Artificial Intelligence for Cognitive Modeling Theory and Practice

This book is written in a clear and thorough way to cover both the traditional and modern uses of artificial intelligence and soft computing. It gives an in-depth look at mathematical models, algorithms, and real-world problems that are hard to solve in MATLAB. The book is intended to provide a broad and in-depth understanding of fuzzy logic controllers, genetic algorithms, neural networks, and hybrid techniques such as ANFIS and the GA-ANN model. Features: A detailed description of basic intelligent techniques (fuzzy logic, genetic algorithm and neural network using MATLAB) A detailed description of the hybrid intelligent technique called the adaptive fuzzy inference technique (ANFIS) Formulation of the nonlinear model like analysis of ANOVA and response surface methodology Variety of solved problems on ANOVA and RSM Case studies of above mentioned intelligent techniques on the different process control systems This book can be used as a handbook and a guide for students of all engineering disciplines, operational research areas, computer applications, and for various professionals who work in the optimization area.

Part A: Artificial Intelligence & Cognitive Computing : Theory &Concept 1. Introduction to AI 1.1 Introduction 1.1.1 Intelligent Control 1.1.2 Expert System 1.1.3 Soft Computing 1.1.3.1 Fuzzy System 1.1.3.2 Neural Network 1.1.3.3 Genetic Algorithm 1.1.3.4 Adaptive Fuzzy Inference System 1.1.4 Real-time System Reference 2. Practical Approach of Fuzzy Logic Controller 2.1 Introduction 2.2 Classical set Properties & Operation 2.2.1 Classical Set 2.2.2 Crisp Set 2.3 Concept of Fuzzy 2.3.1 Fuzzy Set 2.3.2 Operation of Fuzzy Set 2.3.3 Properties of Fuzzy Set 2.3.4 Comparison between Crisp Set & Fuzzy set 2.3.5 Composition of Fuzzy Set 2.3.6 Properties of Fuzzy Composition 2.3.7 Classical tolerance Relation 2.3.8 Features of membership function 2.4 Fuzzification 2.4.1 Intitution 2.4.2 Inference 2.4.3 Rank Ordering 2.4.4 Angular Fuzzy set 2.4.5 Neural network 2.4.5.A Training the neural network 2.4.5.B Testing the neural network 2.4.6 Genetic Algorithm 2.4.7 Inductive reasoning 2.5 Defuzzification 2.5.1 Max – membership principle 2.5.2 Centroid method 2.5.3 Weighted average method 2.5.4 Mean- max membership or middle of maxima 2.5.5 Center of sum methods 2.5.6 Center of largest area 2.6 Example for different Defuzzification methods Reference 3. A Practical Approach to Neural Network Model 3.1 Introduction 3.1.1 Network Topology 3.1.1.A Feed forward Network 3.1.1.B. Feedback Network 3.1.2 Adjustments of Weights or Learning 3.1.2.1 Supervised Learning 3.1.2.2 Unsupervised Learning 3.1.2.3 Reinforcement Learning 3.1.3 Activation Functions 3.1.3.1 Type of Activation Function 3.1.4 Learning rules in neural network 3.1.4.1 Hebbian Learning Rule 3.1.4.2. Perceptron Learning Rule 3.1.4.3 Delta Learning Rule 3.1.4.4 Competitive Learning Rule (Winner-takes-all) 3.1.4.5 Outstar Learning Rule 3.1.5 Mcculloch Pitts neuron 3.1.6 Simple neural nets for pattern classification 3.1.7 Linear Reparability 3.1.8 Perceptron 3.2. Adaptive Linear Neuron (ADALINE) 3.2.1 Madaline (Multiple adaptive linear neurons) 3.2.2 Associative Memory Network 3.2.3 Hetero Associative memory 3.3 Bidirectional associative memory 3.4 Self-Organizing Maps: Kohonen Maps 3.5 Learning vector Quantization (LVQ) 3.6 Counter Propagation Network (CPN) 3.6.1 Full counter propagation network (FCPN) 3.6.2. Forward only counter Propagation network 3.7 ART (Adaptive resonance Theory) 3.8 Standard back propagation architecture 3.9 Boltzmann Machine Learning Reference 4. Introduction to Genetic Algorithm 4.1 Introduction 4.2 Optimization Problems 4.2.1 Steps for solving the optimization problem 4.2.2 Point to point Algorithms (P2P) 4.2.3 A* Search Algorithm 4.2.4 Simulated Annealing 4.2.5 Genetic Algorithm 4.2.5.1 Motivation of GA 4.2.5.2 Basic Terminology 4.2.5.3 Experiments 4.2.5.4 Parameters Tuning Technique in Genetic Algorithm 4.2.5.5 Strategy parameters 4.3 Constrained Optimization 4.4 Multimodal optimization 4.5 Multiobjective Optimization 4.6 Combinatorial Optimization 4.6.1 Differential Evolution Reference 5. Modeling of ANFIS (Adaptive Fuzzy Inference System) System 5.1 Introduction 5.2 Hybrid Systems Sequential Hybrid Systems 5.2.2. Auxiliary Hybrid Systems 5.2.3 Embedded Hybrid Systems 5.3 Neuro-Fuzzy Hybrids 5.3.2 Adaptive Neuro-Fuzzy Interference System (ANFIS) 5.3.2.1 Fuzzy Inference System (FIS) 5.3.2.2 Adaptive Network 5.4 ANFIS Architecture 5.4.1 Hybrid Learning Algorithm 5.4.2 Derivation of Fuzzy Model 5.4.2.1 Extracting the initial fuzzy model 5.4.2.2 