Artificial Intelligence and Robotics

Name: Artificial Intelligence and Robotics | Transformative and Computational Algorithms for Enhanced Problem Solving
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Transformative and Computational Algorithms for Enhanced Problem Solving

Suresh Kumar Krishnadhas Ananth Kumar Tamilarasan Pramod Singh Rathore Abhishek Kumar(Editor)

De Gruyter (Publisher)

1st Edition

Published on 30. March 2026

XVII, 569 pages

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Persons

Dr. Suresh Kumar Krishnadhas is an academician and researcher specializing in Artificial Intelligence, Machine Learning, Deep Learning, NLP, IoT, Blockchain, Networking, and Cybersecurity. He obtained his Ph.D. in Information and Communication Engineering from Anna University, M.Tech. from Manonmaniam Sundaranar University, and B.E. from Anna University. With over 16 years of teaching experience, he currently serves as Assistant Professor at Sri Eshwar College of Engineering (Autonomous), Coimbatore. He has published extensively in SCI/Scopus-indexed journals, conferences, and book chapters, with research interests in AI-driven healthcare, smart cities, image analysis, and quantum machine learning. He holds patents and copyrights, is a Red Hat Certified System Administrator, and is a member of IEEE, ISTE, SDIWC, IRED, and IAENG.

Dr. Ananth Kumar Tamilarasan is an Associate Professor and Research Head at IFET College of Engineering (Autonomous), India. He earned his Ph.D. in VLSI Design from Manonmaniam Sundaranar University, Tirunelveli, and completed his Master's and Bachelor's degrees from Anna University, Chennai. He has published research papers in national and international journals and conferences. His research interests include Networks-on-Chip, Computer Architecture, and ASIC Design. He has received several honors, including the Young Innovator Award, Young Researcher Award, Class A Award from IIT Bombay, and Best Paper Award at INCODS 2017. He holds multiple patents, is a life member of IEEE and ISTE, has edited six books, authored numerous book chapters with Springer, IET Press, and Taylor & Francis, and authored the book Evolutionary Intelligence for Healthcare Applications published by Taylor & Francis.

Dr. Pramod Singh Rathore is working in the Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India. He has completed his Ph. D. in Computer Science & Engineering from the University of Engineering and Management, Jaipur (UEM) and done M. Tech in Computer Sci. & Engineering from the Government Engineering College Ajmer, Rajasthan Technical University, Kota, India. With over 15 years of academic teaching experience, he has published more than 95 papers in reputable, peer-reviewed national and international journals, books, and conferences, including Wiley, IGI GLOBAL, Taylor & Francis, Springer, Elsevier Science Direct, Annals of Computer Science, and IEEE. He has co-authored and edited numerous books with well-known publishers such as Wiley, CRC Press, and USA. His research interests include computer networks, machine learning, and DBMS. He is a senior member of IEEE.

Dr. Abhishek Kumar is currently working as an Assistant Director and Professor in the Department of Computer Science & Engineering at Chandigarh University, Mohali, Punjab, India, and as a Senior Researcher at Ingeniot Lab, Universidad de Castilla-La Mancha (UCLM), Spain, since August 2025. He completed his Post-Doctoral Fellowship from the Ingenium Research Group Lab, Universidad de Castilla-La Mancha, Spain. With over 15 years of teaching and research experience, he has published more than 200 papers in reputed national and international journals, books, and conferences .

