technologies within the steel industry. A number of important developments have been reported and most of them resulted in significant savings. Mathematical modeling is quite important both for understanding and for controlling a process. However, most steelmaking operations are extremely complex and cannot be described mathematically. They are, however, adequately controlled by human operators on the basis of their knowledge and expertise. Because of this, artificial intelligence is an ideal technology for the automation of many steelmaking-related processes.

  18. Distributed Computing and Artificial Intelligence, 12th International Conference

    CERN Document Server

    Malluhi, Qutaibah; Gonzalez, Sara; Bocewicz, Grzegorz; Bucciarelli, Edgardo; Giulioni, Gianfranco; Iqba, Farkhund

    2015-01-01

    The 12th International Symposium on Distributed Computing and Artificial Intelligence 2015 (DCAI 2015) is a forum to present applications of innovative techniques for studying and solving complex problems. The exchange of ideas between scientists and technicians from both the academic and industrial sector is essential to facilitate the development of systems that can meet the ever-increasing demands of today’s society. The present edition brings together past experience, current work and promising future trends associated with distributed computing, artificial intelligence and their application in order to provide efficient solutions to real problems. This symposium is organized by the Osaka Institute of Technology, Qatar University and the University of Salamanca.

  19. Advanced solutions in power systems HVDC, facts, and artificial intelligence

    CERN Document Server

    Liu, Chen-Ching; Edris, Abdel-Aty

    2016-01-01

    Provides insight on both classical means and new trends in the application of power electronic and artificial intelligence techniques in power system operation and control This book presents advanced solutions for power system controllability improvement, transmission capability enhancement and operation planning. The book is organized into three parts. The first part describes the CSC-HVDC and VSC-HVDC technologies, the second part presents the FACTS devices, and the third part refers to the artificial intelligence techniques. All technologies and tools approached in this book are essential for power system development to comply with the smart grid requirements.

  20. Artificial Intelligence Applied to Project Success: A Literature Review

    Directory of Open Access Journals (Sweden)

    Daniel Magaña Martínez

    2015-12-01

    Full Text Available Project control and monitoring tools are based on expert judgement and parametric tools. Projects are the means by which companies implement their strategies. However project success rates are still very low. This is a worrying situation that has a great economic impact so alternative tools for project success prediction must be proposed in order to estimate project success or identify critical factors of success. Some of these tools are based on Artificial Intelligence. In this paper we will carry out a literature review of those papers that use Artificial Intelligence as a tool for project success estimation or critical success factor identification.

  1. Bionics: A Step toward Artificial Intelligence Systems

    Science.gov (United States)

    Dutton, Robert E.

    1970-01-01

    Recent developments and future prospects in the borrowing of biological principles to build problem solving relationships between human intelligence and the information storage and manipulation capacities of computers. Twenty-one references. (LY)

  2. Artificial intelligence and synthetic biology: A tri-temporal contribution.

    Science.gov (United States)

    Bianchini, Francesco

    2016-10-01

    Artificial intelligence can make numerous contributions to synthetic biology. I would like to suggest three that are related to the past, present and future of artificial intelligence. From the past, works in biology and artificial systems by Turing and von Neumann prove highly interesting to explore within the new framework of synthetic biology, especially with regard to the notions of self-modification and self-replication and their links to emergence and the bottom-up approach. The current epistemological inquiry into emergence and research on swarm intelligence, superorganisms and biologically inspired cognitive architecture may lead to new achievements on the possibilities of synthetic biology in explaining cognitive processes. Finally, the present-day discussion on the future of artificial intelligence and the rise of superintelligence may point to some research trends for the future of synthetic biology and help to better define the boundary of notions such as "life", "cognition", "artificial" and "natural", as well as their interconnections in theoretical synthetic biology. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  3. Applying artificial intelligence technology to support decision-making in nursing: A case study in Taiwan.

    Science.gov (United States)

    Liao, Pei-Hung; Hsu, Pei-Ti; Chu, William; Chu, Woei-Chyn

    2015-06-01

    This study applied artificial intelligence to help nurses address problems and receive instructions through information technology. Nurses make diagnoses according to professional knowledge, clinical experience, and even instinct. Without comprehensive knowledge and thinking, diagnostic accuracy can be compromised and decisions may be delayed. We used a back-propagation neural network and other tools for data mining and statistical analysis. We further compared the prediction accuracy of the previous methods with an adaptive-network-based fuzzy inference system and the back-propagation neural network, identifying differences in the questions and in nurse satisfaction levels before and after using the nursing information system. This study investigated the use of artificial intelligence to generate nursing diagnoses. The percentage of agreement between diagnoses suggested by the information system and those made by nurses was as much as 87 percent. When patients are hospitalized, we can calculate the probability of various nursing diagnoses based on certain characteristics. © The Author(s) 2013.

  4. A Multidisciplinary Artificial Intelligence Model of an Affective Robot

    OpenAIRE

    Hooman Aghaebrahimi Samani; Elham Saadatian

    2012-01-01

    A multidisciplinary approach to a novel artificial intelligence system for an affective robot is presented in this paper. The general objective of the system is to develop a robotic system which strives to achieve a high level of emotional bond between humans and robot by exploring human love. Such a relationship is a contingent process of attraction, affection and attachment from humans towards robots, and the belief of the vice versa from robots to humans. The advanced artificial intelli...

  5. The Construction of Intelligent English Teaching Model Based on Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Xiaoguang Li

    2017-12-01

    Full Text Available In order to build a modernized tool platform that can help students improve their English learning efficiency according to their mastery of knowledge and personality, this paper develops an online intelligent English learning system that uses Java and artificial intelligence language Prolog as the software system. This system is a creative reflection of the thoughts of expert system in artificial intelligence. Established on the Struts Spring Hibernate lightweight JavaEE framework, the system modules are coupled with each other in a much lower degree, which is convenient to future function extension. Combined with the idea of expert system in artificial intelligence, the system developed appropriate learning strategies to help students double the learning effect with half the effort; Finally, the system takes into account the forgetting curve of memory, on which basis the knowledge that has been learned will be tested periodically, intending to spare students’ efforts to do a sea of exercises and obtain better learning results.

  6. Artificial Neural Network System for Thyroid Diagnosis

    Directory of Open Access Journals (Sweden)

    Mazin Abdulrasool Hameed

    2017-05-01

    Full Text Available Thyroid disease is one of major causes of severe medical problems for human beings. Therefore, proper diagnosis of thyroid disease is considered as an important issue to determine treatment for patients. This paper focuses on using Artificial Neural Network (ANN as a significant technique of artificial intelligence to diagnose thyroid diseases. The continuous values of three laboratory blood tests are used as input signals to the proposed system of ANN. All types of thyroid diseases that may occur in patients are taken into account in design of system, as well as the high accuracy of the detection and categorization of thyroid diseases are considered in the system. A multilayer feedforward architecture of ANN is adopted in the proposed design, and the back propagation is selected as learning algorithm to accomplish the training process. The result of this research shows that the proposed ANN system is able to precisely diagnose thyroid disease, and can be exploited in practical uses. The system is simulated via MATLAB software to evaluate its performance

  7. Artificial Neural Network Metamodels of Stochastic Computer Simulations

    Science.gov (United States)

    1994-08-10

    23 Haddock, J. and O’Keefe, R., "Using Artificial Intelligence to Facilitate Manufacturing Systems Simulation," Computers & Industrial Engineering , Vol...Feedforward Neural Networks," Computers & Industrial Engineering , Vol. 21, No. 1- 4, (1991), pp. 247-251. 87 Proceedings of the 1992 Summer Computer...Using Simulation Experiments," Computers & Industrial Engineering , Vol. 22, No. 2 (1992), pp. 195-209. 119 Kuei, C. and Madu, C., "Polynomial

  8. Prediction of Modal Shift Using Artificial Neural Networks

    OpenAIRE

    Kadir Akgöl; Metin Mutlu Aydin; Özcan Asilkan; Banihan Günay

    2014-01-01

    Various public transport concepts have been developed to provide solutions to the ever growing problem of traffic in modern times. For instance, intelligent subscription bus service is one of them. This concept aims to provide a means of transport at near private car comfort as well as at near public transport cost. By this means, a shift from other modes of transport, especially private car, to public transport is aimed to encourage use of public transport. An artificial neural network model...

  9. Plant Growth Models Using Artificial Neural Networks

    Science.gov (United States)

    Bubenheim, David

    1997-01-01

    In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.

  10. Body, thought, being-human and artificial intelligence: Merleau ...

    African Journals Online (AJOL)

    The focus then shifts to Merleau-Ponty in order to demonstrate the remarkable extent to which his understanding of human embodiment and related issues such as perception and creativity, paved the way for the work of, among others, Lyotard, and anticipated the critique of artificial intelligence on the part of the latter.

  11. Intellectual Issues in the History of Artificial Intelligence

    Science.gov (United States)

    1982-10-28

    being knowledge-intensive programs. [Goldstein and Papert , 1977.] It may be doubted that these changes amounted to an actual paradigm shift. What...Edward A. and Feldman, Julian (Eds.). Computers and Thought. New York: McGraw-Hill, 1963. Goldstein, Ira and Papert , Seymour. Artificial intelligence

  12. Research Priorities for Robust and Beneficial Artificial Intelligence

    OpenAIRE

    Russell, Stuart; Dewey, Daniel; Tegmark, Max

    2015-01-01

    Success in the quest for artificial intelligence has the potential to bring unprecedented benefits to humanity, and it is therefore worthwhile to investigate how to maximize these benefits while avoiding potential pitfalls. This article gives numerous examples (which should by no means be construed as an exhaustive list) of such worthwhile research aimed at ensuring that AI remains robust and beneficial.

  13. Artificial intelligence system for technical diagnostics of photomasks

    Directory of Open Access Journals (Sweden)

    Kozin A. A.

    2012-02-01

    Full Text Available The developed artificial intelligence system has a high level of noise immunity, so its inclusion in the hardware and software for technical diagnostics of photomasks will reduce the hardware requirements for its execution, and thereby reduce the cost of the complex. As a result it will allow to make a small-scale production profitable.

  14. Artificial intelligence system for technical diagnostics of photomasks

    OpenAIRE

    Kozin A. A.; Kozina Yu. Yu.

    2012-01-01

    The developed artificial intelligence system has a high level of noise immunity, so its inclusion in the hardware and software for technical diagnostics of photomasks will reduce the hardware requirements for its execution, and thereby reduce the cost of the complex. As a result it will allow to make a small-scale production profitable.

  15. Reflections on the relationship between artificial intelligence and operations research

    Science.gov (United States)

    Fox, Mark S.

    1989-01-01

    Historically, part of Artificial Intelligence's (AI's) roots lie in Operations Research (OR). How AI has extended the problem solving paradigm developed in OR is explored. In particular, by examining how scheduling problems are solved using OR and AI, it is demonstrated that AI extends OR's model of problem solving through the opportunistic use of knowledge, problem reformulation and learning.

  16. The Third International Conference on Artificial Intelligence and Education

    OpenAIRE

    Liffick, Blaise W.

    1987-01-01

    The Third International Conference on Artificial Intelligence and Education attracted over 400 participants from all over the world who gathered to present projects reports, exchange views, discuss common problems, and establish contacts concerning AI and education. This article presents a synopsis of the major presentations and an overview of the conference as a whole.

  17. An Artificial Intelligence Course for Liberal Arts Students.

    Science.gov (United States)

    Skala, Helen

    1988-01-01

    Outlines a course in artificial intelligence for liberal arts students that has no programing prerequisites. Topics and projects included in the course are described, including problem solving; natural language; expert systems; image understanding, or character recognition; and robotic systems. (28 references) (Author/LRW)

  18. Application of artificial intelligence in load frequency control of ...

    African Journals Online (AJOL)

    This paper presents the use of artificial intelligence to study the load frequency control of interconnected power system. In the proposed scheme, a ... The control strategies guarantees that the steady state error of frequencies and inadvertent interchange of tie-lines power are maintained in a given tolerance limitations.

  19. Some Notes About Artificial Intelligence as New Mathematical Tool

    Directory of Open Access Journals (Sweden)

    Angel Garrido

    2010-04-01

    Full Text Available Mathematics is a mere instance of First-Order Predicate Calculus. Therefore it belongs to applied Monotonic Logic. So, we found the limitations of classical logic reasoning and the clear advantages of Fuzzy Logic and many other new interesting tools. We present here some of the more usefulness tools of this new field of Mathematics so-called Artificial Intelligence.

  20. Artificial Intelligence Methods: Challenge in Computer Based Polymer Design

    Science.gov (United States)

    Rusu, Teodora; Pinteala, Mariana; Cartwright, Hugh

    2009-08-01

    This paper deals with the use of Artificial Intelligence Methods (AI) in the design of new molecules possessing desired physical, chemical and biological properties. This is an important and difficult problem in the chemical, material and pharmaceutical industries. Traditional methods involve a laborious and expensive trial-and-error procedure, but computer-assisted approaches offer many advantages in the automation of molecular design.

  1. Some Notes About Artificial Intelligence as New Mathematical Tool

    OpenAIRE

    Angel Garrido

    2010-01-01

    Mathematics is a mere instance of First-Order Predicate Calculus. Therefore it belongs to applied Monotonic Logic. So, we found the limitations of classical logic reasoning and the clear advantages of Fuzzy Logic and many other new interesting tools. We present here some of the more usefulness tools of this new field of Mathematics so-called Artificial Intelligence.

  2. John McCarthy–Father of Artificial Intelligence

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 19; Issue 3. John McCarthy – Father of Artificial Intelligence. V Rajaraman. General Article Volume 19 Issue 3 March 2014 pp 198-207. Fulltext. Click here to view fulltext PDF. Permanent link: http://www.ias.ac.in/article/fulltext/reso/019/03/0198-0207 ...

  3. Application of artificial intelligence to risk analysis for forested ecosystems

    Science.gov (United States)

    Daniel L. Schmoldt

    2001-01-01

    Forest ecosystems are subject to a variety of natural and anthropogenic disturbances that extract a penalty from human population values. Such value losses (undesirable effects) combined with their likelihoods of occurrence constitute risk. Assessment or prediction of risk for various events is an important aid to forest management. Artificial intelligence (AI)...

  4. Evolution and Revolution in Artificial Intelligence in Education

    Science.gov (United States)

    Roll, Ido; Wylie, Ruth

    2016-01-01

    The field of Artificial Intelligence in Education (AIED) has undergone significant developments over the last twenty-five years. As we reflect on our past and shape our future, we ask two main questions: What are our major strengths? And, what new opportunities lay on the horizon? We analyse 47 papers from three years in the history of the…

  5. An Artificial Intelligence-Based Distance Education System: Artimat

    Science.gov (United States)

    Nabiyev, Vasif; Karal, Hasan; Arslan, Selahattin; Erumit, Ali Kursat; Cebi, Ayca

    2013-01-01

    The purpose of this study is to evaluate the artificial intelligence-based distance education system called ARTIMAT, which has been prepared in order to improve mathematical problem solving skills of the students, in terms of conceptual proficiency and ease of use with the opinions of teachers and students. The implementation has been performed…

  6. Artificial Intelligence: Is the Future Now for A.I.?

    Science.gov (United States)

    Ramaswami, Rama

    2009-01-01

    In education, artificial intelligence (AI) has not made much headway. In the one area where it would seem poised to lend the most benefit--assessment--the reliance on standardized tests, intensified by the demands of the No Child Left Behind Act of 2001, which holds schools accountable for whether students pass statewide exams, precludes its use.…

  7. APPLYING ARTIFICIAL INTELLIGENCE TECHNIQUES TO HUMAN-COMPUTER INTERFACES

    DEFF Research Database (Denmark)

    Sonnenwald, Diane H.

    1988-01-01

    A description is given of UIMS (User Interface Management System), a system using a variety of artificial intelligence techniques to build knowledge-based user interfaces combining functionality and information from a variety of computer systems that maintain, test, and configure customer telephone...

  8. Human Factors and Computer Interfaces--Implications for Artificial Intelligence.

    Science.gov (United States)

    Norris, Cathleen A.

    1987-01-01

    This second in a series of articles on artificial intelligence emphasizes human factors. The design of video display units and keyboards is discussed, the organizational structure of human memory is described, humans are examined as information processors using inductive and deductive reasoning, and educational implications are explored. (LRW)

  9. Artificial Intelligence Models for Human Problem-Solving.

    Science.gov (United States)

    Goldin, Gerald A.; Luger, George F.

    A theory that there is a correspondence between Piagetian conservation operations and groups of symmetry transformations, and that these symmetry transformations may be used in explaining human problem solving behaviors, is developed in this paper. Current research in artificial intelligence is briefly reviewed, then details of the symmetry…

  10. Artificial intelligent e-learning architecture

    Science.gov (United States)

    Alharbi, Mafawez; Jemmali, Mahdi

    2017-03-01

    Many institutions and university has forced to use e learning, due to its ability to provide additional and flexible solutions for students and researchers. E-learning In the last decade have transported about the extreme changes in the distribution of education allowing learners to access multimedia course material at any time, from anywhere to suit their specific needs. In the form of e learning, instructors and learners live in different places and they do not engage in a classroom environment, but within virtual universe. Many researches have defined e learning based on their objectives. Therefore, there are small number of e-learning architecture have proposed in the literature. However, the proposed architecture has lack of embedding intelligent system in the architecture of e learning. This research argues that unexplored potential remains, as there is scope for e learning to be intelligent system. This research proposes e-learning architecture that incorporates intelligent system. There are intelligence components, which built into the architecture.

  11. Neuroscientific implications for situated and embodied artificial intelligence

    Science.gov (United States)

    Downing, Keith

    2007-03-01

    While classic artificial intelligence systems still struggle to incorporate commonsense knowledge properly, situated and embodied artificial intelligence (SEAI) aims to build animats that acquire a common-sense understanding of the world via interactions between simulated brains, bodies and environments. Neuroscientists believe that much of this common sense involves predictive models for physical activities, but the transfer of sensorimotor skill knowledge to cognition is non-trivial, indicating that SEAI may meet a daunting challenge of its own. This paper considers the neurological bases for implicit procedural and explicit declarative common sense, and the possibilities for its transfer from the former to the latter. This helps assess the prospects for SEAI eventually to surpass GOFAI (good old-fashioned AI) in the quest for generally intelligent systems.

  12. Artificial intelligence - New tools for aerospace project managers

    Science.gov (United States)

    Moja, D. C.

    1985-01-01

    Artificial Intelligence (AI) is currently being used for business-oriented, money-making applications, such as medical diagnosis, computer system configuration, and geological exploration. The present paper has the objective to assess new AI tools and techniques which will be available to assist aerospace managers in the accomplishment of their tasks. A study conducted by Brown and Cheeseman (1983) indicates that AI will be employed in all traditional management areas, taking into account goal setting, decision making, policy formulation, evaluation, planning, budgeting, auditing, personnel management, training, legal affairs, and procurement. Artificial intelligence/expert systems are discussed, giving attention to the three primary areas concerned with intelligent robots, natural language interfaces, and expert systems. Aspects of information retrieval are also considered along with the decision support system, and expert systems for project planning and scheduling.

