SCOPE:

Data-driven methods, particularly deep learning models, have revolutionized image processing tasks such as object detection and segmentation, as well as inverse sensing tasks like monitoring atmospheric conditions, vegetation health, and more. These models enable highly accurate predictions from large, labeled datasets. However, one of the key challenges with current AI systems is their opacity—while they deliver precise results, they often fail to provide clear insights into the underlying mechanisms driving these predictions. The lack of interpretability, especially in the context of remote sensing data, complicates the understanding of how internal features are represented and utilized. This issue is particularly pronounced in smart environment applications, where artificial intelligence and machine learning (ML) techniques are increasingly employed, yet their internal workings remain largely opaque.

Despite the wealth of research on AI and ML, existing books often fail to comprehensively cover the intersection of machine learning, deep learning, and sensing technologies within the context of smart environments. This book aims to fill that gap by offering both theoretical insights and practical solutions. Our goal is to help readers better grasp the concepts necessary for designing and deploying next-generation, sustainable smart environments, while also providing practical solutions to real-world challenges in diverse areas such as smart agriculture, transportation, healthcare, manufacturing, and supply chain management.

The book will introduce new models, practical solutions, and cutting-edge technologies to design sustainable smart environments. It will also explore the potential of machine intelligence and deep learning technologies, with a focus on their application in creating smarter, more sustainable systems. Through a combination of theory, case studies, and real-world applications, this book will serve as a comprehensive resource for both academics and practitioners interested in the intersection of AI and smart environments. 

TOPICS:

Authors are invited to submit previously unpublished chapters to this book. Topics include, but are not limited to:

• Deep Learning Techniques in Smart Sensing Applications

• Advanced Sensing and Internet of Thing Technologies in Smart Cities

• Generalizable Events- based Sensing for Smart City Applications

• Real Time Detection of Unseen Classes for Smart City Applications

• AI-enabled Sensing Techniques and Trust Mechanisms in Smart Environments

• Security Techniques for Heterogeneous Sensing Data

• Adversarial Machine Learning Countermeasures for IoT Sensing Networks

• Advanced Hyperspectral Remote Sensing

• AI-Enabled Sensing Techniques for Smart Farming and Precision Agriculture

• AI and Sensing Techniques for Environmental Monitoring

• Artificial Intelligence and Sensing for Sustainable Smart Supply Chain: The Case of Perishable Food

• Explainable AI (XAI) and Sensing Methods for Spatial Epidemiology

• ...etc.