Search

Browse Subject Areas

For Authors

Submit a Proposal

Nature-Inspired Intelligence for Complex Problems

Edited by Abhishek Kumar, Priya Batta, J.P.Ananth, Oswalt Manoj S, and T. Ananth Kumar
Copyright: 2026   |   Expected Pub Date:2026/08/30
ISBN: 9781394409709  |  Hardcover  |  
592 pages

One Line Description
Discover how to turn nature’s best problem-solving strategies into powerful computational tools with this comprehensive guide to building resilient, adaptive, and next-generation algorithms for healthcare, finance, and engineering.

Audience
Computer scientists, engineers, applied mathematicians, data scientists, and researchers in optimization and complex systems, as well as professionals in healthcare, energy, finance, and technology seeking innovative problem-solving approaches.

Description
Nature-inspired intelligence is a rapidly evolving field that draws from biological and physical phenomena, such as evolution, swarm behavior, neural processing, and immune systems, to develop algorithms capable of handling complexity, uncertainty, and scalability. Unlike conventional computational approaches, these techniques adapt dynamically, mimic resilience, and exhibit problem-solving strategies observed in nature. As industries face increasingly complex and data-intensive challenges, nature-inspired intelligence provides robust, efficient, and innovative solutions, positioning it as a cornerstone of future technological and scientific progress.
This book presents a comprehensive exploration of how biological, physical, and ecological principles can be transformed into powerful computational tools for solving some of today’s most challenging problems. Drawing inspiration from natural processes, the book highlights a broad spectrum of algorithms that push beyond traditional approaches to optimization and decision-making. Blending theory with application, the book demonstrates how nature-inspired intelligence can address complexity across domains including healthcare, energy, finance, engineering, and emerging technologies.
Readers will find the volume:
• Offers an in-depth exploration of a wide range of nature-inspired computational techniques, including evolutionary algorithms, swarm intelligence, neural models, and physics-inspired methods;
• Bridges the gap between natural systems and computational problem-solving, appealing to a diverse audience of researchers and practitioners;
• Features case studies in robotics, healthcare, finance, engineering, and environmental sustainability, and highlights how these algorithms are used to tackle practical challenges across industries;
• Addresses the latest advancements in combining multiple nature-inspired techniques and explores cutting-edge topics like quantum computing and bio-hybrid systems, ensuring the content remains relevant to current research and innovation.

Back to Top
Author / Editor Details
Abhishek Kumar, PhD is an Assistant Director and Professor in the Computer Science and Engineering Department, Chandigarh University, Mohali, Punjab, India. He has more than 230 publications to his credit, including books, chapters in books, and journal articles. His research interests span artificial intelligence, renewable energy systems, image processing, and data mining.

Priya Batta, PhD is an Associate Professor in the Computer Science and Engineering Department, Amity University, Mohali, Punjab, India. She received her Doctorate in Computer Science and Engineering from Chandigarh University. Her research specializes in artificial intelligence, blockchain, and IoT.

J.P. Ananth, PhD is a Professor and Director of the Internal Quality Assurance Cell, Dayananda Sagar University, Bangalore, India, with more than 23 years of experience. He has published more than 60 articles in international journals and conferences. His research interests include computer vision, pattern recognition, artificial intelligence, and data analytics.

S. Oswalt Manoj, PhD is an Associate Professor in the Department of Computer Science and Engineering, Alliance University, Bengaluru, Karnataka, India. He holds a Doctorate in Information Science and Engineering from Anna University in Chennai. His research areas include big data analytics, artificial intelligence, computer vision, machine learning, deep learning, and cloud computing.

T. Ananth Kumar, PhD is an Associate Professor and Research Head in Computer Science and Engineering, IFET College of Engineering, Villupuram, Tamil Nadu, India. He has more than 250 publications to his credit, including books, book chapters, and articles in international journals and conferences. His fields of interest include networks on chips, computer architecture, and ASIC design.