Content

Intro
Preface
Contents
List of Contributors
1 Mathematical and Statistical Techniques in Artificial Intelligence Designing AI Systems Grounded in Math and Statistics
1.1 Introduction to Mathematical and Statistical Techniques
1.2 Fundamentals of Probability and Statistics
1.2.1 Probability: The Foundation of Uncertainty
1.2.1.1 Probability of Events
1.2.1.2 Axioms of Probability
1.2.1.3 Joint, Conditional Probability, and Independence
1.2.2 Random Variables and Probability Distributions
1.2.2.1 Discrete Random Variables
1.2.2.2 Continuous Random Variables
1.2.2.3 Probabilistic Models in AI
1.2.2.4 AI Optimization Techniques
1.3 Optimization by the Gradient
1.3.1 Variants
1.3.2 Extensions
1.4 Optimization Through Heuristics
1.4.1 Convex Optimization
1.4.2 Optimization Using Combinatorial Methods
1.4.3 Optimization via Bayesian
1.4.4 Heuristic Search Methods
1.5 Heuristic Functions
1.5.1 Popular Heuristic Search Algorithms
1.5.2 Application in Optimization Problems
1.5.3 Handling Uncertainty and Probabilistic Heuristics
1.5.4 Continuous and High-Dimensional Spaces
1.6 Statistical Techniques in Machine Learning
1.6.1 Machine Learning Techniques
1.6.2 Optimization and Regularization
1.6.3 Applications of Mathematical and Statistical Techniques in AI
1.7 Advanced ML Techniques
1.7.1 Neural Networks and Deep Learning
1.7.2 Kernel Methods and Support Vector Machines (SVMs)
1.7.3 Ensemble Methods
1.7.4 Graphical Models Based on Probabilities
1.7.5 Reinforcement Learning (RL)
1.8 Reinforcement Learning: Concepts and Applications
1.8.1 Core Concepts of RL
1.8.1.1 The Environment and the Agent
1.8.1.2 State, Action, and Rewards
1.8.1.3 Policy (p)
1.8.2 Q-Function (Q) and Value Function (V)
1.8.3 Investigating as Opposed to Exploiting
1.8.4 Algorithms for Learning
1.8.5 Applications of Reinforcement Learning in AI and Statistics
1.8.6 Risk and Finance Management
1.9 Statistical Learning and Inference in AI
1.9.1 Core Concepts in Statistical Learning
1.9.1.1 Statistical Models and Probabilistic Assumptions
1.9.1.2 Supervised and Unsupervised Learning
1.9.1.3 Parameter Estimation
1.9.1.4 Regularization and Complexity Control
1.9.1.5 Model Evaluation and Selection
1.9.2 AI Statistical Inference
1.9.2.1 Hypothesis Testing and Significance Testing
1.9.2.2 Quantification of Uncertainty and Confidence Intervals
1.9.2.3 Bayesian Inference
1.9.2.4 Resampling and Bootstrapping
1.9.3 Applications of Statistical Learning in AI
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Forecasting and Predictive Modeling
NLP
Computer Vision
Identifying Anomalies
Recommendation Systems
1.9.4 High-Level Statistical Techniques of AI
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Models of Hierarchies
Bayesian Networks and Graphical Models with Probabilities
State-Space Models and HMMs
Gaussian Processes (GPs)
Causal Inference
1.10 Case Studies of Quantitative AI Applications
1.10.1 Predictive Maintenance in Manufacturing
1.10.2 Fraud Detection in Financial Services
1.10.3 Optimizing Supply Chain and Logistics with Predictive Analytics
1.10.4 Personalized Healthcare and Treatment Optimization
1.10.5 Credit Scoring and Risk Assessment in Banking
1.10.6 E-Commerce Dynamic Pricing Optimization
1.10.7 Predicting and Improving Energy Use in Smart Grids
1.11 Future Directions in Mathematical and Statistical AI
1.12 Conclusion
References
2 Linear Algebra in AI and Robotics
2.1 Introduction
2.2 Data Representation Technique Using Linear Algebra
2.3 Optimization and Machine Learning
2.3.1 Data Preprocessing and Noise Reduction
2.3.2 Feature Extraction
2.3.3 Visualization
2.3.4 Improving Model Efficiency
2.3.5 Pattern Recognition and Anomaly Detection
2.3.6 Compression in Image and Signal Processing
2.4 Neural Networks and Deep Learning
2.5 Control Systems in Robotics
2.6 Conclusion
References
3 Advanced Statistical Tools for Data Science
3.1 Introduction
3.1.1 Advanced Statistical Tools and its Application in Data Science
