Neural networks and fuzzy systems Books

Book SynopsisThe big challenge for a successful AI project isn’t deciding which problems you can solve. It’s deciding which problems you should solve. In Managing Successful AI Projects, author and AI consultant Veljko Krunic reveals secrets for succeeding in AI that he developed with Fortune 500 companies, early-stage start-ups, and other business across multiple industries. Key Features · Selecting the right AI project to meet specific business goals · Economizing resources to deliver the best value for money · How to measure the success of your AI efforts in the business terms · Predict if you are you on the right track to deliver your intended business results For executives, managers, team leaders, and business-focused data scientists. No specific technical knowledge or programming skills required. About the technology Companies small and large are initiating AI projects, investing vast sums of money on software, developers, and data scientists. Too often, these AI projects focus on technology at the expense of actionable or tangible business results, resulting in scattershot results and wasted investment. Managing Successful AI Projects sets out a blueprint for AI projects to ensure they are predictable, successful, and profitable. It’s filled with practical techniques for running data science programs that ensure they’re cost effective and focused on the right business goals. Veljko Krunic is an independent data science consultant who has worked with companies that range from start-ups to Fortune 10 enterprises. He holds a PhD in Computer Science and an MS in Engineering Management, both from the University of Colorado at Boulder. He is also a Six Sigma Master Black Belt.

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Book SynopsisLike no other text in this field, authors Jose C. Principe, Neil R. Euliano, and W. Curt Lefebvre have written a unique and innovative text unifying the concepts of neural networks and adaptive filters into a common framework. The text is suitable for senior/graduate courses in neural networks and adaptive filters. It offers over 200 fully functional simulations (with instructions) to demonstrate and reinforce key concepts and help the reader develop an intuition about the behavior of adaptive systems with real data. This creates a powerful self-learning environment highly suitable for the professional audience.Table of ContentsChapter 1 Data Fitting with Linear Models 1 Chapter 2 Pattern Recognition 68 Chapter 3 Multilayer Perceptrons 100 Chapter 4 Designing and Training MLPS 173 Chapter 5 Function Approximation with MLPs, Radial Basis Functions, and Support Vector Machines 223 Chapter 6 Hebbian Learning and Principal Component Analysis 279 Chapter 7 Competitive and Kohonen Networks 333 Chapter 8 Principles of Digital Signal Processing 364 Chapter 9 Adaptive Filters 429 Chapter 10 Temporal Processing with Neural Networks 473 Chapter 11 Training and Using Recurrent Networks 525 Appendix A Elements of Linear Algebra and Pattern Recognition 589 Appendix B NeuroSolutions Tutorial 613 Appendix C Data Directory 637 Glossary 639 Index 647

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Book SynopsisConnectionism and the Mind provides a clear and balanced introduction to connectionist networks and explores theoretical and philosophical implications. Much of this discussion from the first edition has been updated, and three new chapters have been added on the relation of connectionism to recent work on dynamical systems theory, artificial life, and cognitive neuroscience. Read two of the sample chapters on line: Connectionism and the Dynamical Approach to Cognition: http://www.blackwellpublishing.com/pdf/bechtel.pdf Networks, Robots, and Artificial Life: http://www.blackwellpublishing.com/pdf/bechtel2.pdfTrade Review"Much more than just an update, this is a thorough and exciting re-build of the classic text. Excellent new treatments of modularity, dynamics, artificial life, and cognitive neuroscience locate connectionism at the very heart of contemporary debates. A superb combination of detail, clarity, scope, and enthusiasm." Andy Clark, University of Sussex "Connectionism and the Mind is an extraordinarily comprehensive and thoughtful review of connectionism, with particular emphasis on recent developments. This new edition will be a valuable primer to those new to the field. But there is more: Bechtel and Abrahamsen's trenchant and even-handed analysis of the conceptual issues that are addressed by connectionist models constitute an important original theoretical contribution to cognitive science." Jeff Elman, University of California at San DiegoTable of ContentsPreface xiii 1 Networks Versus Symbol Systems: Two Approaches To Modeling Cognition 1 1.1 A Revolution in the Making? 