Sultan Ahmad - Böcker

Harnessing Artificial Intelligence-Enhanced Graph Models for Biological Discovery: Unveiling Biological Frontiers introduces revolutionary techniques that merge artificial intelligence with graph-based methods to uncover complex biological networks. Through detailed examples and case studies, the book provides researchers and practitioners the essential tools to analyze molecular interactions, identify key biomarkers, and hasten the discovery of novel therapeutics. Chapters delve into the sophisticated interplay between advanced AI techniques and graph models, specially designed to decode the intricacies of biological systems. By utilizing cutting-edge AI algorithms, readers can explore complex biological networks, forecast molecular interactions, and pinpoint new drug targets with exceptional precision.Offers an innovative approach by combining artificial intelligence with graph-based techniques to delve into complex biological networksIncludes practical examples and case studies, providing researchers and practitioners with the tools they need to analyze molecular interactions and identify crucial biomarkersEnables researchers to predict molecular interactions and identify novel drug targets with unparalleled accuracy and efficiencyUnlocks new avenues for biological discovery, facilitating precise and effective research outcomes

This book highlights authentication and trust evaluation models in the Industrial Internet of Things. It further discusses data breaches and security issues in various Artificial Intelligence-enabled systems and uses Blockchain to resolve the challenges faced by the Industrial Internet of Things. The text showcases performance quality assessment for the Industrial Internet of Things’ applications.This book:Discusses and evaluates different quality assessment systems and authentication of smart devicesAddresses data handling, data security, confidentiality, and integrity of data in the Industrial Internet of ThingsFocuses on developing framework and standardization of quality assessment for diverse Internet of Things-enabled devicesExplains the designing, developing, and framing of smart machines, that are equipped with tools for tracking and logging data to provide advanced security featuresPresents the convergence of the Internet of Things toward Industry 4.0 through quality assessment via analyzing data security and identifying vulnerabilitiesIt is primarily written for graduate students and academic researchers in the fields of electrical engineering, electronics, and communications engineering, industrial and production engineering, computer science, and engineering.

Machine learning, Internet of Things (IoT) and data analytics are new and fresh technologies that are being increasingly adopted in the field of medicine. This book positions itself at the forefront of this movement, exploring the beneficial applications of these new technologies and how they are gradually creating a smart healthcare system.This book details the various ways in which machine learning, data analytics and IoT solutions are instrumental in disease prediction in smart healthcare. For example, wearable sensors further help doctors and healthcare managers to monitor patients remotely and collect their health parameters in real-time, which can then be used to create datasets to develop machine learning models that can aid in the prediction and detection of any susceptible disease. In this way, smart healthcare can provide novel solutions to traditional medical issues.This book is a useful overview for scientists, researchers, practitioners and academics specialising in the field of intelligent healthcare, as well as containing additional appeal as a reference book for undergraduate and graduate students

Machine learning, Internet of Things (IoT) and data analytics are new and fresh technologies that are being increasingly adopted in the field of medicine. This book positions itself at the forefront of this movement, exploring the beneficial applications of these new technologies and how they are gradually creating a smart healthcare system.This book details the various ways in which machine learning, data analytics and IoT solutions are instrumental in disease prediction in smart healthcare. For example, wearable sensors further help doctors and healthcare managers to monitor patients remotely and collect their health parameters in real-time, which can then be used to create datasets to develop machine learning models that can aid in the prediction and detection of any susceptible disease. In this way, smart healthcare can provide novel solutions to traditional medical issues.This book is a useful overview for scientists, researchers, practitioners and academics specialising in the field of intelligent healthcare, as well as containing additional appeal as a reference book for undergraduate and graduate students

Smart agriculture is an approach to maintaining nature without compromising the basic needs of future generations while at the same time improving the efficiency of farming. The main achievements of smart agriculture in terms of sustainable agriculture are crop rotation, controlling nutrient deficiencies in crops, pest and disease control, recycling, and water harvesting, leading to a safer environment overall. Living organisms depend on the nature of biodiversity and are exposed to pollution due to waste emissions, use of fertilizers and pesticides, degraded dead plants, and so on. The emission of greenhouse gases affects plants, animals, humans, and the environment; hence, this necessitates a better environment for living organisms.The purpose of this book is to provide a comprehensive overview of the latest advancements, challenges, and potential applications of artificial intelligence (AI) technology and the Internet of Things (IoT) in the future of intelligent agriculture. The book is primarily focused on equipping younger researchers, graduates, and professionals in the industry with the necessary knowledge to understand the advantages of AI technology, machine learning, and data analytics methods in improving current agricultural practices.Key features include the following:The book showcases the latest advancements in AI and smart agriculture technologiesThe text emphasizes sustainable practices supported by AI, highlighting how technology can enhance productivity while minimizing environmental impactReaders will learn how to harness big data and analytics to drive informed decision-making and optimize their agricultural yields

Smart agriculture is an approach to maintaining nature without compromising the basic needs of future generations while at the same time improving the efficiency of farming. The main achievements of smart agriculture in terms of sustainable agriculture are crop rotation, controlling nutrient deficiencies in crops, pest and disease control, recycling, and water harvesting, leading to a safer environment overall. Living organisms depend on the nature of biodiversity and are exposed to pollution due to waste emissions, use of fertilizers and pesticides, degraded dead plants, and so on. The emission of greenhouse gases affects plants, animals, humans, and the environment; hence, this necessitates a better environment for living organisms.The purpose of this book is to provide a comprehensive overview of the latest advancements, challenges, and potential applications of artificial intelligence (AI) technology and the Internet of Things (IoT) in the future of intelligent agriculture. The book is primarily focused on equipping younger researchers, graduates, and professionals in the industry with the necessary knowledge to understand the advantages of AI technology, machine learning, and data analytics methods in improving current agricultural practices.Key features include the following:The book showcases the latest advancements in AI and smart agriculture technologiesThe text emphasizes sustainable practices supported by AI, highlighting how technology can enhance productivity while minimizing environmental impactReaders will learn how to harness big data and analytics to drive informed decision-making and optimize their agricultural yields