3.1.2 Advanced Statistical Tools in Robotics
3.2 Markov Chain Monte Carlo methods
3.3 Canonical Correlation Analysis (CCA)
3.4 Discriminant Analysis (LDA and QDA)
3.5 Multivariate ANOVA (MANOVA)
3.6 Mixed-Effects Models (Hierarchical Models)
3.6.1 The Structure of a Mixed-Effects Model
3.7 Linear Mixed-Effects Model Fit by ML
3.8 Survival and Hazard Models
3.9 Causal Inference
3.9.1 Fundamental Problem of Causal Inference, Confounding Variables, and Selection Bias, Along with Expanded Examples and Implications
3.9.2 Structural Causal Models (SCMs)
3.9.3 Do-Calculus: Mathematical Framework for Causal Inference
3.10 High-Dimensional Statistics
3.10.1 Lasso and Elastic Net Regression
3.10.2 Random Forests
3.10.3 Gradient Boosting Machines
3.11 Time Series Analysis
3.11.1 Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA)
3.11.2 Long Short-Term Memory (LSTM) Networks
3.12 Nonparametric Methods
3.12.1 Kernel Density Estimation
3.12.2 Spline Regression
References
4 The Heartbeat of Innovation: Investigating the Essential Role of Differential Equations in the Evolution of Digital Technologies
4.1 Introduction
4.1.1 Historical Context of Differential Equations
4.1.2 Evolution of Digital Technologies
4.1.3 Literature Survey
4.2 The Mathematical Foundations of Differential Equations
4.2.1 Types of Differential Equations
4.2.1.1 Ordinary Differential Equations (ODEs)
4.2.1.2 Partial Differential Equations (PDEs)
4.2.1.3 Linear Versus Nonlinear Differential Equations
4.2.1.4 Homogeneous Versus Nonhomogeneous Differential Equations
4.2.2 Methods of Solution and Analysis
4.2.2.1 Analytical Methods
4.2.2.2 Numerical Methods
4.3 Applications in Digital Technologies
4.3.1 Signal Processing and Communication Systems
4.3.1.1 Signal Representation and Filtering
4.3.1.2 System Dynamics and Response Analysis
4.3.1.3 Modulation and Demodulation Techniques
4.3.1.4 Signal Reconstruction and Sampling Theory
4.3.1.5 Adaptive Filtering and Machine Learning Applications
4.3.2 Control Systems and Robotics
4.3.2.1 Modeling Dynamic Systems
4.3.2.2 Control Design Techniques
4.3.2.3 Stability Analysis
4.3.2.4 Robotics and Kinematic Modeling
4.3.2.5 Path Planning and Motion Control
4.3.2.6 Adaptive and Intelligent Control
4.3.3 Machine Learning and Data Science
4.3.3.1 Dynamic Systems and State-Space Models
4.3.3.2 Learning Dynamics through Differential Equations
4.3.3.3 Neural Networks and Differential Equations
4.3.3.4 Regularization and Differential Equations
4.3.3.5 Partial Differential Equations in Data Analysis
4.3.3.6 Bayesian Inference and Stochastic Differential Equations (SDEs)
4.3.3.6.1 Stochastic Processes and Noise in Machine Learning
4.4 Case Studies
4.4.1 Differential Equations in Engineering Applications
4.4.1.1 Mechanical Vibrations
4.4.1.1.1 Case Study: Spring-Mass-Damper System
4.4.1.2 Electrical Circuit Analysis
4.4.1.2.1 Case Study: RLC Circuit
4.4.1.3 Structural Analysis
4.4.1.3.1 Case Study: Beam Bending
4.4.1.4 Fluid Dynamics
4.4.1.4.1 Case Study: Navier-Stokes Equations
4.4.1.5 Control Systems in Robotics
4.4.1.5.1 Case Study: Inverted Pendulum
4.4.2 Real-World Examples in Computer Science
4.4.2.1 Image Processing
4.4.2.1.1 Example: Image Denoising
4.4.2.2 Neural Networks and Deep Learning
4.4.2.2.1 Example: Neural Ordinary Differential Equations (ODEs)
4.4.2.3 Robotics and Path Planning
4.4.2.3.1 Example: Optimal Control in Robotic Arms
4.4.2.4 Network Theory and Graph Algorithms
4.4.2.4.1 Example: Epidemic Modeling on Networks
4.4.2.5 Computational Fluid Dynamics (CFD)
4.4.2.5.1 Example: Simulating Airflow Around Objects
4.5 Challenges and Limitations
4.5.1 Numerical Methods and Computational Complexity
4.5.1.1 Numerical Solution of Differential Equations
4.5.1.2 Stiffness in Differential Equations
4.5.1.3 Computational Complexity
4.5.1.4 Resource Requirements
4.5.1.5 Error Propagation and Stability
4.5.2 Addressing Nonlinear Dynamics
4.5.2.1 Nature of Nonlinear Dynamics
4.5.2.2 Challenges of Nonlinear Dynamics
