Padmesh Tripathi – författare

This book gives comprehensive insights into the application of AI, machine learning, and deep learning in developing efficient and optimal surveillance systems for both indoor and outdoor environments, addressing the evolving security challenges in public and private spaces. Mathematical Models Using Artificial Intelligence for Surveillance Systems aims to collect and publish basic principles, algorithms, protocols, developing trends, and security challenges and their solutions for various indoor and outdoor surveillance applications using artificial intelligence (AI). The book addresses how AI technologies such as machine learning (ML), deep learning (DL), sensors, and other wireless devices could play a vital role in assisting various security agencies. Security and safety are the major concerns for public and private places in every country. Some places need indoor surveillance, some need outdoor surveillance, and, in some places, both are needed. The goal of this book is to provide an efficient and optimal surveillance system using AI, ML, and DL-based image processing. The blend of machine vision technology and AI provides a more efficient surveillance system compared to traditional systems. Leading scholars and industry practitioners are expected to make significant contributions to the chapters. Their deep conversations and knowledge, which are based on references and research, will result in a wonderful book and a valuable source of information.

This essential book provides a comprehensive, expert-led guide on how federated learning can revolutionize crop yield, enhance resource management, and ensure a pathway to sustainable food quality and safety. The convergence of artificial intelligence, machine learning, and data science with agriculture and food, provides remarkable opportunities to improve quality, sustainability, and productivity in the agricultural sector. Federated Learning is a promising technology that has emerged at this intersection. In the context of smart agriculture, federated learning holds promise for improving crop yield, resource management, and decision-making. Additionally, federated learning provides greater clarity and understanding in the world of agriculture, encouraging stakeholders to explore and adopt this technology for improved farm management. Readers will find the book: Explores the integration of federated learning, a novel machine learning technique, into the realm of agriculture and food quality enhancement, showcasing the latest advancements;Introduces real-world applications of federated learning in agriculture, and demonstrates the way this technology can transform farming practices, crop monitoring, pest control, and food quality assurance;By bridging the fields of agriculture, machine learning, and food science, it offers a holistic perspective on leveraging technology to address challenges in food production and quality management;Emphasizes the importance of sustainability in agriculture, exploring how federated learning can contribute to more efficient resource utilization, reduced environmental impact, and the overall sustainability of food production systems;Discusses the future directions of smart agriculture and food quality enhancement, envisioning how federated learning and other emerging technologies can continue to shape the industry and address evolving challenges.Audience Agriculture specialists, agricultural engineers, professionals associated with food safety, crop managers, quality assurance professionals, IT professionals, data scientists, and academics working towards improved quality and sustainability in agriculture.