Bentham Science Publishers

This book demonstrates several use cases of how artificial intelligence (AI) and machine learning (ML) are revolutionizing problem-solving across various industries. The book presents 18 edited chapters beginning with the latest advancements in human-AI interactions and neuromorphic computing, setting the stage for practical applications.

Chapters focus on AI and ML applications such as fingerprint recognition, glaucoma detection, and lung cancer identification using image processing. The book also explores the role of AI in professional operations such as UX design, event detection, and content analysis. Additionally, the book includes content that examines AI's impact on technical operations wireless communication, VLSI systems, and advanced manufacturing processes. Each chapter contains summaries and references for addressing the needs of beginner and advanced readers.

This comprehensive guide is an essential resource for anyone seeking to understand AI's transformative role in modern problem-solving in professional industries.

A Primer on Wireless Technology and IoT Basics covers the journey of wireless networks from the early 1G systems to the anticipated capabilities of 6G, while also explaining essential wireless technologies such as GSM, WLAN, WPANs, MANETs, and Wireless Sensor Networks (WSNs). It further delves into how these technologies underpin IoT ecosystems, emphasizing their role in data collection, transmission, and smart system integration.

Chapters explore key developments in wireless communication, types of networks, sensor architectures, IoT system layers, and real-world implementation challenges. Special attention is given to IoT applications across diverse sectors and how wireless protocols support their functionality.

This book is particularly useful for readers seeking a strong foundational understanding of wireless systems and IoT, making it ideal for both technical and non-technical audiences.

Key Features:

Explain key generations of wireless technology from 1G to 6G.

Clarify concepts related to GSM, WLAN, WPANs, MANETs, and WSNs

Illustrate IoT architecture, protocols, and system layers

Highlight real-world IoT use cases and applications

Address implementation challenges and data handling in IoT systems

A Treatise on Ecological Science provides a fresh perspective on modern ecological thought by exploring topics often overlooked in traditional ecological texts. The book opens with a discussion of Network Ecology, unraveling the interconnectedness of ecosystems, and transitions to the intricate relationship between biodiversity and climate change. It further addresses Human Ecology, highlighting its central role in shaping ecological discourse, and explores the principles of Industrial Ecology, emphasizing sustainable practices in industrial systems. The final chapter critically examines the Sustainable Development Goals, offering a balanced view of their benefits and limitations.

Designed to bridge gaps between ecology, industry, and global sustainability, this book serves as an insightful resource for students, researchers, and policymakers interested in contemporary ecological science and its practical applications.

Key Features:

- Examines underexplored topics in ecological science.

- Connects biodiversity, climate change, and human ecology.

- Highlights the role of industrial ecology in sustainability.

- Provides a critical analysis of Sustainable Development Goals.

This two-part series examines how AI-powered image analysis and IoT-enabled devices enhance diagnostic precision, facilitate real-time monitoring and early detection through connected sensors. Together, these technologies bridge gaps in access, reduce diagnostic subjectivity, and support remote patient care. The books also address vital considerations such as data privacy, security, and ethical implications in digital healthcare. Highlighting both current applications and future directions, the series emphasizes interdisciplinary collaboration to advance AI and IoT-driven dermatology for improved clinical outcomes and equitable healthcare delivery.

Key Features:

Explores the convergence of AI and IoT in skin cancer detection and care.

Highlights advanced imaging, data analytics, and sensor-based monitoring.

Discusses challenges related to privacy, ethics, and healthcare accessibility.

Showcases current research and innovations targeting early and precise diagnosis.

Bridges medical, technological, and policy perspectives for holistic insight.

The book aims to provide a deeper understanding of the synergistic impact of Artificial intelligence (AI) and the Internet of Things (IoT) for disease detection. It presents a collection of topics designed to explain methods to detect different diseases in humans and plants. Chapters are edited by experts in IT and machine learning, and are structured to make the volume accessible to a wide range of readers.

Key Features:

- 17 Chapters present information about the applications of AI and IoT in clinical medicine and plant biology

- Provides examples of algorithms for heart diseases, Alzheimer's disease, cancer, pneumonia and more

- Includes techniques to detect plant disease

- Includes information about the application of machine learning in specific imaging modalities

- Highlights the use of a variety of advanced Deep learning techniques like Mask R-CNN

- Each chapter provides an introduction and literature review and the relevant protocols to follow

The book is an informative guide for data and computer scientists working to improve disease detection techniques in medical and life sciences research. It also serves as a reference for engineers working in the healthcare delivery sector.

AI and ML in Early Warning Systems for Natural Disasters bridges the gap between advanced computational models and real-world disaster management practices by highlighting how data-driven intelligence can enhance resilience planning and reduce risks in the face of climate change and extreme environmental events.