Back to Top

Table of Contents
Preface
Part I: Foundations and Introduction to Nature-Inspired Intelligence
1. Introduction to Nature-Inspired Intelligence
Simarpreet Kaur and Vikas Wasson
1.1 Overview of Nature-Inspired Computing
1.1.1 Classification of Nature-Inspired Computing Algorithms
1.2 The Need for Bio-Inspired Solutions
1.2.1 Bird Flocking (BF)
1.2.2 Fish School Search (FSS) Algorithm
1.2.3 Genetic Bee Colony (GBC) Approach
1.2.4 Whale Optimization Algorithm (WOA)
1.2.5 Artificial Algae Algorithm (AAA)
1.2.6 Elephant Search Algorithm (ESA)
1.2.7 Cuckoo Search Optimization Algorithm (CSOA)
1.2.8 Moth Flame Optimization (MFO)
1.2.9 Firefly Algorithm (FA)
1.2.10 Flower Pollination Algorithm (FPA)
1.2.11 Krill Herd (KH)
1.2.12 Crested Porcupine Optimizer (CPO)
1.3 Key Characteristics of Nature-Inspired Algorithms
1.4 Conclusion and Future Scope
List of Abbreviations
References
2. Exploring Swarm Intelligence: A Comparative Analysis of Nature-Inspired Optimization Techniques
Inderdeep Kaur and Aleem Ali
2.1 Introduction to Swarm Intelligence
2.2 Fundamentals of Swarm Intelligence
2.2.1 The Characteristics of Swarm Intelligence
2.2.2 Comparison with Traditional Optimization Techniques
2.2.3 Advantages and Challenges of Swarm-Based Approaches
2.2.3.1 Advantages
2.2.3.2 Challenges
2.3 Ant Colony Optimization (ACO)
2.3.1 Biological Inspiration: Ant Foraging Behavior
2.3.2 Algorithm Steps and Workflow
2.3.2.1 Pheromone Update Mechanism
2.3.3 Variants of ACO
2.3.3.1 Ant System
2.3.3.2 MAX-MIN Ant System
2.3.3.3 Ant Colony System
2.3.4 Applications of ACO
2.4 Particle Swarm Optimization (PSO)
2.4.1 Biological Inspiration: Social Behavior of Birds and Fish
2.4.2 Algorithm Steps
2.4.2.1 Role of Inertia Weight
2.4.3 Variants of PSO
2.4.4 Applications of PSO
2.4.5 Strengths and Limitations of PSO
2.4.5.1 Strengths
2.4.5.2 Limitations
2.5 Grey Wolf Optimizer (GWO)
2.5.1 Biological Inspiration: Social Hierarchy and Hunting
Behavior of Grey Wolves
2.5.2 Algorithm Steps and Workflow
2.5.2.1 Role of Alpha, Beta, Delta, and Omega Wolves with Examples
2.5.2.2 Encircling and Attacking Prey Mechanism
2.5.3 Variants of GWO
2.5.4 Applications of GWO
2.5.5 Strengths and Limitations of GWO
2.5.5.1 Strengths
2.5.5.2 Limitations of Grey Wolf Optimizer (GWO)
2.6 Comparative Analysis of Swarm Intelligence Algorithms
2.6.1 Evaluation
2.6.1.1 Evaluation Parameters
2.6.1.2 Real-World Problems
2.6.1.3 Statistical Analysis
2.6.2 Suitability for Different Domains
2.7 Applications of Swarm Intelligence Algorithms in Real-World Problems
2.7.1 Optimization Problems
2.7.1.1 Traveling Salesman Problem (TSP)
2.7.1.2 Vehicle Routing Problem (VRP)
2.7.1.3 Job Shop Scheduling Problem (JSSP)
2.7.2 Machine Learning and Data Mining
2.7.2.1 Hyperparameter Tuning
2.7.2.2 Feature Selection
2.7.3 Robotics and Engineering Design
2.7.3.1 Path Planning
2.7.3.2 Structural Optimization
2.7.4 Bioinformatics and Healthcare
2.7.4.1 Protein Folding
2.7.4.2 Drug Discovery
2.8 Challenges and Future Research Directions
2.9 Conclusion
References
3. Swarm Dynamics in Optimization: A Deep Dive into PSO
Benjamin Franklin S., Justin Jayaraj K., Monisha A., Balasubramaniam V., Sasi Kala and N.S. Kavitha
3.1 Particle Swarm Optimization (PSO)
3.1.1 Introduction
3.1.2 Exploration and Exploitation in Particle Swarm Optimization (PSO)
3.1.2.1 Exploration in PSO
3.1.2.2 PSO Exploitation
3.1.3 Challenges and Limitations of Particle Swarm Optimization (PSO)
3.1.3.1 Premature Convergence
3.1.4 Sensitivity to Parameter Settings
3.1.5 Performance in High-Dimensional Spaces