  13. A Suitable Artificial Intelligence Model for Inventory Level Optimization

    Directory of Open Access Journals (Sweden)

    Tereza Sustrova

    2016-05-01

    Full Text Available Purpose of the article: To examine suitable methods of artificial neural networks and their application in business operations, specifically to the supply chain management. The article discusses construction of an artificial neural networks model that can be used to facilitate optimization of inventory level and thus improve the ordering system and inventory management. For the data analysis from the area of wholesale trade with connecting material is used. Methodology/methods: Methods used in the paper consists especially of artificial neural networks and ANN-based modelling. For data analysis and preprocessing, MS Office Excel software is used. As an instrument for neural network forecasting MathWorks MATLAB Neural Network Tool was used. Deductive quantitative methods for research are also used. Scientific aim: The effort is directed at finding whether the method of prediction using artificial neural networks is suitable as a tool for enhancing the ordering system of an enterprise. The research also focuses on finding what architecture of the artificial neural networks model is the most suitable for subsequent prediction. Findings: Artificial neural networks models can be used for inventory management and lot-sizing problem successfully. A network with the TRAINGDX training function and TANSIG transfer function and 6-8-1 architecture can be considered the most suitable for artificial neural network, as it shows the best results for subsequent prediction. Conclusions: It can be concluded that the created model of artificial neural network can be successfully used for predicting order size and therefore for improving the order cycle of an enterprise. Conclusions resulting from the paper are beneficial for further research.

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

    Science.gov (United States)

    Das, Nilakash; Topalovic, Marko; Janssens, Wim

    2018-03-01

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

  15. Artificial intelligence techniques for clutter identification with polarimetric radar signatures

    Science.gov (United States)

    Islam, Tanvir; Rico-Ramirez, Miguel A.; Han, Dawei; Srivastava, Prashant K.

    2012-06-01

    The use of different artificial intelligence (AI) techniques for clutter signals identification in the context of radar based precipitation estimation is presented. The clutter signals considered are because of ground clutter, sea clutter and anomalous propagation whereas the explored AI techniques include the support vector machine (SVM), the artificial neural network (ANN), the decision tree (DT), and the nearest neighbour (NN) systems. Eight different radar measurement combinations comprising of various polarimetric spectral signatures — the reflectivity (ZH), differential reflectivity (ZDR), differential propagation phase (ΦDP), cross-correlation coefficient (ρHV), velocity (V) and spectral width (W) from a C-band polarimetric radar are taken into account as input vectors to the AI systems. The results reveal that all four AI classifiers can identify the clutter echoes with around 98-99% accuracy when all radar input signatures are used. As standalone input vectors, the polarimetric textures of the ΦDP and the ZDR have also demonstrated excellent skills distinguishing clutter echoes with an accuracy of 97-98% approximately. If no polarimetric signature is available, a combination of the texture of ZH, V and W representing typical measurements from a single-polarization Doppler radar may be used for clutter identification, but with a lower accuracy when compared to the use of polarimetric radar measurements. In contrast, the use of ZH or W alone is found less reliable for clutter classification. Among the AI techniques, the SVM has a slightly better score in terms of various clutter identification indicators as compared to the others. Conversely, the NN algorithm has shown a lower performance in identifying the clutter echoes correctly considering the standalone radar signatures as inputs. Despite this, the performance among the different AI techniques is comparable indicating the suitability of the developed systems, and this is further supported when

  16. Computer-aided diagnosis and artificial intelligence in clinical imaging.

    Science.gov (United States)

    Shiraishi, Junji; Li, Qiang; Appelbaum, Daniel; Doi, Kunio

    2011-11-01

    Computer-aided diagnosis (CAD) is rapidly entering the radiology mainstream. It has already become a part of the routine clinical work for the detection of breast cancer with mammograms. The computer output is used as a "second opinion" in assisting radiologists' image interpretations. The computer algorithm generally consists of several steps that may include image processing, image feature analysis, and data classification via the use of tools such as artificial neural networks (ANN). In this article, we will explore these and other current processes that have come to be referred to as "artificial intelligence." One element of CAD, temporal subtraction, has been applied for enhancing interval changes and for suppressing unchanged structures (eg, normal structures) between 2 successive radiologic images. To reduce misregistration artifacts on the temporal subtraction images, a nonlinear image warping technique for matching the previous image to the current one has been developed. Development of the temporal subtraction method originated with chest radiographs, with the method subsequently being applied to chest computed tomography (CT) and nuclear medicine bone scans. The usefulness of the temporal subtraction method for bone scans was demonstrated by an observer study in which reading times and diagnostic accuracy improved significantly. An additional prospective clinical study verified that the temporal subtraction image could be used as a "second opinion" by radiologists with negligible detrimental effects. ANN was first used in 1990 for computerized differential diagnosis of interstitial lung diseases in CAD. Since then, ANN has been widely used in CAD schemes for the detection and diagnosis of various diseases in different imaging modalities, including the differential diagnosis of lung nodules and interstitial lung diseases in chest radiography, CT, and position emission tomography/CT. It is likely that CAD will be integrated into picture archiving and

  17. Operation optimization of distributed generation using artificial intelligent techniques

    Directory of Open Access Journals (Sweden)

    Mahmoud H. Elkazaz

    2016-06-01

    Full Text Available Future smart grids will require an observable, controllable and flexible network architecture for reliable and efficient energy delivery. The use of artificial intelligence and advanced communication technologies is essential in building a fully automated system. This paper introduces a new technique for online optimal operation of distributed generation (DG resources, i.e. a hybrid fuel cell (FC and photovoltaic (PV system for residential applications. The proposed technique aims to minimize the total daily operating cost of a group of residential homes by managing the operation of embedded DG units remotely from a control centre. The target is formed as an objective function that is solved using genetic algorithm (GA optimization technique. The optimal settings of the DG units obtained from the optimization process are sent to each DG unit through a fully automated system. The results show that the proposed technique succeeded in defining the optimal operating points of the DGs that affect directly the total operating cost of the entire system.

  18. Artificial Intelligent Control for a Novel Advanced Microwave Biodiesel Reactor

    Energy Technology Data Exchange (ETDEWEB)

    Wali, W A; Hassan, K H; Cullen, J D; Al-Shamma' a, A I; Shaw, A; Wylie, S R, E-mail: w.wali@2009.ljmu.ac.uk [Built Environment and Sustainable Technologies Institute (BEST), School of the Built Environment, Faculty of Technology and Environment Liverpool John Moores University, Byrom Street, Liverpool L3 3AF (United Kingdom)

    2011-08-17

    Biodiesel, an alternative diesel fuel made from a renewable source, is produced by the transesterification of vegetable oil or fat with methanol or ethanol. In order to control and monitor the progress of this chemical reaction with complex and highly nonlinear dynamics, the controller must be able to overcome the challenges due to the difficulty in obtaining a mathematical model, as there are many uncertain factors and disturbances during the actual operation of biodiesel reactors. Classical controllers show significant difficulties when trying to control the system automatically. In this paper we propose a comparison of artificial intelligent controllers, Fuzzy logic and Adaptive Neuro-Fuzzy Inference System(ANFIS) for real time control of a novel advanced biodiesel microwave reactor for biodiesel production from waste cooking oil. Fuzzy logic can incorporate expert human judgment to define the system variables and their relationships which cannot be defined by mathematical relationships. The Neuro-fuzzy system consists of components of a fuzzy system except that computations at each stage are performed by a layer of hidden neurons and the neural network's learning capability is provided to enhance the system knowledge. The controllers are used to automatically and continuously adjust the applied power supplied to the microwave reactor under different perturbations. A Labview based software tool will be presented that is used for measurement and control of the full system, with real time monitoring.

  19. Artificial Intelligent Control for a Novel Advanced Microwave Biodiesel Reactor

    Science.gov (United States)

    Wali, W. A.; Hassan, K. H.; Cullen, J. D.; Al-Shamma'a, A. I.; Shaw, A.; Wylie, S. R.

    2011-08-01

    Biodiesel, an alternative diesel fuel made from a renewable source, is produced by the transesterification of vegetable oil or fat with methanol or ethanol. In order to control and monitor the progress of this chemical reaction with complex and highly nonlinear dynamics, the controller must be able to overcome the challenges due to the difficulty in obtaining a mathematical model, as there are many uncertain factors and disturbances during the actual operation of biodiesel reactors. Classical controllers show significant difficulties when trying to control the system automatically. In this paper we propose a comparison of artificial intelligent controllers, Fuzzy logic and Adaptive Neuro-Fuzzy Inference System(ANFIS) for real time control of a novel advanced biodiesel microwave reactor for biodiesel production from waste cooking oil. Fuzzy logic can incorporate expert human judgment to define the system variables and their relationships which cannot be defined by mathematical relationships. The Neuro-fuzzy system consists of components of a fuzzy system except that computations at each stage are performed by a layer of hidden neurons and the neural network's learning capability is provided to enhance the system knowledge. The controllers are used to automatically and continuously adjust the applied power supplied to the microwave reactor under different perturbations. A Labview based software tool will be presented that is used for measurement and control of the full system, with real time monitoring.

  20. Artificial Intelligence (AI) Based Tactical Guidance for Fighter Aircraft

    Science.gov (United States)

    McManus, John W.; Goodrich, Kenneth H.

    1990-01-01

    A research program investigating the use of Artificial Intelligence (AI) techniques to aid in the development of a Tactical Decision Generator (TDG) for Within Visual Range (WVR) air combat engagements is discussed. The application of AI programming and problem solving methods in the development and implementation of the Computerized Logic For Air-to-Air Warfare Simulations (CLAWS), a second generation TDG, is presented. The Knowledge-Based Systems used by CLAWS to aid in the tactical decision-making process are outlined in detail, and the results of tests to evaluate the performance of CLAWS versus a baseline TDG developed in FORTRAN to run in real-time in the Langley Differential Maneuvering Simulator (DMS), are presented. To date, these test results have shown significant performance gains with respect to the TDG baseline in one-versus-one air combat engagements, and the AI-based TDG software has proven to be much easier to modify and maintain than the baseline FORTRAN TDG programs. Alternate computing environments and programming approaches, including the use of parallel algorithms and heterogeneous computer networks are discussed, and the design and performance of a prototype concurrent TDG system are presented.

  1. Evolving Spiking Neural Networks for Control of Artificial Creatures

    Directory of Open Access Journals (Sweden)

    Arash Ahmadi

    2013-10-01

    Full Text Available To understand and analysis behavior of complicated and intelligent organisms, scientists apply bio-inspired concepts including evolution and learning to mathematical models and analyses. Researchers utilize these perceptions in different applications, searching for improved methods andapproaches for modern computational systems. This paper presents a genetic algorithm based evolution framework in which Spiking Neural Network (SNN of artificial creatures are evolved for higher chance of survival in a virtual environment. The artificial creatures are composed ofrandomly connected Izhikevich spiking reservoir neural networks using population activity rate coding. Inspired by biological neurons, the neuronal connections are considered with different axonal conduction delays. Simulations results prove that the evolutionary algorithm has thecapability to find or synthesis artificial creatures which can survive in the environment successfully.

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

    Science.gov (United States)

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

    2017-01-01

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

  3. Artificial intelligence and Internet of Things in a “smart home” context

    DEFF Research Database (Denmark)

    Lynggaard, Per

    We are currently witnessing an evolution from building and home automation to smart homes, driven by progressing maturity of the Internet of Things and the use of artificial intelligence. However, significant technological challenges such as immature home intelligence, huge network and central...... of the distributed system is comparable to state-of-the-art centralized smart home architectures. In a larger perspective, the proposed framework supports and facilitates the coming era of Internet of Things. The distributed approach and elements of the framework can be applied in many related areas, such as ambient...

  4. The use of artificial neural networks in experimental data acquisition and aerodynamic design

    Science.gov (United States)

    Meade, Andrew J., Jr.

    1991-01-01

    It is proposed that an artificial neural network be used to construct an intelligent data acquisition system. The artificial neural networks (ANN) model has a potential for replacing traditional procedures as well as for use in computational fluid dynamics validation. Potential advantages of the ANN model are listed. As a proof of concept, the author modeled a NACA 0012 airfoil at specific conditions, using the neural network simulator NETS, developed by James Baffes of the NASA Johnson Space Center. The neural network predictions were compared to the actual data. It is concluded that artificial neural networks can provide an elegant and valuable class of mathematical tools for data analysis.

  5. Artificial Intelligence Applied to the Command, Control, Communications, and Intelligence of the U.S. Central Command.

    Science.gov (United States)

    1983-06-06

    these components will be presented. 4.17 °°,. CHAPTER III FOOTNOTES 1. Arron Barr and Edward A. Feigenbaum, eds., Te Handbook gf Artificial Inteligence ol...RD-R137 205 ARTIFICIAL INTELLIGENCE APPLIED TO THE COMIMAND CONTROL i/i COMMUNICATIONS RND..(U) ARMY WAR COLL CARLISLE BARRACKS U PA J N ENVART 06...appropriate mlitary servic or *swesmment aency. ARTIFICIAL INTELLIGENCE APPLIED TO THE COMMAND, CONTROL, COMMUNICATIONS, AND INTELLIGENCE OF THE U.S. CENTRAL

  6. Clinical Note Creation, Binning, and Artificial Intelligence

    OpenAIRE

    Deliberato, Rodrigo Oct?vio; Celi, Leo Anthony; Stone, David J.

    2017-01-01

    The creation of medical notes in software applications poses an intrinsic problem in workflow as the technology inherently intervenes in the processes of collecting and assembling information, as well as the production of a data-driven note that meets both individual and healthcare system requirements. In addition, the note writing applications in currently available electronic health records (EHRs) do not function to support decision making to any substantial degree. We suggest that artifici...

  7. Artificial Intelligence (AI techniques to analyze the determinants attributes in housing prices

    Directory of Open Access Journals (Sweden)

    Julia M. Núñez Tabale

    2016-12-01

    Full Text Available The econometric approach to obtain the value of a property began with hedonic modelling, which were based on a set of property attributes, internal or external, associated to each particular dwelling. The final sale value can be estimated, and also the marginal prices of each exogenous explanatory variable. A good alternative to the hedonic approach is based on several Artificial Intelligence (AI techniques, such as artificial neural networks (ANN, these tend to be more precise. Both methodologies are compared, and a case study is developed using data from Seville, the larger town in the South of Spain.

  8. Application of artificial intelligence methods for prediction of steel mechanical properties

    Directory of Open Access Journals (Sweden)

    Z. Jančíková

    2008-10-01

    Full Text Available The target of the contribution is to outline possibilities of applying artificial neural networks for the prediction of mechanical steel properties after heat treatment and to judge their perspective use in this field. The achieved models enable the prediction of final mechanical material properties on the basis of decisive parameters influencing these properties. By applying artificial intelligence methods in combination with mathematic-physical analysis methods it will be possible to create facilities for designing a system of the continuous rationalization of existing and also newly developing industrial technologies.

  9. Autonomous operations through onboard artificial intelligence

    Science.gov (United States)

    Sherwood, R. L.; Chien, S.; Castano, R.; Rabideau, G.

    2002-01-01

    The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

  10. Philosophy and Theory of Artificial Intelligence

    CERN Document Server

    2013-01-01

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

  11. A Survey on Using Artificial Intelligence Techniques in the Software Development Process

    OpenAIRE

    K. Hema Shankari; Dr. R.Thirumalaiselvi

    2014-01-01

    Software engineering and artificial intelligence are the two important fields of the computer science. Artificial Intelligence is about making machines intelligent, while Software engineering is knowledge –intensive activity, requiring extensive knowledge of the application domain and of the target software itself. This study intends to review the techniques developed in artificial intelligence from the standpoint of their application in software engineering. The goal of this rese...

  12. Application of Artificial Intelligence and Data Mining Techniques to Financial Markets

    OpenAIRE

    Katarína Hilovska; Peter Koncz

    2012-01-01

    The aim of artificial intelligence is to discover mechanisms of adaptation in a changing environment with utilisation of intelligence, for instance in the ability to exclude unlikely solutions. Artificial intelligence methods have extensive application in different fields such as medicine, games, transportation, or heavy industry. This paper deals with interdisciplinary issues – interconnection of artificial intelligence and finance. The paper briefly describes techniques of data mining, expe...

  13. Providing Language Instructor with Artificial Intelligence Assistant

    Directory of Open Access Journals (Sweden)

    K. Pietroszek

    2007-12-01

    Full Text Available Abstract—This paper presents the preliminary results ofdeveloping HAL for CALL, an artificial intelligenceassistant for language instructor. The assistant consists of achatbot, an avatar (a three-dimensional visualization of thechatbot, a voice (text-to-speech engine interface andinterfaces to external sources of language knowledge. Sometechniques used in adapting freely available chatbot for theneed of a language learning system are presented.Integration of HAL with Second Life virtual world isproposed. We will discuss technical challenges and possiblefuture work directions.

  14. Macromolecular networks and intelligence in microorganisms

    Directory of Open Access Journals (Sweden)

    Hans V Westerhoff

    2014-07-01

    Full Text Available Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks constitute a type of Information and Communication Technology (ICT: they receive information from the outside and inside of cells, integrate and interpret this information, and then activate a response. Biological networks enable molecules within cells, and even cells themselves, to communicate with each other and their environment. We have become accustomed to associating brain activity – particularly activity of the human brain – with a phenomenon we call intelligence. Yet, four billion years of evolution could have selected networks with topologies and dynamics that confer traits analogous to this intelligence, even though they were outside the intercellular networks of the brain. Here, we explore how macromolecular networks in microbes confer intelligent characteristics, such as memory, anticipation, adaptation and reflection and we review current understanding of how network organization reflects the type of intelligence required for the environments in which they were selected. We propose that, if we were to leave terms such as human and brain out of the defining features of intelligence, all forms of life – from microbes to humans – exhibit some or all characteristics consistent with intelligence. We then review advances in genome-wide data production and analysis, especially in microbes, that provide a lens into microbial intelligence and propose how the insights derived from quantitatively characterizing biomolecular networks may enable synthetic biologists to create intelligent molecular networks for biotechnology, possibly generating new forms of intelligence, first in silico and then in vivo.

  15. INTEGRATING ARTIFICIAL NEURAL NETWORKS FOR DEVELOPING TELEMEDICINE SOLUTION

    Directory of Open Access Journals (Sweden)

    Mihaela GHEORGHE

    2015-06-01

    Full Text Available Artificial intelligence is assuming an increasing important role in the telemedicine field, especially neural networks with their ability to achieve meaning from large sets of data characterized by lacking exactness and accuracy. These can be used for assisting physicians or other clinical staff in the process of taking decisions under uncertainty. Thus, machine learning methods which are specific to this technology are offering an approach for prediction based on pattern classification. This paper aims to present the importance of neural networks in detecting trends and extracting patterns which can be used within telemedicine domains, particularly for taking medical diagnosis decisions.

