Hybrid Computational Intelligent Systems: Modeling, Simulation and Optimization

Description

Hybrid Computational Intelligent Systems – Modeling, Simulation and Optimization unearths the latest advances in evolving hybrid intelligent modeling and simulation of human-centric data-intensive applications optimized for real-time use, thereby enabling researchers to come up with novel breakthroughs in this ever-growing field. Salient features include the fundamentals of modeling and simulation with recourse to knowledge-based simulation, interaction paradigms, and human factors, along with the enhancement of the existing state of art in a high-performance computing setup. In addition, this book presents optimization strategies to evolve robust and failsafe intelligent system modeling and simulation. The volume also highlights novel applications for different engineering problems including signal and data processing, speech, image, sensor data processing, innovative intelligent systems, and swarm intelligent manufacturing systems. Features: A self-contained approach to integrating the principles of hybrid computational ntelligence with system modeling and simulation Well-versed foundation of computational intelligence and its application to real life engineering problems Elucidates essential background, concepts, definitions, and theories thereby putting forward a complete treatment on the subject Effective modeling of hybrid intelligent systems forms the backbone of almost every operative system in real-life Proper simulation of real-time hybrid intelligent systems is a prerequisite for deriving any real-life system solution Optimized system modeling and simulation enable real-time and failsafe operations of the existing hybrid intelligent system solutions Information presented in an accessible way for researchers, engineers, developers, and practitioners from academia and industry working in all major areas and interdisciplinary areas of hybrid computational intelligence and communication systems to evolve human-centered modeling and simulations of real-time data-intensive intelligent systems. Cover Half Title Series Page Title Page Copyright Page Dedication Table of Contents Preface Editors Contributors 1 Creating Ratings of Agricultural Universities based on their Digital Footprint 1.1 Introduction 1.2 Methodology for the Formation of University Rankings Based on the Passive Digital Footprint 1.3 Methodology for Generating Rating Based On a Semi-Active Digital Footprint 1.4 Methodology for Generating Ratings Based on an Active Digital Footprint 1.5 Rating Calculation Results 1.6 Conclusion References 2 Mechatronic Complex’s Fuzzy System for Fixating Moving Objects 2.1 Introduction 2.2 Composition and Principle of Operation of the MC 2.3 Kinematic Model of the MC 2.4 Fuzzy SISO-system 2.4.1 Fuzzification 2.4.2 Input and output Membership Functions 2.4.3 Defuzzification 2.5 Experimental Research 2.6 Conclusions References 3 Quad Sensor-based Soil-moisture Prediction using Machine Learning 3.1 Introduction 3.2 Related Works 3.3 Proposed System 3.3.1 Sensors used in the Proposed Framework 3.3.2 Hardware Architecture 3.3.3 Dataset 3.3.4 Data Analysis 3.3.5 Performance Evaluation Measures 3.4 Results and Discussion 3.5 Conclusion References 4 Stability Analysis for a Diffusive Ratio-dependent Predator-prey Model Involving Two Delays 4.1 Introduction 4.2 Stability Analysis 4.3 Examples and Simulations 4.4 Conclusion Acknowledgment References 5 Analysis and Prediction of Physical Fitness test Data of College Students based on Grey Model 5.1 Introduction 5.2 Grey Model Related Theories 5.2.1 Introduction to the Grey Model 5.2.2 Grey GM (1,1) Prediction Model Algorithm 5.2.3 Grey