3.1.5.1 Computational Cost for Large Populations
3.2 Engineering Designs in PSO
3.2.1 Aerospace Engineering
3.2.2 Electrical Engineering
3.2.3 Mechanical Engineering
3.2.4 Automotive Engineering
3.2.5 Civil Engineering
3.2.6 Computer Science and Engineering
3.3 Variants of PSO
3.3.1 Introduction
3.3.1.1 Application of QPSO
3.3.1.2 Mathematical Model QPSO
3.3.2 Multi-Swarm Particle Swarm Optimization (MS-PSO)
3.3.2.1 Key Mechanisms in Multi-Swarm PSO
3.3.2.2 Mathematical Formulation of MS-PSO
3.3.2.3 Advantages of Multi-Swarm PSO
3.3.2.4 Application of MS-PSO
3.3.3 Hybrid Particle Swarm Optimization (HPSO)
3.3.3.1 Key Approaches in Hybrid PSO
3.3.3.2 Applications of Hybrid PSO
3.3.3.3 Conclusion of Hybrid PSO
3.4 Swarm Intelligence in PSO
3.4.1 Introduction
3.4.2 Major Principles of Swarm Intelligence in PSO
3.4.2.1 Decentralization and Self-Organization
3.4.2.2 Cooperation and Information Sharing
3.4.2.3 Balance between Exploration and Exploitation
3.4.2.4 Adaptability and Dynamic Behavior
3.5 PSO in Healthcare and Logistic
3.5.1 Introduction
3.5.2 PSO in Medical Diagnosis and Treatment Planning
3.5.2.1 PSO in Medical Resource Allocation
3.5.3 PSO in Route Optimization and Vehicle Scheduling
3.5.3.1 PSO in Warehouse and Inventory Management
3.6 Enhancements in PSO for Improved Performance
3.6.1 Introduction
3.6.2 Hybridization and Diversity Maintenance in PSO
3.6.3 Adaptive Parameter Tuning
3.6.4 Hybridization with Other Algorithms
3.6.5 Diversity Maintenance Techniques
3.6.6 Future Scope and Conclusion
Bibliography
4. Genetic Algorithms: Fundamentals and Applications
Satya Reddy Satti, Chanchal Alam, Ajay Sharma and Shamneesh Sharma
Introduction
Fundamentals of Genetic Algorithms
Chromosome Representation
Fitness Function
Selection
Crossover
Mutation
Termination Criteria
Mathematical Foundations of Genetic Algorithms
Schema Theorem and Building Block Hypothesis
Markov Chain Analysis
Population Genetics in GAs
Multi-Objective Optimization
Mutation and Diversity Maintenance
Convergence Analysis
Example: Analyzing Schema Survival
Advanced Techniques and Hybrid Models in Genetic Algorithms
Applications of Genetic Algorithms
Challenges and Mitigations
Practical Implementation for Genetic Algorithms
Problem Definition
Implementation
Future Directions in Genetic Algorithm Research
Integration with Quantum Computing
Neuroevolution and Artificial General Intelligence (AGI)
Ethical AI
Edge Computing
Explainable AI (XAI)
Climate and Sustainability Applications
Collaborative Human-GA Systems
Conclusion
References
5. Challenges and Future Directions in Nature-Inspired Intelligence
Thayanithi C.A., Elipe Arjun and Priyanka Singh
5.1 Introduction
5.1.1 Overview of Nature-Inspired Intelligence
5.1.2 Current State of the Field
5.2 Contemporary Challenges
5.2.1 Algorithmic Limitations
5.2.1.1 Convergence Issues
5.2.1.2 Scalability Problems
5.2.1.3 Parameter Optimization
5.2.2 Implementation Barriers
5.2.2.1 System Integration
5.2.2.2 Resource Constraints
5.2.2.3 Real-Time Processing
5.2.2.4 Standardization Issues
5.2.3 Application-Specific Challenges
5.2.3.1 Domain Adaptation
5.2.3.2 Reliability and Robustness
5.2.3.3 Practical Implementation
5.2.3.4 Integration with Existing Systems
5.3 Emerging Technologies and Trends
5.3.1 Quantum Computing Integration
5.3.1.1 Quantum-Inspired Algorithms
5.3.1.2 Hybrid Quantum-Classical Systems
5.3.1.3 Bio-Quantum Interfaces
5.3.2 AI and Machine Learning Convergence
5.3.2.1 Deep Learning Integration
5.3.2.2 Reinforcement Learning Applications
5.3.2.3 Neural Architecture Optimization