  16. Northeast Artificial Intelligence Consortium Annual Report. Volume 2. 1988 Discussing, Using, and Recognizing Plans (NLP)

    Science.gov (United States)

    1989-10-01

    Encontro Portugues de Inteligencia Artificial (EPIA), Oporto, Portugal, September 1985. [15] N. J. Nilsson. Principles Of Artificial Intelligence. Tioga...FI1 F COPY () RADC-TR-89-259, Vol II (of twelve) Interim Report October 1969 AD-A218 154 NORTHEAST ARTIFICIAL INTELLIGENCE CONSORTIUM ANNUAL...7a. NAME OF MONITORING ORGANIZATION Northeast Artificial Of p0ilcabe) Intelligence Consortium (NAIC) Rome_____ Air___ Development____Center

  17. ARTIFICIAL INTELLIGENCE EFFECTIVENESS IN JOB SHOP ENVIRONMENTS

    Directory of Open Access Journals (Sweden)

    OMAR CASTRILLON

    2011-01-01

    Full Text Available El objetivo del presente trabajo, es definir una nueva metodología la cual permita comparar la efectividad de algunas de las principales técnicas de inteligencia artificial (aleatorias, búsqueda tabú, minería de datos, algoritmos evolutivos. Esta metodología es aplicada en los procesos de secuenciación de la producción en ambientes job shop, en un problema con N pedidos y M máquinas, donde cada uno de los pedidos debe pasar por todas las máquinas sin importar el orden. Estas técnicas son medidas en las variables tiempo total de proceso, tiempo total muerto y porcentaje de utilización de las máquinas. Inicialmente, una revisión teórica fue realizada, esta muestra la utilidad y efectividad de la inteligencia artificial en los procesos de secuenciación de la producción. Posteriormente y con base en la experimentación planteada, los resultados obtenidos, muestran que estas técnicas presentan una efectividad superior al 95%, con un intervalo de confiabilidad del 99.5% medido en las variables objeto de estudio.

  18. Artificial Intelligent Controller for a DC Motor

    Science.gov (United States)

    Delavari, Hadi; Ranjbar Noiey, Abolzafl; Minagar, Sara

    The Speed and position control of DC motors is addressed in this paper. An optimal intelligent control scheme is proposed for the system. Preliminary a PID controller is designed using Genetic Algorithms (GA). The proposed controller is implemented by using optimal integral state feedback control with GA and Kalman filter. In the proposed scheme, performance depends on choosing weighting matrices Q and R in the cost function, and accordingly GA is used to find these proper weighting matrices. In order to reduce the control performance degradation due to system parameters variation, a Kalman filter is gained. The performance of the proposed technique (ISF) is compared with PID controller. Computer simulation validates the effectiveness of the proposed scheme even in presence of uncertainties.

  19. Dynamic Analysis of Emotions through Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Susana Mejía M.

    2016-04-01

    Full Text Available Emotions have been demonstrated to be an important aspect of human intelligence and to play a significant role in human decision-making processes. Emotions are not only feelings but also processes of establishing, maintaining or disrupting the relation between the organism and the environment. In the present paper, several features of social and developmental Psychology are introduced, especially concepts that are related to Theories of Emotions and the Mathematical Tools applied in psychology (i.e., Dynamic Systems and Fuzzy Logic. Later, five models that infer emotions from a single event, in AV-Space, are presented and discussed along with the finding that fuzzy logic can measure human emotional states

  20. 11th International Conference on Distributed Computing and Artificial Intelligence

    CERN Document Server

    Bersini, Hugues; Corchado, Juan; Rodríguez, Sara; Pawlewski, Paweł; Bucciarelli, Edgardo

    2014-01-01

    The 11th International Symposium on Distributed Computing and Artificial Intelligence 2014 (DCAI 2014) is a forum to present applications of innovative techniques for studying and solving complex problems. The exchange of ideas between scientists and technicians from both the academic and industrial sector is essential to facilitate the development of systems that can meet the ever-increasing demands of today’s society. The present edition brings together past experience, current work and promising future trends associated with distributed computing, artificial intelligence and their application in order to provide efficient solutions to real problems. This year’s technical program presents both high quality and diversity, with contributions in well-established and evolving areas of research (Algeria, Brazil, China, Croatia, Czech Republic, Denmark, France, Germany, Ireland, Italy, Japan, Malaysia, Mexico, Poland, Portugal, Republic of Korea, Spain, Taiwan, Tunisia, Ukraine, United Kingdom), representing ...

  1. Artificial neural networks for static security assessment

    Energy Technology Data Exchange (ETDEWEB)

    Niebur, D.; Fischl, R.

    1997-12-31

    A reliable, continuous supply of electric energy is essential for the functioning of today`s complex societies. Due to a combination of increasing energy consumption and impediments of various kinds to the extension of existing electric transmission networks, these power systems are operated closer and closer to their limits. This situation requires a significantly less conservative power system operation and control regime which, in turn, is possible only by monitoring the system state in much more detail than was necessary previously. Fortunately, the large quantity of information required can be provided in many cases through recent advances in telecommunications and computing techniques. There is, however, a lack of evaluation techniques required to extract the salient information and to use it for higher-order processing. Whilst the sheer quantity of available information is always a problem, this situation is aggravated in emergency situations when rapid decisions are required. Furthermore, the behaviour of power systems is highly non-linear. Monitoring and control involves several hundred variables which are only partly available by measurements. Load demands and dynamic loads are difficult to model. Therefore models appropriate for normal situations might become invalid in emergency situations. These problems provide important motivation to explore novel data processing and programming techniques from the vast pool of artificial intelligence techniques. The following section gives a short introduction to static security assessment. (Author)

  2. Artificial Intelligence Techniques for Controlling Spacecraft Power System

    OpenAIRE

    Hanaa T. El-Madany; Faten H. Fahmy; Ninet M. A. El-Rahman; Hassen T. Dorrah

    2011-01-01

    Advancements in the field of artificial intelligence (AI) made during this decade have forever changed the way we look at automating spacecraft subsystems including the electrical power system. AI have been used to solve complicated practical problems in various areas and are becoming more and more popular nowadays. In this paper, a mathematical modeling and MATLAB–SIMULINK model for the different components of the spacecraft power system is presented. Also, a control sys...

  3. [I, Robot: artificial intelligence, uniqueness and self-consciousness].

    Science.gov (United States)

    Agrest, Martín

    2008-01-01

    The cinematographic version of the science fiction classical book by Isaac Asimov (I, Robot) is used as a starting point, from the Artificial Intelligence perspective, in order to analyze what it is to have a self. Uniqueness or the exchange impossibility and the continuity of being one self are put forward to understand the movie's characters as well as the possibilities of feeling self conscious.

  4. Micromechanics as a testbed for artificial intelligence methods evaluation

    OpenAIRE

    Kussul, Ernst; Baidyk, Tatiana; Lara Rosano, Felipe; Makeyev, Oleksandr; Martín, Anabel; Wunsch, Donald

    2006-01-01

    Some of the artificial intelligence (AI) methods could be used to improve the performance of automation systems in manufacturing processes. However, the application of these methods in the industry is not widespread because of the high cost of the experiments with the AI systems applied to the conventional manufacturing systems. To reduce the cost of such experiments, we have developed a special micromechanical equipment, similar to conventional mechanical equipment, but of a lot smaller o...

  5. Artificial Intelligence as a Business Forecasting and Error Handling Tool

    OpenAIRE

    Md. Tabrez Quasim; Rupak Chattopadhyay

    2015-01-01

     Any business enterprise must rely a lot on how well it can predict the future happenings. To cope up with the modern global customer demand, technological challenges, market competitions etc., any organization is compelled to foresee the future having maximum impact and least chances of errors. The traditional forecasting approaches have some limitations. That is why the business world is adopting the modern Artificial Intelligence based forecasting techniques. This paper has tried to presen...

  6. Artificial intelligence and dynamic systems for geophysical applications

    CERN Document Server

    Gvishiani, Alexei

    2002-01-01

    The book presents new clustering schemes, dynamical systems and pattern recognition algorithms in geophysical, geodynamical and natural hazard applications. The original mathematical technique is based on both classical and fuzzy sets models. Geophysical and natural hazard applications are mostly original. However, the artificial intelligence technique described in the book can be applied far beyond the limits of Earth science applications. The book is intended for research scientists, tutors, graduate students, scientists in geophysics and engineers

  7. Artificial intelligence, expert systems, computer vision, and natural language processing

    Science.gov (United States)

    Gevarter, W. B.

    1984-01-01

    An overview of artificial intelligence (AI), its core ingredients, and its applications is presented. The knowledge representation, logic, problem solving approaches, languages, and computers pertaining to AI are examined, and the state of the art in AI is reviewed. The use of AI in expert systems, computer vision, natural language processing, speech recognition and understanding, speech synthesis, problem solving, and planning is examined. Basic AI topics, including automation, search-oriented problem solving, knowledge representation, and computational logic, are discussed.

  8. Determination of rock depth using artificial intelligence techniques

    OpenAIRE

    Viswanathan, R.; Samui, Pijush

    2016-01-01

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

  9. Robotic air vehicle. Blending artificial intelligence with conventional software

    Science.gov (United States)

    Mcnulty, Christa; Graham, Joyce; Roewer, Paul

    1987-01-01

    The Robotic Air Vehicle (RAV) system is described. The program's objectives were to design, implement, and demonstrate cooperating expert systems for piloting robotic air vehicles. The development of this system merges conventional programming used in passive navigation with Artificial Intelligence techniques such as voice recognition, spatial reasoning, and expert systems. The individual components of the RAV system are discussed as well as their interactions with each other and how they operate as a system.

  10. Artificial Intelligence Research at GTE Laboratories (Research in Progress)

    OpenAIRE

    Frawley, William; Goyal, Shri

    1984-01-01

    GTE Laboratories is the central corporate research and development facility for the sixty subsidiaries of the worldwide GTE corporation. Located in the Massachusetts Route 128 high technology area, the five laboratories that comprise GTE Laboratories generate the ideas, products, systems, and services that provide technical leadership for GTE. The two laboratories which conduct artificial intelligence research are the Computer Science Laboratory (CSL) and the Fundamental Research Laboratory (...

  11. Distributed Computing and Artificial Intelligence, 12th International Conference

    OpenAIRE

    Omatu, Sigeru; Malluhi, Qutaibah M.; Rodríguez González, Sara; Bocewicz, Grzegorz; Bucciarelli, Edgardo; Giulioni, Gianfranco; Iqba, Farkhund

    2017-01-01

    The 12th International Symposium on Distributed Computing and Artificial Intelligence 2015 (DCAI 2015) is a forum to present applications of innovative techniques for studying and solving complex problems. The exchange of ideas between scientists and technicians from both the academic and industrial sector is essential to facilitate the development of systems that can meet the ever-increasing demands of today’s society. The present edition brings together past experience, current work and pro...

  12. Artificial Intelligence in Sports Biomechanics: New Dawn or False Hope?

    OpenAIRE

    Bartlett, Roger

    2006-01-01

    This article reviews developments in the use of Artificial Intelligence (AI) in sports biomechanics over the last decade. It outlines possible uses of Expert Systems as diagnostic tools for evaluating faults in sports movements ('techniques') and presents some example knowledge rules for such an expert system. It then compares the analysis of sports techniques, in which Expert Systems have found little place to date, with gait analysis, in which they are routinely used. Consideration is then ...

  13. Artificial Intelligence in Video Games: Towards a Unified Framework

    OpenAIRE

    Safadi, Firas

    2015-01-01

    The work presented in this dissertation revolves around the problem of designing artificial intelligence (AI) for video games. This problem becomes increasingly challenging as video games grow in complexity. With modern video games frequently featuring sophisticated and realistic environments, the need for smart and comprehensive agents that understand the various aspects of these environments is pressing. Although machine learning techniques are being successfully applied in a multitude of d...

  14. Artificial intelligence programming languages for computer aided manufacturing

    Science.gov (United States)

    Rieger, C.; Samet, H.; Rosenberg, J.

    1979-01-01

    Eight Artificial Intelligence programming languages (SAIL, LISP, MICROPLANNER, CONNIVER, MLISP, POP-2, AL, and QLISP) are presented and surveyed, with examples of their use in an automated shop environment. Control structures are compared, and distinctive features of each language are highlighted. A simple programming task is used to illustrate programs in SAIL, LISP, MICROPLANNER, and CONNIVER. The report assumes reader knowledge of programming concepts, but not necessarily of the languages surveyed.

  15. Artificial Intelligence Systems as Prognostic and Predictive Tools in Ovarian Cancer.

    Science.gov (United States)

    Enshaei, A; Robson, C N; Edmondson, R J

    2015-11-01

    The ability to provide accurate prognostic and predictive information to patients is becoming increasingly important as clinicians enter an era of personalized medicine. For a disease as heterogeneous as epithelial ovarian cancer, conventional algorithms become too complex for routine clinical use. This study therefore investigated the potential for an artificial intelligence model to provide this information and compared it with conventional statistical approaches. The authors created a database comprising 668 cases of epithelial ovarian cancer during a 10-year period and collected data routinely available in a clinical environment. They also collected survival data for all the patients, then constructed an artificial intelligence model capable of comparing a variety of algorithms and classifiers alongside conventional statistical approaches such as logistic regression. The model was used to predict overall survival and demonstrated that an artificial neural network (ANN) algorithm was capable of predicting survival with high accuracy (93 %) and an area under the curve (AUC) of 0.74 and that this outperformed logistic regression. The model also was used to predict the outcome of surgery and again showed that ANN could predict outcome (complete/optimal cytoreduction vs. suboptimal cytoreduction) with 77 % accuracy and an AUC of 0.73. These data are encouraging and demonstrate that artificial intelligence systems may have a role in providing prognostic and predictive data for patients. The performance of these systems likely will improve with increasing data set size, and this needs further investigation.

  16. Distinct neurocognitive strategies for comprehensions of human and artificial intelligence.

    Science.gov (United States)

    Ge, Jianqiao; Han, Shihui

    2008-07-30

    Although humans have inevitably interacted with both human and artificial intelligence in real life situations, it is unknown whether the human brain engages homologous neurocognitive strategies to cope with both forms of intelligence. To investigate this, we scanned subjects, using functional MRI, while they inferred the reasoning processes conducted by human agents or by computers. We found that the inference of reasoning processes conducted by human agents but not by computers induced increased activity in the precuneus but decreased activity in the ventral medial prefrontal cortex and enhanced functional connectivity between the two brain areas. The findings provide evidence for distinct neurocognitive strategies of taking others' perspective and inhibiting the process referenced to the self that are specific to the comprehension of human intelligence.

  17. Expertik: Experience with Artificial Intelligence and Mobile Computing

    Directory of Open Access Journals (Sweden)

    José Edward Beltrán Lozano

    2013-06-01

    Full Text Available This article presents the experience in the development of services based in Artificial Intelligence, Service Oriented Architecture, mobile computing. It aims to combine technology offered by mobile computing provides techniques and artificial intelligence through a service provide diagnostic solutions to problems in industrial maintenance. It aims to combine technology offered by mobile computing and the techniques artificial intelligence through a service to provide diagnostic solutions to problems in industrial maintenance. For service creation are identified the elements of an expert system, the knowledge base, the inference engine and knowledge acquisition interfaces and their consultation. The applications were developed in ASP.NET under architecture three layers. The data layer was developed conjunction in SQL Server with data management classes; business layer in VB.NET and the presentation layer in ASP.NET with XHTML. Web interfaces for knowledge acquisition and query developed in Web and Mobile Web. The inference engine was conducted in web service developed for the fuzzy logic model to resolve requests from applications consulting knowledge (initially an exact rule-based logic within this experience to resolve requests from applications consulting knowledge. This experience seeks to strengthen a technology-based company to offer services based on AI for service companies Colombia.

  18. The Outline of Personhood Law Regarding Artificial Intelligences and Emulated Human Entities

    Science.gov (United States)

    Muzyka, Kamil

    2013-12-01

    On the verge of technological breakthroughs, which define and revolutionize our understanding of intelligence, cognition, and personhood, especially when speaking of artificial intelligences and mind uploads, one must consider the legal implications of granting personhood rights to artificial intelligences or emulated human entities

  19. Improved Local Weather Forecasts Using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Wollsen, Morten Gill; Jørgensen, Bo Nørregaard

    2015-01-01

    Solar irradiance and temperature forecasts are used in many different control systems. Such as intelligent climate control systems in commercial greenhouses, where the solar irradiance affects the use of supplemental lighting. This paper proposes a novel method to predict the forthcoming weather...... using an artificial neural network. The neural network used is a NARX network, which is known to model non-linear systems well. The predictions are compared to both a design reference year as well as commercial weather forecasts based upon numerical modelling. The results presented in this paper show...... that the network outperforms the commercial forecast for lower step aheads (forecast. However, the neural network approach is fast, fairly precise and allows for further expansion with higher resolution....

  20. Searching for exoplanets using artificial intelligence

    Science.gov (United States)

    Pearson, Kyle A.; Palafox, Leon; Griffith, Caitlin A.

    2018-02-01

    In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify. Here we present a new method of detecting exoplanet candidates in large planetary search projects which, unlike current methods uses a neural network. Neural networks, also called "deep learning" or "deep nets" are designed to give a computer perception into a specific problem by training it to recognize patterns. Unlike past transit detection algorithms deep nets learn to recognize planet features instead of relying on hand-coded metrics that humans perceive as the most representative. Our convolutional neural network is capable of detecting Earth-like exoplanets in noisy time-series data with a greater accuracy than a least-squares method. Deep nets are highly generalizable allowing data to be evaluated from different time series after interpolation without compromising performance. As validated by our deep net analysis of Kepler light curves, we detect periodic transits consistent with the true period without any model fitting. Our study indicates that machine learning will facilitate the characterization of exoplanets in future analysis of large astronomy data sets.

  1. Artificial Intelligence Procedures for Tree Taper Estimation within a Complex Vegetation Mosaic in Brazil.

    Science.gov (United States)

    Nunes, Matheus Henrique; Görgens, Eric Bastos

    2016-01-01

    Tree stem form in native tropical forests is very irregular, posing a challenge to establishing taper equations that can accurately predict the diameter at any height along the stem and subsequently merchantable volume. Artificial intelligence approaches can be useful techniques in minimizing estimation errors within complex variations of vegetation. We evaluated the performance of Random Forest® regression tree and Artificial Neural Network procedures in modelling stem taper. Diameters and volume outside bark were compared to a traditional taper-based equation across a tropical Brazilian savanna, a seasonal semi-deciduous forest and a rainforest. Neural network models were found to be more accurate than the traditional taper equation. Random forest showed trends in the residuals from the diameter prediction and provided the least precise and accurate estimations for all forest types. This study provides insights into the superiority of a neural network, which provided advantages regarding the handling of local effects.

  2. Artificial Intelligence Procedures for Tree Taper Estimation within a Complex Vegetation Mosaic in Brazil.

    Directory of Open Access Journals (Sweden)

    Matheus Henrique Nunes

    Full Text Available Tree stem form in native tropical forests is very irregular, posing a challenge to establishing taper equations that can accurately predict the diameter at any height along the stem and subsequently merchantable volume. Artificial intelligence approaches can be useful techniques in minimizing estimation errors within complex variations of vegetation. We evaluated the performance of Random Forest® regression tree and Artificial Neural Network procedures in modelling stem taper. Diameters and volume outside bark were compared to a traditional taper-based equation across a tropical Brazilian savanna, a seasonal semi-deciduous forest and a rainforest. Neural network models were found to be more accurate than the traditional taper equation. Random forest showed trends in the residuals from the diameter prediction and provided the least precise and accurate estimations for all forest types. This study provides insights into the superiority of a neural network, which provided advantages regarding the handling of local effects.