1 1.2 Forerunners of Connectionism: Pandemonium and Perceptrons 2 1.3 The Allure of Symbol Manipulation 7 1.3.1 From logic to artificial intelligence 7 1.3.2 From linguistics to information processing 10 1.3.3 Using artificial intelligence to simulate human information processing 11 1.4 The Decline and Re-emergence of Network Models 12 1.4.1 Problems with perceptrons 12 1.4.2 Re-emergence: The new connectionism 13 1.5 New Alliances and Unfinished Business 15 Notes 17 Sources and Suggested Readings 17 2 Connectionist Architectures 19 2.1 The Flavor of Connectionist Processing: A Simulation of Memory Retrieval 19 2.1.1 Components of the model 20 2.1.2 Dynamics of the model 22 2.1.2.1 Memory retrieval in the Jets and Sharks network 22 2.1.2.2 The equations 23 2.1.3 Illustrations of the dynamics of the model 24 2.1.3.1 Retrieving properties from a name 24 2.1.3.2 Retrieving a name from other properties 26 2.1.3.3 Categorization and prototype formation 26 2.1.3.4 Utilizing regularities 28 2.2 The Design Features of a Connectionist Architecture 29 2.2.1 Patterns of connectivity 29 2.2.1.1 Feedforward networks 29 2.2.1.2 Interactive networks 31 2.2.2 Activation rules for units 32 2.2.2.1 Feedforward networks 32 2.2.2.2 Interactive networks: Hopfield networks and Boltzmann machines 34 2.2.2.3 Spreading activation vs. interactive connectionist models 37 2.2.3 Learning principles 38 2.2.4 Semantic interpretation of connectionist systems 40 2.2.4.1 Localist networks 41 2.2.4.2 Distributed networks 41 2.3 The Allure of the Connectionist Approach 45 2.3.1 Neural plausibility 45 2.3.2 Satisfaction of soft constraints 46 2.3.3 Graceful degradation 48 2.3.4 Content-addressable memory 49 2.3.5 Capacity to learn from experience and generalize 51 2.4 Challenges Facing Connectionist Networks 51 2.5 Summary 52 Notes 52 Sources and Recommended Readings 53 3 Learning 54 3.1 Traditional and Contemporary Approaches to Learning 54 3.1.1 Empiricism 54 3.1.2 Rationalism 55 3.1.3 Contemporary cognitive science 56 3.2 Connectionist Models of Learning 57 3.2.1 Learning procedures for two-layer feedforward networks 58 3.2.1.1 Training and testing a network 58 3.2.1.2 The Hebbian rule 58 3.2.1.3 The delta rule 60 3.2.1.4 Comparing the Hebbian and delta rules 67 3.2.1.5 Limitations of the delta rule: The XOR problem 67 3.2.2 The backpropagation learning procedure for multi-layered networks 69 3.2.2.1 Introducing hidden units and backpropagation learning 69 3.2.2.2 Using backpropagation to solve the XOR problem 74 3.2.2.3 Using backpropagation to train a network to pronounce words 77 3.2.2.4 Some drawbacks of using backpropagation 78 3.2.3 Boltzmann learning procedures for non-layered networks 79 3.2.4 Competitive learning 80 3.2.5 Reinforcement learning 81 3.3 Some Issues Regarding Learning 82 3.3.1 Are connectionist systems associationist? 82 3.3.2 Possible roles for innate knowledge 84 3.3.2.1 Networks and the rationalist–empiricist continuum 84 3.3.2.2 Rethinking innateness: Connectionism and emergence 85 Notes 87 Sources and Suggested Readings 88 4 Pattern Recognition and Cognition 89 4.1 Networks as Pattern Recognition Devices 90 4.1.1 Pattern recognition in two-layer networks 90 4.1.2 Pattern recognition in multi-layered networks 93 4.1.2.1 McClelland and Rumelhart’s interactive activation model of word recognition 93 4.1.2.2 Evaluating the interactive activation model of word recognition 100 4.1.3 Generalization and similarity 101 4.2 Extending Pattern Recognition to Higher Cognition 102 4.2.1 Smolensky’s proposal: Reasoning in harmony networks 103 4.2.2 Margolis’s proposal: Cognition as sequential pattern recognition 103 4.3 Logical Inference as Pattern Recognition 106 4.3.1 What is it to learn logic? 106 4.3.2 A network for evaluating validity of arguments 109 4.3.3 Analyzing how a network evaluates arguments 112 4.3.4 A network for constructing derivations 115 4.4 Beyond Pattern Recognition 117 Notes 118 Sources and Suggested Readings 119 5 Are Rules Required to Process Representations? 120 5.1 Is Language Use Governed by Rules? 120 5.2 Rumelhart and McClelland’s Model of Past-tense Acquisition 122 5.2.1 A pattern associator with Wickelfeature encodings 122 5.2.2 Activation function and learning procedure 126 5.2.3 Overregularization in a simpler network: The rule of 78 127 5.2.4 Modeling U-shaped learning 130 5.2.5 Modeling differences between different verb classes 133 5.3Pinker and Prince’s Arguments for Rules 135 5.3.1 Overview of the critique of Rumelhart and McClelland’s model 135 5.3.2 Putative linguistic inadequacies 136 5.3.3 Putative behavioral inadequacies 139 5.3.4 Do the inadequacies reflect inherent limitations of PDP networks? 140 5.4 Accounting for the U-shaped Learning Function 141 5.4.1 The role of input for children 142 5.4.2 The role of input for networks: The rule of 78 revisited 146 5.4.3 Plunkett and Marchman’s simulations of past-tense acquisition 148 5.5 Conclusion 152 Notes 153 Sources and Suggested Readings 155 6 Are Syntactically Structured Representations Needed? 