Amlan Chakrabarti – författare

Application of Artificial Intelligence in Early Detection of Lung Cancer presents the most up-to-date computer-aided diagnosis techniques used to effectively predict and diagnose lung cancer. The presence of pulmonary nodules on lung parenchyma is often considered an early sign of lung cancer, thus using machine and deep learning technologies to identify them is key to improve patients' outcome and decrease the lethal rate of such disease. The book discusses topics such as basics of lung cancer imaging, pattern recognition techniques, deep learning, and nodule detection and localization. In addition, the book discusses risk prediction based on radiological analysis and 3D modeling.This is a valuable resource for cancer researchers, oncologists, graduate students, radiologists, and members of biomedical field who are interested in the potential of AI technologies in the diagnosis of lung cancer.

Despite the increasing population (the Food and Agriculture Organization of the United Nations estimates 70% more food will be needed in 2050 than was produced in 2006), issues related to food production have yet to be completely addressed. In recent years, Internet of Things technology has begun to be used to address different industrial and technical challenges to meet this growing need. These Agro-IoT tools boost productivity and minimize the pitfalls of traditional farming, which is the backbone of the world''s economy. Aided by the IoT, continuous monitoring of fields provides useful and critical information to farmers, ushering in a new era in farming. The IoT can be used as a tool to combat climate change through greenhouse automation; monitor and manage water, soil and crops; increase productivity; control insecticides/pesticides; detect plant diseases; increase the rate of crop sales; cattle monitoring etc.

Agricultural Informatics: Automation Using the IoT and Machine Learning focuses on all these topics, including a few case studies, and they give a clear indication as to why these techniques should now be widely adopted by the agriculture and farming industries.

Despite the increasing population (the Food and Agriculture Organization of the United Nations estimates 70% more food will be needed in 2050 than was produced in 2006), issues related to food production have yet to be completely addressed. In recent years, Internet of Things technology has begun to be used to address different industrial and technical challenges to meet this growing need. These Agro-IoT tools boost productivity and minimize the pitfalls of traditional farming, which is the backbone of the world''s economy. Aided by the IoT, continuous monitoring of fields provides useful and critical information to farmers, ushering in a new era in farming. The IoT can be used as a tool to combat climate change through greenhouse automation; monitor and manage water, soil and crops; increase productivity; control insecticides/pesticides; detect plant diseases; increase the rate of crop sales; cattle monitoring etc.

Agricultural Informatics: Automation Using the IoT and Machine Learning focuses on all these topics, including a few case studies, and they give a clear indication as to why these techniques should now be widely adopted by the agriculture and farming industries.

Introduction to Artificial Intelligence accompanies AI courses being rolled out at universities worldwide. The title covers all the modules needed for a non-technical introduction to AI concepts, for students across disciplines. This practical curriculum is now available through MyLab, Pearson’s premier digital courseware, which pairs an eTextbook with AI tools and comprehensive assessments.

Requiring only basic digital literacy, the text introduces technical concepts using intuitive explanations instead of overly mathematical analysis. By linking foundations to modern uses, the authors show how AI is shaping society and industry, across regions and sectors, while highlighting efforts to enforce responsible uses of AI. They cover all the classic concepts, including rule-based AI, machine learning, deep learning, and vision and language, with up-to-date coverage of LLMs, prompt engineering, multimodal generation and agentic AI.

The book commences with an introductory section on reconfigurable computing and thereafter delves into its applications in critical infrastructures. This book analyzes how such systems may be affected due to vulnerabilities and also discusses how these can be attacked by adversaries or cybercriminals. This book also discusses protection mechanisms related to such threats. It initially starts with an introduction to the various industrial revolutions and the changes in security needs, followed by a basic description of reconfigurable hardware based critical infrastructures. Further, the book contains discussion on security administration and planning, auditing, monitoring and analysis, risk-response and recovery, control and countermeasures highlighting  their need in reconfigurable hardware-based platforms. In addition to these, new concepts are introduced such as AI/ML for cybersecurity, digital forensics related to reconfigurable hardware based critical infrastructures. Finally, a hands on learning experience is presented to provide the readers with a practical flavour.  

The book will be useful to students (graduate and undergraduate), researchers, academicians and industry professionals.

This edited volume presents the latest high-quality technical contributions and research results in the areas of computing, informatics, and information management. The book deals with state-of art topics, discussing challenges and possible solutions, and explores future research directions. The main goal of this volume is not only to summarize new research findings but also place these in the context of past work. This volume is designed for professional audience, composed of researchers, practitioners, scientists and engineers in both the academia and the industry.

The book presents the latest, high-quality, technical contributions and research findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It discusses state-of-the-art topics as well as the challenges and solutions for future development. It includes original and previously unpublished international research work highlighting research domains from different perspectives. This book is mainly intended for researchers and practitioners in academia and industry.