  3. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  4. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Science.gov (United States)

    Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  5. A genetic-neural artificial intelligence approach to resins optimization; Uma metodologia baseada em inteligencia artificial para otimizacao de resinas

    Energy Technology Data Exchange (ETDEWEB)

    Cabral, Denise C.; Barros, Marcio P.; Lapa, Celso M.F.; Pereira, Claudio M.N.A. [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil)]. E-mail: lapa@ien.gov.br; mbarros@ien.gov.br

    2005-07-01

    This work presents a preliminary study about the viability and adequacy of a new methodology for the definition of one of the main properties of ion exchange resins used for isotopic separation. Basically, the main problem is the definition of pelicule diameter in case of pelicular ion exchange resins, in order to achieve the best performance in the shortest time. In order to achieve this, a methodology was developed, based in two classic techniques of Artificial Intelligence (AI). At first, an artificial neural network (NN) was trained to map the existing relations between the nucleus radius and the resin's efficiency associated with the exchange time. Later on, a genetic algorithm (GA) was developed in order to find the best pelicule dimension. Preliminary results seem to confirm the potential of the method, and this can be used in any chemical process employing ion exchange resins. (author)

  6. International Conference on Artificial Neural Networks (ICANN)

    CERN Document Server

    Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

    2015-01-01

    The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

  7. Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

    OpenAIRE

    Chandra Prasetyo Utomo; Aan Kardiana; Rika Yuliwulandari

    2014-01-01

    Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural Networks (ANN) has been widely used in intelligent breast cancer diagnosis. However, the standard Gradient-Based Back Propagation Artificial Neural Networks...

  8. Searching for Exoplanets using Artificial Intelligence

    Science.gov (United States)

    Pearson, Kyle Alexander; Palafox, Leon; Griffith, Caitlin Ann

    2017-10-01

    In the last decade, over a million stars were monitored to detect transiting planets. The large volume of data obtained from current and future missions (e.g. Kepler, K2, TESS and LSST) requires automated methods to detect the signature of a planet. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify. Here we present a new method of detecting exoplanet candidates in large planetary search projects which, unlike current methods uses a neural network. Neural networks, also called ``deep learning'' or ``deep nets'', are a state of the art machine learning technique designed to give a computer perception into a specific problem by training it to recognize patterns. Unlike past transit detection algorithms, the deep net learns to characterize the data instead of relying on hand-coded metrics that humans perceive as the most representative. Exoplanet transits have different shapes, as a result of, e.g. the planet's and stellar atmosphere and transit geometry. Thus, a simple template does not suffice to capture the subtle details, especially if the signal is below the noise or strong systematics are present. Current false-positive rates from the Kepler data are estimated around 12.3% for Earth-like planets and there has been no study of the false negative rates. It is therefore important to ask how the properties of current algorithms exactly affect the results of the Kepler mission and, future missions such as TESS, which flies next year. These uncertainties affect the fundamental research derived from missions, such as the discovery of habitable planets, estimates of their occurrence rates and our understanding about the nature and evolution of planetary systems.

  9. Knowledge-Based Aircraft Automation: Managers Guide on the use of Artificial Intelligence for Aircraft Automation and Verification and Validation Approach for a Neural-Based Flight Controller

    Science.gov (United States)

    Broderick, Ron

    1997-01-01

    The ultimate goal of this report was to integrate the powerful tools of artificial intelligence into the traditional process of software development. To maintain the US aerospace competitive advantage, traditional aerospace and software engineers need to more easily incorporate the technology of artificial intelligence into the advanced aerospace systems being designed today. The future goal was to transition artificial intelligence from an emerging technology to a standard technology that is considered early in the life cycle process to develop state-of-the-art aircraft automation systems. This report addressed the future goal in two ways. First, it provided a matrix that identified typical aircraft automation applications conducive to various artificial intelligence methods. The purpose of this matrix was to provide top-level guidance to managers contemplating the possible use of artificial intelligence in the development of aircraft automation. Second, the report provided a methodology to formally evaluate neural networks as part of the traditional process of software development. The matrix was developed by organizing the discipline of artificial intelligence into the following six methods: logical, object representation-based, distributed, uncertainty management, temporal and neurocomputing. Next, a study of existing aircraft automation applications that have been conducive to artificial intelligence implementation resulted in the following five categories: pilot-vehicle interface, system status and diagnosis, situation assessment, automatic flight planning, and aircraft flight control. The resulting matrix provided management guidance to understand artificial intelligence as it applied to aircraft automation. The approach taken to develop a methodology to formally evaluate neural networks as part of the software engineering life cycle was to start with the existing software quality assurance standards and to change these standards to include neural network

  10. An Examination of Application of Artificial Neural Network in Cognitive Radios

    Science.gov (United States)

    Bello Salau, H.; Onwuka, E. N.; Aibinu, A. M.

    2013-12-01

    Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined.

  11. Artificial intelligence for diagnostic purposes: principles, procedures and limitations.

    Science.gov (United States)

    Cleophas, Ton J; Cleophas, Toine F

    2010-02-01

    Back propagation (BP) artificial neural networks are a distribution-free method for data analysis based on layers of artificial neurons that transduce imputed information. It has been recognized as having a number of advantages compared to traditional methods including the possibility to process imperfect data, and complex non-linear data. The objective of this study was to review the principles, procedures, and limitations of BP artificial neural networks for a non-mathematical readership. A real data sample of weight, height and measured body surface area from 90 individuals was used as an example. SPSS 17.0 with neural network add-on was used for the analysis. The predicted body surface from a two hidden layer BP neural network was compared to the body surface calculated by the Haycock equation. Both the predicted values from the neural network and from the Haycock equation were close to the measured values. A linear regression analysis with neural network as predictor produced an r(2)-value of 0.983, while the Haycock equation produced an r(2)-value of 0.995 (r(2)>0.95 is a criterion for accurate diagnostic testing). BP neural networks may, sometimes, predict clinical diagnoses with accuracies similar to those of other methods. However, traditional statistical procedures, such as regression analyses need to be added for testing their accuracies against alternative methods. Nonetheless, BP neural networks have great potential through their ability to learn by example instead of learning by theory.

  12. Artificial intelligence techniques for voltage control

    Energy Technology Data Exchange (ETDEWEB)

    Ekwue, A.; Cheng, D.T.Y.; Macqueen, J.F.

    1997-12-31

    In electric power systems, the advantages of reactive power dispatching or optimisation include improved utilisation of reactive power sources and hence reduction in reactive power flows and real losses of the system; unloading of the system and equipment as a result of reactive flow reduction; the power factors of generation are improved and system security is enhanced; reduced voltage gradients and somewhat higher voltages which result across the system from improved operation; deferred capital investment is new reactive power sources as a result of improved utilisation of existing equipment; and for the National Grid Company plc (NGC), the main advantage is reduced out-of-merit operation. The problem of reactive power control has been studied and widely reported in the literature. Non-linear programming methods as well as linear programming techniques for constraint dispatch have been described. Static optimisation of reactive power sources by the use of sensitivity analysis was described by Kishore and Hill. Long range optimum var planning has been considered and the optimum amount and location of network reactive compensation so as to maintain the system voltage within the desired limits, while operating under normal and various insecurity states, have also been studied using several methods. The objective of this chapter is therefore to review conventional methods as well as AI techniques for reactive power control. (Author)

  13. Artificial-intelligence-based optimization of the management of snow removal assets and resources.

    Science.gov (United States)

    2002-10-01

    Geographic information systems (GIS) and artificial intelligence (AI) techniques were used to develop an intelligent : snow removal asset management system (SRAMS). The system has been evaluated through a case study examining : snow removal from the ...

  14. Application of Artificial Intelligence Techniques in Unmanned Aerial Vehicle Flight

    Science.gov (United States)

    Bauer, Frank H. (Technical Monitor); Dufrene, Warren R., Jr.

    2003-01-01

    This paper describes the development of an application of Artificial Intelligence for Unmanned Aerial Vehicle (UAV) control. The project was done as part of the requirements for a class in Artificial Intelligence (AI) at Nova southeastern University and as an adjunct to a project at NASA Goddard Space Flight Center's Wallops Flight Facility for a resilient, robust, and intelligent UAV flight control system. A method is outlined which allows a base level application for applying an AI method, Fuzzy Logic, to aspects of Control Logic for UAV flight. One element of UAV flight, automated altitude hold, has been implemented and preliminary results displayed. A low cost approach was taken using freeware, gnu, software, and demo programs. The focus of this research has been to outline some of the AI techniques used for UAV flight control and discuss some of the tools used to apply AI techniques. The intent is to succeed with the implementation of applying AI techniques to actually control different aspects of the flight of an UAV.

  15. Artificial Intelligence Research Capabilities of the Air Force Institute of Technology

    OpenAIRE

    Milne, Robert; Cross, Stephen

    1985-01-01

    The Air Force Institute of Technology (AFIT) provides master's degree education to Air Force and Army Officers in various engineering fields It is in a unique position to educate and perform research in the area of applications of artificial intelligence to military problems. Its two AI faculty members are the only military officers with PhD's in Artificial Intelligence. In the past two years, the artificial intelligence Laboratory of AFIT has become a major focal point for AI research and ap...

  16. Analysis of Computer-Aided and Artificial Intelligence Technologies and Solutions in Service Industries in Russia

    OpenAIRE

    Rezanov, Vladislav

    2013-01-01

    The primary objective of this research study was to investigate the relationship between Computer-Aided and Artificial Intelligence Technologies and customer satisfaction in the context of businesses in Russia. The research focuses on methods of Artificial Intelligence technology application in business and its effect on customer satisfaction. The researcher introduces Artificial Intelligence and studies the forecasting approaches in relation to business operations. The rese...

  17. ARTIFICIAL INTELLIGENCE CAPABILITIES FOR INCREASING ORGANIZATIONAL-TECHNOLOGICAL RELIABILITY OF CONSTRUCTION

    OpenAIRE

    Ginzburg Alexander Vital`evich; Ryzhkova Anastasiya Igorevna

    2018-01-01

    The technology of artificial intelligence is actively being mastered in the world but there is not much talk about the capabilities of artificial intelligence in construction industry and this issue requires additional elaboration. As a rule, the decision to invest in a particular construction project is made on the basis of an assessment of the organizational and technological reliability of the construction process. Artificial intelligence can be a convenient quality tool for identifying, a...

  18. Proceedings of the conference on artificial intelligence in the Department of Energy Complex

    Energy Technology Data Exchange (ETDEWEB)

    Marinuzzi, J.G. (comp.)

    1990-05-01

    This report contains the following discussions: the defense program simulation of rocky flats plant; spatial representation and reasoning for automated mesh generation; INEL support to modernization efforts at the aberdeen proving ground; artificial intelligence applications at the ICPP; an expert system for tuning particle beam accelerators; quality control expert system; an easily maintained knowledge-based system for interactive delivery of detailed technical information; workload scheduling in DOE production complex; turning operations planning system; a nuclear power plant operator advisor based on artificial intelligence technology; a impact of artificial intelligence on the new production reactor; using expert systems in treaty verification; knowledge-based systems technology transfer in Oak Ridge; applications of AI to nuclear power plants; knowledge-based computer security systems; robotic grasping of unknown objects: a knowledge-based approach; applying expertise to data in the geologist's assistant expert system; feature recognition based automatic part classification and coding; object-oriented inventories for simulation of manufacturing process; expert system at AWE; plating expert system; inspection process planning expert; troubleshooting local area networks at Savannah River Site; maintenance importance generator; joint theater level simulator; a system for authoring of tutorials including video capture and annotation, links to manuals, and links to executable code; a personal computer based expert system for documenting compliance with the National Environmental Protection Act; spatial representation and reasoning for automated mesh generation; robotic grasping of unknown objects: a knowledge-based approach; and synthesis of engineering anticipatory systems.

  19. Neurolinguistically constrained simulation of sentence comprehension: integrating artificial intelligence and brain theory

    Energy Technology Data Exchange (ETDEWEB)

    Gigley, H.M.

    1982-01-01

    An artificial intelligence approach to the simulation of neurolinguistically constrained processes in sentence comprehension is developed using control strategies for simulation of cooperative computation in associative networks. The desirability of this control strategy in contrast to ATN and production system strategies is explained. A first pass implementation of HOPE, an artificial intelligence simulation model of sentence comprehension, constrained by studies of aphasic performance, psycholinguistics, neurolinguistics, and linguistic theory is described. Claims that the model could serve as a basis for sentence production simulation and for a model of language acquisition as associative learning are discussed. HOPE is a model that performs in a normal state and includes a lesion simulation facility. HOPE is also a research tool. Its modifiability and use as a tool to investigate hypothesized causes of degradation in comprehension performance by aphasic patients are described. Issues of using behavioral constraints in modelling and obtaining appropriate data for simulated process modelling are discussed. Finally, problems of validation of the simulation results are raised; and issues of how to interpret clinical results to define the evolution of the model are discussed. Conclusions with respect to the feasibility of artificial intelligence simulation process modelling are discussed based on the current state of research.

  20. XII International Conference of the Italian Association on Artificial Intelligence

    CERN Document Server

    Semeraro, Giovanni; Vargiu, Eloisa; New Challenges in Distributed Information Filtering and Retrieval : DART 2011: Revised and Invited Papers

    2013-01-01

    This volume focuses on new challenges in distributed Information Filtering and Retrieval. It collects invited chapters and extended research contributions from the DART 2011 Workshop, held in Palermo (Italy), on September 2011, and co-located with the XII International Conference of the Italian Association on Artificial Intelligence. The main focus of DART was to discuss and compare suitable novel solutions based on intelligent techniques and applied to real-world applications. The chapters of this book present a comprehensive review of related works and state of the art. Authors, both practitioners and researchers, shared their results in several topics such as "Multi-Agent Systems", "Natural Language Processing", "Automatic Advertisement", "Customer Interaction Analytics", "Opinion Mining".

  1. Deep into the Brain: Artificial Intelligence in Stroke Imaging.

    Science.gov (United States)

    Lee, Eun-Jae; Kim, Yong-Hwan; Kim, Namkug; Kang, Dong-Wha

    2017-09-01

    Artificial intelligence (AI), a computer system aiming to mimic human intelligence, is gaining increasing interest and is being incorporated into many fields, including medicine. Stroke medicine is one such area of application of AI, for improving the accuracy of diagnosis and the quality of patient care. For stroke management, adequate analysis of stroke imaging is crucial. Recently, AI techniques have been applied to decipher the data from stroke imaging and have demonstrated some promising results. In the very near future, such AI techniques may play a pivotal role in determining the therapeutic methods and predicting the prognosis for stroke patients in an individualized manner. In this review, we offer a glimpse at the use of AI in stroke imaging, specifically focusing on its technical principles, clinical application, and future perspectives.

  2. Artificial intelligence support for scientific model-building

    Science.gov (United States)

    Keller, Richard M.

    1992-01-01

    Scientific model-building can be a time-intensive and painstaking process, often involving the development of large and complex computer programs. Despite the effort involved, scientific models cannot easily be distributed and shared with other scientists. In general, implemented scientific models are complex, idiosyncratic, and difficult for anyone but the original scientific development team to understand. We believe that artificial intelligence techniques can facilitate both the model-building and model-sharing process. In this paper, we overview our effort to build a scientific modeling software tool that aids the scientist in developing and using models. This tool includes an interactive intelligent graphical interface, a high-level domain specific modeling language, a library of physics equations and experimental datasets, and a suite of data display facilities.

  3. Artificial intelligence techniques for scheduling Space Shuttle missions

    Science.gov (United States)

    Henke, Andrea L.; Stottler, Richard H.

    1994-01-01

    Planning and scheduling of NASA Space Shuttle missions is a complex, labor-intensive process requiring the expertise of experienced mission planners. We have developed a planning and scheduling system using combinations of artificial intelligence knowledge representations and planning techniques to capture mission planning knowledge and automate the multi-mission planning process. Our integrated object oriented and rule-based approach reduces planning time by orders of magnitude and provides planners with the flexibility to easily modify planning knowledge and constraints without requiring programming expertise.

  4. Artificial Intelligence in the service of system administrators

    CERN Multimedia

    CERN. Geneva

    2012-01-01

    Research has been performed to try to automatize (some) system administration tasks, starting in 2001 when IBM defined the so-called “self objectives” supposed to lead to “autonomic computing”. In this context, we present a framework that makes use of artificial intelligence and machine learning to monitor and diagnose at a low level and in a non intrusive way  Linux-based systems and their interaction with software. Moreover, the multi agent approach we use, coupled with a "object oriented paradigm" architecture should increase a lot our learning speed, and highlight relations between probl...

  5. The application of artificial intelligence technology to aeronautical system design

    Science.gov (United States)

    Bouchard, E. E.; Kidwell, G. H.; Rogan, J. E.

    1988-01-01

    This paper describes the automation of one class of aeronautical design activity using artificial intelligence and advanced software techniques. Its purpose is to suggest concepts, terminology, and approaches that may be useful in enhancing design automation. By understanding the basic concepts and tasks in design, and the technologies that are available, it will be possible to produce, in the future, systems whose capabilities far exceed those of today's methods. Some of the tasks that will be discussed have already been automated and are in production use, resulting in significant productivity benefits. The concepts and techniques discussed are applicable to all design activity, though aeronautical applications are specifically presented.

  6. THE POTENTIAL OF ARTIFICIAL INTELLIGENCE IN SOUTH AFRICAN MANUFACTURING

    Directory of Open Access Journals (Sweden)

    A.R. Greef

    2012-01-01

    Full Text Available This paper provides an introduction to the most commonly used Knowledge Based Systems (KBS's called Rule Based Systems, presents some benefits of using these systems if the application warrants their attention and provides an over-view of current R&D as well as industrial systems already implemented. Areas of manUfacturing that could use KES's within the South African context are suggested. A research programme investigating the use of KBS's in robotics in progress at the University of Stellenbosch demonstrating a number of useful properties associated with programming Artificial Intelligence (AI techniques using logic programming, is discussed.

  7. Chips challenging champions games, computers and artificial intelligence

    CERN Document Server

    Schaeffer, J

    2002-01-01

    One of the earliest dreams of the fledgling field of artificial intelligence (AI) was to build computer programs that could play games as well as or better than the best human players. Despite early optimism in the field, the challenge proved to be surprisingly difficult. However, the 1990s saw amazing progress. Computers are now better than humans in checkers, Othello and Scrabble; are at least as good as the best humans in backgammon and chess; and are rapidly improving at hex, go, poker, and shogi. This book documents the progress made in computers playing games and puzzles. The book is the

  8. Effect of altering local protein fluctuations using artificial intelligence

    Science.gov (United States)

    Nishiyama, Katsuhiko

    2017-03-01

    The fluctuations in Arg111, a significantly fluctuating residue in cathepsin K, were locally regulated by modifying Arg111 to Gly111. The binding properties of 15 dipeptides in the modified protein were analyzed by molecular simulations, and modeled as decision trees using artificial intelligence. The decision tree of the modified protein significantly differed from that of unmodified cathepsin K, and the Arg-to-Gly modification exerted a remarkable effect on the peptide binding properties. By locally regulating the fluctuations of a protein, we may greatly alter the original functions of the protein, enabling novel applications in several fields.