4.5.2.3 Approaches to Address Nonlinear Dynamics
4.5.2.3.1 Numerical Methods
4.5.2.3.2 Approximation Techniques
4.5.2.3.3 Bifurcation Analysis
4.5.2.3.4 Machine Learning and Data-Driven Approaches
4.5.2.3.5 Lyapunov Stability Theory
4.6 Future Trends and Innovations
4.6.1 Emerging Applications in AI and Big Data
4.6.1.1 Enhanced Machine Learning Models
4.6.1.2 Predictive Analytics and Time Series Forecasting
4.6.1.3 Optimization and Control in Smart Systems
4.6.1.4 Data-Driven Discovery of Dynamics
4.6.1.5 Computational Efficiency and Scalability
4.6.2 The Role of Differential Equations in Next-Generation Technologies
4.6.2.1 Quantum Computing
4.6.2.2 Biotechnology and Systems Biology
4.6.2.3 Advanced Materials and Manufacturing
4.6.2.4 Environmental Modeling and Climate Science
4.6.2.5 Robotics and Autonomous Systems
4.7 Ethical Implications of Mathematical Models in Digital Technologies
4.7.1 Ethical Challenges Raised through Mathematical Models
4.7.2 Using Mathematical Models to Mitigate Ethical Problems
4.8 Conclusion
4.8.1 Summary of Key Insights
4.8.2 The Continued Relevance of Differential Equations in Digital Evolution
References
5 Mathematical Tools for Digital Signal Processing
5.1 Introduction
5.2 Comprehending Signal Analysis
5.2.1 Complex Analysis Function in Signal Processing
5.2.2 The Delta Function in Signal Analysis
5.2.3 Fourier Series with Half-Range Expansions and Fourier Series in Signal Analysis
5.2.4 Fourier and Alternative Integral Transforms in Signal Analysis
5.3 Computational Linear Algebra in Digital Signal Analysis
5.3.1 Matrix Theory and Algebra in DSP
5.3.2 Direct Approaches to Resolution in Digital Signal Analysis
5.3.3 Vector and Matrix Standards in Digital Signal Analysis
5.3.4 Digital Signal Analysis Iterative Methods of Solution
5.3.5 Wavelets and Iterative Methods in Quantitative AI
5.4 Software Engineering and Programming in DSP and Analysis
5.4.1 Software Engineering in DSP
5.4.2 Enhancement of DSP Algorithms
5.4.3 Software Tools and Frameworks for DSP
5.4.4 Utilization of DSP in Practical Systems
5.5 DSP: Techniques, Algorithms, and Library Development in Signal Analysis
5.5.1 An Overview of DSP
5.5.2 Algorithms in DSP
5.5.3 Constructing a DSP Library: Procedures and Elements
5.5.4 Utilization of DSP Libraries
5.5.5 Comparative Analysis: DSP Techniques and Algorithms
5.5.6 DSP-AI Real-Time Applications and Future Research Directions
5.6 Conclusion
References
6 The Role of Differential Equation in Digital Technologies
6.1 Overview of Differential Equations
6.1.1 Historical Evolution and Relevance in Digital Systems
6.1.2 Chapter Objectives and Scope
6.2 Fundamentals of Differential Equations
6.2.1 Initial and Boundary Conditions
Common Solution Methods
Linear Versus Nonlinear Equations
6.3 Modeling Digital Technologies with Differential Equations
6.3.1 Signal Processing and Filter Design
6.3.2 Circuit Analysis and RLC Networks
6.3.3 Control Systems in Automation
6.4 Application in Digital Communication Systems
6.4.1 Wave Propagation and Transmission Lines
6.4.2 Modulation Techniques and Channel Modeling
6.4.3 Error Prediction and Correction Dynamics
6.5 Differential Equations in Computer Graphics and Vision
6.5.1 Image Processing and Edge Detection
6.5.2 Heat Equation for Denoising
6.5.3 Shape Deformation and Morphing Techniques
6.6 Use in Machine Learning and AI
6.6.1 Neural Differential Equations
6.6.2 Gradient Dynamics and Optimization
6.6.3 Reinforcement Learning Models
6.7 Simulation Tools and Computational Techniques
6.7.1 Finite Difference and Finite Element Methods
6.7.1.1 Finite Difference Method
6.7.1.2 Finite Element Method (FEM)
6.7.2 MATLAB, Simulink, and Python Libraries
6.7.2.1 MATLAB
6.7.2.2 Simulink
Python Libraries
6.7.3 Stability and Convergence Analysis
6.7.3.1 Convergence
6.8 Case Studies and Practical Implementations
6.8.1 Smart Sensors and IoT Devices
6.8.2 Real-Time Systems Modeling
6.8.3 Biomedical Signal Analysis
Example: ECG Signal Filtering (Denoising)