Beginning with an overview of traditional early warning systems and the limitations they face in accuracy and timeliness The book sheds light on to AI- and ML-driven approaches, detailing predictive analytics, anomaly detection, sensor networks, geospatial data integration, and IoT-enabled monitoring systems. Case studies on earthquake prediction, flood forecasting, cyclone tracking, and wildfire detection illustrate the practical applicability of AI-powered models across diverse contexts. Later chapters examine legal frameworks, ethical considerations, and community-based strategies that ensure responsible, sustainable, and inclusive deployment of these technologies.

Key Features

Presents AI and ML techniques for predictive analytics, anomaly detection, and risk modeling in disaster scenarios.

Demonstrates real-world applications through case studies on earthquakes, floods, cyclones, and wildfires.

Explores integration of satellite imagery, remote sensing, and IoT-based sensor networks for real-time monitoring.

Assesses legal, regulatory, and ethical frameworks shaping AI use in disaster preparedness.

Provides multidisciplinary insights, blending computer science, engineering, and disaster management for resilient community planning.

Positioned at the intersection of intelligent transportation systems (ITS), computer vision, and machine learning, this book presents a comprehensive examination of how artificial intelligence and statistical techniques are reshaping traffic monitoring, management, and urban mobility in the era of smart cities.

The book begins with the core principles of AI and traffic systems, introducing statistical modeling, data acquisition, and image processing for traffic analysis. Midway, it transitions into deep learning-powered applications such as object detection, vehicle tracking, congestion forecasting, and real-time incident recognition. Later sections address legal, regulatory, and ethical frameworks, while concluding chapters highlight IoT-enabled models and future trajectories in AI-powered traffic management.

Key Features:

Introduces principles of AI, machine learning, and statistical modeling for traffic systems

Demonstrates applications of deep learning in congestion prediction, incident detection, and vehicle tracking

Examines AI-driven traffic optimization, urban mobility solutions, and self-driving technologies

Evaluates security, data privacy, and legal considerations in AI-based traffic surveillance

Integrates AI with IoT frameworks for real-time monitoring in smart city infrastructure

Highlights future directions and policy implications for sustainable and ethical traffic management.

AI-Driven Innovations in Electrochemical Technologies for Sustainable Energy Solutions blends principles of data science, materials engineering, and energy technology to offer a forward-looking exploration of how Artificial Intelligence is reshaping the design, optimisation, and performance of electrochemical technologies.

Across nine well-structured chapters, the book progresses from foundational principles of AI-integrated electrochemical systems to advanced applications in batteries, fuel cells, supercapacitors, and electrolysers. Topics include AI-assisted materials discovery, performance optimisation, real-time monitoring, predictive maintenance, and system-level energy management. Ethical, environmental, and policy considerations surrounding AI adoption are also examined, along with case studies that demonstrate real-world impact.

With its interdisciplinary perspective and emphasis on practical innovations, this book equips readers with a deep understanding of how AI reduces experimentation time, streamlines design processes, and drives next-generation sustainable energy solutions.

Key Features

Comprehensive coverage of AI-enhanced electrochemical systems

Real-world case studies and application-driven insights

Focus on AI-assisted material discovery, optimisation, and predictive maintenance

Discussions on the ethical, environmental, and policy dimensions

Forward-looking perspectives on integrating AI with sustainable energy and healthcare technologies

AI-Powered Innovations in Ophthalmic Diagnosis and Treatment is a forward-looking assessment of how artificial intelligence is redefining modern eye care. The book connects computational advancements with clinical practice, presenting a structured, multidisciplinary roadmap for integrating AI across diagnostic workflows, therapeutic decisions, patient monitoring, and future-ready developments in ophthalmology.

Across seven insightful chapters, the book establishes AI-driven frameworks that enhance ophthalmic workflows through federated, reinforcement, and prompt-based learning. Subsequent chapters detail AI-enabled diagnostics for retinal, glaucoma, and ocular surface diseases, alongside governance challenges in real-world deployment. The book also explores personalized therapeutics by integrating AI with Traditional Chinese Medicine, stem cell strategies, and precision care models.

It further examines next-generation technologies including LLMs, AIGC, and multi-agent systems and their transformative roles in clinical research, drug discovery, and medical education. Broader applications are highlighted through AI's connection to systemic disease surveillance and wearable healthcare. The final chapters address regulatory, ethical, and legal considerations, concluding with a visionary outlook on quantum computing and global collaboration shaping the future of ophthalmic AI.

Uniting clinical evidence, computational methodologies, and patient-centred perspectives, this book provides actionable insights for implementing responsible, explainable, and scalable AI within ophthalmic practice. Key Features

Introduces AI-driven frameworks enhancing diagnostic and clinical workflows in ophthalmology.

Presents practical AI applications for retinal, glaucoma, and ocular surface disease detection.

Explores AI-guided personalized therapeutics, including TCM integration and stem cell innovations.

Examines emerging technologies such as LLMs, AIGC, and multi-agent systems in research and education.

Addresses ethical, regulatory, and governance challenges in deploying medical AI.

Highlights AI's role in systemic disease prediction, wearable monitoring, and next-generation healthcare models.

Envisions future innovations shaped by quantum computing and global AI collaboration.