  9. Artificial intelligence in medicine: humans need not apply?

    Science.gov (United States)

    Diprose, William; Buist, Nicholas

    2016-05-06

    Artificial intelligence (AI) is a rapidly growing field with a wide range of applications. Driven by economic constraints and the potential to reduce human error, we believe that over the coming years AI will perform a significant amount of the diagnostic and treatment decision-making traditionally performed by the doctor. Humans would continue to be an important part of healthcare delivery, but in many situations, less expensive fit-for-purpose healthcare workers could be trained to 'fill the gaps' where AI are less capable. As a result, the role of the doctor as an expensive problem-solver would become redundant.

  10. Determination of rock depth using artificial intelligence techniques

    Directory of Open Access Journals (Sweden)

    R. Viswanathan

    2016-01-01

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

  11. Artificial Intelligence For A Safer And More Efficient Car Driving

    Science.gov (United States)

    Adorni, Giovanni

    1989-03-01

    In this paper a project, PROMETHEUS, is described in which fourteen of Europe's leading car manufacturers are to join with approximately forty research institutes and governmental agencies to make the traffic of Europe safer, more efficient and more economical. PROMETHEUS project is divided into seven areas. In this paper one of the seven areas, PRO-ART, is described. PRO-ART is aimed at clarifying the need for and the principles of the artificial intelligence to be used in the next generation automobile. After a brief description of the overhall project, the description of the seven years PRO-ART Italian research programme will be given.

  12. Natural language processing in psychiatry. Artificial intelligence technology and psychopathology.

    Science.gov (United States)

    Garfield, D A; Rapp, C; Evens, M

    1992-04-01

    The potential benefit of artificial intelligence (AI) technology as a tool of psychiatry has not been well defined. In this essay, the technology of natural language processing and its position with regard to the two main schools of AI is clearly outlined. Past experiments utilizing AI techniques in understanding psychopathology are reviewed. Natural language processing can automate the analysis of transcripts and can be used in modeling theories of language comprehension. In these ways, it can serve as a tool in testing psychological theories of psychopathology and can be used as an effective tool in empirical research on verbal behavior in psychopathology.

  13. Narrative Generation in Entertainment: Using Artificial Intelligence Planning

    OpenAIRE

    George, Richard A

    2015-01-01

    From the field of artificial intelligence (AI) there is a growing stream of technology capable of being embedded in software that will reshape the way we interact with our environment in our everyday lives. This ‘AI software’ is often used to tackle more mundane tasks that are otherwise dangerous or meticulous for a human to accomplish. One particular area, explored in this paper, is for AI software to assist in supporting the enjoyable aspects of the lives of humans. Entertainment is one of ...

  14. Artificial intelligence and expert systems in-flight software testing

    Science.gov (United States)

    Demasie, M. P.; Muratore, J. F.

    1991-01-01

    The authors discuss the introduction of advanced information systems technologies such as artificial intelligence, expert systems, and advanced human-computer interfaces directly into Space Shuttle software engineering. The reconfiguration automation project (RAP) was initiated to coordinate this move towards 1990s software technology. The idea behind RAP is to automate several phases of the flight software testing procedure and to introduce AI and ES into space shuttle flight software testing. In the first phase of RAP, conventional tools to automate regression testing have already been developed or acquired. There are currently three tools in use.

  15. Using artificial intelligence to control fluid flow computations

    Science.gov (United States)

    Gelsey, Andrew

    1992-01-01

    Computational simulation is an essential tool for the prediction of fluid flow. Many powerful simulation programs exist today. However, using these programs to reliably analyze fluid flow and other physical situations requires considerable human effort and expertise to set up a simulation, determine whether the output makes sense, and repeatedly run the simulation with different inputs until a satisfactory result is achieved. Automating this process is not only of considerable practical importance but will also significantly advance basic artificial intelligence (AI) research in reasoning about the physical world.

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

    Directory of Open Access Journals (Sweden)

    Gys Albertus Marthinus Meiring

    2015-12-01

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

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

    Science.gov (United States)

    Meiring, Gys Albertus Marthinus; Myburgh, Hermanus Carel

    2015-12-04

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

  18. Estimation of urban runoff and water quality using remote sensing and artificial intelligence.

    Science.gov (United States)

    Ha, S R; Park, S Y; Park, D H

    2003-01-01

    Water quality and quantity of runoff are strongly dependent on the landuse and landcover (LULC) criteria. In this study, we developed a more improved parameter estimation procedure for the environmental model using remote sensing (RS) and artificial intelligence (AI) techniques. Landsat TM multi-band (7bands) and Korea Multi-Purpose Satellite (KOMPSAT) panchromatic data were selected for input data processing. We employed two kinds of artificial intelligence techniques, RBF-NN (radial-basis-function neural network) and ANN (artificial neural network), to classify LULC of the study area. A bootstrap resampling method, a statistical technique, was employed to generate the confidence intervals and distribution of the unit load. SWMM was used to simulate the urban runoff and water quality and applied to the study watershed. The condition of urban flow and non-point contaminations was simulated with rainfall-runoff and measured water quality data. The estimated total runoff, peak time, and pollutant generation varied considerably according to the classification accuracy and percentile unit load applied. The proposed procedure would efficiently be applied to water quality and runoff simulation in a rapidly changing urban area.

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

    OpenAIRE

    Gys Albertus Marthinus Meiring; Hermanus Carel Myburgh

    2015-01-01

    In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying...

  20. Prediction of Availability Indicator of Water Pipes Using Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Kutyłowska Małgorzata

    2017-01-01

    Full Text Available The paper presents the results of artificial neural networks application to the availability indicator prediction. The forecasted results indicate that artificial networks may be used to model the reliability level of the water supply systems. The network was trained using 147 and 173 operational data from one Polish medium-sized city (distribution pipes and house connections, respectively. 50% of all data was chosen for learning, 25% for testing and 25% for validation. In prognosis phase, the best created network used 100% of 114 and 133 values for testing. Following functions were used to activate neurons in hidden and output layers: linear, logistic, hyperbolic tangent, exponential. The learning of the artificial network was performed using following input parameters: material, total length, diameter. In the optimal models hyperbolic tangent was chosen to activate the hidden and output neurons in modeling the availability indicator of house connections during 68 epochs of training. Hidden and output neurons were activated (20 epochs of learning respectively by hyperbolic tangent and linear function during the prediction of availability indicator of distribution pipes. The maximum relative errors in learning and prognosis step were equal to 0.10% and 1.20% as well as 0.27% and 1.15% for distribution pipes and house connections, respectively.

  1. Protein subcellular localization prediction using artificial intelligence technology.

    Science.gov (United States)

    Nair, Rajesh; Rost, Burkhard

    2008-01-01

    Proteins perform many important tasks in living organisms, such as catalysis of biochemical reactions, transport of nutrients, and recognition and transmission of signals. The plethora of aspects of the role of any particular protein is referred to as its "function." One aspect of protein function that has been the target of intensive research by computational biologists is its subcellular localization. Proteins must be localized in the same subcellular compartment to cooperate toward a common physiological function. Aberrant subcellular localization of proteins can result in several diseases, including kidney stones, cancer, and Alzheimer's disease. To date, sequence homology remains the most widely used method for inferring the function of a protein. However, the application of advanced artificial intelligence (AI)-based techniques in recent years has resulted in significant improvements in our ability to predict the subcellular localization of a protein. The prediction accuracy has risen steadily over the years, in large part due to the application of AI-based methods such as hidden Markov models (HMMs), neural networks (NNs), and support vector machines (SVMs), although the availability of larger experimental datasets has also played a role. Automatic methods that mine textual information from the biological literature and molecular biology databases have considerably sped up the process of annotation for proteins for which some information regarding function is available in the literature. State-of-the-art methods based on NNs and HMMs can predict the presence of N-terminal sorting signals extremely accurately. Ab initio methods that predict subcellular localization for any protein sequence using only the native amino acid sequence and features predicted from the native sequence have shown the most remarkable improvements. The prediction accuracy of these methods has increased by over 30% in the past decade. The accuracy of these methods is now on par with

  2. Artificial neural network in cosmic landscape

    Science.gov (United States)

    Liu, Junyu

    2017-12-01

    In this paper we propose that artificial neural network, the basis of machine learning, is useful to generate the inflationary landscape from a cosmological point of view. Traditional numerical simulations of a global cosmic landscape typically need an exponential complexity when the number of fields is large. However, a basic application of artificial neural network could solve the problem based on the universal approximation theorem of the multilayer perceptron. A toy model in inflation with multiple light fields is investigated numerically as an example of such an application.

  3. Assessing Landslide Hazard Using Artificial Neural Network

    DEFF Research Database (Denmark)

    Farrokhzad, Farzad; Choobbasti, Asskar Janalizadeh; Barari, Amin

    2011-01-01

    failure" which is main concentration of the current research and "liquefaction failure". Shear failures along shear planes occur when the shear stress along the sliding surfaces exceed the effective shear strength. These slides have been referred to as landslide. An expert system based on artificial...... neural network has been developed for use in the stability evaluation of slopes under various geological conditions and engineering requirements. The Artificial neural network model of this research uses slope characteristics as input and leads to the output in form of the probability of failure...

  4. Artificial neural networks in high voltage transmission line problems

    Science.gov (United States)

    Ekonomou, L.; Kontargyri, V. T.; Kourtesi, St.; Maris, T. I.; Stathopulos, I. A.

    2007-07-01

    According to the literature high voltage transmission line problems are faced using conventional analytical methods, which include in most cases empirical and/or approximating equations. Artificial intelligence and more specifically artificial neural networks (ANN) are addressed in this work, in order to give accurate solutions to high voltage transmission line problems using in the calculations only actual field data. Two different case studies are studied, i.e., the estimation of critical flashover voltage on polluted insulators and the estimation of lightning performance of high voltage transmission lines. ANN models are developed and are tested on operating high voltage transmission lines and polluted insulators, producing very satisfactory results. These two ANN models can be used in electrical engineers' studies aiming at the more effective protection of high voltage equipment.

  5. Artificial intelligence-assisted occupational lung disease diagnosis.

    Science.gov (United States)

    Harber, P; McCoy, J M; Howard, K; Greer, D; Luo, J

    1991-08-01

    An artificial intelligence expert-based system for facilitating the clinical recognition of occupational and environmental factors in lung disease has been developed in a pilot fashion. It utilizes a knowledge representation scheme to capture relevant clinical knowledge into structures about specific objects (jobs, diseases, etc) and pairwise relations between objects. Quantifiers describe both the closeness of association and risk, as well as the degree of belief in the validity of a fact. An independent inference engine utilizes the knowledge, combining likelihoods and uncertainties to achieve estimates of likelihood factors for specific paths from work to illness. The system creates a series of "paths," linking work activities to disease outcomes. One path links a single period of work to a single possible disease outcome. In a preliminary trial, the number of "paths" from job to possible disease averaged 18 per subject in a general population and averaged 25 per subject in an asthmatic population. Artificial intelligence methods hold promise in the future to facilitate diagnosis in pulmonary and occupational medicine.

  6. An advanced artificial intelligence tool for menu design.

    Science.gov (United States)

    Khan, Abdus Salam; Hoffmann, Achim

    2003-01-01

    The computer-assisted menu design still remains a difficult task. Usually knowledge that aids in menu design by a computer is hard-coded and because of that a computerised menu planner cannot handle the menu design problem for an unanticipated client. To address this problem we developed a menu design tool, MIKAS (menu construction using incremental knowledge acquisition system), an artificial intelligence system that allows the incremental development of a knowledge-base for menu design. We allow an incremental knowledge acquisition process in which the expert is only required to provide hints to the system in the context of actual problem instances during menu design using menus stored in a so-called Case Base. Our system incorporates Case-Based Reasoning (CBR), an Artificial Intelligence (AI) technique developed to mimic human problem solving behaviour. Ripple Down Rules (RDR) are a proven technique for the acquisition of classification knowledge from expert directly while they are using the system, which complement CBR in a very fruitful way. This combination allows the incremental improvement of the menu design system while it is already in routine use. We believe MIKAS allows better dietary practice by leveraging a dietitian's skills and expertise. As such MIKAS has the potential to be helpful for any institution where dietary advice is practised.

  7. Artificial intelligence techniques for automatic screening of amblyogenic factors.

    Science.gov (United States)

    Van Eenwyk, Jonathan; Agah, Arvin; Giangiacomo, Joseph; Cibis, Gerhard

    2008-01-01

    To develop a low-cost automated video system to effectively screen children aged 6 months to 6 years for amblyogenic factors. In 1994 one of the authors (G.C.) described video vision development assessment, a digitizable analog video-based system combining Brückner pupil red reflex imaging and eccentric photorefraction to screen young children for amblyogenic factors. The images were analyzed manually with this system. We automated the capture of digital video frames and pupil images and applied computer vision and artificial intelligence to analyze and interpret results. The artificial intelligence systems were evaluated by a tenfold testing method. The best system was the decision tree learning approach, which had an accuracy of 77%, compared to the "gold standard" specialist examination with a "refer/do not refer" decision. Criteria for referral were strabismus, including microtropia, and refractive errors and anisometropia considered to be amblyogenic. Eighty-two percent of strabismic individuals were correctly identified. High refractive errors were also correctly identified and referred 90% of the time, as well as significant anisometropia. The program was less correct in identifying more moderate refractive errors, below +5 and less than -7. Although we are pursuing a variety of avenues to improve the accuracy of the automated analysis, the program in its present form provides acceptable cost benefits for detecting ambylogenic factors in children aged 6 months to 6 years.

  8. AN ARTIFICIAL INTELLIGENCE-BASED DISTANCE EDUCATION SYSTEM: Artimat

    Directory of Open Access Journals (Sweden)

    Vasif NABIYEV

    2013-04-01

    Full Text Available The purpose of this study is to evaluate the artificial intelligence-based distance education system called as ARTIMAT, which has been prepared in order to improve mathematical problem solving skills of the students, in terms of conceptual proficiency and ease of use with the opinions of teachers and students. The implementation has been performed with 4 teachers and 59 students in 10th grade in an Anatolian High School in Trabzon. Many institutions and organizations in the world approach seriously to distance education besides traditional education. It is inevitable to use the distance education in teaching the problem solving skills in this different dimension of the education. In the studies in Turkey and abroad in the field of mathematics teaching, problem solving skills are generally stated not to be at the desired level and often expressed to have difficulty in teaching. For this reason, difficulties of the students in problem solving have initially been evaluated and the system has been prepared utilizing artificial intelligence algorithms according to the obtained results. In the evaluation of the findings obtained from the application, it has been concluded that the system is responsive to the needs of the students and is successful in general, but that conceptual changes should be made in order that students adapt to the system quickly.

  9. Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction.

    Science.gov (United States)

    Park, Seong Ho; Han, Kyunghwa

    2018-03-01

    The use of artificial intelligence in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysis of medical images. Adoption of an artificial intelligence tool in clinical practice requires careful confirmation of its clinical utility. Herein, the authors explain key methodology points involved in a clinical evaluation of artificial intelligence technology for use in medicine, especially high-dimensional or overparameterized diagnostic or predictive models in which artificial deep neural networks are used, mainly from the standpoints of clinical epidemiology and biostatistics. First, statistical methods for assessing the discrimination and calibration performances of a diagnostic or predictive model are summarized. Next, the effects of disease manifestation spectrum and disease prevalence on the performance results are explained, followed by a discussion of the difference between evaluating the performance with use of internal and external datasets, the importance of using an adequate external dataset obtained from a well-defined clinical cohort to avoid overestimating the clinical performance as a result of overfitting in high-dimensional or overparameterized classification model and spectrum bias, and the essentials for achieving a more robust clinical evaluation. Finally, the authors review the role of clinical trials and observational outcome studies for ultimate clinical verification of diagnostic or predictive artificial intelligence tools through patient outcomes, beyond performance metrics, and how to design such studies. © RSNA, 2018.

  10. Social networks a framework of computational intelligence

    CERN Document Server

    Chen, Shyi-Ming

    2014-01-01

    This volume provides the audience with an updated, in-depth and highly coherent material on the conceptually appealing and practically sound information technology of Computational Intelligence applied to the analysis, synthesis and evaluation of social networks. The volume involves studies devoted to key issues of social networks including community structure detection in networks, online social networks, knowledge growth and evaluation, and diversity of collaboration mechanisms.  The book engages a wealth of methods of Computational Intelligence along with well-known techniques of linear programming, Formal Concept Analysis, machine learning, and agent modeling.  Human-centricity is of paramount relevance and this facet manifests in many ways including personalized semantics, trust metric, and personal knowledge management; just to highlight a few of these aspects. The contributors to this volume report on various essential applications including cyber attacks detection, building enterprise social network...

  11. Natural and Artificial Intelligence, Language, Consciousness, Emotion, and Anticipation

    Science.gov (United States)

    Dubois, Daniel M.

    2010-11-01

    The classical paradigm of the neural brain as the seat of human natural intelligence is too restrictive. This paper defends the idea that the neural ectoderm is the actual brain, based on the development of the human embryo. Indeed, the neural ectoderm includes the neural crest, given by pigment cells in the skin and ganglia of the autonomic nervous system, and the neural tube, given by the brain, the spinal cord, and motor neurons. So the brain is completely integrated in the ectoderm, and cannot work alone. The paper presents fundamental properties of the brain as follows. Firstly, Paul D. MacLean proposed the triune human brain, which consists to three brains in one, following the species evolution, given by the reptilian complex, the limbic system, and the neo-cortex. Secondly, the consciousness and conscious awareness are analysed. Thirdly, the anticipatory unconscious free will and conscious free veto are described in agreement with the experiments of Benjamin Libet. Fourthly, the main section explains the development of the human embryo and shows that the neural ectoderm is the whole neural brain. Fifthly, a conjecture is proposed that the neural brain is completely programmed with scripts written in biological low-level and high-level languages, in a manner similar to the programmed cells by the genetic code. Finally, it is concluded that the proposition of the neural ectoderm as the whole neural brain is a breakthrough in the understanding of the natural intelligence, and also in the future design of robots with artificial intelligence.

  12. Artificial neural networks a practical course

    CERN Document Server

    da Silva, Ivan Nunes; Andrade Flauzino, Rogerio; Liboni, Luisa Helena Bartocci; dos Reis Alves, Silas Franco

    2017-01-01

    This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.

