Related eBooks

Decoding AI: Unleashing the Future of Business and Finance

eISBN: 979-8-89881-387-1
ISBN: 979-8-89881-388-8

Introduction

Decoding AI: Unleashing the Future of Business and Finance

Decoding AI: Unleashing the Future of Business and Finance examines the transformative impact of Artificial Intelligence on modern business and financial ecosystems. With a strong emphasis on innovation, risk management, ethical deployment, and predictive intelligence, the book offers a comprehensive view of how AI is reshaping sectors such as banking, insurance, accounting, fintech, and beyond.

Across twelve insightful chapters, the book traces the evolution of AI-driven transformation, from the emergence of banking in the metaverse and AI-enabled climate risk mitigation to fraud detection, underwriting, credit scoring, and accounting practices, with a special focus on emerging markets such as India. Later chapters explore fintech risk management, cross-sector predictive analytics, and the ethical challenges posed by generative AI. The concluding sections focus on trust, governance frameworks, workforce transformation, and the future trajectory of AI-powered organisations.

By blending academic depth with real-world relevance, this book equips readers with strategic, technical, and ethical perspectives needed to navigate AI-led digital transformation.

Key Features:

Real-world case studies supported by industry data (2019–2024)

Covers emerging technologies, including Generative AI and blockchain

Provides practical insights into AI-driven risk management and predictive analytics

Ethical, regulatory, and governance perspectives for responsible AI adoption

Discusses applications across finance, healthcare, marketing, and manufacturing

Advanced Information Retrieval System: Theoretical and Experimental Perspective

eISBN: 979-8-89881-366-6
ISBN: 979-8-89881-367-3

Introduction

Advanced Information Retrieval System: Theoretical and Experimental Perspective

Advanced Information Retrieval System: Theoretical and Experimental Perspective blends foundational theory with practicality to provide an integrative exploration of modern information retrieval (IR) systems. This volume examines a wide range of IR methodologies, from classical indexing and ranking techniques to cutting-edge AI-driven approaches, demonstrating how these systems can be applied across diverse domains, including web search, recommendation systems, sentiment analysis, and multimedia retrieval.

The book takes a structured approach towards guiding readers from traditional IR models to advanced, hybrid frameworks. The early chapters focus on classical and modern retrieval techniques with comparative analyses of different methods. Subsequent chapters focus on applied scenarios such as tourism recommender systems, sentiment mining from YouTube comments, book and medicine recommendation engines, and image-audio-based retrieval systems. Advanced topics include semantic role classification using BERT, hybrid filtering methods, personalised web crawlers, and experimental studies on smoothing techniques. Real-world case studies and experimental evaluations illustrate how theoretical models translate into effective, domain-specific IR applications.

Key Features

Comprehensive coverage of traditional, modern, and hybrid IR techniques
Practical frameworks for recommendation systems, sentiment analysis, and web crawling
Integration of AI and machine learning methods, including BERT and TF-IDF models
Experimental evaluations and comparative analyses across multiple domains
Real-world applications spanning tourism, healthcare, fashion, and multimedia retrieval

Cognitive Computing with Intelligent Engineering Platforms

eISBN: 979-8-89881-441-0
ISBN: 979-8-89881-442-7

Introduction

Cognitive Computing with Intelligent Engineering Platforms

Cognitive Computing with Intelligent Engineering Platforms is an explorative study anchored in the transformative role of cloud-based artificial intelligence and cognitive computing in advancing smart engineering systems across industrial, urban, energy, and healthcare domains. The volume highlights how AI-driven platforms, Industrial IoT, and cloud infrastructures are reshaping decision-making, control systems and human–machine collaboration.

Through focused chapters, the book examines AI-powered control architectures, cognitive technologies for smart manufacturing, and intelligent support systems for operators in next-generation industries. It further addresses collaborative energy management for smart cities using hybrid optimisation and IoT data fusion, advanced deep learning models for real-time electric vehicle battery state-of-charge estimation, and cognitive Industrial IoT applications in healthcare for intelligent care delivery. By integrating theoretical models with applied case studies, the book offers a holistic perspective on intelligent, sustainable, and human-centric engineering solutions.

