Artificial Intelligence and Edge Computing for Sustainable Ocean Health explores the transformative role of AI and edge computing in preserving and enhancing ocean health. The growing influence of Artificial Intelligence (AI), along with the Internet of Things (IoT) in generating wide coverage of sensor networks, and Edge Computing (EC) has paved the way for investigation of underwater as well as massive marine data, thereby generating huge potential for credible research opportunities for these domains. This book’s journey begins with a broad overview of Artificial Intelligence for Sustainable Ocean Health, setting the foundation for understanding AI's potential in marine conservation. The subsequent chapter, Role of Artificial Intelligence and Technologies in Improving Ocean Health in Promoting Tourism, illustrates the synergy between technological advancements and sustainable tourism practices, demonstrating how AI can enhance the attractiveness and preservation of marine destinations. The identification, restoration, and monitoring of marine resources along with the utilization of technology continues in Utilization of Underwater Wireless Sensor Network through Supervising a Random Network Environment in the Ocean Environment has been extensively dealt with. The technical challenges of underwater imaging, essential for accurate data collection and analysis has been discussed. The importance of Explainable AI is discussed in chapters like Sustainable Development Goal 14: Explainable AI (XAI) for Ocean Health, Explainable AI (XAI) for Ocean Health: Exploring the Role of Explainable AI in Enhancing Ocean Health, and A Comprehensive Study of AI (XAI) for Ocean Health Monitoring, which emphasize transparency and trust in AI systems. Further, Revolutionizing Internet of Underwater Things with Federated Learning, Underwater Drone, Underwater Imagery with AI/ML and IoT in ROV Technology and Ocean Cleanup has been demonstrated using innovative approaches to addressing underwater challenges. The book also includes a Review on the Optics and Photonics in Environmental Sustainability, focusing on the role of optics in marine conservation. Security issues are tackled in Intelligent Hash Function Based Key-Exchange Scheme for Ocean Underwater Data Transmission, and the overarching potential of AI in marine resource management is discussed in Artificial Intelligence as Key-enabler for Safeguarding the Marine Resources.

This open access book invites readers to learn how to develop artificial intelligence (AI)-based algorithms to perform their research in oceanography. Various examples are exhibited to guide details of how to feed the big ocean data into the AI models to analyze and achieve optimized results. The number of scholars engaged in AI oceanography research will increase exponentially in the next decade. Therefore, this book will serve as a benchmark providing insights for scholars and graduate students interested in oceanography, computer science, and remote sensing.

The world is addressing the insistent challenge of climate change, and the need for innovative solutions has become paramount. In this period of technical developments, artificial intelligence (AI) has emerged as a powerful instrument with enormous prospects to combat climate change and other environmental subjects. AI's ability to process vast amounts of data, identify patterns, and make intelligent predictions offers unprecedented opportunities to tackle this global crisis. High-Performance Computing (HPC) or super-computing environments address these large and complex challenges with individual nodes (computers) working together in a cluster (connected group) to perform massive amounts of computing in a short period. Creating and removing these clusters is often automated in the cloud to reduce costs. Computer networks, communication systems, and other IT infrastructures have a growing environmental footprint due to significant energy consumption and greenhouse gas emissions. To address this seemingly self-defeating conundrum, and create a truly sustainable environment, new energy models, algorithms, methodologies, platforms, tools, and systems are required to support next-generation computing and communication infrastructures. Harnessing High-Performance Computing and AI for Environmental Sustainability navigates through AI-driven solutions from sustainable agriculture and land management to energy optimization and smart grids. It unveils how AI algorithms can analyze colossal datasets, offering unprecedented insights into climate modeling, weather prediction, and long-term climate trends. Integrating AI-powered optimization algorithms revolutionizes energy systems, propelling the transition towards a low-carbon future by reducing greenhouse gas emissions and enhancing efficiency. This book is ideal for educators, environmentalists, industry professionals, and researchers alike, and it explores the ethical dimensions and policies surrounding AI's contribution to environmental development.

This book provides an insight into ways of inculcating the need for applying mobile edge data analytics in bioinformatics and medicine. The book is a comprehensive reference that provides an overview of the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to the healthcare field. Topics include deep learning methods for applications in object detection and identification, object tracking, human action recognition, and cross-modal and multimodal data analysis. High performance computing systems for applications in healthcare are also discussed. The contributors also include information on microarray data analysis, sequence analysis, genomics based analytics, disease network analysis, and techniques for big data Analytics and health information technology.

Smart health care development : challenges and solutions / R. Sujatha, E.P. Ephzibah and S. Sree Dharinya -- Working of mobile intelligent agents on the web-a survey / P.R. Joe Dhanith and B. Surendiran -- Power management scheme for photovoltaic/battery hybrid system in smart grid / T. Bharani Prakash and S. Nagakumararaj -- Analysis : a neural network equalizer for channel equalization by particle swarm optimization for various channel models / M. Muthumari, D.C. Diana and C. Ambika Bhuvaneswari -- Implementing hadoop container migrations in opennebula private cloud environment / P. Kalyanaraman, K.R. Jothi, P. Balakrishnan, R.G. Navya, A. Shah and V. Pandey -- Transmission line inspection using unmanned aerial vehicle / A. Mahaboob Subahani, M. Kathiresh and S. Sanjeev -- Smart city infrastructure management system using IoT / S. Ramamoorthy, M. Kowsigan, P. Balasubramanie and P. John Paul -- Lightweight cryptography algorithms for IoT resource starving devices / S. Aruna, G. Usha, P. Madhavan and M.V. Ranjith Kumar -- Pre-learning-based semantic segmentation for LiDAR point cloud data using self-organized map / K. Rajathi and P. Sarasu -- Smart load balancing algorithms in cloud computing-a review / K.R. Jothi, S. Anto, M. Kohar, M. Chadha and P. Madhavan -- A low-cost wearable remote healthcare monitoring system / Konguvel Elango and Kannan Muniandi -- IoT-based secure smart infrastructure data management / R. Poorvadevi, M. Kowsigan, P. Balasubramanie -- A study of addiction behavior for smart psychological health care system / V. Sabapathi and K.P. Vijayakumar -- A custom cluster design with Raspberry Pi for parallel programming and deployment of private cloud / Sukesh, B., Venkatesh, K. and Srinivas, L.N.B. -- Energy efficient load balancing technique for distributed data transmission using edge computing / Karthikeyan, K. and Madhavan, P. -- Blockchain-based SDR signature scheme with time-stamp / Swathi Singh, Divya Satish and Sree Rathna Lakshmi.

The rapid proliferation of connected devices in our daily lives, from smart homes to industrial sensors, has led to an explosion of data that requires processing before it is useful to experts. However, modern devices often have limited resources, making it challenging to decode and utilize this data effectively. Additionally, the need for real-time decision-making further complicates this issue, as traditional data processing methods take far too long to be able to keep up with the required volume and speed. Developments Towards Next Generation Intelligent Systems for Sustainable Development offers a comprehensive solution to these challenges by integrating novel technologies such as AI, edge computing, federated learning, quantum computing, and more. The book shows how intelligent systems can maximize computing power by leveraging these technologies to process large volumes of data efficiently and autonomously and make real-time decisions. The proposed architectures and frameworks focus on real-time analysis, faster decision-making, enhanced privacy, and efficient data processing.