156 6.1 Fodor and Pylyshyn’s Critique: The Need for Symbolic Representations with Constituent Structure 156 6.1.1 The need for compositional syntax and semantics 156 6.1.2 Connectionist representations lack compositionality 158 6.1.3 Connectionism as providing mere implementation 160 6.2 First Connectionist Response: Explicitly Implementing Rules and Representations 163 6.2.1 Implementing a production system in a network 163 6.2.2 The variable binding problem 165 6.2.3 Shastri and Ajjanagadde’s connectionist model of variable binding 166 6.3Second Connectionist Response: Implementing Functionally Compositional Representations 170 6.3.1 Functional vs. concatenative compositionality 170 6.3.2 Developing compressed representations using Pollack’s RAAM networks 171 6.3.3 Functional compositionality of compressed representations 175 6.3.4 Performing operations on compressed representations 177 6.4 Third Connectionist Response: Employing Procedural Knowledge with External Symbols 178 6.4.1 Temporal dependencies in processing language 179 6.4.2 Achieving short-term memory with simple recurrent networks 180 6.4.3 Elman’s first study: Learning grammatical categories 181 6.4.4 Elman’s second study: Respecting dependency relations 184 6.4.5 Christiansen’s extension: Pushing the limits of SRNs 187 6.5 Using External Symbols to Provide Exact Symbol Processing 190 6.6 Clarifying the Standard: Systematicity and Degree of Generalizability 194 6.7 Conclusion 197 Notes 198 Sources and Suggested Readings 199 7 Simulating Higher Cognition: a Modular Architecture For Processing Scripts 200 7.1 Overview of Scripts 200 7.2 Overview of Miikkulainen’s DISCERN System 201 7.3Modular Connectionist Architectures 203 7.4 FGREP: An Architecture that Allows the System to Devise Its Own Representations 206 7.4.1 Why FGREP? 206 7.4.2 Exploring FGREP in a simple sentence parser 208 7.4.3 Exploring representations for words in categories 210 7.4.4 Moving to multiple modules: The DISCERN system 212 7.5 A Self-organizing Lexicon Using Kohonen Feature Maps 212 7.5.1 Innovations in lexical design 212 7.5.2 Using Kohonen feature maps in DISCERN’s lexicon 213 7.5.2.1 Orthography: From high-dimensional vector representations to map units 213 7.5.2.2 Associative connections: From the orthographic map to the semantic map 216 7.5.2.3 Semantics: From map unit to high-dimensional vector representations 216 7.5.2.4 Reversing direction: From semantic to orthographic representations 216 7.5.3 Advantages of Kohonen feature maps 216 7.6 Encoding and Decoding Stories as Scripts 217 7.6.1 Using recurrent FGREP modules in DISCERN 217 7.6.2 Using the Sentence Parser and Story Parser to encode stories 218 7.6.3 Using the Story Generator and Sentence Generator to paraphrase stories 221 7.6.4 Using the Cue Former and Answer Producer to answer questions 223 7.7 A Connectionist Episodic Memory 223 7.7.1 Making Kohonen feature maps hierarchical 223 7.7.2 How role-binding maps become self-organized 225 7.7.3 How role-binding maps become trace feature maps 225 7.8 Performance: Paraphrasing Stories and Answering Questions 228 7.8.1 Training and testing DISCERN 228 7.8.2 Watching DISCERN paraphrase a story 229 7.8.3 Watching DISCERN answer questions 229 7.9 Evaluating DISCERN 231 7.10 Paths Beyond the First Decade of Connectionism 233 Notes 234 Sources and Suggested Readings 234 8 Connectionism and the Dynamical Approach to Cognition 235 8.1 Are We on the Road to a Dynamical Revolution? 235 8.2 Basic Concepts of DST: The Geometry of Change 237 8.2.1 Trajectories in state space: Predators and prey 237 8.2.2 Bifurcation diagrams and chaos 240 8.2.3 Embodied networks as coupled dynamical systems 242 8.3Using Dynamical Systems Tools to Analyze Networks 243 8.3.1 Discovering limit cycles in network controllers for robotic insects 244 8.3.2 Discovering multiple attractors in network models of reading 246 8.3.2.1 Modeling the semantic pathway 248 8.3.2.2 Modeling the phonological pathway 249 8.3.3 Discovering trajectories in SRNs for sentence processing 253 8.3.4 Dynamical analyses of learning in networks 256 8.4 Putting Chaos to Work in Networks 257 8.4.1 Skarda and Freeman’s model of the olfactory bulb 257 8.4.2 Shifting interpretations of ambiguous displays 260 8.5 Is Dynamicism a Competitor to Connectionism? 264 8.5.1 Van Gelder and Port’s critique of classic connectionism 264 8.5.2 Two styles of modeling 265 8.5.3 Mechanistic versus covering-law explanations 266 8.5.4 Representations: Who needs them? 270 8.6 Is Dynamicism Complementary to Connectionism? 276 8.7 Conclusion 280 Notes 280 Sources and Suggested Readings 281 9 Networks, Robots, and Artificial Life 282 9.1 Robots and the Genetic Algorithm 282 9.1.1 The robot as an artificial lifeform 282 9.1.2 The genetic algorithm for simulated evolution 283 9.2 Cellular Automata and the Synthetic Strategy 284 9.2.1 Langton’s vision: The synthetic strategy 284 9.2.2 Emergent structures from simple beings: Cellular automata 286 9.2.3 Wolfram’s four classes of cellular automata 288 9.2.4 Langton and l at the edge of chaos 289 9.3Evolution and Learning in Food-seekers 291 9.3.1 Overview and study 1: Evolution without learning 291 9.3.2 The Baldwin effect and study 2: Evolution with learning 293 9.4 Evolution and Development in Khepera 295 9.4.1 Introducing Khepera 295 9.4.2 The development of phenotypes from genotypes 296 9.4.3 The evolution of genotypes 298 9.4.4 Embodied networks: Controlling real robots 298 9.5 The Computational Neuroethology of Robots 300 9.6 When Philosophers Encounter Robots 301 9.6.1 No Cartesian split in embodied agents? 301 9.6.2 No representations in subsumption architectures? 302 9.6.3 No intentionality in robots and Chinese rooms? 303 9.6.4 No armchair when Dennett does philosophy? 