Key Features

Integrated perspectives on cloud-based AI with smart engineering platforms
Focus on cognitive computing and Industry 5.0 paradigms
Discusses applications across manufacturing, energy, EV systems, smart cities, and healthcare
Presents advanced AI models for real-time monitoring and optimisation
Emphasises on sustainability, human–AI collaboration, and intelligent automation

Cloud Computing and IoT Strategies for Industry 5.0 Innovation

eISBN: 978-981-5322-64-4
ISBN: 978-981-5322-65-1

Introduction

Cloud Computing and IoT Strategies for Industry 5.0 Innovation

Cloud Computing and IoT Strategies for Industry 5.0 Innovation is a comprehensive study centred on Industry 5.0 principles, highlighting the integration of human-centric, sustainable, and resilient intelligent digital technologies. The book presents a comprehensive exploration of how cloud computing, the Internet of Things (IoT), edge intelligence, and advanced data analytics are transforming the modern industrial ecosystem

With ten structured chapters, the book progresses from foundational concepts such as Industry 5.0 evolution, cloud service models, and IoT architectures to advanced topics including secure cloud infrastructures, cryptographic frameworks, and machine-learning-based threat detection. It further explores the synergy between edge and cloud computing for real-time processing, cloud-driven data analytics, and integration frameworks across sectors such as manufacturing, agriculture, and smart cities. The concluding chapters address critical issues of cybersecurity, data privacy, and emerging trends shaping the future of cloud–IoT convergence.

Key Features:

Comprehensive coverage of cloud computing, IoT, edge intelligence, and Industry 5.0 technologies

Detailed exploration of secure cloud infrastructures, cryptography, and cybersecurity frameworks

Integrated perspectives on AI, blockchain, and edge analytics for intelligent industrial applications

Practical frameworks and real-world case studies across manufacturing, smart cities, and agriculture

Focuses on human–machine collaboration, resilience, and sustainable industrial innovation

Discussions of emerging trends, privacy challenges, and future research directions

Quantum-Enhanced Cloud AI: The Next Frontier in Machine Learning and Deep Learning

eISBN: 979-8-89881-321-5
ISBN: 979-8-89881-322-2

Introduction

Quantum-Enhanced Cloud AI: The Next Frontier in Machine Learning and Deep Learning

Quantum-Enhanced Cloud AI: The Next Frontier in Machine Learning and Deep Learning presents a scholarly and comprehensive examination of the convergence of quantum computing, cloud infrastructure, and advanced artificial intelligence. As classical computing approaches approach their practical and physical limits, this volume positions quantum-enhanced cloud platforms as a transformative foundation for next-generation machine learning and deep learning systems.

The book systematically introduces core principles of quantum computing, cloud-based quantum architectures, and hybrid quantum–classical models, establishing a strong conceptual foundation before advancing to practical implementations. It critically examines quantum algorithms, error correction challenges, coherence limitations, and architectural considerations that shape the feasibility of Quantum AI. Through detailed case studies and comparative analyses, the book demonstrates how quantum machine learning techniques can outperform classical approaches in domains such as healthcare, bioinformatics, finance, cybersecurity, agriculture, smart cities, and Internet of Vehicles systems. Emerging topics including edge intelligence, quantum-enabled IoT, sustainable supply chains, and secure cloud-based healthcare frameworks are also explored.

Written in a rigorous yet accessible style, this reference work serves graduate students, researchers, and professionals seeking to understand both the theoretical underpinnings and real-world implications of quantum-enhanced AI. It offers valuable insights into the technological, industrial, and societal impact of this rapidly evolving field.

Key Features

· Comprehensive coverage of quantum computing principles and cloud-based AI architectures

· In-depth analysis of hybrid quantum–classical machine learning models and algorithms

· Comparative evaluation of quantum and classical AI approaches across multiple application domains

· Practical insights into implementation challenges, scalability, and security

· Forward-looking perspectives on the future of Quantum AI and intelligent computing systems.

Applications of Modern High Performance Networks

Editor(s) : Junaid Ahmed Zubairi

Book Web Price: US $

After 9% Discount 116

Related eBooks

Decoding AI: Unleashing the Future of Business and Finance

Advanced Information Retrieval System: Theoretical and Experimental Perspective

Cognitive Computing with Intelligent Engineering Platforms

Cloud Computing and IoT Strategies for Industry 5.0 Innovation

Quantum-Enhanced Cloud AI: The Next Frontier in Machine Learning and Deep Learning