Subtractive Clustering Technique 5.4.2.3 Grid Partitioning Technique 5.3.2.4 C- Mean Clustering Reference 6. Machine Learning Techniques for Cognitive Modeling 6.1 Introduction 6.2 Classification of Machine Learning 6.2.1 Supervised Learning 6.2.1.1 Inductive Learning 6.2.1.2 Learning by Version Space 6.2.1.3 Learning by Decision Tree (DT) 6.2.1.4 Analogical Learning 6.2.2 Unsupervised Learning 6.2.3 Reinforcement Learning 6.2.3.1 Learning Automata 6.2.3.2 Adaptive Dynamic Programming 6.2.3.3 Q learning 6.2.3.4 Temporal difference learning 6.2.4 Learning by Inductive Logic Programming (ILP) Reference Part B: Artificial Intelligence & Cognitive Computing : Practices 7. Parametric Optimization of N Channel JFET using Bio Inspired Optimization Techniques 7.1 Introduction 7.2 Mathematical Description 7.2.1 Current Equation for JFET 7.2.2 Flower Pollination Algorithm 7.2.3 Objective Function 7.3 Methodology 7.4. Result & Discussion 7.5 Conclusion Reference 8. AI based Model of Clinical and Epidemiological Factors for COVID19 8.1 Introduction 8.2 Related Work 8.3 Artificial Neural Network Based Model 8.3.1 Modeling of Artificial Neural Network 8.3.1.1 Collection, pre-processing and division of data 8.3.1.2 Implementation of neural network 8.3.2 Performance of Training, Testing & Validation of network 8.3.3 Performance evaluation of Training Functions 8.4 Results & Discussion 8.5 Conclusions Reference 9. Fuzzy Logic Based Parametric Optimization Technique of Electro Chemical Discharge Micro-Machining (µ-CDM) Process during Micro-Channel Cutting on Silica Glass 9.1 Introduction 9.2 Development of the Set up 9.3 Experimental Methodology & Result Analysis 9.3.1 Effects of process parameters on MRR, OC and MD 9.3.2 Determination of optimized condition 9.4 Conclusions References 10. Study of ANFIS model to Forecast the Average Localization Error (ALE) with Applications to Wireless Sensor Networks (WSN) 10.1 Introduction 10.2 System Model 10.2.1 Distance calculation for generalization of Optimization problem 10.2.2 Simulation Setup 10.2.3 Experimental Results and Performance Analysis 10.2.3.1 The Effect of Anchor Density 10.2.3.2 The Effect of Communication Range 10.3 Adaptive Neuro-Fuzzy Inference Architecture 10.3.1 Hybrid Learning ANFIS 10.3.2 ANFIS Training Process 10.4 Result Analysis 10.5 Conclusions References 11. Performance Estimation of Photovoltaic Cell using Hybrid Genetic Algorithm & Particle Swarm Optimization 11.1 Introduction 11.2 Mathematics model & objective function of the Solar Cell 11.2.1 Single diode model (SDM) 11.2.2 Double diode model (DDM) 11.2.3 PV module model 11.3 Objective function 11.4 Proposed Methodology 11.4.1 Improved Cuckoo Search Optimization 11.5 Results and Discussion 11.5.1 Test information 11.5.1.1 Fitness Test 11.5.1.2 Reliability Test 11.5.1.3 Computational Efficiency Test 11.5.1.4 Convergence Test 11.5.1.5 Accuracy Test 11.5.2 Overall Efficiency 11.5.3 Validation between manufacturer’s datasheet & experimental datasheets 11.5.3.1 Case study 1: Single diode model 11.5.3.2 Case study 2: Double diode model 11.6 Conclusions References 12. Bio inspired Optimization based PID Controller Tuning for a Non-Linear Cylindrical Tank System 12.1 Introduction 12.2 Methodology 12.2.1 Mathematical model of Cylindrical Tank 12.2.2 Description of Metaheuristic techniques 12.2.2.1 Flower Pollination Algorithm (FPA) 12.2.2.2 Bacterial Foraging Optimization Algorithm (BFOA): 12.3 Result & Discussion 12.4 Conclusion References 13. A Hybrid Algorithm Based on CSO & PSO for Parametric Optimization of Liquid Flow model 13.1 Introduction 13.2 Experimental Setup Liquid flow control process 13.3 Modeling of the liquid flow process 13.4 Proposed Methodology 13.4.1 Hybrid GAPSO 13.4.2 Parameters Setting 13.5 Performance Analysis 13.5.1 Computational Efficiency Test 13.5.2 Convergence Speed 13.5.3 Accuracy Test 13.6 Finding optimal condition for Liquid flow 13.7 Conclusions References 14. Modelling of Improved Deep Learning Algorithm for Detection of Type 2 Diabetes 14.1 Introduction 14.2 Methodology 14.2.1 Datasets 14.2.2 Imbalance datasets 14.2.3 Synthetic Minority over Sampling Technique (SMOTE) 14.3 Proposed flow Diagram 14.4 Deep Neural Network for Data classification 14.5 Experimental Result Analysis 14.5.1 Performance Measure 14.5.2 Comparison with existing system 14.6 Conclusions References 15. Human Activity Recognition (HAR), Prediction & Analysis using Machine Learning 15.1