  13. QUESTION ANSWERING SYSTEM BERBASIS ARTIFICIAL INTELLIGENCE MARKUP LANGUAGE SEBAGAI MEDIA INFORMASI

    Directory of Open Access Journals (Sweden)

    Fajrin Azwary

    2016-04-01

    Full Text Available Artificial intelligence technology nowadays, can be processed with a variety of forms, such as chatbot, and the various methods, one of them using Artificial Intelligence Markup Language (AIML. AIML using template matching, by comparing the specific patterns in the database. AIML template design process begins with determining the necessary information, then formed into questions, these questions adapted to AIML pattern. From the results of the study, can be known that the Question-Answering System in the chatbot using Artificial Intelligence Markup Language are able to communicate and deliver information. Keywords: Artificial Intelligence, Template Matching, Artificial Intelligence Markup Language, AIML Teknologi kecerdasan buatan saat ini dapat diolah dengan berbagai macam bentuk, seperti ChatBot, dan berbagai macam metode, salah satunya menggunakan Artificial Intelligence Markup Language (AIML. AIML menggunakan metode template matching yaitu dengan membandingkan pola-pola tertentu pada database. Proses perancangan template AIML diawali dengan menentukan informasi yang diperlukan, kemudian dibentuk menjadi pertanyaan, pertanyaan tersebut disesuaikan dengan bentuk pattern AIML. Hasil penelitian dapat diperoleh bahwa Question-Answering System dalam bentuk ChatBot menggunakan Artificial Intelligence Markup Language dapat berkomunikasi dan menyampaikan informasi. Kata kunci : Kecerdasan Buatan, Pencocokan Pola, Artificial Intelligence Markup Language, AIML

  14. "It's Going to Kill Us!" and Other Myths about the Future of Artificial Intelligence

    Science.gov (United States)

    Atkinson, Robert D.

    2016-01-01

    Given the promise that artificial intelligence (AI) holds for economic growth and societal advancement, it is critical that policymakers not only avoid retarding the progress of AI innovation, but also actively support its further development and use. This report provides a primer on artificial intelligence and debunks five prevailing myths that,…

  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. Artificial Intelligence Measurement System, Overview and Lessons Learned. Final Project Report.

    Science.gov (United States)

    Baker, Eva L.; Butler, Frances A.

    This report summarizes the work conducted for the Artificial Intelligence Measurement System (AIMS) Project which was undertaken as an exploration of methodology to consider how the effects of artificial intelligence systems could be compared to human performance. The research covered four areas of inquiry: (1) natural language processing and…

  17. Perspectives on Research in Artificial Intelligence and Artificial General Intelligence Relevant to DoD

    Science.gov (United States)

    2017-01-01

    spatial architecture for energy -efficient dataflow for convolutional neural networks. 2016. 67 Karl Freund. Google’s tpu chip creates more questions...4 2.3 Structure and History of AI as a Field of Research...3.2 Generic Network Architecture ........................................................................................ 11 3.3 Neuron Non

  18. Heart failure analysis dashboard for patient's remote monitoring combining multiple artificial intelligence technologies.

    Science.gov (United States)

    Guidi, G; Pettenati, M C; Miniati, R; Iadanza, E

    2012-01-01

    In this paper we describe an Heart Failure analysis Dashboard that, combined with a handy device for the automatic acquisition of a set of patient's clinical parameters, allows to support telemonitoring functions. The Dashboard's intelligent core is a Computer Decision Support System designed to assist the clinical decision of non-specialist caring personnel, and it is based on three functional parts: Diagnosis, Prognosis, and Follow-up management. Four Artificial Intelligence-based techniques are compared for providing diagnosis function: a Neural Network, a Support Vector Machine, a Classification Tree and a Fuzzy Expert System whose rules are produced by a Genetic Algorithm. State of the art algorithms are used to support a score-based prognosis function. The patient's Follow-up is used to refine the diagnosis.

  19. Artificial neural networks and support vector mac

    Indian Academy of Sciences (India)

    Quantitative structure-property relationships of electroluminescent materials: Artificial neural networks and support vector machines to predict electroluminescence of organic molecules. ALANA FERNANDES GOLIN and RICARDO STEFANI. ∗. Laboratório de Estudos de Materiais (LEMAT), Instituto de Ciências Exatas e da ...

  20. Where Artificial Intelligence and Neuroscience Meet: The Search for Grounded Architectures of Cognition

    Directory of Open Access Journals (Sweden)

    Frank van der Velde

    2010-01-01

    Full Text Available The collaboration between artificial intelligence and neuroscience can produce an understanding of the mechanisms in the brain that generate human cognition. This article reviews multidisciplinary research lines that could achieve this understanding. Artificial intelligence has an important role to play in research, because artificial intelligence focuses on the mechanisms that generate intelligence and cognition. Artificial intelligence can also benefit from studying the neural mechanisms of cognition, because this research can reveal important information about the nature of intelligence and cognition itself. I will illustrate this aspect by discussing the grounded nature of human cognition. Human cognition is perhaps unique because it combines grounded representations with computational productivity. I will illustrate that this combination requires specific neural architectures. Investigating and simulating these architectures can reveal how they are instantiated in the brain. The way these architectures implement cognitive processes could also provide answers to fundamental problems facing the study of cognition.

  1. Application of artificial intelligence to characterize naturally fractured zones in Hassi Messaoud Oil Field, Algeria

    Energy Technology Data Exchange (ETDEWEB)

    El Ouahed, Abdelkader Kouider; Mazouzi, Amine [Sonatrach, Rue Djenane Malik, Hydra, Algiers (Algeria); Tiab, Djebbar [Mewbourne School of Petroleum and Geological Engineering, The University of Oklahoma, 100 East Boyd Street, SEC T310, Norman, OK, 73019 (United States)

    2005-12-15

    In highly heterogeneous reservoirs classical characterization methods often fail to detect the location and orientation of the fractures. Recent applications of Artificial Intelligence to the area of reservoir characterization have made this challenge a possible practice. Such a practice consists of seeking the complex relationship between the fracture index and some geological and geomechanical drivers (facies, porosity, permeability, bed thickness, proximity to faults, slopes and curvatures of the structure) in order to obtain a fracture intensity map using Fuzzy Logic and Neural Network. This paper shows the successful application of Artificial Intelligence tools such as Artificial Neural Network and Fuzzy Logic to characterize naturally fractured reservoirs. A 2D fracture intensity map and fracture network map in a large block of Hassi Messaoud field have been developed using Artificial Neural Network and Fuzzy Logic. This was achieved by first building the geological model of the permeability, porosity and shale volume using stochastic conditional simulation. Then by applying some geomechanical concepts first and second structure directional derivatives, distance to the nearest fault, and bed thickness were calculated throughout the entire area of interest. Two methods were then used to select the appropriate fracture intensity index. In the first method well performance was used as a fracture index. In the second method a Fuzzy Inference System (FIS) was built. Using this FIS, static and dynamic data were coupled to reduce the uncertainty, which resulted in a more reliable Fracture Index. The different geological and geomechanical drivers were ranked with the corresponding fracture index for both methods using a Fuzzy Ranking algorithm. Only important and measurable data were selected to be mapped with the appropriate fracture index using a feed forward Back Propagation Neural Network (BPNN). The neural network was then used to obtain a fracture intensity

  2. ON THE PREDICTION OF CHAOTIC DYNAMICS WITH ARTIFICIAL INTELLIGENCE TECHNIQUES

    OpenAIRE

    CIOBANU DUMITRU

    2012-01-01

    Because of sensitive dependence on initial conditions (SDIC), characteristic to chaotic systems, the prediction of such system can be made with an accepted accuracy only for relatively small number of steps ahead. Using artificial techniques like neural networks and support vector machine to predict chaotic dynamics present advantages over traditional methods and usually they offers better results. In this paper, I highlight some of these advantages.

  3. Artificial Intelligence and ‘learning regions’: an outline to a research adventure

    OpenAIRE

    de Souza, Peter; Yildirim, Sule

    2007-01-01

    Can we usefully improve the understanding of a ‘learning (knowledge) region’ by using an Artificial Intelligence (AI) metaphor? Could one capture the dynamics of a learning, intelligent, society in a geographical or a functional form with an analytical approach like the Artificial Intelligence (AI)? Specifically, could this approach, as a metaphorical example, contribute to the understanding of the functioning of the learning (LR) or knowledge regions. Simultaneously, with the focus on th...

  4. Robotics and autonomous systems in the 50th anniversary of artificial intelligence

    OpenAIRE

    Casals, Alicia; Fernández Caballero, Antonio

    2007-01-01

    The special issue on ?Robotics and Autonomous Systems in the 50th Anniversary of Artificial Intelligence? collects a subset of the best papers in the fields of Robotics and Autonomous Systems presented at the Campus Multidisciplinary in Perception and Intelligence, CMPI-2006. The CMPI-2006 international conference, held in Albacete, Spain, from July 10 to 14, 2006, resulted in a forum for scientists in commemoration of the 50th Anniversary of Artificial Intelligence, which successfully report...

  5. E-LEARNING EXPERIENCE WITH ARTIFICIAL INTELLIGENCE SUPPORTED SOFTWARE: An International Application on English Language Courses

    OpenAIRE

    Kose, Utku; Arslan, Ahmet

    2015-01-01

    Nowadays, artificial intelligence supported e-learning scenarios are widely employed by educational institutions in order to ensure better teaching and learning experiences along educational activities. In the context of performed scientific studies, positive results often encourage such institutions to apply their intelligent e-learning systems on different types of courses and report advantages of artificial intelligent in especially education field. It seems that the future of education w...

  6. On The Comparison of Artificial Neural Network (ANN) and ...

    African Journals Online (AJOL)

    West African Journal of Industrial and Academic Research ... This work presented the results of an experimental comparison of two models: Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) for ... Keywords: Multinomial Logistic Regression, Artificial Neural Network, Correct classification rate.

  7. Capacity of Intelligent Underlay and Overlay Network

    DEFF Research Database (Denmark)

    Ling, Yim; Elling, Jan; Nielsen, Thomas Toftegaard

    1996-01-01

    This paper suggests a method of dimensioning the cell configuration of radio sites in a cellular network using the intelligent overlay and underlay (IOU) technique. The traffic capacity has been analysed by means of a teletraffic model, which is developed based on the classical theory of overflow...... traffic. The formulas of the model have been implemented with the use of MatLab. To verify the model, measurement methods have been developed to collect the teletraffic information in a real-live GSM network. The measured data indicates that the teletraffic model describes the capacity with high accuracy...... and therefore can be used to dimension the network. The model shows that the increase of capacity for a GSM network with 34 frequencies is about 30%. Further capacity enhancement can be achieved by intelligent frequency planning method which is currently being developed...

  8. Network Neuroscience Theory of Human Intelligence.

    Science.gov (United States)

    Barbey, Aron K

    2018-01-01

    An enduring aim of research in the psychological and brain sciences is to understand the nature of individual differences in human intelligence, examining the stunning breadth and diversity of intellectual abilities and the remarkable neurobiological mechanisms from which they arise. This Opinion article surveys recent neuroscience evidence to elucidate how general intelligence, g, emerges from individual differences in the network architecture of the human brain. The reviewed findings motivate new insights about how network topology and dynamics account for individual differences in g, represented by the Network Neuroscience Theory. According to this framework, g emerges from the small-world topology of brain networks and the dynamic reorganization of its community structure in the service of system-wide flexibility and adaptation. Copyright © 2017 The Author. Published by Elsevier Ltd.. All rights reserved.

  9. The Cyber Intelligence Challenge of Asyngnotic Networks

    Directory of Open Access Journals (Sweden)

    Edward M. Roche

    2015-09-01

    Full Text Available The intelligence community is facing a new type of organization, one enabled by the world’s information and communications infrastructure. These asyngnotic networks operate without leadership and are self-organizing in nature. They pose a threat to national security because they are difficult to detect in time for intelligence to provide adequate warning. Social network analysis and link analysis are important tools but can be supplemented by application of neuroscience principles to understand the forces that drive asyngnotic self-organization and triggering of terrorist events. Applying Living Systems Theory (LST to a terrorist attack provides a useful framework to identify hidden asyngnotic networks. There is some antecedent work in propaganda analysis that may help uncover hidden asyngnotic networks, but computerized SIGINT methods face a number of challenges.

  10. Prediction of Metabolism of Drugs using Artificial Intelligence: How far have we reached?

    Science.gov (United States)

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

    2016-01-01

    Information about drug metabolism is an essential component of drug development. Modeling the drug metabolism requires identification of the involved enzymes, rate and extent of metabolism, the sites of metabolism etc. There has been continuous attempts in the prediction of metabolism of drugs using artificial intelligence in effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are number of predictive models available for metabolism using Support vector machines, Artificial neural networks, Bayesian classifiers etc. There is an urgent need to review their progress so far and address the existing challenges in prediction of metabolism. In this attempt, we are presenting the currently available literature models and some of the critical issues regarding prediction of drug metabolism.

  11. ANALYSIS DATA SETS USING HYBRID TECHNIQUES APPLIED ARTIFICIAL INTELLIGENCE BASED PRODUCTION SYSTEMS INTEGRATED DESIGN

    Directory of Open Access Journals (Sweden)

    Daniel-Petru GHENCEA

    2017-06-01

    Full Text Available The paper proposes a prediction model of behavior spindle from the point of view of the thermal deformations and the level of the vibrations by highlighting and processing the characteristic equations. This is a model analysis for the shaft with similar electro-mechanical characteristics can be achieved using a hybrid analysis based on artificial intelligence (genetic algorithms - artificial neural networks - fuzzy logic. The paper presents a prediction mode obtaining valid range of values for spindles with similar characteristics based on measured data sets from a few spindles test without additional measures being required. Extracting polynomial functions of graphs resulting from simultaneous measurements and predict the dynamics of the two features with multi-objective criterion is the main advantage of this method.

  12. Wireless intelligent network: infrastructure before services?

    Science.gov (United States)

    Chu, Narisa N.

    1996-01-01

    The Wireless Intelligent Network (WIN) intends to take advantage of the Advanced Intelligent Network (AIN) concepts and products developed from wireline communications. However, progress of the AIN deployment has been slow due to the many barriers that exist in the traditional wireline carriers' deployment procedures and infrastructure. The success of AIN has not been truly demonstrated. The AIN objectives and directions are applicable to the wireless industry although the plans and implementations could be significantly different. This paper points out WIN characteristics in architecture, flexibility, deployment, and value to customers. In order to succeed, the technology driven AIN concept has to be reinforced by the market driven WIN services. An infrastructure suitable for the WIN will contain elements that are foreign to the wireline network. The deployment process is expected to seed with the revenue generated services. Standardization will be achieved by simplifying and incorporating the IS-41C, AIN, and Intelligent Network CS-1 recommendations. Integration of the existing and future systems impose the biggest challenge of all. Service creation has to be complemented with service deployment process which heavily impact the carriers' infrastructure. WIN deployment will likely start from an Intelligent Peripheral, a Service Control Point and migrate to a Service Node when sufficient triggers are implemented in the mobile switch for distributed call control. The struggle to move forward will not be based on technology, but rather on the impact to existing infrastructure.

  13. BNAIC 2008, Proceedings 20th Belgian-Netherlands Conference on Artificial Intelligence

    OpenAIRE

    Nijholt, Antinus; Pantic, Maja; Poel, Mannes; Hondorp, G.H.W.; Unknown

    2008-01-01

    This book contains the proceedings of the 20th edition of the Belgian-Netherlands Conference on Artificial Intelligence. The conference was organized by the Human Media Interaction group of the University of Twente. As usual, the conference was under the auspices of the Belgian-Dutch Association for Artificial Intelligence (BNVKI) and the Dutch Research School for Information and Knowledge Systems (SIKS). The conference aims at presenting an overview of state-of-the-art research in artificial...

  14. AIonAI: a humanitarian law of artificial intelligence and robotics.

    Science.gov (United States)

    Ashrafian, Hutan

    2015-02-01

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

  15. iWordNet: A New Approach to Cognitive Science and Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Mark Chang

    2017-01-01

    Full Text Available One of the main challenges in artificial intelligence or computational linguistics is understanding the meaning of a word or concept. We argue that the connotation of the term “understanding,” or the meaning of the word “meaning,” is merely a word mapping game due to unavoidable circular definitions. These circular definitions arise when an individual defines a concept, the concepts in its definition, and so on, eventually forming a personalized network of concepts, which we call an iWordNet. Such an iWordNet serves as an external representation of an individual’s knowledge and state of mind at the time of the network construction. As a result, “understanding” and knowledge can be regarded as a calculable statistical property of iWordNet topology. We will discuss the construction and analysis of the iWordNet, as well as the proposed “Path of Understanding” in an iWordNet that characterizes an individual’s understanding of a complex concept such as a written passage. In our pilot study of 20 subjects we used a regression model to demonstrate that the topological properties of an individual’s iWordNet are related to his IQ score, a relationship that suggests iWordNets as a potential new methodology to studying cognitive science and artificial intelligence.

  16. Analysis of Boiler Operational Variables Prior to Tube Leakage Fault by Artificial Intelligent System

    Directory of Open Access Journals (Sweden)

    Al-Kayiem Hussain H.

    2014-07-01

    Full Text Available Steam boilers are considered as a core of any steam power plant. Boilers are subjected to various types of trips leading to shut down of the entire plant. The tube leakage is the worse among the common boiler faults, where the shutdown period lasts for around four to five days. This paper describes the rules of the Artificial Intelligent Systems to diagnosis the boiler variables prior to tube leakage occurrence. An Intelligent system based on Artificial Neural Network was designed and coded in MATLAB environment. The ANN was trained and validated using real site data acquired from coal fired power plant in Malaysia. Ninety three boiler operational variables were identified for the present investigation based on the plant operator experience. Various neural networks topology combinations were investigated. The results showed that the NN with two hidden layers performed better than one hidden layer using Levenberg-Maquardt training algorithm. Moreover, it was noticed that hyperbolic tangent function for input and output nodes performed better than other activation function types.