Example: Glucose-Level Prediction in Diabetes
6.9 Challenges and Future Trends
6.9.1 Computational Complexity and Real-Time Constraints
6.9.1.1 Problem 1: High Computational Load
6.9.1.2 Problem 2: Real-Time Response Constraint
6.9.2 Hybrid Modeling Approaches
6.9.3 Integration with Quantum and Edge Computing
6.9.3.1 Quantum Computing
6.9.3.2 Edge Computing
6.10 Conclusion
References
7 Disease Prediction with Decision Trees Optimized by MapReduce Algorithm
7.1 Introduction
7.1.1 Predicting Diseases with MapReduce's Algorithm
7.1.2 MapReduce's Benefits for Disease Prediction
7.1.3 Applications in Healthcare
7.2 Learning Algorithm about Disease Prediction
7.2.1 Real-Time Disease Prediction with Decision Trees and MapReduce
7.2.2 Using Streaming Data for Real-Time Disease Prediction
7.2.3 Disease Prediction Using a MapReduce-Optimized Decision Tree Algorithm
7.3 Working of Disease Prediction
7.4 Implementation
7.5 Conclusion
References
8 AI-Assisted Predator-Prey Framework for Understanding COVID-19 Transmission Dynamics
8.1 Introduction
8.2 Model Formulation
8.3 Boundedness and Positivity of the Theorem
8.4 Analysis of Nonlinear Systems and Stability
8.4.1 Critical Point
8.4.2 Analysis of equilibrium point's existence and stability
8.5 Numerica1 Results
8.6 Discussions and Conclusions
References
9 Predicting Mathematics Achievement in Secondary Education Using Regression Analysis
9.1 Introduction
9.2 Literature Survey
9.3 Proposed Methodology
9.3.1 Data Collection
9.3.1.1 Null Hypothesis
9.3.1.2 Preprocessing
9.3.2 Feature Selection
9.3.3 Prediction
9.3.3.1 Linear Regression
9.3.3.2 Logistic Regression (LogR)
9.3.3.3 Support Vector Regression
9.3.3.4 Decision Tree Regression (DTR)
9.3.3.5 Random Forest Regression (RFR)
9.4 Experimental Results
9.4.1 Correlation Analysis of Learning Behavior and Mathematics Achievement
9.4.2 Mathematics Achievement Prediction
9.5 Conclusion
References
10 Performance Enhancement of Artificial Intelligence Models Utilizing Hybrid Optimization Techniques
10.1 Introduction
10.2 Challenges in Single Optimization Techniques
10.2.1 High Dimensionality
10.2.2 Noise
10.2.3 Scalability
10.2.4 Convergence
10.2.5 Computational Cost
10.2.6 Hyperparameter Tuning
10.2.7 Data Efficiency
10.2.8 Unreliable Results
10.2.9 Limited Memory
10.2.10 Overfitting
10.2.11 Outliers
10.3 Distinct Hybrid Optimization Techniques
10.3.1 Genetic Algorithm and Simulated Annealing (GA-SA)
10.3.2 Particle Swarm Optimization and Genetic Algorithm (PSO-GA)
10.3.3 Differential Evolution and Particle Swarm Optimization (DE-PSO)
10.3.4 Genetic Algorithm and Neural Networks (GA-NN)
10.3.5 Ant Colony Optimization and Particle Swarm Optimization (ACO-PSO)
10.3.6 Simulated Annealing and Tabu Search (SA-TS)
10.3.7 Memetic Algorithms (MAs)
10.3.8 Genetic Algorithm and Ant Colony Optimization (GA-ACO)
10.3.9 Differential Evolution and Simulated Annealing (DE-SA)
10.3.10 Harmony Search and Particle Swarm Optimization (HS-PSO)
10.3.11 Artificial Bee Colony and Genetic Algorithm (ABC-GA)
10.3.12 Whale Optimization Algorithm and Genetic Algorithm (WOA-GA)
10.4 Applications of Hybrid Optimization Techniques
10.4.1 Scheduling Flights
10.4.2 Supply Chain Management
10.4.3 Energy Distribution
10.4.4 Manufacturing Processes
10.4.5 Healthcare Scheduling
10.4.6 Telecommunication Networks
10.4.7 Urban Planning
10.4.8 Agricultural Planning
10.4.9 Water Resource Management
10.4.10 Traffic Management
10.4.11 Disaster Response
10.4.12 Network Design
10.4.13 Vehicle Design
10.4.14 Drug Discovery
10.5 Background Studies in Hybrid Optimization Techniques
10.6 Conclusion and Future Scope
References
11 Smart Fuzzy Logic in AI Systems for Accurate Medical Diagnostics