304 9.7 Conclusion 305 Sources and Suggested Readings 305 10 Connectionism and the Brain 306 10.1 Connectionism Meets Cognitive Neuroscience 306 10.2 Four Connectionist Models of Brain Processes 309 10.2.1 What/Where streams in visual processing 309 10.2.2 The role of the hippocampus in memory 313 10.2.2.1 The basic design and functions of the hippocampal system 313 10.2.2.2 Spatial navigation in rats 315 10.2.2.3 Spatial versus declarative memory accounts 316 10.2.2.4 Declarative memory in humans and monkeys 318 10.2.3 Simulating dyslexia in network models of reading 323 10.2.3.1 Double dissociations in dyslexia 323 10.2.3.2 Modeling deep dyslexia 327 10.2.3.3 Modeling surface dyslexia 331 10.2.3.4 Two pathways versus dual routes 335 10.2.4 The computational power of modular structure in neocortex 338 10.3The Neural Implausibility of Many Connectionist Models 341 10.3.1 Biologically implausible aspects of connectionist networks 342 10.3.2 How important is neurophysiological plausibility? 343 10.4 Whither Connectionism? 346 Notes 347 Sources and Suggested Readings 348 Appendix A: Notation 349 Appendix B: Glossary 350 Bibliography 363 Name Index 384 Subject Index 395

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Book SynopsisFUZZY COMPUTING IN DATA SCIENCE This book comprehensively explains how to use various fuzzy-based models to solve real-time industrial challenges. The book provides information about fundamental aspects of the field and explores the myriad applications of fuzzy logic techniques and methods. It presents basic conceptual considerations and case studies of applications of fuzzy computation. It covers the fundamental concepts and techniques for system modeling, information processing, intelligent system design, decision analysis, statistical analysis, pattern recognition, automated learning, system control, and identification. The book also discusses the combination of fuzzy computation techniques with other computational intelligence approaches such as neural and evolutionary computation. Audience Researchers and students in computer science, artificial intelligence, machine learning, big data analytics, and information and communication technology.Table of ContentsPreface xvii Acknowledgement xxi 1 Band Reduction of HSI Segmentation Using FCM 1 V. Saravana Kumar, S. Anantha Sivaprakasam, E.R. Naganathan, Sunil Bhutada and M. Kavitha 1.1 Introduction 2 1.2 Existing Method 3 1.2.1 K-Means Clustering Method 3 1.2.2 Fuzzy C-Means 3 1.2.3 Davies Bouldin Index 4 1.2.4 Data Set Description of HSI 4 1.3 Proposed Method 5 1.3.1 Hyperspectral Image Segmentation Using Enhanced Estimation of Centroid 5 1.3.2 Band Reduction Using K-Means Algorithm 6 1.3.3 Band Reduction Using Fuzzy C-Means 7 1.4 Experimental Results 8 1.4.1 DB Index Graph 8 1.4.2 K-Means–Based PSC (EEOC) 9 1.4.3 Fuzzy C-Means–Based PSC (EEOC) 10 1.5 Analysis of Results 12 1.6 Conclusions 16 References 17 2 A Fuzzy Approach to Face Mask Detection 21 Vatsal Mishra, Tavish Awasthi, Subham Kashyap, Minerva Brahma, Monideepa Roy and Sujoy Datta 2.1 Introduction 22 2.2 Existing Work 23 2.3 The Proposed Framework 26 2.4 Set-Up and Libraries Used 26 2.5 Implementation 27 2.6 Results and Analysis 29 2.7 Conclusion and Future Work 33 References 34 3 Application of Fuzzy Logic to the Healthcare Industry 37 Biswajeet Sahu, Lokanath Sarangi, Abhinadita Ghosh and Hemanta Kumar Palo 3.1 Introduction 38 3.2 Background 41 3.3 Fuzzy Logic 42 3.4 Fuzzy Logic in Healthcare 45 3.5 Conclusions 49 References 50 4 A Bibliometric Approach and Systematic Exploration of Global Research Activity on Fuzzy Logic in Scopus Database 55 Sugyanta Priyadarshini and Nisrutha Dulla 4.1 Introduction 56 4.2 Data Extraction and Interpretation 58 4.3 Results and Discussion 59 4.3.1 Per Year Publication and Citation Count 59 4.3.2 Prominent Affiliations Contributing Toward Fuzzy Logic 60 4.3.3 Top Journals Emerging in Fuzzy Logic in Major Subject Areas 61 4.3.4 Major Contributing Countries Toward Fuzzy Research Articles 63 4.3.5 Prominent Authors Contribution Toward the Fuzzy Logic Analysis 66 4.3.6 Coauthorship of Authors 67 4.3.7 Cocitation Analysis of Cited Authors 68 4.3.8 Cooccurrence of Author Keywords 68 4.4 Bibliographic Coupling of Documents, Sources, Authors, and Countries 70 4.4.1 Bibliographic Coupling of Documents 70 4.4.2 Bibliographic Coupling of Sources 71 4.4.3 Bibliographic Coupling of Authors 72 4.4.4 Bibliographic Coupling of Countries 73 4.5 Conclusion 74 References 76 5 Fuzzy Decision Making in Predictive Analytics and Resource Scheduling 79 Rekha A. Kulkarni, Suhas H. Patil and Bithika Bishesh 5.1 Introduction 80 5.2 History of Fuzzy Logic and Its Applications 81 5.3 Approximate Reasoning 82 5.4 Fuzzy Sets vs Classical Sets 83 5.5 Fuzzy Inference System 84 5.5.1 Characteristics of FIS 85 5.5.2 Working of FIS 85 5.5.3 Methods of FIS 86 5.6 Fuzzy Decision Trees 86 5.6.1 Characteristics of Decision Trees 87 5.6.2 Construction of Fuzzy Decision Trees 87 5.7 Fuzzy Logic as Applied to Resource Scheduling in a Cloud Environment 88 5.8 Conclusion 90 References 91 6 Application of Fuzzy Logic and Machine Learning Concept in Sales Data Forecasting Decision Analytics Using ARIMA Model 93 S. Mala and V. Umadevi 6.1 Introduction 94 6.1.1 Aim and Scope 94 6.1.2 R-Tool 94 6.1.3 Application of Fuzzy Logic 94 6.1.4 Dataset 95 6.2 Model Study 96 6.2.1 Introduction to Machine Learning Method 96 6.2.2 Time Series Analysis 96 6.2.3 Components of a Time Series 97 6.2.4 Concepts of Stationary 