Mathematical Methods in Artificial Intelligence
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Über dieses Buch
In today’s data-driven era, the convergence of mathematics, computing, artificial intelligence, and blockchain is emerging as a significant area at the intersection of applied mathematics and computer science, particularly in decision-making. This book explores the applications of advanced mathematical models and computational algorithms to AI-driven strategies and blockchain technologies.
It covers advanced linear algebra techniques, probability theory, optimization methods, game theory, cryptography, and statistical learning, providing deep mathematical insights into AI, blockchain, and data-driven decision-making. The book delves into matrix computations and eigenvalue problems relevant to deep learning, Bayesian inference for predictive modeling, and reinforcement learning for dynamic decision-making.
Additionally, optimization methods such as convex programming and Lagrangian multipliers enhance resource allocation, while cryptographic protocols ensure the security of blockchain systems. By integrating these mathematical frameworks, this book provides researchers, professionals, and students with practical tools for addressing complex business challenges ranging from fraud detection to automated contract execution.
- Integrates advanced mathematics, computing, AI, and blockchain for data-driven management.
- Covers optimization, probability, game theory, predictive analytics, automation, and secure transactions.
- Bridges theory and practice for researchers
Information zu Autoren / Herausgebern
Dr. Abhishek Kumar, Senior Member of IEEE, is an Assistant Director and Professor in the Computer Science & Engineering Department at Chandigarh University, Punjab, India. With over 13 years of teaching experience, he has published 180+ peer-reviewed papers and successfully supervised four Ph.D. scholars, with four more currently under his guidance, along with 30+ M.Tech projects. He holds a Ph.D. from the University of Madras and completed postdoctoral research at Universidad de Castilla-La Mancha, Spain. His research interests span artificial intelligence, renewable energy systems, image processing, and data mining. An award-winning researcher, Dr. Kumar has received several accolades, including the Sir C.V. Raman National Award (2018), and holds a patent. An accomplished author and editor, he has authored seven books and edited 51 volumes with reputed publishers like IET, Elsevier, Wiley, Springer, and De Gruyter. Dr. Kumar also serves as Series Editor for book series such as Quantum Computing (De Gruyter), Intelligent Energy Systems (Elsevier), and MMDA De Gruyter.
Reyes Jose holds a full professor position at UABC, Tijuana campus, Baja California, Mexico. He is the President of the Mexican Network of Software Engineering (REDMIS,https://conisoft.org/redmis/). He is a member of the National System of Researchers of Mexico(SNI), Level 2, and leads several research projects in collaboration with Industry. His research areas are Software Engineering (uncertainty in agile methodologies, quality improvement in Scrum), Human-Computer Interaction (user-centered design, adaptive user interfaces), and he is currently working with Quantum Computing. He was the General Chair for the National and International Conference on Software Engineering Research and Innovation (CONISOFT).
Angeles Quezada holds a Doctorate in Sciences from the Autonomous University of Baja California, a Master's degree in Computer Science from the Technological Institute of Tijuana, and a Bachelor's degree in Computer Science from the Technological Institute of Tapachula, Chiapas. She is currently a research professor pursuing a Master's Degree in Information Technologies at the Tijuana Technological Institute, where she participates in research projects and teaching. She is the author of various scientific publications, including indexed journals, book chapters, and conference articles. She is a member of the National System of Researchers SNI level 1 and a member of the Mexican Thematic Network of Software Engineering (REDMIS). Research areas include Human Computer Interaction, Artificial Intelligence, and Machine Learning.
Dhaya Chinnathambi is currently a computer science and engineering professor at Adhiparasakthi Engineering College, Tamil Nadu, India. She received her Bachelor’s degree from Madras University, her Master’s degree from Anna University, and her Doctorate from Pondicherry University. She has published papers in reputed International Journals, Conferences, and has published patents. Her areas of specialization include Machine Learning, Data Science, Software Architecture Evaluation, Genetic Algorithms, and MCDM. She served as a reviewer for Elsevier, ETRI, and some reputed journals and as an author for Book chapters in Wiley and IGI Global. Her academic dedication has been recognized through various awards, including the "Women Leadership Award" by the Computer Society of India and the "Young Researcher Award" for contributions to Science and Technology.