11.1 Introduction
11.1.1 Contribution
11.2 Proposed Methodology
11.2.1 Data Layer
11.2.1.1 Patient ID Description
11.2.1.2 Heart Rate (bpm)
11.2.1.3 SpO2 (%) Measurement
11.2.1.4 Temperature (°C) Description
11.2.1.5 Blood Pressure (mmHg)
11.2.2 Neuro-Fuzzy Layer
11.2.2.1 Feature Extraction in Neuro-Fuzzy Systems
11.2.2.2 Feature Transformation Using Membership Functions and Fuzzy Sets
11.2.2.3 Neural Network Architecture and Training
11.2.2.4 Combining Features and Assessing Rules
11.2.2.5 Neural Network Optimization for Learning and Adaptation
11.2.2.6 Mathematical Model for Neural Fuzzy System
11.2.3 AI Layer
11.2.4 Practical Deployment of the Proposed System in a Real World
11.3 Experimental Results and Discussion
11.3.1 Classification of Data in This Dataset
11.3.1.1 Patients in the Low Category (Patients 1, 2, and 3)
11.3.1.2 Patients Within the Normal Range (Patients 4, 5, 8, 9, and 11)
11.3.1.3 Patients in High Range (Patients 6, 7, 10, 12, and 13)
11.3.1.4 t-Test and ANOVA Test
11.4 Conclusion
References
12 2-Domination in Fuzzy Digraphs and Its Applications in Digital Systems
12.1 Introduction
12.2 Preliminaries
12.3 Main Results
12.4 Application of 2-Domination Fuzzy Digraph on Digital System
12.4.1 Algorithms to Find 2-Dominating Set in Fuzzy Digraph
12.4.2 Seven-Robot Coordination Network Using 2-Domination in Fuzzy Digraph
12.4.2.1 Illustration
12.4.3 Benefits
12.4.4 AI-Driven 2-Domination in Traffic Management for Smart Cities
12.4.4.1 Illustration
12.5 Conclusion
References
13 Optimizing Machine Learning and Robotics for Precision in Cancer Prediction and Treatment
13.1 Introduction
13.2 Related Works
13.3 Proposed Work for Cancer Prediction and Treatment Optimization
13.3.1 Cancer Prediction as a Classification Task
13.3.2 Treatment Optimization as a Decision-Making Problem
13.3.3 Mathematical Models
13.3.3.1 Cancer Prediction Using Transformer Models
13.3.3.2 Real-Time Robotic Treatment Planning with Reinforcement Learning
13.3.3.3 Cross-Domain Attention Mechanism
13.4 Experimental Setup
13.5 Simulation Framework
13.5.1 Dataset
13.5.2 Simulation Setup
13.5.3 Evaluation Metrics
13.5.4 Performance of Cancer Prediction Models
13.6 Conclusion
References
14 Applications of Split Divisor Equitable Domination in Fuzzy Graphs to Network Theory
14.1 Introduction
14.2 Preliminaries
14.3 Main Results
14.4 Split Equitable Domination in Computer Networks
14.4.1 Illustration
14.5 Conclusion
References
15 Optimizing Image Preprocessing Methods for Crop Disease Detection in Precision Agriculture
15.1 Introduction
15.2 Literature Review
15.3 Overview of Deep Learning for Crop Disease Detection
15.4 Understanding Image Processing Algorithms in the Context of Deep Learning
15.4.1 Hough Transform
15.4.1.1 Challenges in Edge Linking
15.4.1.2 Line Detection Algorithm
15.4.2 Otsu Adaptive Thresholding
15.4.3 NDVI
15.4.4 Histogram Equalization
15.4.5 SLIC: Simple Linear Iterative Clustering
15.4.6 Gaussian Blur
15.5 ResNet-50
15.5.1 Introduction
15.5.2 What Are Convolution, Pooling, and Relu?
15.5.3 Pooling
15.5.4 Batch Normalization
15.5.5 RELU
15.5.6 Need for ResNet-50
15.5.7 Architecture and Internal Implementation
15.6 Experimentation and Evaluation
15.7 Analyzing the Challenges in Image Preprocessing and Deep Learning for Precision Agriculture
15.8 Conclusion
15.9 Future Directions in Preprocessing and Deep Learning for Precision Agriculture
References
16 AI-Powered Web Application for Early Detection of Heart Disease Using Computational Algorithms
16.1 Introduction
16.2 Survey on Cardiac Disease Forecasting by Using Ml Algorithms
16.3 Materials and Method
16.4 Results and Discussion