5.3.3 Advanced Computing Paradigms
5.3.3.1 Edge Computing
5.3.3.2 Distributed Systems
5.3.3.3 Cloud Integration
5.4 Future Research Directions
5.4.1 Algorithm Development
5.4.1.1 Hybrid Approaches
5.4.1.2 Self-Adaptive Systems
5.4.1.3 Multi-Objective Optimization
5.4.2 Application Domains
5.4.2.1 Smart Cities and Urban Planning
5.4.2.2 Healthcare and Drug Discovery
5.4.2.3 Environmental Sustainability
5.4.2.4 Industrial Optimization
5.4.3 Methodological Advances
5.4.3.1 Benchmarking Standards
5.4.3.2 Validation Frameworks
5.4.3.3 Performance Metrics
5.5 Implementation Strategies
5.5.1 Technical Considerations
5.5.1.1 Architecture Design
5.5.1.2 Resource Management
5.5.2 Development Frameworks
5.5.2.1 Open-Source Tools
5.5.2.2 Software Libraries
5.5.2.3 Development Guidelines
5.5.3 Best Practices
5.5.3.1 Code Optimization
5.5.3.2 Testing Strategies
5.5.3.3 Documentation Standards
5.6 Impact Analysis
5.6.1 Technological Impact
5.6.1.1 Computational Efficiency
5.6.1.2 Innovation Potential
5.6.2 Societal Impact
5.6.2.1 Sustainability
5.6.2.2 Healthcare Advancement
5.6.2.3 Quality of Life
5.6.3 Economic Impact
5.6.3.1 Cost Reduction
5.6.3.2 Resource Optimization
5.6.3.3 Market Opportunities
5.7 Future Recommendations
5.7.1 Research Priorities
5.7.1.1 Short-Term Goals
5.7.1.2 Long-Term Objectives
5.7.1.3 Research Gaps
5.8 Conclusion
5.8.1 Summary of Key Points
5.8.2 Future Outlook
References
Part II: Methods and Hybrid Models
6. Hybrid Swarm Intelligence for Enhancing Optimization through Multi Swarm and Quantum Inspired Models in Decision Making and Robotics
Barakkath Nisha U., Yasir Abdullah R., Sindhu V., Raihana A.
and Anitha G.
6.1 Introduction
6.1.1 The Need for Optimized Decision-Making in Robotics and AI
6.1.2 Limitations of Traditional Single-Swarm Optimization Techniques
6.1.3 The Rise of Hybrid Swarm Intelligence for Superior
Optimization
6.1.4 Motivational Study
6.2 Background and Related Work
6.2.1 Traditional Swarm Intelligence Techniques
6.2.2 Evolution of Hybrid Swarm Intelligence Models
6.2.3 Quantum-Inspired Swarm Intelligence for Superior Decision-Making
6.2.4 Applications of Swarm Intelligence
6.3 Framework of Hybrid Swarm Intelligence
6.3.1 Multi-Swarm Optimization (MSO)
6.3.2 Quantum-Inspired Swarm Intelligence (QPSO)
6.3.3 Hybrid PSO-ACO Models
6.3.4 Pseudocode for Hybrid Multi-Swarm Optimization Models
6.4 Applications of Hybrid Swarm Intelligence
6.4.1 Industrial Automation
6.4.2 Medical Robotics
6.4.3 Decision Support Systems
6.4.4 Optimization in IoT and Smart Cities
6.5 Experimental Results and Performance Analysis
6.5.1 Challenges and Future Directions in Hybrid Swarm Intelligence
6.5.1.1 Challenges in Hybrid Swarm Intelligence
6.5.1.2 Future Directions in Hybrid Swarm Intelligence
6.6 Conclusion
References
7. Swarm Intelligence and Differential Evolution in Robotics and Decision-Making
Devendra Babu Pesarlanka, Abhinav Kumar, Ajay Sharma, Arun Malik and Shamneesh Sharma
7.1 Introduction
7.1.1 Importance of Decentralized Decision-Making
7.2 Fundamentals of Swarm Intelligence
7.2.1 Biological Inspiration of Swarm Intelligence
7.2.2 Principles of Self-Organization
7.3 Key Swarm Intelligence Algorithms
7.3.1 Ant Colony Optimization (ACO)
7.3.1.1 Biological Inspiration: Ant Foraging Behavior
7.3.2 Mechanism of ACO
7.3.2.1 Pheromone Trails and Staggery
7.3.2.2 Probabilistic Solution Construction
7.3.3 Applications of ACO
7.3.3.1 Traveling Salesman Problem (TSP)
7.3.3.2 Network Routing and Load Balancing
7.3.3.3 Resource Allocation in Decision-Making