99 6.2.5 Model Parsimony 100 6.3 Methodology 100 6.3.1 Exploratory Data Analysis 100 6.3.1.1 Seed Types—Analysis 101 6.3.1.2 Comparison of Location and Seeds 101 6.3.1.3 Comparison of Season (Month) and Seeds 103 6.3.2 Forecasting 103 6.3.2.1 Auto Regressive Integrated Moving Average (ARIMA) 103 6.3.2.2 Data Visualization 106 6.3.2.3 Implementation Model 108 6.4 Result Analysis 108 6.5 Conclusion 110 References 110 7 Modified m-Polar Fuzzy Set ELECTRE-I Approach 113 Madan Jagtap, Prasad Karande and Pravin Patil 7.1 Introduction 114 7.1.1 Objectives 114 7.2 Implementation of m-Polar Fuzzy ELECTRE-I Integrated Shannon’s Entropy Weight Calculations 115 7.2.1 The m-Polar Fuzzy ELECTRE-I Integrated Shannon’s Entropy Weight Calculation Method 115 7.3 Application to Industrial Problems 118 7.3.1 Cutting Fluid Selection Problem 118 7.3.2 Results Obtained From m-Polar Fuzzy ELECTRE-I for Cutting Fluid Selection Problem 122 7.3.3 FMS Selection Problem 125 7.3.4 Results Obtained From m-Polar Fuzzy ELECTRE-I for FMS Selection 130 7.4 Conclusions 143 References 143 8 Fuzzy Decision Making: Concept and Models 147 Bithika Bishesh 8.1 Introduction 148 8.2 Classical Set 149 8.3 Fuzzy Set 150 8.4 Properties of Fuzzy Set 151 8.5 Types of Decision Making 153 8.5.1 Individual Decision Making 153 8.5.2 Multiperson Decision Making 157 8.5.3 Multistage Decision Making 158 8.5.4 Multicriteria Decision Making 160 8.6 Methods of Multiattribute Decision Making (MADM) 162 8.6.1 Weighted Sum Method (WSM) 162 8.6.2 Weighted Product Method (WPM) 162 8.6.3 Weighted Aggregates Sum Product Assessment (WASPAS) 163 8.6.4 Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) 166 8.7 Applications of Fuzzy Logic 167 8.8 Conclusion 169 References 169 9 Use of Fuzzy Logic for Psychological Support to Migrant Workers of Southern Odisha (India) 173 Sanjaya Kumar Sahoo and Sukanta Chandra Swain 9.1 Introduction 174 9.2 Objectives and Methodology 175 9.2.1 Objectives 175 9.2.2 Methodology 176 9.3 Effect of COVID-19 on the Psychology and Emotion of Repatriated Migrants 176 9.3.1 Psychological Variables Identified 176 9.3.2 Fuzzy Logic for Solace to Migrants 176 9.4 Findings 178 9.5 Way Out for Strengthening the Psychological Strength of the Migrant Workers through Technological Aid 178 9.6 Conclusion 179 References 180 10 Fuzzy-Based Edge AI Approach: Smart Transformation of Healthcare for a Better Tomorrow 181 B. RaviKrishna, Sirisha Potluri, J. Rethna Virgil Jeny, Guna Sekhar Sajja and Katta Subba Rao 10.1 Significance of Machine Learning in Healthcare 182 10.2 Cloud-Based Artificial Intelligent Secure Models 183 10.3 Applications and Usage of Machine Learning in Healthcare 183 10.3.1 Detecting Diseases and Diagnosis 183 10.3.2 Drug Detection and Manufacturing 183 10.3.3 Medical Imaging Analysis and Diagnosis 184 10.3.4 Personalized/Adapted Medicine 185 10.3.5 Behavioral Modification 185 10.3.6 Maintenance of Smart Health Data 185 10.3.7 Clinical Trial and Study 185 10.3.8 Crowdsourced Information Discovery 185 10.3.9 Enhanced Radiotherapy 186 10.3.10 Outbreak/Epidemic Prediction 186 10.4 Edge AI: For Smart Transformation of Healthcare 186 10.4.1 Role of Edge in Reshaping Healthcare 186 10.4.2 How AI Powers the Edge 187 10.5 Edge AI-Modernizing Human Machine Interface 188 10.5.1 Rural Medicine 188 10.5.2 Autonomous Monitoring of Hospital Rooms—A Case Study 188 10.6 Significance of Fuzzy in Healthcare 189 10.6.1 Fuzzy Logic—Outline 189 10.6.2 Fuzzy Logic-Based Smart Healthcare 190 10.6.3 Medical Diagnosis Using Fuzzy Logic for Decision Support Systems 191 10.6.4 Applications of Fuzzy Logic in Healthcare 193 10.7 Conclusion and Discussions 193 References 194 11 Video Conferencing (VC) Software Selection Using Fuzzy TOPSIS 197 Rekha Gupta 11.1 Introduction 197 11.2 Video Conferencing Software and Its Major Features 199 11.2.1 Video Conferencing/Meeting Software (VC/MS) for Higher Education Institutes 199 11.3 Fuzzy TOPSIS 203 11.3.1 Extension of TOPSIS Algorithm: Fuzzy TOPSIS 203 11.4 Sample Numerical Illustration 207 11.5 Conclusions 213 References 213 12 Estimation of Nonperforming Assets of Indian Commercial Banks Using Fuzzy AHP and Goal Programming 215 Kandarp Vidyasagar and Rajiv Kr. Dwivedi 12.1 Introduction 216 12.1.1 Basic Concepts of Fuzzy AHP and Goal Programming 217 12.2 Research Model 221 12.2.1 Average Growth Rate Calculation 227 12.3 Result and Discussion 233 12.4 Conclusion 234 References 234 13 Evaluation of Ergonomic Design for the Visual Display Terminal Operator at Static Work Under FMCDM Environment 237 Bipradas Bairagi 13.1 Introduction 238 13.2 Proposed Algorithm 240 13.3 An Illustrative Example on Ergonomic Design Evaluation 245 13.4 Conclusions 249 References 249 14 Optimization of Energy Generated from Ocean Wave Energy Using Fuzzy Logic 253 S. B. Goyal, Pradeep Bedi, Jugnesh Kumar and Prasenjit Chatterjee 14.1 Introduction 254 14.2 Control Approach in Wave Energy Systems 255 14.3 Related Work 257 14.4 Mathematical Modeling for Energy Conversion from Ocean Waves 259 14.5 Proposed Methodology 260 14.5.1 Wave Parameters 261 14.5.2 Fuzzy-Optimizer 262 14.6 Conclusion 264 References 264 15 The m-Polar Fuzzy TOPSIS Method for NTM Selection 267 Madan Jagtap and Prasad Karande 15.1 Introduction 268 15.2 Literature Review 268 15.3 Methodology 