16.5 Conclusion
References
17 Analytical Optimization on a Two-Echelon Supply Chain with Fuzzy Production Rate and Environmental Effects
17.1 Introduction
17.2 Symbols and Hypotheses
17.2.1 Symbols and Descriptions
17.2.2 Hypotheses
17.3 Mathematical Formulation
17.3.1 Integrated SC Model
17.3.2 Fuzzy to Crisp Conversion
17.3.3 Solution Procedure
17.4 Numerical Analysis
17.4.1 Examples
17.4.2 Numerical Results and Discussion
17.5 Managerial Insights
17.6 Conclusion and Possible Extensions
References
18 Verdcure: Automatic Identification of Plant Diseases Using Convolutional Neural Networks
18.1 Introduction
18.1.1 Survey on Automatic Identification of Plant Diseases
18.2 Materials and Methods
18.2.1 Workflow
18.2.2 Data Normalization and Activation Functions
18.2.3 Feature Extraction and Model Building with CNN Using PyTorch
18.2.4 Training the Model
18.3 Results and Discussion
18.4 Conclusion
References
19 Lightweight Deep Learning Models for Crab Species Recognition
19.1 Introduction
19.2 Literature Survey
19.3 Proposed Method
19.3.1 CNN-Based Crab Species Classification
19.3.1.1 Convolutional Layer
19.3.1.2 Activation Functions
19.3.1.3 Batch Normalization
19.3.1.4 Pooling Layer
19.3.1.5 Flatten Layer
19.3.1.6 Fully Connected (Dense) Layer
19.3.1.7 Dropout Layer
19.3.2 VGG-16-based Crab Species Classification
19.3.3 EfficientNet-B0
Compound Scaling Equation
19.3.4 ShuffleNetV2
19.3.5 SqueezeNet
19.4 Results and Discussion
19.4.1 Dataset Details
19.4.2 Evaluation Metrics on CNN-Based Model
19.4.3 Performance Evaluation on Lightweight Models
19.5 Conclusion
References
20 Ethical and Social Implications of Mathematical Models in AI and Robotics
20.1 Introduction
20.2 Literature Survey
20.3 Ethical and Social Implications of Mathematical Models in AI
20.3.1 Ethical Considerations in AI Development
20.3.2 Ethical and Social Implications of Mathematical Models in Robotics
20.4 Conclusion
References
21 Efficient Trade Optimization Using Artificial Intelligence and Amazon Web Service Platform
21.1 Introduction
21.1.1 Amazon Web Service
21.1.2 Online Trading Strategies
21.1.3 Background
21.2 Related Works
21.3 Proposed Methodology
21.3.1 Dataset
21.3.2 Preprocessing
21.3.3 Train and Test Split-Up
21.3.4 Feature Selection
21.3.5 Classification
21.3.6 Prediction
21.4 Results and Discussion
21.5 Conclusion
References
22 AI Bias and Fairness: Unveiling Solutions for Ethical Algorithmic Practices
22.1 Introduction
22.1.1 Training Data and Bias
22.2 Types of Bias
22.2.1 Algorithmic Bias
22.2.2 Contextual Bias
22.2.3 Transparency Bias
22.2.4 Application Bias
22.2.5 In-Group Bias
22.2.6 Financial Bias
22.2.7 Confirmation Bias
22.2.8 Data Bias
22.2.9 Demographic Bias
22.2.10 Cognitive Bias
22.2.11 Social Bias
22.2.12 Temporal Bias
22.2.13 Interpretation Bias
22.2.14 Power Imbalance Bias
22.2.15 Availability Bias
22.3 Bias in AI: Key Relationships
22.3.1 Bias and Discrimination
22.3.2 Bias and Variance
22.3.3 Bias and F1 Score
22.4 Bias in Emerging AI Techniques
22.4.1 Bias and Retrieval-Augmented Generation
22.4.2 Bias Amplification in Deep Models
22.4.3 Bias in Transfer Learning
22.4.4 Bias and Algorithmic Oppression
22.5 Measuring and Quantifying Bias
22.5.1 Measuring Bias
22.6 Impact of Bias in AI: Real-World Consequences
22.6.1 Case Study 1
22.6.2 Case Study 2
22.6.3 Case Study 3
22.6.4 Case Study 4
22.6.5 Case Study 5
22.6.6 Case Study 6
22.6.7 Technology Leaders
22.6.8 Facial Recognition Technology:
22.7 Feedback Loops: How Algorithms Can Influence Algorithms?
22.8 Challenges and Limitations Encountered
22.9 Recommendations for Fixing Algorithmic Bias
22.9.1 Accountability and Transparency
