Artificial Intelligence for Cognitive Modeling Theory and Practice

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

3.1 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

4.1 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

5.1 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

6.1 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

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

8.1 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

10.1 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

11.1 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

12.1 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

13.1 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

14.1 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

15.1 Introduction

15.2 Related Works

15.3 Proposed Method for Human Action Recognition

15.3.1 Data Collection overview

15.3.2 Signal processing

15.3.3 Feature selection

15.3.4 Exploratory Data Analysis

15.3.5 Data preprocessing

15.3.6 Exploratory Data Analysis for Static and dynamic activities

15.3.7 Visualizing data using t-SNE

15.4 Machine learning Algorithm

15.4.1 Logistics Regression

15.4.2 Random Forest

15.4.3 Decision Tree

15.4.4 Support vector machine

15.4.5 K nearest neighbor

15.4.6 Naïve Bayes

15.4.7 Data preprocessing

15.5 Experimental Results

15.6 Conclusion

Reference

Biography

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 Cengiz received 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 Professor at the College of Information Technology in University of Fujairah, UAE. Dr. Cengiz is the author of over 40 SCI / SCI-E articles including IEEE Internet of Things Journal, IEEE Access, Expert Systems with Applications, Knowledge Based Systems and ACM Transactions on Sensor Networks, 5 international patents, more than 10 book chapters, and 1 book in Turkish. He is editor of more than 10 books. His research interests include wireless sensor networks, wireless communications, statistical signal processing, indoor positioning systems, internet of things, power electronics and 5G. He is the Associate Editor of IEEE Potentials Magazine, Handling Editor of Microprocessors and Microsystems, Elsevier, Associate Editor of IET Electronics Letters, IET Networks. He has also Guest Editorial Positions in IEEE Internet of Things Magazine. He serves several reviewer positions for IEEE Internet of Things Journal, IEEE Sensors Journal and IEEE Access. He serves several book editorial positions in IEEE, Springer, Elsevier, Wiley and CRC. He presented 40+ keynote talks in reputed IEEE and Springer Conferences about WSNs, IoT and 5G. He is Senior Member, IEEE and Professional Member of ACM. Dr. Cengiz’ s awards and honors include the Tubitak Priority Areas PhD Scholarship, the Kadir Has University PhD Student Scholarship, best presentation award in ICAT 2016 Conference and best paper award in ICAT 2018 Conference.