270 15.3.1 Steps of the mFS TOPSIS 270 15.4 Case Study 272 15.4.1 Effect of Analytical Hierarchy Process (AHP) Weight Calculation on the mFS TOPSIS Method 273 15.4.2 Effect of Shannon’s Entropy Weight Calculation on the m-Polar Fuzzy Set TOPSIS Method 277 15.5 Results and Discussions 281 15.5.1 Result Validation 281 15.6 Conclusions and Future Scope 283 References 284 16 Comparative Analysis on Material Handling Device Selection Using Hybrid FMCDM Methodology 287 Bipradas Bairagi 16.1 Introduction 288 16.2 MCDM Techniques 289 16.2.1 Fahp 289 16.2.2 Entropy Method as Weights (Influence) Evaluation Technique 290 16.3 The Proposed Hybrid and Super Hybrid FMCDM Approaches 291 16.3.1 Topsis 291 16.3.2 FMOORA Method 292 16.3.3 FVIKOR 292 16.3.4 Fuzzy Grey Theory (FGT) 293 16.3.5 COPRAS –G 293 16.3.6 Super Hybrid Algorithm 294 16.4 Illustrative Example 295 16.5 Results and Discussions 298 16.5.1 FTOPSIS 298 16.5.2 FMOORA 298 16.5.3 FVIKRA 298 16.5.4 Fuzzy Grey Theory (FGT) 299 16.5.5 COPRAS-G 299 16.5.6 Super Hybrid Approach (SHA) 299 16.6 Conclusions 302 References 302 17 Fuzzy MCDM on CCPM for Decision Making: A Case Study 305 Bimal K. Jena, Biswajit Das, Amarendra Baral and Sushanta Tripathy 17.1 Introduction 306 17.2 Literature Review 307 17.3 Objective of Research 308 17.4 Cluster Analysis 308 17.4.1 Hierarchical Clustering 309 17.4.2 Partitional Clustering 309 17.5 Clustering 310 17.6 Methodology 314 17.7 TOPSIS Method 316 17.8 Fuzzy TOPSIS Method 318 17.9 Conclusion 325 17.10 Scope of Future Study 326 References 326 Index 329

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Book SynopsisPrinciples of Soft Computing Using Python Programming An accessible guide to the revolutionary techniques of soft computing Soft computing is a computing approach designed to replicate the human mind's unique capacity to integrate uncertainty and imprecision into its reasoning. It is uniquely suited to computing operations where rigid analytical models will fail to account for the variety and ambiguity of possible solutions. As machine learning and artificial intelligence become more and more prominent in the computing landscape, the potential for soft computing techniques to revolutionize computing has never been greater. Principles of Soft Computing Using Python Programming provides readers with the knowledge required to apply soft computing models and techniques to real computational problems. Beginning with a foundational discussion of soft or fuzzy computing and its differences from hard computing, it describes different models for soft computing and

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Book SynopsisExamines different kinds of models and investigates some of the basic properties of connectionism in the context of synthetic psychology, including accounts of how the internal structure of connectionist networks can be interpreted. This title investigates basic properties of connectionism in the context of synthetic psychology.Trade Review"In this remarkable book, Dawson refines and develops synthetic psychology – an approach to explaining mental capacities that takes as its inspiration the investigation of simple systems exhibiting emergent behavior. Rich with examples, the book shows with extraordinary clarity how ideas from embodied cognitive science, robotics, artificial life, and connectionism can be combined to shed new light on the workings of the mind. It's hard to imagine a better book for anyone wishing to understand the latest advances in cognitive science." Larry Shapiro, University of Wisconsin "Minds and Machines provides an easily understood introduction to synthetic psychology – start with simple processes, see what emerges, and analyze the resulting system. Dawson lays a solid foundation describing the strengths and weaknesses of various modeling approaches in psychology, and then builds on this by giving concrete examples of how connectionism – using the synthetic approach – can be used to provide simple explanations of seemingly complex cognitive phenomena." David A. Medler, The Medical College of Wisconsin "Thisis a wonderful book, both in terms of the thought-provoking technical content and the delightfully conversational style that readers have come to expect from the author of Understanding Cognitive Science. Dawson has a real gift for presenting complex ideas in an accessible and engaging way that does not dilute the scientific or philosophical intricacies involved." Stefan C. Kremer, University of Guelph, Canada "An important virtue of this book is that the content and order of presentation has clearly been tested at length in the classroom of a dedicated and creative teacher. The book has many illustrations from teaching practice, and would be an excellent basis for a senior undergraduate or introductory graducate course on cognitive modelling, and I'd be delighted to use it for that purpose myself ... This is a fine book, and I suspect it would be a valuable resource for those who don't know much about synthetic psychology but would like to get a clear sense of the lie of the land." David Spurrett, University of KwaZulu-Natal, Psychology in Society, 30, 2004, 77-79Table of ContentsList of Figures. List of Tables. 1. The Kids in the Hall. Synthetic Versus Analytic Traditions. . 