Multimedia and Multimodal Intelligence – serie

The multimodal and multimedia data need to be secured and preserved properly in the present scenario. The methodologies proposed in the book will offer a new paradigm in AI-based techniques. With these approaches, the social good of modern society will be enhanced. Multimedia, multimodal data and intelligent systems will deliver insightful comments for sustainable development. Therefore, these challenges have proven exciting for multimedia and multimodal AI researchers. These facts served as the impetus for the writing of this book, which is intended for researchers who fulfill modern society’s requirements.Key Features:Emphasis on interdisciplinary approaches, novel techniques and solutions to provide recent advancements in multimedia and multimodal intelligence for sustainable development of modern society.Includes new concepts, state-of-the-art security approaches, theories and practices focusing on multimedia and multimodal intelligence for modern society.Highlights the security of multimodal data utilizing a fusion of artificial intelligence and soft computing techniques.The book can be used as a reference book for practicing engineers, scientists, researchers and technologists. It will also be helpful for senior undergraduate and graduate students, practitioners from government and industry and professionals working on state-of-the-art multimedia and multimodal intelligence solutions for modern society applications.

This book highlights the integration of cloud, edge, and network computing for sustainable farming and future trends in smart farming, including the role of UAVs, 5G, and advanced artificial intelligence. It covers the use of real‑time environmental data for forecasting and tracking agricultural products and livestock health monitoring.Key FeaturesOffers a detailed exploration of the integration of Internet of Things, artificial intelligence, and multimodal intelligence in precision agriculture, covering a wide range of applications from crop management to livestock monitoringIdentifies and discusses the challenges of using artificial intelligence and multimodal data in agriculture, providing solutions and techniques to overcome these obstaclesDiscusses advanced technologies like multispectral and hyperspectral imaging, Internet of Things sensors, and data fusion techniquesHighlights emerging trends and future directions in smart farming, including UAVs, 5G, cloud‑edge continuum integration, and federated learningIncludes case studies and practical examples demonstrating successful applications of multimodal artificial intelligence in precision farmingThe text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science engineering, agricultural engineering, and information technology.

The text begins with a detailed introduction to the core principles of remote sensing, offering readers foundational knowledge before delving into the growing role of artificial intelligence. It systematically explores how artificial intelligence-driven methods are transforming the way satellite imagery and other forms of remote sensing data are processed, analyzed, and interpreted across various domains.This book:Includes in-depth analysis of the latest research findings and real-world case studies highlighting successful applications of artificial intelligence in remote sensing. Addresses emerging trends like explainable artificial intelligence and federated learning, ensuring that the readers understand the future of artificial intelligence-driven remote sensing. Presents advanced machine learning and deep learning methods for spectral and spatial feature extraction in remote sensing. Explains artificial intelligence for unmanned aerial vehicle (UAV) and hyperspectral remote sensing. Explores big data analytics for remote sensing, and quantum machine learning for high dimensional remote sensing data. It is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, environmental engineering, and information technology.

This book provides a comprehensive examination of artificial intelligence techniques that integrate heterogeneous healthcare data to support intelligent, secure, and sustainable healthcare systems. It presents both theoretical foundations and practical methodologies for multimodal data fusion, representation learning, and decision-making using medical images, bio signals, electronic health records, wearable sensors, text, and multimedia data.Features:Illustrates analyzing and integrating multiple modalities of healthcare data, such as medical images, electronic health records, bio signals, and genomic data.Emphasizes the role of multimedia intelligence in healthcare by leveraging advanced artificial intelligence techniques to analyze diverse data formats, including text, images, audio, and video.Presents practical applications that ensure secure healthcare data management and sustainable solutions, focusing on improving patient care and enhancing diagnostics.Covers the different security mechanisms applied in multimodal and multimedia-based healthcare applications.Showcases the role of advanced artificial intelligence and machine learning methods, including privacy-preserving technologies, blockchain, and explainable artificial intelligence tailored for secure healthcare.The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, and biomedical engineering.

This book showcases the potential of artificial intelligence to transform diagnostics, predictive analytics, personalized treatment, and healthcare resource optimization. It presents real-world applications and emerging trends, such as quantum computing, federated learning, and edge artificial intelligence. Features:Explores the intersection of artificial intelligence and neuroscience, including applications in understanding brain disorders, cognitive functions, and neuroimaging data processing.Provides insights into emerging technologies such as quantum computing and their potential to revolutionize medical data processing, accelerate artificial intelligence algorithms, and solve previously intractable problems in healthcare.Analyzes advanced strategies for identifying and mitigating biases in artificial intelligence models, ensuring compliance with global healthcare regulations and fostering ethical artificial intelligence adoption.Discusses the application of multi-agent artificial intelligence systems in optimizing hospital operations, emergency response, and resource management in healthcare ecosystems.Covers the use of generative models, such as generative adversarial network and variational autoencoders, in drug design, protein structure prediction, and the creation of synthetic biomedical datasets.The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, biomedical engineering, drug delivery, and information technology.