7.3.4 Advantages and Limitations of ACO
7.4 Particle Swarm Optimization (PSO)
7.4.1 Biological Inspiration: Flocking and Swarming Behaviors
7.4.2 Mechanism of PSO
7.4.2.1 Particle Movement and Velocity Update
7.4.2.2 Personal Best and Global Best Strategies
7.4.3 Applications of PSO
7.4.3.1 Continuous Function Optimization
7.4.3.2 Machine Learning and Hyperparameter Tuning
7.4.3.3 Energy Management and Smart Grids
7.4.4 Advantages and Limitations of PSO
7.5 Applications of Swarm Intelligence in Robotics
7.5.1 Overview of Swarm Robotics
7.5.2 Autonomous Navigation and Path Planning
7.5.3 Task Allocation and Multi-Robot Coordination
7.5.4 Applications in Search and Rescue Missions
7.5.5 Challenges in Swarm Robotics
7.6 Swarm Intelligence in Decision-Making
7.6.1 Multi-Objective Optimization in Decision-Making
7.6.2 Real-Time Decision Support Systems
7.6.3 Swarm Intelligence in Logistics and Supply Chain Management
7.6.4 Applications in Healthcare Resource Allocation
7.6.5 Swarm Intelligence in Smart Cities
7.7 Challenges and Future Directions
7.7.1 Computational Complexity and Scalability Issues
7.7.2 Algorithmic Stability and Convergence Challenges
7.7.3 Hybrid Approaches: Integrating SI with Deep Learning
7.7.4 Ethical and Privacy Considerations in SI-Based Systems
7.8 Conclusion
7.8.1 Summary of Key Insights
7.8.2 The Future of Swarm Intelligence in Robotics and Decision-Making
References
8. Hybrid Nature-Inspired Systems: A Computational Intelligence Perspective
Anitha Subbarayan
8.1 Evolutionary Computation for Global Search Optimization
8.1.1 Genetic Algorithms (GA)
8.1.2 Differential Evolution (DE)
8.1.3 Particle Swarm Optimization (PSO)
8.1.4 Evolution Strategies (ES)
8.1.5 Comparative Performance Analysis
8.2 Swarm Intelligence in Local Search and Refinement
8.2.1 Particle Swarm Optimization (PSO)
8.2.2 Ant Colony Optimization (ACO)
8.2.3 Artificial Bee Colony (ABC)
8.2.4 Firefly Algorithm (FA)
8.2.5 Cuckoo Search Algorithm (CS)
8.2.6 Evaluation of Swarm Intelligence Algorithms
8.2.7 Applications of Swarm Intelligence in Local Search and Refinement
8.3 Neuro-Evolutionary Models for Adaptive Learning
8.3.1 Neural Networks (NN)
8.3.2 Evolutionary Algorithms (EA)
8.3.3 Adaptive Learning Mechanisms
8.4 Hybridization Strategies for Balancing Exploration and Exploitation
8.5 Co-Evolutionary and Memetic Algorithms
8.6 Applications of Hybrid Nature-Inspired Systems
8.6.1 Engineering
8.6.2 Healthcare & Biomedical Research
8.6.3 Agriculture & Food Security
8.6.4 Renewable Energy & Sustainability
8.6.5 Aerospace & Defense
8.6.6 Telecommunications & Network Optimization
8.6.7 Transportation & Autonomous Systems
8.6.8 Education & E-Learning
8.6.9 Entertainment & Media
8.7 Performance Metrics and Computational Efficiency of Hybrid Nature-Inspired Systems
References
9. Optimizing Engineering Systems: Differential Evolution
Algorithm and Hybrid Approaches for PID Controller
G. Saravanan, C. Pazhanimuthu, P.N. Senthil Prakash and N.R. Wilfred Blessing
9.1 Introduction
9.2 Related Works
9.3 Algorithms
9.3.1 Differential Evolutionary Algorithm
9.3.2 Hybrid DEA
9.4 System Model
9.4.1 Automotive Cruise Control System Model
9.4.2 DC Motor Control System
9.4.3 Liquid Level Control System
9.4.4 PID Controller
9.5 Simulation Results and Discussion
9.5.1 Simulation and Analysis
9.5.2 Transient Analysis
9.5.3 Robustness Analysis
Conclusion
References
10. Novel Aspects of Ant Colony Optimization and Particle
Swarm Optimization
Rohan Gupta and Gurpreet Singh
10.1 MANET Routing Strategies
10.2 Routing Protocols