Fachgebiete
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Frontmatter
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Contents
V - Theme 1: Algorithm Optimization
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Theme 1: Algorithm Optimization
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Adaptive AI Models for Energy Optimization Through User Behavior Analysis
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Mathematical Modeling-Based Optimization for Regulating Overcrowding in Unreserved Rail Coaches Using Capacity-Constrained Ticketing Algorithms
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AI for Financial Risk Management: Combining CatBoost and Genetic Algorithms for Portfolio Optimization
23 -
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Optimizing Transportation Systems in Smart Cities with Support Vector Machines (SVMs) and Particle Swarm Optimization
35 -
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Predictive Maintenance in Smart Systems with Temporal Convolutional Networks (TCN) and Autoencoders
49 -
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Cascade Forward Backpropagation Neural Networks for Precise Surface Roughness Prediction in Monel 400 Machining
63 -
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Corn Seed Sorting for Production Maintenance Using Metaheruistic Optimized Neural Networks
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Sorting Corn Seeds Using Metaheruistic Optimal Neural Networks for Production Maintenance
85 -
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Advanced Optimization of Load Balancing in Distributed Cloud Systems Using Star Cocoloring Techniques
95 -
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MFO_LK_MLP: Moth Flame Optimized Lattice Kohonen Multiple Layer Perceptron Neural Network-Based Cell Imbalance Prediction Among Autonomous Vehicles for Effective Battery Management
103 -
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Mathematical Modeling and Optimization of AI-Driven Hostel Allocation Systems for Smart Accommodation Management
117 -
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Optimized Online Service Booking Portal Enhanced with Artificial Intelligence and Mathematical Modeling
131 -
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A Quantitative Modeling Approach to AI-Integrated Smart Trip Planning with Real-Time Route Optimization and Cost Minimization
145 -
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Mathematical Modeling and AI-Driven Optimization of Blood Bank Management Systems Using Real-Time Analytics and Predictive Matching
157 -
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A Mathematical Approach Polygonal Models and Their Applications in Image Processing
169 -
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Deep Learning-Based Real-Time Energy Distribution Optimization for Hybrid Energy Storage Systems in Electric Vehicles
183 -
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Enhancing Early Prediction of Gestational Diabetes Mellitus Using Advanced Machine Learning and Feature Optimization Techniques
195 -
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Optimizing Pre-owned Car Valuation with SVR, XGBoost, KNN, and ANN Models
209 -
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Data Science Applications Using Extreme Gradient Boosting (XGBoost) and Random Forest for Predictive Analytics in Financial Sectors
223 -
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Developing Continuous Integration/Continuous Deployment for Microservices Architecture Using DevOps
235 -
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Real-Time Inventory Management System for Retail Chains: A Data-Driven Approach
247 -
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Automated Software Deployment System with Integrated Testing Pipelines
261 -
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A Novel DevOps Monitoring and Incident Response System: Methodology and Performance Evaluation
275 -
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AI-Enabled Data-Driven Decision Support Systems for Corporate Management
289 -
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Deep Learning Applications in Predictive Analytics for Business Management
303 -
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Machine Learning in Smart Cities: Leveraging Particle Swarm Optimization (PSO) and Decision Trees for Urban Development Planning
317 -
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Smart Systems Powered by K-Nearest Neighbors (KNN) and Bidirectional LSTMs for Real-Time Data Processing
329 -
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AI-Powered Disaster Management in Smart Cities Using YOLOv5 and Ant Colony Optimization (ACO)
341 - Theme 2: Blockchain
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Theme 2: Blockchain
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Blockchain-Driven Data Mining in Federated Learning Environments Enhancing Privacy and Security
357 -
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Leveraging Blockchain for Secure and Transparent Federated Learning in Data Mining Applications
371 -
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A Decentralized Approach to Data Mining: Integrating Blockchain with Federated Learning
385 -
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Optimizing Data Mining Processes with Blockchain-Enabled Federated Learning
399 -
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Federated Learning and Blockchain: Synergizing Privacy-Preserving Data Mining Techniques
413 -
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Decentralized Identity Security System Using Blockchain
427 - Theme 3: Cryptography
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Theme 3: Cryptography
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Digital Rights Management System with RC4-2S Encryption Technique
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Adaptive Correlative Approach for Enhanced Biometric Security Using EEG Signal Interface
453 - Theme 4: Cybersecurity
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Theme 4: Cybersecurity
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Firewall-Z: Leveraging AI Mathematical Modeling for Real-Time Threat Detection
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Intelligent Cyber Threat Detection Using Deep Neural Networks with PSO Optimization
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A Mathematical Modeling Approach to AI-Driven Threat Detection Using Cloud-Based Honeypots
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Next-Generation Network Data Security: Advanced Threat Detection, Encryption Techniques, and AI-Driven Cyber Defense Mechanisms for Safeguarding Digital Infrastructures
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An Intelligent Deep Learning-Based Adaptive Framework for Multilayered Intrusion Detection and Threat Mitigation in Modern Information Security Architectures
523 -
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Application-Driven Criminal Investigation Uncovering Reports and Discoveries
535 -
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Streamlining Network Security: A Convolutional Neural Network-Based System for Real-Time SIP Signal Analysis and Attack Detection
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Privacy-Preserving Federated Deep Learning for Emotion and Engagement Analytics in Smart Classrooms
557 -
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Fortifying Cyber-Physical Systems: Current Trends and Future Directions in Security Algorithms
569 -
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Data Anonymization Using Pseudonym System to Preserve Data Privacy
581 -
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Advance Real-Time System for Criminal Identification Using Facial Recognition
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Crime Scene Anomaly Prediction Using Generative Adversarial Networks
603
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