2. Advantages and Disadvantages of Modeling. What Is A Model?. Advantages and Disadvantages of Models. . 3. Models of Data. An Example of a Model of Data. Properties of Models of Data. . 4. Mathematical Models. An Example Mathematical Model. Mathematical Models vs. Models of Data. . 5. Computer Simulations. A Sample Computer Simulation. Connectionist Models. Properties of Computer Simulations. . 6. First Steps Toward Synthetic Psychology. Introduction. Building a Thoughtless Walker. Step 1: Synthesis. Step 2: Emergence. Step 3: Analysis. Issues Concerning Synthetic Psychology. . 7. Uphill Analysis, Downhill Synthesis. Introduction. From Homeostats to Tortoises. Ashby’s Homeostat. Vehicles. Synthesis and Emergence: Some Modern Examples. The Law of Uphill Analysis and Downhill Synthesis. . 8. Connectionism As Synthetic Psychology. Introduction. Beyond Sensory Reflexes. Connectionism, Synthesis, and Representation. Summary and Conclusions. . 9. Building Associations. From Associationism To Connectionism. Building An Associative Memory. Beyond the Limitations of Hebb Learning. Associative Memory and Synthetic Psychology. . 10. Making Decisions. The Limits of Linearity. A Fundamental Nonlinearity. Building a Perceptron: A Nonlinear Associative Memory. The Psychology of Perceptrons. The Need for Layers. . 11. Sequences of Decisions. The Logic of Layers. Training Multilayered Networks. A Simple Case Study: Exclusive Or. A Second Case Study: Classifying Musical Chords. A Third Case Study: From Connectionism to Selectionism. . 12. From Synthesis To Analysis. Representing Musical Chords in a Pdp Network. Interpreting the Internal Structure of Value Unit Networks. Network Interpretation and Synthetic Psychology. . 13. From Here To Synthetic Psychology. References. Index

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Book SynopsisNonlinear modelling has become increasingly important and widely used in economics. This valuable book brings together recent advances in the area including contributions covering cross-sectional studies of income distribution and discrete choice models, time series models of exchange rate dynamics and jump processes, and artificial neural network and genetic algorithm models of financial markets. Attention is given to the development of theoretical models as well as estimation and testing methods with a wide range of applications in micro and macroeconomics, labour and finance.The book provides valuable introductory material that is accessible to students and scholars interested in this exciting research area, as well as presenting the results of new and original research. Nonlinear Economic Models provides a sequel to Chaos and Nonlinear Models in Economics by the same editors.Trade Review'This collection provides valuable introductory material that is accessible to students and scholars interested in this research area.' -- Business HorizonsTable of ContentsContents: Part I: Introduction 1. Nonlinear Modelling: An Introduction Part II: Cross-sectional Applications 2. A Model of Income Distribution 3. Truncated Distribution Families 4. Betit: A Flexible Binary Choice Model 5. Estimation of Generalised Distributions 6. Age and the Distribution of Earnings 7. Count Data and Discrete Distributions Part III: Time Series Applications 8. A Model of the Real Exchange Rate 9. Jump Models and Higher Moments 10. A Topological Test of Chaos 11. Genetic Algorithms and Trading Rules Part IV: Neural Network Applications 12. Artificial Neural Networks 13. An ANN Model of the Stock Market 14. Exchange Rate Forecasting Models Index

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Book SynopsisThis book contains the proceedings of the 22nd EANN “Engineering Applications of Neural Networks” 2021 that comprise of research papers on both theoretical foundations and cutting-edge applications of artificial intelligence. Based on the discussed research areas, emphasis is given in advances of machine learning (ML) focusing on the following algorithms-approaches: Augmented ML, autoencoders, adversarial neural networks, blockchain-adaptive methods, convolutional neural networks, deep learning, ensemble methods, learning-federated learning, neural networks, recurrent – long short-term memory. The application domains are related to: Anomaly detection, bio-medical AI, cyber-security, data fusion, e-learning, emotion recognition, environment, hyperspectral imaging, fraud detection, image analysis, inverse kinematics, machine vision, natural language, recommendation systems, robotics, sentiment analysis, simulation, stock market prediction.Table of ContentsAutomatic Facial Expression Neutralisation Using Generative Adversarial Network.- Creating Ensembles of Generative Adversarial Network Discriminators for One-class Classification.- A Hybrid Deep Learning Ensemble for Cyber Intrusion Detection.- Anomaly Detection by Robust Feature Reconstruction.- Deep Learning of Brain Asymmetry Images and Transfer Learning for Early Diagnosis of Dementia.- Deep learning topology-preserving EEG-based images for autism detection in infants.- Improving the Diagnosis of Breast Cancer by Combining Visual and Semantic Feature Descriptors.- Liver cancer trait detection and classification through Machine Learning on smart mobile devices.