10.2.1 Destination Sequenced Distance Vector (DSDV)
10.2.2 Dynamic Source Routing (DSR)
10.2.3 Ad-Hoc On-Demand Distance Vector (AODV)
10.2.4 Ad-Hoc On-Demand Multipath Distance (AOMDV)
10.2.5 Optimized Device State Routing (OLSR)
10.2.6 Zone Routing Protocol (ZRP)
10.2.7 The Wireless Routing Protocol (WRP)
10.2.8 Cluster Based Routing Protocol (CBRP)
10.3 Ant Based Routing Protocols
10.3.1 AntHocNet
10.3.2 AntNet
10.3.3 Probabilistic Emergent Routing Algorithm (PERA)
10.4 PSO Routing Protocols
10.4.1 PSO-AODV Routing Protocol
10.4.2 PSO-LBR (Particle Swarm Optimization-Based Load Balancing Routing)
10.4.3 PSO-Based Energy-Efficient Routing Protocol (EERPSO)
10.4.4 PSO-Based QoS Routing Protocol
10.4.5 PSO-Based Multipath Routing Protocol
10.4.6 PSO-Based Secure Routing Protocol
10.5 Hybrid Routing Protocols
10.5.1 HOPNET
10.5.2 The Hybrid Ad Hoc Direction-Finding Protocol (HARP)
10.6 Results and Discussion
10.7 Conclusion
References
11. Physics-Inspired Algorithms: Applications in Energy and Environmental Systems
Naman Srivastava, Samyak Varia, Scaria Alex, Aswathy K. Cherian, Ashwini S. and Arshey M.
11.1 Introduction
11.1.1 The Increasing Complexity of Energy Grids and Environmental Systems
11.1.2 The Need for Intelligent Computational Techniques to Enhance Decision-Making and Optimization
11.1.3 Limitations of Traditional Optimization Methods
11.1.4 Challenges in Handling Nonlinearity, Uncertainty, and Large-Scale Data
11.1.5 Issues with Deterministic and Gradient-Based Methods
11.2 Foundations of Physics-Inspired Algorithms (PIAs)
11.2.1 The Science Behind PIAs
11.2.2 The Physical Foundations of PIAs
11.2.2.1 Science of Heat and Energy
11.2.2.2 Fluid Dynamics—Learning from Water and Air
11.2.2.3 Quantum Mechanics—The Influence of Small Elements
11.2.2.4 Mechanics and Swarm Intelligence—Moving Together Towards Solutions
11.3 Application of Physics-Inspired Algorithms (PIAs) in Energy Systems [1492 and 0%]
11.3.1 Smart Grid Optimization
11.3.2 Demand-Side Management (DSM)
11.3.3 Load Balancing and Scheduling
11.3.4 Renewable Energy Integration
11.3.4.1 Forecasting of Wind and Solar Power
11.3.4.2 Distributed Energy Resource Optimization
11.3.4.3 Microgrid Stability and Control
11.3.5 Battery Energy Management in the Hybrid Renewable Energy System (HRES)
11.3.5.1 Energy Storage Optimization within EMS
11.3.5.2 Duty Cycle Scheduling for Electric Vehicles (EVs) Coupled with the HRES
11.4 Application of PIAs in Environmental Systems
11.4.1 Climate Modeling and Prediction
11.4.1.1 Simulation of Climate Patterns Using PIAs
11.4.1.2 Extreme Weather Events Prediction
11.4.2 Pollution Control and Emission Mitigation
11.4.2.1 Monitoring Air and Water Pollution
11.4.2.2 Optimization of Industrial Processes to Reduce the Manufacturing Carbon Footprint
11.4.2.3 Reduced Strategies of the Greenhouse Gas Emission
11.4.3 Water Resource Management
11.5 Case Studies and Real-Life Implementations
11.5.1 Case Study: Smart Grid Optimization by Particle Swarm Optimization (PSO)
11.5.2 Case Study: Renewable Energy Forecasting Using Quantum-Inspired Algorithms
11.5.3 Case Study: Real-Time Thermal Process Monitoring and Prediction Using Physics-Informed Neural Networks
11.6 Challenges and Way Forward
11.6.1 Computational Complexity and Scalability Issues
11.6.2 Hybrid Approaches: PIAs + Machine Learning and Big Data Analytics
11.6.3 Integration with Quantum Computing for Enhanced Optimization
11.6.4 Policy and Regulatory Considerations in Energy and Environment Applications
11.7 Conclusion
11.7.1 Future Research Opportunities