  17. Neutron spectrometry with artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Rodriguez, J.M.; Mercado S, G.A. [Universidad Autonoma de Zacatecas, A.P. 336, 98000 Zacatecas (Mexico); Iniguez de la Torre Bayo, M.P. [Universidad de Valladolid, Valladolid (Spain); Barquero, R. [Hospital Universitario Rio Hortega, Valladolid (Spain); Arteaga A, T. [Envases de Zacatecas, S.A. de C.V., Zacatecas (Mexico)]. e-mail: rvega@cantera.reduaz.mx

    2005-07-01

    An artificial neural network has been designed to obtain the neutron spectra from the Bonner spheres spectrometer's count rates. The neural network was trained using 129 neutron spectra. These include isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra from mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-bin ned to 31 energy groups using the MCNP 4C code. Re-binned spectra and UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and the respective spectrum was used as output during neural network training. After training the network was tested with the Bonner spheres count rates produced by a set of neutron spectra. This set contains data used during network training as well as data not used. Training and testing was carried out in the Mat lab program. To verify the network unfolding performance the original and unfolded spectra were compared using the {chi}{sup 2}-test and the total fluence ratios. The use of Artificial Neural Networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  18. Neutron spectrometry using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Vega-Carrillo, Hector Rene [Unidad Academica de Estudios Nucleares, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico)]|[Unidad Academica de Ing. Electrica, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico)]|[Unidad Academica de Matematicas, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico)]. E-mail: fermineutron@yahoo.com; Martin Hernandez-Davila, Victor [Unidad Academica de Estudios Nucleares, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico)]|[Unidad Academica de Ing. Electrica, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico); Manzanares-Acuna, Eduardo [Unidad Academica de Estudios Nucleares, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico); Mercado Sanchez, Gema A. [Unidad Academica de Matematicas, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico); Pilar Iniguez de la Torre, Maria [Depto. Fisica Teorica, Molecular y Nuclear, Universidad de Valladolid, Valladolid (Spain); Barquero, Raquel [Hospital Universitario Rio Hortega, Valladolid (Spain); Palacios, Francisco; Mendez Villafane, Roberto [Depto. Fisica Teorica, Molecular y Nuclear, Universidad de Valladolid, Valladolid (Spain)]|[Universidad Europea Miguel de Cervantes, C. Padre Julio Chevalier No. 2, 47012 Valladolid (Spain); Arteaga Arteaga, Tarcicio [Unidad Academica de Estudios Nucleares, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico)]|[Envases de Zacatecas, SA de CV, Parque Industrial de Calera de Victor Rosales, Zac. (Mexico); Manuel Ortiz Rodriguez, Jose [Unidad Academica de Estudios Nucleares, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico)]|[Unidad Academica de Ing. Electrica, Universidad Autonoma de Zacatecas, Apdo. Postal 336, 98000 Zacatecas, Zac. (Mexico)

    2006-04-15

    An artificial neural network has been designed to obtain neutron spectra from Bonner spheres spectrometer count rates. The neural network was trained using 129 neutron spectra. These include spectra from isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra based on mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. The re-binned spectra and the UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and their respective spectra were used as output during the neural network training. After training, the network was tested with the Bonner spheres count rates produced by folding a set of neutron spectra with the response matrix. This set contains data used during network training as well as data not used. Training and testing was carried out using the Matlab{sup (R)} program. To verify the network unfolding performance, the original and unfolded spectra were compared using the root mean square error. The use of artificial neural networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated with this ill-conditioned problem.

  19. Computer Aided Automatic Control - CAAC artificial intelligence block

    Energy Technology Data Exchange (ETDEWEB)

    Balate, J.; Chramcov, B.; Princ, M. [Brno Univ. of Technology (Czech Republic). Faculty of Technology in Zlin

    2000-07-01

    The aim of the plan to build up the system CAAC - Computer Aided Automatic Control is to create modular setup of partial computing programs including theory of automatic control, algorithms of programs for processing signals and programs of control algorithms. To approach its informative contents to students and professional public the CAAC system utilizes Internet services http in the form of WWW pages. The CAAC system is being processed at the Institute of Automation and Control Technique of the Faculty of Technology in Zlin of the Brno University of Technology and is determined particularly for pedagogic purposes. Recently also the methods of artificial intelligence have been included to the open CAAC system and that is comprised in this article. (orig.)

  20. Application of artificial intelligence to pharmacy and medicine.

    Science.gov (United States)

    Dasta, J F

    1992-04-01

    Artificial intelligence (AI) is a branch of computer science dealing with solving problems using symbolic programming. It has evolved into a problem solving science with applications in business, engineering, and health care. One application of AI is expert system development. An expert system consists of a knowledge base and inference engine, coupled with a user interface. A crucial aspect of expert system development is knowledge acquisition and implementing computable ways to solve problems. There have been several expert systems developed in medicine to assist physicians with medical diagnosis. Recently, several programs focusing on drug therapy have been described. They provide guidance on drug interactions, drug therapy monitoring, and drug formulary selection. There are many aspects of pharmacy that AI can have an impact on and the reader is challenged to consider these possibilities because they may some day become a reality in pharmacy.

  1. Experimental Autonomous Road Vehicle with Logical Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Sergey Sergeevich Shadrin

    2017-01-01

    Full Text Available This article describes some technical issues regarding the adaptation of a production car to a platform for the development and testing of autonomous driving technologies. A universal approach to performing the reverse engineering of electric power steering (EPS for the purpose of external control is also presented. The primary objective of the related study was to solve the problem associated with the precise prediction of the dynamic trajectory of an autonomous vehicle. This was accomplished by deriving a new equation for determining the lateral tire forces and adjusting some of the vehicle parameters under road test conductions. A Mivar expert system was also integrated into the control system of the experimental autonomous vehicle. The expert system was made more flexible and effective for the present application by the introduction of hybrid artificial intelligence with logical reasoning. The innovation offers a solution to the major problem of liability in the event of an autonomous transport vehicle being involved in a collision.

  2. Artificial intelligence in the service of system administrators

    CERN Document Server

    Haen, Christophe; Bonaccorsi, E; Neufeld, N

    2012-01-01

    The LHCb online system relies on a large and heterogeneous IT infrastructure made from thousands of servers on which many different applications are running. They run a great variety of tasks: critical ones such as data taking and secondary ones like web servers. The administration of such a system and making sure it is working properly represents a very important workload for the small expert-operator team. Research has been performed to try to automatize (some) system administration tasks, starting in 2001 when IBM defined the so-called self objectives supposed to lead to autonomic computing. In this context, we present a framework that makes use of artificial intelligence and machine learning to monitor and diagnose at a low level and in a non intrusive way Linux-based systems and their interaction with software. Moreover, the multi agent approach we use, coupled with an object oriented paradigm architecture should increase our learning speed a lot and highlight relations between problems.

  3. Application of Artificial Intelligence for Bridge Deterioration Model

    Directory of Open Access Journals (Sweden)

    Zhang Chen

    2015-01-01

    Full Text Available The deterministic bridge deterioration model updating problem is well established in bridge management, while the traditional methods and approaches for this problem require manual intervention. An artificial-intelligence-based approach was presented to self-updated parameters of the bridge deterioration model in this paper. When new information and data are collected, a posterior distribution was constructed to describe the integrated result of historical information and the new gained information according to Bayesian theorem, which was used to update model parameters. This AI-based approach is applied to the case of updating parameters of bridge deterioration model, which is the data collected from bridges of 12 districts in Shanghai from 2004 to 2013, and the results showed that it is an accurate, effective, and satisfactory approach to deal with the problem of the parameter updating without manual intervention.

  4. Artificial intelligence for breast cancer screening: Opportunity or hype?

    Science.gov (United States)

    Houssami, Nehmat; Lee, Christoph I; Buist, Diana S M; Tao, Dacheng

    2017-12-01

    Interpretation of mammography for breast cancer (BC) screening can confer a mortality benefit through early BC detection, can miss a cancer that is present or fast growing, or can result in false-positives. Efforts to improve screening outcomes have mostly focused on intensifying imaging practices (double instead of single-reading, more frequent screens, or supplemental imaging) that may add substantial resource expenditures and harms associated with population screening. Less attention has been given to making mammography screening practice 'smarter' or more efficient. Artificial intelligence (AI) is capable of advanced learning using large complex datasets and has the potential to perform tasks such as image interpretation. With both highly-specific capabilities, and also possible un-intended (and poorly understood) consequences, this viewpoint considers the promise and current reality of AI in BC detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. 13th International Conference on Distributed Computing and Artificial Intelligence

    CERN Document Server

    Silvestri, Marcello; González, Sara

    2016-01-01

    The special session Decision Economics (DECON) 2016 is a scientific forum by which to share ideas, projects, researches results, models and experiences associated with the complexity of behavioral decision processes aiming at explaining socio-economic phenomena. DECON 2016 held in the University of Seville, Spain, as part of the 13th International Conference on Distributed Computing and Artificial Intelligence (DCAI) 2016. In the tradition of Herbert A. Simon’s interdisciplinary legacy, this book dedicates itself to the interdisciplinary study of decision-making in the recognition that relevant decision-making takes place in a range of critical subject areas and research fields, including economics, finance, information systems, small and international business, management, operations, and production. Decision-making issues are of crucial importance in economics. Not surprisingly, the study of decision-making has received a growing empirical research efforts in the applied economic literature over the last ...

  6. Application of Artificial Intelligence to Reservoir Characterization - An Interdisciplinary Approach

    Energy Technology Data Exchange (ETDEWEB)

    Kelkar, B.G.; Gamble, R.F.; Kerr, D.R.; Thompson, L.G.; Shenoi, S.

    2000-01-12

    The primary goal of this project is to develop a user-friendly computer program to integrate geological and engineering information using Artificial Intelligence (AI) methodology. The project is restricted to fluvially dominated deltaic environments. The static information used in constructing the reservoir description includes well core and log data. Using the well core and the log data, the program identifies the marker beds, and the type of sand facies, and in turn, develops correlation's between wells. Using the correlation's and sand facies, the program is able to generate multiple realizations of sand facies and petrophysical properties at interwell locations using geostatistical techniques. The generated petrophysical properties are used as input in the next step where the production data are honored. By adjusting the petrophysical properties, the match between the simulated and the observed production rates is obtained.

  7. Enabling Autonomous Space Mission Operations with Artificial Intelligence

    Science.gov (United States)

    Frank, Jeremy

    2017-01-01

    For over 50 years, NASA's crewed missions have been confined to the Earth-Moon system, where speed-of-light communications delays between crew and ground are practically nonexistent. This ground-centered mode of operations, with a large, ground-based support team, is not sustainable for NASAs future human exploration missions to Mars. Future astronauts will need smarter tools employing Artificial Intelligence (AI) techniques make decisions without inefficient communication back and forth with ground-based mission control. In this talk we will describe several demonstrations of astronaut decision support tools using AI techniques as a foundation. These demonstrations show that astronauts tasks ranging from living and working to piloting can benefit from AI technology development.

  8. Implementing Artificial Intelligence Behaviors in a Virtual World

    Science.gov (United States)

    Krisler, Brian; Thome, Michael

    2012-01-01

    In this paper, we will present a look at the current state of the art in human-computer interface technologies, including intelligent interactive agents, natural speech interaction and gestural based interfaces. We describe our use of these technologies to implement a cost effective, immersive experience on a public region in Second Life. We provision our Artificial Agents as a German Shepherd Dog avatar with an external rules engine controlling the behavior and movement. To interact with the avatar, we implemented a natural language and gesture system allowing the human avatars to use speech and physical gestures rather than interacting via a keyboard and mouse. The result is a system that allows multiple humans to interact naturally with AI avatars by playing games such as fetch with a flying disk and even practicing obedience exercises using voice and gesture, a natural seeming day in the park.

  9. Artificial intelligence for multi-mission planetary operations

    Science.gov (United States)

    Atkinson, David J.; Lawson, Denise L.; James, Mark L.

    1990-01-01

    A brief introduction is given to an automated system called the Spacecraft Health Automated Reasoning Prototype (SHARP). SHARP is designed to demonstrate automated health and status analysis for multi-mission spacecraft and ground data systems operations. The SHARP system combines conventional computer science methodologies with artificial intelligence techniques to produce an effective method for detecting and analyzing potential spacecraft and ground systems problems. The system performs real-time analysis of spacecraft and other related telemetry, and is also capable of examining data in historical context. Telecommunications link analysis of the Voyager II spacecraft is the initial focus for evaluation of the prototype in a real-time operations setting during the Voyager spacecraft encounter with Neptune in August, 1989. The preliminary results of the SHARP project and plans for future application of the technology are discussed.

  10. The application of artificial intelligence for the identification of the maceral groups and mineral components of coal

    Science.gov (United States)

    Mlynarczuk, Mariusz; Skiba, Marta

    2017-06-01

    The correct and consistent identification of the petrographic properties of coal is an important issue for researchers in the fields of mining and geology. As part of the study described in this paper, investigations concerning the application of artificial intelligence methods for the identification of the aforementioned characteristics were carried out. The methods in question were used to identify the maceral groups of coal, i.e. vitrinite, inertinite, and liptinite. Additionally, an attempt was made to identify some non-organic minerals. The analyses were performed using pattern recognition techniques (NN, kNN), as well as artificial neural network techniques (a multilayer perceptron - MLP). The classification process was carried out using microscopy images of polished sections of coals. A multidimensional feature space was defined, which made it possible to classify the discussed structures automatically, based on the methods of pattern recognition and algorithms of the artificial neural networks. Also, from the study we assessed the impact of the parameters for which the applied methods proved effective upon the final outcome of the classification procedure. The result of the analyses was a high percentage (over 97%) of correct classifications of maceral groups and mineral components. The paper discusses also an attempt to analyze particular macerals of the inertinite group. It was demonstrated that using artificial neural networks to this end makes it possible to classify the macerals properly in over 91% of cases. Thus, it was proved that artificial intelligence methods can be successfully applied for the identification of selected petrographic features of coal.

  11. Parametric Identification of Aircraft Loads: An Artificial Neural Network Approach

    Science.gov (United States)

    2016-03-30

    Undergraduate Student Paper Postgraduate Student Paper Parametric Identification of Aircraft Loads: An Artificial Neural Network Approach...monitoring, flight parameter, nonlinear modeling, Artificial Neural Network , typical loadcase. Introduction Aircraft load monitoring is an... Neural Networks (ANN), i.e. the BP network and Kohonen Clustering Network , are applied and revised by Kalman Filter and Genetic Algorithm to build

  12. Reducing unnecessary lab testing in the ICU with artificial intelligence.

    Science.gov (United States)

    Cismondi, F; Celi, L A; Fialho, A S; Vieira, S M; Reti, S R; Sousa, J M C; Finkelstein, S N

    2013-05-01

    To reduce unnecessary lab testing by predicting when a proposed future lab test is likely to contribute information gain and thereby influence clinical management in patients with gastrointestinal bleeding. Recent studies have demonstrated that frequent laboratory testing does not necessarily relate to better outcomes. Data preprocessing, feature selection, and classification were performed and an artificial intelligence tool, fuzzy modeling, was used to identify lab tests that do not contribute an information gain. There were 11 input variables in total. Ten of these were derived from bedside monitor trends heart rate, oxygen saturation, respiratory rate, temperature, blood pressure, and urine collections, as well as infusion products and transfusions. The final input variable was a previous value from one of the eight lab tests being predicted: calcium, PTT, hematocrit, fibrinogen, lactate, platelets, INR and hemoglobin. The outcome for each test was a binary framework defining whether a test result contributed information gain or not. Predictive modeling was applied to recognize unnecessary lab tests in a real world ICU database extract comprising 746 patients with gastrointestinal bleeding. Classification accuracy of necessary and unnecessary lab tests of greater than 80% was achieved for all eight lab tests. Sensitivity and specificity were satisfactory for all the outcomes. An average reduction of 50% of the lab tests was obtained. This is an improvement from previously reported similar studies with average performance 37% by [1-3]. Reducing frequent lab testing and the potential clinical and financial implications are an important issue in intensive care. In this work we present an artificial intelligence method to predict the benefit of proposed future laboratory tests. Using ICU data from 746 patients with gastrointestinal bleeding, and eleven measurements, we demonstrate high accuracy in predicting the likely information to be gained from proposed future

  13. Artificial neural networks in neutron dosimetry

    Energy Technology Data Exchange (ETDEWEB)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Mercado, G.A.; Perales M, W.A.; Robles R, J.A. [Unidades Academicas de Estudios Nucleares, UAZ, A.P. 336, 98000 Zacatecas (Mexico); Gallego, E.; Lorente, A. [Depto. de Ingenieria Nuclear, Universidad Politecnica de Madrid, (Spain)

    2005-07-01

    An artificial neural network has been designed to obtain the neutron doses using only the Bonner spheres spectrometer's count rates. Ambient, personal and effective neutron doses were included. 187 neutron spectra were utilized to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in Bonner spheres spectrometer and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing was carried out in Mat lab environment. The artificial neural network performance was evaluated using the {chi}{sup 2}- test, where the original and calculated doses were compared. The use of Artificial Neural Networks in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  14. Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

    Directory of Open Access Journals (Sweden)

    Pedro U. Lima

    2008-11-01

    Full Text Available This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior T?cnico (ISR/IST in Lisbon. The acronym of the project stands both for "Society of Robots" and "Soccer Robots", the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots and dynamic (moving robots obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams.

  15. Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

    Directory of Open Access Journals (Sweden)

    Pedro U. Lima

    2004-09-01

    Full Text Available This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Técnico (ISR/IST in Lisbon. The acronym of the project stands both for “Society of Robots” and “Soccer Robots”, the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots and dynamic (moving robots obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams.

  16. Artificial Intelligence Methodologies and Their Application to Diabetes.

    Science.gov (United States)

    Rigla, Mercedes; García-Sáez, Gema; Pons, Belén; Hernando, Maria Elena

    2018-03-01

    In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches. New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future. All these parameters, when analyzed, can be helpful for patients and doctors' decision support. Similar new scenarios have appeared in most medical fields, in such a way that in recent years, there has been an increased interest in the development and application of the methods of artificial intelligence (AI) to decision support and knowledge acquisition. Multidisciplinary research teams integrated by computer engineers and doctors are more and more frequent, mirroring the need of cooperation in this new topic. AI, as a science, can be defined as the ability to make computers do things that would require intelligence if done by humans. Increasingly, diabetes-related journals have been incorporating publications focused on AI tools applied to diabetes. In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas. This recently needed knowledge includes AI tools, which have become part of the diabetes health care. The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers-doctors and nurses-in this field.

  17. Natural and Artificial Intelligence in Neurosurgery: A Systematic Review.

    Science.gov (United States)

    Senders, Joeky T; Arnaout, Omar; Karhade, Aditya V; Dasenbrock, Hormuzdiyar H; Gormley, William B; Broekman, Marike L; Smith, Timothy R

    2017-09-07

    Machine learning (ML) is a domain of artificial intelligence that allows computer algorithms to learn from experience without being explicitly programmed. To summarize neurosurgical applications of ML where it has been compared to clinical expertise, here referred to as "natural intelligence." A systematic search was performed in the PubMed and Embase databases as of August 2016 to review all studies comparing the performance of various ML approaches with that of clinical experts in neurosurgical literature. Twenty-three studies were identified that used ML algorithms for diagnosis, presurgical planning, or outcome prediction in neurosurgical patients. Compared to clinical experts, ML models demonstrated a median absolute improvement in accuracy and area under the receiver operating curve of 13% (interquartile range 4-21%) and 0.14 (interquartile range 0.07-0.21), respectively. In 29 (58%) of the 50 outcome measures for which a P -value was provided or calculated, ML models outperformed clinical experts ( P .05), while in 3 of 50 (6%) clinical experts outperformed ML models ( P < .05). All 4 studies that compared clinicians assisted by ML models vs clinicians alone demonstrated a better performance in the first group. We conclude that ML models have the potential to augment the decision-making capacity of clinicians in neurosurgical applications; however, significant hurdles remain associated with creating, validating, and deploying ML models in the clinical setting. Shifting from the preconceptions of a human-vs-machine to a human-and-machine paradigm could be essential to overcome these hurdles.