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Book SynopsisThis book describes how to use expert knowledge—which is often formulated by using imprecise (fuzzy) words from a natural language. In the 1960s, Zadeh designed special "fuzzy" techniques for such use. In the 1980s, fuzzy techniques started controlling trains, elevators, video cameras, rice cookers, car transmissions, etc. Now, combining fuzzy with neural, genetic, and other intelligent methods leads to new state-of-the-art results: in aerospace industry (from drones to space flights), in mobile robotics, in finances (predicting the value of crypto-currencies), and even in law enforcement (detecting counterfeit banknotes, detecting online child predators and in creating explainable AI systems). The book describes these (and other) applications—as well as foundations and logistics of fuzzy techniques. This book can be recommended to specialists—both in fuzzy and in various application areas—who will learn latest techniques and their applications, and to students interested in innovative ideas.Table of ContentsPowerset operators in categories with fuzzy relations dened by monads.- Improved Fuzzy Q-Learning with Replay Memory.- The ulem package: underlining for emphasis.- A Dynamic Hierarchical Genetic-Fuzzy Sugeno Network.- Fuzzy Mathematical Morphology and Applications in Image Processing.

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Book SynopsisThe three-volume set LNCS 13623, 13624, and 13625 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022.The 146 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications.The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.Table of ContentsTheory and Algorithms.- Solving Partial Differential Equations using Point-based Neural Networks.- Patch Mix Augmentation with Dual Encoders for Meta-Learning.- Tacit Commitments Emergence in Multi-agent Reinforcement Learning.- Saccade Direction Information Channel.- Shared-Attribute Multi-Graph Clustering with Global Self-Attention.- Mutual Diverse-Label Adversarial Training.- Multi-Agent Hyper-Attention Policy Optimization.- Filter Pruning via Similarity Clustering for Deep Convolutional Neural Networks.- FPD: Feature Pyramid Knowledge Distillation.- An effective ensemble model related to incremental learning in neural machine translation.- Local-Global Semantic Fusion Single-shot Classification Method.- Self-Reinforcing Feedback Domain Adaptation Channel.- General Algorithm for Learning from Grouped Uncoupled Data and Pairwise Comparison Data.- Additional Learning for Joint Probability Distribution Matching in BiGAN.- Multi-View Self-Attention for Regression Domain Adaptation with Feature Selection.- EigenGRF: Layer-Wise Eigen-Learning for Controllable Generative Radiance Fields.- Partial Label learning with Gradually Induced Error-Correction Output Codes.- HMC-PSO: A Hamiltonian Monte Carlo and Particle Swarm Optimization-based optimizer.- Heterogeneous Graph Representation for Knowledge Tracing.- Intuitionistic fuzzy universum support vector machine.- Support vector machine based models with sparse auto-encoder based features for classification problem.- Selectively increasing the diversity of GAN-generated samples.- Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning.- Differentiable Causal Discovery Under Heteroscedastic Noise.- IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels.- Adaptive Scaling for U-Net in Time Series Classification.- Permutation Elementary Cellular Automata: Analysis and Application of Simple Examples.- SSPR: A Skyline-Based Semantic Place Retrieval Method.- Double Regularization-based RVFL and edRVFL Networks for Sparse-Dataset Classification.- Adaptive Tabu Dropout for Regularization of Deep Neural Networks.- Class-Incremental Learning with Multiscale Distillation for Weakly Supervised Temporal Action Localization.- Nearest Neighbor Classifier with Margin Penalty for Active Learning.- Factual Error Correction in Summarization with Retriever-Reader Pipeline.- Context-adapted Multi-policy Ensemble Method for Generalization in Reinforcement Learning.- Self-attention based multi-scale graph convolutional networks.- Synesthesia Transformer with Contrastive Multimodal Learning.- Context-based Point Generation Network for Point Cloud Completion.- Temporal Neighborhood Change Centrality for Important Node Identification in Temporal Networks.- DOM2R-Graph: A Web Attribute Extraction Architecture with Relation-aware Heterogeneous Graph Transformer.- Sparse Linear Capsules for Matrix Factorization-based Collaborative Filtering.- PromptFusion: a Low-cost Prompt-based Task Composition for Multi-task Learning.- A fast and efficient algorithm for filtering the training dataset.- Entropy-minimization Mean Teacher for Source-Free Domain Adaptive Object Detection.- IA-CL: A Deep Bidirectional Competitive Learning Method for Traveling Salesman Problem.- Boosting Graph Convolutional Networks With Semi-Supervised Training.- Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs.- VAAC: V-value Attention Actor-Critic for Cooperative Multi-agent Reinforcement Learning.- An Analytical Estimation of Spiking Neural Networks Energy Efficiency.- Correlation Based Semantic Transfer with Application to Domain Adaptation.- Minimum Variance Embedded Intuitionistic Fuzzy Weighted Random Vector Functional Link Network.- Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction.

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