11.7.2 Role of PIAs in Advancing Sustainable Energy and Environmental Solutions
References
Part III: Applications Across Domains
12. Optimization-Driven Deep CNN with PFCM Clustering for Enhanced MRI-Based Brain Tumor Detection
P. Sathish, Sashikanth Reddy Avula and Channabasava
12.1 Introduction
12.2 Related Work
12.3 Proposed Exponential Cuckoo-Based DCNN for Automatic Brain Tumor Classification
12.3.1 Pre-Processing
12.3.2 Segmentation Using FCM Clustering
12.3.3 Feature Extraction
12.3.4 Optimization-Based DCNN for Brain Tumor Classification
12.3.5 Training Phase
12.4 Discussion of Results
12.4.1 Investigational Analysis
12.4.2 Implementation Metrics
12.4.3 Investigational Results
12.4.4 Implementation Analysis
12.4.5 Analysis on BRATS Dataset
12.4.6 Analysis on SIMBRATS Dataset
12.5 Summary
References
13. Explainable AI and Ensemble Learning for Genetic Disorder Diagnosis Advancing Accuracy and Interpretability in Healthcare Predictions
Ishdeep and Neetu Rani
13.1 Introduction
13.2 Literature Review
13.3 Materials and Methods
13.3.1 Tools and Libraries
13.3.2 Data Collection & Exploratory Data Analysis (EDA)
13.3.3 Null Management
13.3.4 Feature Selection
13.3.5 Workflow
13.4 Results and Discussion
13.4.1 Addressing Model Interpretability in Sensitive Healthcare Applications
13.4.2 Bar Plots
13.4.3 Waterfall Plots
13.5 Conclusion
13.6 Future Scope
References
14. Optimizing Complex Weights of Linear Antenna Array for Combating Real World Wireless Traffic Congestion
Surekha Rani and Himanshu Sharma
14.1 Introduction
14.2 Problem Formulation
14.2.1 Polynomial Notation of Array Factor for Amplitude Calculation
14.2.2 Trigonometric Notation of Array Factor for Phase Calculation
14.2.3 Algorithm for Complex Weight Method
14.3 Simulation and Results
14.3.1 Null Steering by Schelkunoff Polynomial Method
14.3.2 Null Steering by Phase Control Method
14.3.3 Null Steering by Complex Weight Method
14.3.4 Comparisons among Different Methods
14.4 Conclusion and Future Scope
Bibliography
15. Nature-Inspired Intelligence for Enhanced Disease Detection in Medical Image Analysis
R. Karthick Manoj, Aasha Nandhini S. and D. Lakshmi
15.1 Introduction
15.2 Related Work
15.3 Proposed Methodology
15.4 Result and Discussion
15.4.1 Performance Evaluation
15.5 Conclusion and Future Work
References
16. Nature-Inspired Hybrid Model for Dysgraphia Diagnosis in Educational Settings
A. Devi, B. Elizebeth Caroline, J. Vidhya, D. Sathish Kumar,
T.D. Subha and L. Manimegalai
16.1 Introduction
16.1.1 Different Types of SLD
16.1.2 SLD Problem in and Around the World
16.1.3 Need for Dysgraphia Identification
16.1.4 Causes and Symptoms of Dysgraphia
16.1.5 Scope of the Research Study
16.1.6 Organization of the Chapter
16.2 Related Works
16.3 Proposed Hybrid Model
16.3.1 Data Collections
16.3.2 Preprocessing
16.3.3 Proposed DL Models for Dysgraphia Identification
16.3.3.1 Traditional U-Net
16.3.3.2 ResNet-50
16.3.3.3 DenseNet121
16.3.3.4 MHSA U-Net
16.4 Feature Selection Using ACO
16.4.1 Rationale for Using ACO in Feature Selection
16.4.2 Ant Colony Optimization (ACO): Overview
16.5 Results and Discussions
16.5.1 Training and Testing Phases
16.5.2 Hyperparameter Tuning Using PSO
16.5.2.1 PSO-Based Hyperparameter Optimization Process
16.5.3 Performance Metrics
16.5.4 Analysis of Handwritten Text Images
16.5.5 Analysis of Model Performance Based on Confusion Matrices
16.5.6 Analysis of Training and Testing Accuracies Comparison
16.6 Conclusion
References
17. Particle Swarm Optimization for Effective Feature Selection in Smart Logistics