Forecasting Model Accuracy Test 5.3 Analysis and Prediction of Physique Based on Grey Model 5.3.1 Data Preprocessing 5.3.2 Data Analysis of Student Fitness Test 5.3.3 Prediction of Student Physical test data Based on Grey Model 5.3.4 Prediction of Physique Development Trend based on Grey Model 5.4 Conclusion Acknowledgment References 6 Analysis and Research on Book Borrowing Tendency based on Apriori Algorithm 6.1 Introduction 6.2 Relevant Theories 6.2.1 Association Rules 6.2.2 Apriori Algorithm 6.3 Analysis of Book Borrowing Data based on Apriori Algorithm 6.3.1 Data Preprocessing 6.3.2 Description and Analysis of Influencing Factors of book Borrowing Volume 6.3.2.1 Relationship Between Book Types and book Borrowing 6.3.2.2 Relationship Between Grade and book Borrowing 6.3.2.3 Relationship between time Period and book Borrowing 6.3.2.4 Relationship between College and book Borrowing 6.3.2.5 Relationship Between Major and book Borrowing 6.3.2.6 Borrowing Preference 6.3.3 Implementation of Apriori Algorithm based on Python 6.3.4 Visualization of Apriori Model 6.3.5 Evaluation and Application of Borrowing Data Tendency 6.4 Conclusion Acknowledgment References 7 Performance Evaluation of Cargo Inspection: Systems with the Function of Materials Recognition 7.1 Introduction 7.2 Key Parameters and Features of High Energy IS with Material Recognition 7.3 Mathematical Model of IS with Dual Energy Recognition of Materials 7.4 Performance Estimation of IS with Material Recognition 7.5 Conclusions and Results References 8 Automated Medical Report Generation on Chest X-ray: Images using Co-attention Mechanism 8.1 Introduction 8.2 Materials and Methods 8.2.1 Dataset 8.2.2 Experimental Setup 8.2.2.1 Natural Language Processing (NLP) Pipeline 8.2.2.2 Convolutional neural Network 8.2.2.3 Recurrent Neural Network 8.2.2.4 Sequence-to-sequence Model with Co-attention Mechanism 8.3 Results and Discussion 8.4 Conclusions and Future Works References 9 An Energy-efficient Secured Arduino-based Home Automation using Android Interface 9.1 Introduction 9.2 Literature Review 9.3 System Architecture 9.3.1 Experimental Devices 9.3.1.1 RFID-RC522 Module 9.3.1.2 Servomotor 9.3.1.3 Arduino UNO 9.3.1.4 Bluetooth HC05 Module 9.3.1.5 Relay Switch 9.3.1.6 Android System 9.3.2 Process Diagram 9.4 Result and Discussion 9.4.1 Experimental Setup 9.4.2 Performance Analysis 9.5 Conclusion References 10 A Multithreaded Android App to Notify Available ‘CoWIN’ Vaccination Slots to Multiple Recipients 10.1 Introduction 10.2 Related Works 10.3 Detailed Description of Application 10.3.1 Functional Features 10.3.2 User Feedback Module 10.3.3 APIs Used in this Application 10.3.4 Design Descriptions 10.4 Advantages of ‘Amar Protishedhak Bondhu’ 10.5 Usability Analysis 10.6 Conclusions and Future Scopes References 11 Binary MMBAIS for Feature Selection Problem 11.1 Introduction: Feature Selection and Metaheuristic Algorithm 11.2 Related Work 11.3 MMBAIS Algorithm 11.4 Binary MMBAIS 11.4.1 Objective Function 11.4.2 Structure of Binary MMBAIS 11.5 Methodology 11.6 Experimental Setup 11.6.1 Datasets 11.6.2 Classification Methods 11.7 Result and Performance Analysis 11.8 Conclusion References 12 Audio to Indian Sign Language Interpreter (AISLI) using Machine Translation and NLP Techniques 12.1 Introduction 12.2 Literature Review 12.3 Speech Recognition 12.3.1 Google API 12.3.2 Sign Language 12.3.3 Indian Sign Language (ISL) and Alphabets 12.4 Implementing ISL in AISLI 12.4.1 AISLI Demonstration 12.5 Conclusions References 13 Fragile Medical Image Watermarking using Auto-generated Adaptive Key-based Encryption 13.1 Introduction 13.2 Proposed Methodology 13.2.1 Watermark Embedding 13.2.2 Extraction of Watermark and EPR 13.3 Results