22.9.2 Race-aware Algorithm (RCA)
22.9.3 Algorithmic Greenlining (AGL)
22.9.4 Data Preprocessing Techniques
22.9.5 Algorithmic Adjustments
22.9.6 Explainable AI Approaches (EAA)
22.9.7 Differential Privacy Techniques
22.9.8 Federated Learning Techniques (FLT)
22.9.9 Counterfactual Fairness
22.9.10 Causal Inference Techniques (CIT)
22.9.11 Contextual Bandit Algorithms (CBA)
22.9.12 Bias Auditing Frameworks
22.9.13 Human-in-the-Loop Approaches
22.9.14 SMOTE: Synthetic Minority Over Sampling Technique
22.10 Need for Global Regulation Frameworks and Guidelines
22.11 Existing Initiatives in Fairness Frameworks and Guidelines
22.12 The Ethical Implications of Mathematical Models in the Digital Age
22.13 Conclusion: Charting a Course Toward Algorithmic Equity: A Multifarious Approach
References
23 Image Steganography and Attacks Analysis for Secured Data Communication
23.1 Introduction
23.1.1 Steganography Techniques
23.2 Related Works
23.3 The Proposed LSB-Based Steganography Strategy
23.3.1 Encoding the Secret Message Using LSB-Based Steganography
23.3.2 Decoding of Secret Message Using the LSB-Based Steganography
23.4 Experimental Setup and Results
23.5 Conclusions and Future Work
References
24 Real-World Intelligence: Applications of Fuzzy Logic in Modern AI Systems
24.1 Introduction to Fuzzy Logic
24.1.1 Origin and Evolution
24.1.2 Comparison with Classical Logic
24.1.3 Importance in Soft Computing
24.2 Mathematical Foundations of Fuzzy Sets)
24.2.1 Fuzzy Sets and Membership Functions
24.2.2 Operations on Fuzzy Sets
24.2.3 Fuzzy Relations and Their Properties
24.2.4 Fuzzy Logic in Machine Learning
24.3 Fuzzy Decision Trees
24.3.1 Fuzzy Clustering (Fuzzy C-Means) (Fuzzy Clustering: FCM Algorithm)
24.3.2 Neuro-Fuzzy Systems
24.3.3 Fuzzy Logic and Deep Learning
24.4 Fuzzy Layers in Neural Networks
24.4.1 Interpretability in Deep Models Using Fuzzy Concepts
24.4.2 Case Studies with Fuzzy CNN/RNN Architectures
24.4.3 Fuzzy Logic in Expert Systems
24.5 Knowledge Representation
24.5.1 Rule Generation and Uncertainty Handling
24.5.2 Fuzzy-Based Medical and Legal Expert Systems
24.5.3 Fuzzy Logic in Robotics and Automation
24.6 Motion Control and Navigation
24.6.1 Fuzzy Controllers for Dynamic Environments
24.6.2 Human-Robot Interaction Using Fuzzy Models
24.6.3 Use of Fuzzy Systems in Natural Language Processing (NLP)
24.7 Sentiment and Emotion Analysis (Using Fuzzy Logic in Sentiment Analysis)
24.7.1 Fuzzy Ontologies for Semantic Understanding
24.8 Fuzzy Logic in Computer Vision
24.8.1 Image Segmentation and Enhancement
24.8.2 Object Detection with Fuzzy Classifiers
24.8.3 Fuzzy Logic in Surveillance Systems
24.9 Fuzzy Control Systems in Industry
24.9.1 Applications in Manufacturing and Process Control
24.9.2 Adaptive Control in Smart Grids and IoT
24.9.3 Case Studies from Industrial Automation
24.10 Challenges and Opportunities in Fuzzy-AI Integration
24.10.1 Scalability and Computation Cost
24.10.2 Standardization and Benchmarking
24.10.3 Future Research Directions
References
25 Trustworthy Stock Market Forecasting: Time Series Modeling Under Generative AI Manipulation Risks
25.1 Introduction
25.2 Literature Survey
25.3 Methodology
25.3.1 Data Collection
25.3.2 Data Preprocessing
25.3.3 Dataset Creation for LSTM
25.3.4 Reshaping Data for LSTM
25.3.5 LSTM Model Creation
25.3.6 Model Training
25.3.7 Prediction
25.3.8 Implementation of Prophet Model
25.3.9 Hybrid Model Approach Methodology
25.3.10 Evaluation Metrics
25.3.11 Visualization
25.4 Results and Discussion
25.4.1 LSTM Model
25.4.2 Hybrid Model
25.5 Conclusion
References
Index
Mathematical Methods in the Digital Age

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