Prof. Pijush Dutta received his B.Tech. and M.Tech. in Electronics and Communication Engineering & Mechatronics Engineering from WBUT, India in 2007 and 2012, respectively & pursuing his PhD degree ( Submitted) from the Department of Electronics & Communication Engineering, Mewar University, India. Currently, he is working as the Assistant Professor & Head of the Department of Electronics and Communication Engineering Department, Greater Kolkata College of Engineering & Management, Baruipur, India. He was the former Head of ECE Department, Global Institute of Management Technology, Krishnagar, India from 2018 to 2022. His current research interests include Sensor & transducer, nonlinear process control system, Mechatronics system & Control, optimization, intelligent system, Internet of Things (IOT), machine learning & Deep learning. He has 50+ publications in peer-reviewed journals, and in national and international conferences. He also published 14 National & International patents so far. Prof. Dutta has authored 3 books in his credit. He is also editorial and review board member of many peer review international journals. Dr. Souvik Pal is an Associate Professor in the Department of Computer Science and Engineering at Sister Nivedita University (Techno India Group), Kolkata, India. Dr. Pal received his M. Tech, and PhD degrees in the field of Computer Science and Engineering from KIIT University, Bhubaneswar, India. He has more than a decade of academic experience. He is author or co-editor of more than 18 books from reputed publishers, including Elsevier, Springer, CRC Press, and Wiley, and he holds three patents. He has more than 75 Publications in his credit in Scopus / SCI/SCIE Journals and conferences. He is serving as a Series Editor for "Advances in Learning Analytics for Intelligent Cloud-IoT Systems", published by Scrivener-Wiley Publishing (Scopus-indexed); "Internet of Things: Data-Centric Intelligent Computing, Informatics, and Communication", published CRC Press, Taylor & Francis Group, USA; "Conference Proceedings Series on Intelligent Systems, Data Engineering, and Optimization", published CRC Press, Taylor & Francis Group, USA. He is the organizing chair of RICE 2019, Vietnam; RICE 2020 Vietnam; ICICIT 2019, Tunisia. He has been invited as a keynote speaker at ICICCT 2019, Turkey, and ICTIDS 2019, 2021 Malaysia. He has also served as Proceedings Editor of ICICCT 2019, 2020; ICMMCS 2020, 2021; ICWSNUCA 2021, India. His professional activities include roles as Associate Editor, Guest Editor, and Editorial Board member for more than 100+ international journals and conferences of high repute and impact. His research area includes cloud computing, big data, internet of things, wireless sensor network, and data analytics. He is a member of many professional organizations, including MIEEE; MCSI; MCSTA/ACM, USA; MIAENG, Hong Kong; MIRED, USA; MACEEE, New Delhi; MIACSIT, Singapore; and MAASCIT, USA. Dr. Asok Kumar received his B.Tech & M.Tech Degree in radio physics and Electronics from Calcutta University in 1997 & 1999 respectively and received his PhD degree from Jadavpur University, 2007. Currently Dr. Kumar working a Dean of the Student welfare Department at Vidyasagar University, West Bengal, India. He has more than 21 years of teaching & more than 15 years of research experience. Apart from teaching & research activity, he has more than 10 years of administrative experience as a HOD, Dean of Academics, NBA Coordinator & Principal of several reputated Engineering Colleges. Dr. Kumar research includes data security, Wireless Communication, Information Theory & Coding, Computer Networking, Intelligent Control, Soft Computing, Optimization, and Sensor & Transducer. He has more than 90 research articles in his credit, published in national, international & conference proceedings. He is a reviewer of more than 10 national international journals .he has authored 2 Author book & 7 book chapters in the field of data security & networking. He has been invited as a keynote speaker at many national level institutes & organizations. He is a member of various professional bodies like IE (India), IEEE, ISTE, IACSIT, etc. Dr. Korhan Cengizreceived the BSc Degree in Electronics and Communication Engineering from Kocaeli University and Business Administration from Anadolu University, Turkey in 2008 and 2009 respectively. He took his MSc degree in Electronics and Communication Engineering from Namik Kemal University, Turkey in 2011, and the PhD degree in Electronics Engineering from Kadir Has University, Turkey in 2016. Since September 2022, He has been associate professor in the department of Computer Engineering, Istinye University, Istanbul, Turkey. Since April 2022, he has been the chair of the research committee of University of Fujairah, United Arab Emirates. Since August 2021, he has been an Assistant