and Discussion 13.4 Conclusion References 14 Designing of a Solution Model for Global Warming and Climate Change using Machine Learning and data Engineering Techniques 14.1 Introduction 14.2 Related Work 14.3 Solution Technique 14.3.1 Prediction Technique Selection 14.4 Solution Architecture 14.5 Result and Analysis 14.6 Conclusion References 15 Human age Estimation using Sit-to-stand Exercise Data-driven Decision-making by Neural Network 15.1 Introduction 15.2 Literature Review 15.2.1 Motivation 15.2.1.1 Specific Contribution 15.3 Methodology 15.4 Results and Discussion 15.5 Conclusions and Future Scope References 16 Feature-based Suicide-ideation Detection from Twitter Data using Machine Learning Techniques 16.1 Introduction 16.2 Related Work 16.3 Proposed Methodology 16.3.1 Data Set Exploration and Preparation 16.3.2 Feature Extraction and Analysis 16.4 Empirical Evaluation 16.4.1 Classification Model 16.4.2 Classification Results 16.5 Conclusion and Future Scope References 17 Analyzing the Role of Indian Media during the Second wave of COVID using Topic Modeling 17.1 Introduction 17.2 Literature Survey 17.3 Methodology 17.3.1 Data Collection 17.3.2 Data Pre-processing 17.3.3 Topic Modeling 17.4 Results and Discussion 17.5 Conclusion References 18 Hardware-efficient FIR Filter Design using fast Converging flower Pollination Algorithm – A case Study of Denoising PCG Signal 18.1 Introduction 18.2 Problem Formulations 18.2.1 Specification of Desired Filter Characteristics 18.2.2 Filter Coefficients Computation 18.2.3 Design filter Architecture 18.3 Hardware-efficient FIR Filter Design using fast Convergingflower Pollination Algorithm 18.3.1 Flower Pollination Algorithm 18.3.2 Fast Converging Flower Pollination Algorithm 18.4 Simulation Results 18.5 Case Study 18.6 Conclusions Bibliography 19 Voice Recognition System using Deep Learning 19.1 Introduction 19.2 Related Works 19.3 Dataset 19.4 Methods 19.4.1 Detection and Fine-tuning 19.4.2 Finding the Optimal Window Size 19.4.3 Training the Model 19.5 Results and Analysis 19.6 Conclusions References 20 Modified Harris Hawk Optimization Algorithm for Multilevel image Thresholding 20.1 Introduction 20.2 Harris’ Hawks Optimization 20.3 Quantitative Evaluation Measure 20.3.1 Modified Otsu 20.3.2 Entropy Yen 20.4 Proposed Methodology 20.4.1 Exploration Phase 20.4.2 Transition from Exploration to Exploitation Phase 20.4.3 Exploitation Phase 20.5 Experimental Results 20.5.1 Details of the Experimental Data 20.5.2 Performance Measures 20.5.3 Results 20.6 Conclusion and Future Direction References 21 An Automatic Probabilistic Framework for Detection and Segmentation of Tumor in brain MRI Images 21.1 Introduction 21.2 Related Work 21.3 Proposed Framework and Methodology 21.4 Results and Discussion 21.5 Conclusion References 22 Comparative Study of Generative Adversarial Networks for Sensor data Generation-based Remaining Useful life Classification 22.1 Introduction and Motivation 22.2 Related Work 22.3 Proposed Technique 22.4 Research Methodology 22.5 Experimental Setup 22.6 Result and Discussion 22.7 Conclusion References 23 Toward a Framework for Implementation of Quantum-inspired Evolutionary Algorithm on Noisy Intermediate Scale Quantum Devices (IBMQ) for Solving Knapsack Problems 23.1 Introduction 23.2 Overview 23.2.1 Motivation 23.2.2 Knapsack Problem 23.2.3 QIEA 23.2.4 QIEA for Knapsack 23.3 Adaptation of EA on IBMQ 23.3.1 Quantum Routine 23.3.2 Quantum Circuit 23.3.3 Proposed Methodology 23.4 Framework and Experimentation Parameters 23.4.1 Framework 23.4.2 Experimentation 23.5 Results 23.5.1 Readings 23.5.2 Graphical Representation 23.5.3 Time Evaluation 23.5.4 Evaluation of Results 23.6 Conclusion Notes Bibliography Index