  18. Using artificial intelligence to predict the risk for posterior capsule opacification after phacoemulsification.

    Science.gov (United States)

    Mohammadi, Seyed-Farzad; Sabbaghi, Mostafa; Z-Mehrjardi, Hadi; Hashemi, Hassan; Alizadeh, Somayeh; Majdi, Mercede; Taee, Farough

    2012-03-01

    To apply artificial intelligence models to predict the occurrence of posterior capsule opacification (PCO) after phacoemulsification. Farabi Eye Hospital, Tehran, Iran. Clinical-based cross-sectional study. The posterior capsule status of eyes operated on for age-related cataract and the need for laser capsulotomy were determined. After a literature review, data polishing, and expert consultation, 10 input variables were selected. The QUEST algorithm was used to develop a decision tree. Three back-propagation artificial neural networks were constructed with 4, 20, and 40 neurons in 2 hidden layers and trained with the same transfer functions (log-sigmoid and linear transfer) and training protocol with randomly selected eyes. They were then tested on the remaining eyes and the networks compared for their performance. Performance indices were used to compare resultant models with the results of logistic regression analysis. The models were trained using 282 randomly selected eyes and then tested using 70 eyes. Laser capsulotomy for clinically significant PCO was indicated or had been performed 2 years postoperatively in 40 eyes. A sample decision tree was produced with accuracy of 50% (likelihood ratio 0.8). The best artificial neural network, which showed 87% accuracy and a positive likelihood ratio of 8, was achieved with 40 neurons. The area under the receiver-operating-characteristic curve was 0.71. In comparison, logistic regression reached accuracy of 80%; however, the likelihood ratio was not measurable because the sensitivity was zero. A prototype artificial neural network was developed that predicted posterior capsule status (requiring capsulotomy) with reasonable accuracy. No author has a financial or proprietary interest in any material or method mentioned. Copyright © 2012 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

  19. Artificial Neural Network-Based System for PET Volume Segmentation

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

    Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  20. Gap Filling of Daily Sea Levels by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Lyubka Pashova

    2013-06-01

    Full Text Available In the recent years, intelligent methods as artificial neural networks are successfully applied for data analysis from different fields of the geosciences. One of the encountered practical problems is the availability of gaps in the time series that prevent their comprehensive usage for the scientific and practical purposes. The article briefly describes two types of the artificial neural network (ANN architectures - Feed-Forward Backpropagation (FFBP and recurrent Echo state network (ESN. In some cases, the ANN can be used as an alternative on the traditional methods, to fill in missing values in the time series. We have been conducted several experiments to fill the missing values of daily sea levels spanning a 5-years period using both ANN architectures. A multiple linear regression for the same purpose has been also applied. The sea level data are derived from the records of the tide gauge Burgas, which is located on the western Black Sea coast. The achieved results have shown that the performance of ANN models is better than that of the classical one and they are very promising for the real-time interpolation of missing data in the time series.

  1. Investment Valuation Analysis with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Hüseyin İNCE

    2017-07-01

    Full Text Available This paper shows that discounted cash flow and net present value, which are traditional investment valuation models, can be combined with artificial neural network model forecasting. The main inputs for the valuation models, such as revenue, costs, capital expenditure, and their growth rates, are heavily related to sector dynamics and macroeconomics. The growth rates of those inputs are related to inflation and exchange rates. Therefore, predicting inflation and exchange rates is a critical issue for the valuation output. In this paper, the Turkish economy’s inflation rate and the exchange rate of USD/TRY are forecast by artificial neural networks and implemented to the discounted cash flow model. Finally, the results are benchmarked with conventional practices.

  2. Artificial Neural Network for Displacement Vectors Determination

    Directory of Open Access Journals (Sweden)

    P. Bohmann

    1997-09-01

    Full Text Available An artificial neural network (NN for displacement vectors (DV determination is presented in this paper. DV are computed in areas which are essential for image analysis and computer vision, in areas where are edges, lines, corners etc. These special features are found by edges operators with the following filtration. The filtration is performed by a threshold function. The next step is DV computation by 2D Hamming artificial neural network. A method of DV computation is based on the full search block matching algorithms. The pre-processing (edges finding is the reason why the correlation function is very simple, the process of DV determination needs less computation and the structure of the NN is simpler.

  3. Medical image analysis with artificial neural networks.

    Science.gov (United States)

    Jiang, J; Trundle, P; Ren, J

    2010-12-01

    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. Copyright © 2010 Elsevier Ltd. All rights reserved.

  4. Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma.

    Science.gov (United States)

    Rajpara, S M; Botello, A P; Townend, J; Ormerod, A D

    2009-09-01

    Dermoscopy improves diagnostic accuracy of the unaided eye for melanoma, and digital dermoscopy with artificial intelligence or computer diagnosis has also been shown useful for the diagnosis of melanoma. At present there is no clear evidence regarding the diagnostic accuracy of dermoscopy compared with artificial intelligence. To evaluate the diagnostic accuracy of dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis and to compare the diagnostic accuracy of the different dermoscopic algorithms with each other and with digital dermoscopy/artificial intelligence for the detection of melanoma. A literature search on dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis was performed using several databases. Titles and abstracts of the retrieved articles were screened using a literature evaluation form. A quality assessment form was developed to assess the quality of the included studies. Heterogeneity among the studies was assessed. Pooled data were analysed using meta-analytical methods and comparisons between different algorithms were performed. Of 765 articles retrieved, 30 studies were eligible for meta-analysis. Pooled sensitivity for artificial intelligence was slightly higher than for dermoscopy (91% vs. 88%; P = 0.076). Pooled specificity for dermoscopy was significantly better than artificial intelligence (86% vs. 79%; P artificial intelligence, which were not significantly different (P = 0.783). There were no significance differences in diagnostic odds ratio among the different dermoscopic diagnostic algorithms. Dermoscopy and artificial intelligence performed equally well for diagnosis of melanocytic skin lesions. There was no significant difference in the diagnostic performance of various dermoscopy algorithms. The three-point checklist, the seven-point checklist and Menzies score had better diagnostic odds ratios than the others; however, these results need to be confirmed by a large-scale high

  5. Artificial neural network cardiopulmonary modeling and diagnosis

    Science.gov (United States)

    Kangas, Lars J.; Keller, Paul E.

    1997-01-01

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

  6. Daily water level forecasting using wavelet decomposition and artificial intelligence techniques

    Science.gov (United States)

    Seo, Youngmin; Kim, Sungwon; Kisi, Ozgur; Singh, Vijay P.

    2015-01-01

    Reliable water level forecasting for reservoir inflow is essential for reservoir operation. The objective of this paper is to develop and apply two hybrid models for daily water level forecasting and investigate their accuracy. These two hybrid models are wavelet-based artificial neural network (WANN) and wavelet-based adaptive neuro-fuzzy inference system (WANFIS). Wavelet decomposition is employed to decompose an input time series into approximation and detail components. The decomposed time series are used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WANN and WANFIS models, respectively. Based on statistical performance indexes, the WANN and WANFIS models are found to produce better efficiency than the ANN and ANFIS models. WANFIS7-sym10 yields the best performance among all other models. It is found that wavelet decomposition improves the accuracy of ANN and ANFIS. This study evaluates the accuracy of the WANN and WANFIS models for different mother wavelets, including Daubechies, Symmlet and Coiflet wavelets. It is found that the model performance is dependent on input sets and mother wavelets, and the wavelet decomposition using mother wavelet, db10, can further improve the efficiency of ANN and ANFIS models. Results obtained from this study indicate that the conjunction of wavelet decomposition and artificial intelligence models can be a useful tool for accurate forecasting daily water level and can yield better efficiency than the conventional forecasting models.

  7. Artificial Intelligence Techniques for the Estimation of Direct Methanol Fuel Cell Performance

    Science.gov (United States)

    Hasiloglu, Abdulsamet; Aras, Ömür; Bayramoglu, Mahmut

    2016-04-01

    Artificial neural networks and neuro-fuzzy inference systems are well known artificial intelligence techniques used for black-box modelling of complex systems. In this study, Feed-forward artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are used for modelling the performance of direct methanol fuel cell (DMFC). Current density (I), fuel cell temperature (T), methanol concentration (C), liquid flow-rate (q) and air flow-rate (Q) are selected as input variables to predict the cell voltage. Polarization curves are obtained for 35 different operating conditions according to a statistically designed experimental plan. In modelling study, various subsets of input variables and various types of membership function are considered. A feed -forward architecture with one hidden layer is used in ANN modelling. The optimum performance is obtained with the input set (I, T, C, q) using twelve hidden neurons and sigmoidal activation function. On the other hand, first order Sugeno inference system is applied in ANFIS modelling and the optimum performance is obtained with the input set (I, T, C, q) using sixteen fuzzy rules and triangular membership function. The test results show that ANN model estimates the polarization curve of DMFC more accurately than ANFIS model.

  8. Application of CAN-bus Network in Intelligent parking

    Science.gov (United States)

    Jianjun, WU; Juan, Hu

    Convenient life makes intelligent parking every- where. This paper describes how to connect the various separate modern terminal of intelligent parking with CAN-bus network, and the competitive advantage of the system by using the CAN-bus network. From a technical point of view, this paper describes the development trend of intelligent parking in next few years.

  9. Virtual Reality for Artificial Intelligence: human-centered simulation for social science.

    Science.gov (United States)

    Cipresso, Pietro; Riva, Giuseppe

    2015-01-01

    There is a long last tradition in Artificial Intelligence as use of Robots endowing human peculiarities, from a cognitive and emotional point of view, and not only in shape. Today Artificial Intelligence is more oriented to several form of collective intelligence, also building robot simulators (hardware or software) to deeply understand collective behaviors in human beings and society as a whole. Modeling has also been crucial in the social sciences, to understand how complex systems can arise from simple rules. However, while engineers' simulations can be performed in the physical world using robots, for social scientist this is impossible. For decades, researchers tried to improve simulations by endowing artificial agents with simple and complex rules that emulated human behavior also by using artificial intelligence (AI). To include human beings and their real intelligence within artificial societies is now the big challenge. We present an hybrid (human-artificial) platform where experiments can be performed by simulated artificial worlds in the following manner: 1) agents' behaviors are regulated by the behaviors shown in Virtual Reality involving real human beings exposed to specific situations to simulate, and 2) technology transfers these rules into the artificial world. These form a closed-loop of real behaviors inserted into artificial agents, which can be used to study real society.

  10. [Medical use of artificial neural networks].

    Science.gov (United States)

    Molnár, B; Papik, K; Schaefer, R; Dombóvári, Z; Fehér, J; Tulassay, Z

    1998-01-04

    The main aim of the research in medical diagnostics is to develop more exact, cost-effective and handsome systems, procedures and methods for supporting the clinicians. In their paper the authors introduce a new method that recently came into the focus referred to as artificial neural networks. Based on the literature of the past 5-6 years they give a brief review--highlighting the most important ones--showing the idea behind neural networks, what they are used for in the medical field. The definition, structure and operation of neural networks are discussed. In the application part they collect examples in order to give an insight in the neural network application research. It is emphasised that in the near future basically new diagnostic equipment can be developed based on this new technology in the field of ECG, EEG and macroscopic and microscopic image analysis systems.

  11. 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.

  12. Intelligent Classification and Visualization of Network Scans

    Energy Technology Data Exchange (ETDEWEB)

    Chen, L; Muelder, C; Ma, K; Bartoletti, A

    2007-03-01

    Network scans are a common first step in a network intrusion attempt. In order to gain information about a potential network intrusion, it is beneficial to analyze these network scans. Statistical methods such as wavelet scalogram analysis have been used along with visualization techniques in previous methods. However, applying these statistical methods to reduce the data causes a substantial amount of data loss. This paper presents a study of using associative memory learning techniques to directly compare network scans in order to create a classification which can be used by itself or in conjunction with existing visualization techniques to better characterize the sources of these scans. This produces an integrated system of visual and intelligent analysis which is applicable to real world data.

  13. Intelligent Communication Module for Wireless Biosensor Networks

    OpenAIRE

    Naik, R.; Singh, J.; Le, H. P.

    2010-01-01

    This chapter presented a new paradigm of biosensors which have processing capability with an intelligent and adaptive wireless communication module. The adaptive communication module efficiently reconfigures its hardware components according to the changes in operating environment in order to reduce system power consumption and optimally utilise resources. The chapter presented several significant applications of wireless biosensor networks which hold enormous potential to benefit the communi...

  14. Prediction of shear wave velocity using empirical correlations and artificial intelligence methods

    Science.gov (United States)

    Maleki, Shahoo; Moradzadeh, Ali; Riabi, Reza Ghavami; Gholami, Raoof; Sadeghzadeh, Farhad

    2014-06-01

    Good understanding of mechanical properties of rock formations is essential during the development and production phases of a hydrocarbon reservoir. Conventionally, these properties are estimated from the petrophysical logs with compression and shear sonic data being the main input to the correlations. This is while in many cases the shear sonic data are not acquired during well logging, which may be for cost saving purposes. In this case, shear wave velocity is estimated using available empirical correlations or artificial intelligent methods proposed during the last few decades. In this paper, petrophysical logs corresponding to a well drilled in southern part of Iran were used to estimate the shear wave velocity using empirical correlations as well as two robust artificial intelligence methods knows as Support Vector Regression (SVR) and Back-Propagation Neural Network (BPNN). Although the results obtained by SVR seem to be reliable, the estimated values are not very precise and considering the importance of shear sonic data as the input into different models, this study suggests acquiring shear sonic data during well logging. It is important to note that the benefits of having reliable shear sonic data for estimation of rock formation mechanical properties will compensate the possible additional costs for acquiring a shear log.

  15. Prediction of shear wave velocity using empirical correlations and artificial intelligence methods

    Directory of Open Access Journals (Sweden)

    Shahoo Maleki

    2014-06-01

    Full Text Available Good understanding of mechanical properties of rock formations is essential during the development and production phases of a hydrocarbon reservoir. Conventionally, these properties are estimated from the petrophysical logs with compression and shear sonic data being the main input to the correlations. This is while in many cases the shear sonic data are not acquired during well logging, which may be for cost saving purposes. In this case, shear wave velocity is estimated using available empirical correlations or artificial intelligent methods proposed during the last few decades. In this paper, petrophysical logs corresponding to a well drilled in southern part of Iran were used to estimate the shear wave velocity using empirical correlations as well as two robust artificial intelligence methods knows as Support Vector Regression (SVR and Back-Propagation Neural Network (BPNN. Although the results obtained by SVR seem to be reliable, the estimated values are not very precise and considering the importance of shear sonic data as the input into different models, this study suggests acquiring shear sonic data during well logging. It is important to note that the benefits of having reliable shear sonic data for estimation of rock formation mechanical properties will compensate the possible additional costs for acquiring a shear log.

  16. Role of Artificial Intelligence Techniques (Automatic Classifiers) in Molecular Imaging Modalities in Neurodegenerative Diseases.

    Science.gov (United States)

    Cascianelli, Silvia; Scialpi, Michele; Amici, Serena; Forini, Nevio; Minestrini, Matteo; Fravolini, Mario Luca; Sinzinger, Helmut; Schillaci, Orazio; Palumbo, Barbara

    2017-01-01

    Artificial Intelligence (AI) is a very active Computer Science research field aiming to develop systems that mimic human intelligence and is helpful in many human activities, including Medicine. In this review we presented some examples of the exploiting of AI techniques, in particular automatic classifiers such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification Tree (ClT) and ensemble methods like Random Forest (RF), able to analyze findings obtained by positron emission tomography (PET) or single-photon emission tomography (SPECT) scans of patients with Neurodegenerative Diseases, in particular Alzheimer's Disease. We also focused our attention on techniques applied in order to preprocess data and reduce their dimensionality via feature selection or projection in a more representative domain (Principal Component Analysis - PCA - or Partial Least Squares - PLS - are examples of such methods); this is a crucial step while dealing with medical data, since it is necessary to compress patient information and retain only the most useful in order to discriminate subjects into normal and pathological classes. Main literature papers on the application of these techniques to classify patients with neurodegenerative disease extracting data from molecular imaging modalities are reported, showing that the increasing development of computer aided diagnosis systems is very promising to contribute to the diagnostic process.

  17. A review on integration of artificial intelligence into water quality modelling.

    Science.gov (United States)

    Chau, Kwok-wing

    2006-07-01

    With the development of computing technology, numerical models are often employed to simulate flow and water quality processes in coastal environments. However, the emphasis has conventionally been placed on algorithmic procedures to solve specific problems. These numerical models, being insufficiently user-friendly, lack knowledge transfers in model interpretation. This results in significant constraints on model uses and large gaps between model developers and practitioners. It is a difficult task for novice application users to select an appropriate numerical model. It is desirable to incorporate the existing heuristic knowledge about model manipulation and to furnish intelligent manipulation of calibration parameters. The advancement in artificial intelligence (AI) during the past decade rendered it possible to integrate the technologies into numerical modelling systems in order to bridge the gaps. The objective of this paper is to review the current state-of-the-art of the integration of AI into water quality modelling. Algorithms and methods studied include knowledge-based system, genetic algorithm, artificial neural network, and fuzzy inference system. These techniques can contribute to the integrated model in different aspects and may not be mutually exclusive to one another. Some future directions for further development and their potentials are explored and presented.

  18. A review on the integration of artificial intelligence into coastal modeling.

    Science.gov (United States)

    Chau, Kwokwing

    2006-07-01

    With the development of computing technology, mechanistic models are often employed to simulate processes in coastal environments. However, these predictive tools are inevitably highly specialized, involving certain assumptions and/or limitations, and can be manipulated only by experienced engineers who have a thorough understanding of the underlying theories. This results in significant constraints on their manipulation as well as large gaps in understanding and expectations between the developers and practitioners of a model. The recent advancements in artificial intelligence (AI) technologies are making it possible to integrate machine learning capabilities into numerical modeling systems in order to bridge the gaps and lessen the demands on human experts. The objective of this paper is to review the state-of-the-art in the integration of different AI technologies into coastal modeling. The algorithms and methods studied include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems. More focus is given to knowledge-based systems, which have apparent advantages over the others in allowing more transparent transfers of knowledge in the use of models and in furnishing the intelligent manipulation of calibration parameters. Of course, the other AI methods also have their individual contributions towards accurate and reliable predictions of coastal processes. The integrated model might be very powerful, since the advantages of each technique can be combined.

  19. Applying Artificial Neural Networks for Face Recognition

    Directory of Open Access Journals (Sweden)

    Thai Hoang Le

    2011-01-01

    Full Text Available This paper introduces some novel models for all steps of a face recognition system. In the step of face detection, we propose a hybrid model combining AdaBoost and Artificial Neural Network (ABANN to solve the process efficiently. In the next step, labeled faces detected by ABANN will be aligned by Active Shape Model and Multi Layer Perceptron. In this alignment step, we propose a new 2D local texture model based on Multi Layer Perceptron. The classifier of the model significantly improves the accuracy and the robustness of local searching on faces with expression variation and ambiguous contours. In the feature extraction step, we describe a methodology for improving the efficiency by the association of two methods: geometric feature based method and Independent Component Analysis method. In the face matching step, we apply a model combining many Neural Networks for matching geometric features of human face. The model links many Neural Networks together, so we call it Multi Artificial Neural Network. MIT + CMU database is used for evaluating our proposed methods for face detection and alignment. Finally, the experimental results of all steps on CallTech database show the feasibility of our proposed model.

  20. ARTIFICIAL INTELLIGENCE IN SPORTS ON THE EXAMPLE OF WEIGHT TRAINING

    Directory of Open Access Journals (Sweden)

    risto Novatchkov

    2013-03-01

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