Asha K. and Nakul Ramesh Varma
17.1 Introduction
17.2 Particle Swarm Optimization
17.2.1 Swarm Intelligence for Smart Logistics
17.2.2 Relevance of PSO over Other Algorithms
17.2.3 Feature Selection
17.2.4 Modeling of Smart Logistics Using PSO
17.3 Literature Survey
17.4 Computational Analysis on Realtime-Case
17.4.1 Vehicle Routing Optimization
17.4.2 Warehouse Management
17.4.3 Inventory Management
17.4.4 Supply Chain Optimization
17.5 Legal and Ethical Considerations
17.6 Methodology
17.7 Implementation and Results
17.7.1 Dataset Used
17.7.2 Visualization of Pareto Fronts
17.8 Conclusion
Bibliography
18. The Integration of IoT and Blockchain for Enhanced Security and Real-Time Updates
Priya Batta and Abhishek Kumar
18.1 Introduction
18.2 Related Works
18.3 Proposed Methodology
18.4 Results and Discussions
18.5 Conclusion and Future Scope
References
19. Advancing Rehabilitation with Virtual Reality
Charu Chhabra, Fowquiya, Sohrab A. Khan and Ifra Aman
19.1 Introduction to Virtual Reality
19.2 Methodology
19.3 Literature
19.3.1 Virtual Reality in Healthcare
19.3.2 Use of Virtual Reality for Rehabilitation
19.3.2.1 Virtual Reality for Diagnostics and Evaluation
19.3.2.2 Virtual Reality for Posture, Balance and Gait
19.3.2.3 Virtual Reality as an Intervention Tool
19.3.2.4 Virtual Reality for Motor Cognition Sequencing
19.3.2.5 Virtual Reality for Balance Training
19.3.2.6 Virtual Reality for Motor Training
19.3.2.7 Virtual Reality for Burns and Phantom Limb Pain
19.3.3 Domain Specific Application of Virtual Reality
19.3.3.1 Virtual Reality in Neurorehabilitation
19.3.3.2 Virtual Reality in Musculoskeletal Rehabilitation
19.3.3.3 Virtual Reality in Sports Rehabilitation
19.3.3.4 Virtual Reality in Cardiopulmonary Rehabilitation
19.3.3.5 Virtual Reality in Telerehabilitation
19.3.4 Ethical Issues with Virtual Reality
19.4 Discussion
19.5 Result
19.6 Conclusion
References
Part IV: Case Studies and Specific Implementations
20. AI for Preserving Indian Knowledge Systems and Philosophy
Aditya Atal, Shaurya Sharma and G.Y. Rajaa Vikhram
20.1 Introduction
20.2 AI in Preserving Ancient Hindu Texts and Literature
20.3 AI-Driven Religious Chatbots and Q&A Systems
20.4 AI and Digital Preservation of Oral Traditions and Folklore
20.5 AI in Ayurveda and Traditional Healing
20.6 AI in Yoga and Meditation Guidance
20.7 AI-Powered Knowledge Systems for Hindu Ethics and Philosophy
20.8 Role of AI in Hindu Astrology and Vedic Mathematics
20.9 Ethical and Theological Considerations in AI-Based Hindu Studies
20.10 Role of Blockchain and Quantum Computing in Hindu Knowledge Systems
20.11 Future Scope and Challenges
20.12 Conclusion and Research Directions
20.13 Research Gaps and Areas for Further Exploration
References
21. Nature-Inspired Algorithms and Their Applications: A Healthcare Case Study with the Bee Algorithm
Puneet Kumar and Deepika Kumar
21.1 Introduction
21.1.1 Key Characteristics of Nature-Inspired Algorithms
21.2 Classification of Nature-Inspired Algorithms
21.2.1 Swarm Intelligence Algorithms
21.2.2 Bio-Inspired Evolutionary Algorithms
21.2.3 Physics- and Chemistry-Inspired Algorithms
21.2.4 Other Nature-Inspired Algorithms
21.3 Bees Algorithm: Foundation
21.3.1 Algorithm Steps
21.3.2 Benefits of the Bees Algorithm
21.3.3 Applications of the Bees Algorithm
21.4 Case Study: Bees Algorithm in Healthcare
21.4.1 Problem Description and Dataset
21.4.2 Methodology
21.4.3 Results and Performance Evaluation
References
Index

Back to Top



Description
Author/Editor Details
Table of Contents
Bookmark this page