Harnessing Data Science For Sustainable Agriculture And Natural Resource Management
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Harnessing Data Science for Sustainable Agriculture and Natural Resource Management
This book is a comprehensive resource that handles the issues of sustainable agriculture and natural resource management, aligned with the United Nations' Sustainable Development Goals (SDGs). The book is organized into five sections, Understanding the Problem, Data Collection and Cleaning, Exploratory Data Analysis and Visualization, Model Building, and Model Deployment. Each section covers a critical aspect of data science in this context and addresses specific SDGs 2—zero hunger, 6—clean water and sanitation, 12—responsible consumption and production, 13—climate action, and 15—Life on land. The organized sections are arranged to seamlessly follow the data science pipeline and provide practical guidance from problem understanding to its model deployment and stakeholder management. The book is useful for researchers, students, practitioners, and policymakers.
Data Science in Agriculture and Natural Resource Management
This book aims to address emerging challenges in the field of agriculture and natural resource management using the principles and applications of data science (DS). The book is organized in three sections, and it has fourteen chapters dealing with specialized areas. The chapters are written by experts sharing their experiences very lucidly through case studies, suitable illustrations and tables. The contents have been designed to fulfil the needs of geospatial, data science, agricultural, natural resources and environmental sciences of traditional universities, agricultural universities, technological universities, research institutes and academic colleges worldwide. It will help the planners, policymakers and extension scientists in planning and sustainable management of agriculture and natural resources. The authors believe that with its uniqueness the book is one of the important efforts in the contemporary cyber-physical systems.
Revolutionizing Cybersecurity With Deep Learning and Large Language Models
As cyber threats grow, national security measures struggle to keep pace with sophisticated attacks. Deep learning and large language models (LLMs) revolutionize cybersecurity by enabling advanced threat detection automated response mechanisms and analytics. AI technologies can analyze vast amounts of data, recognize patterns, and identify threats to security systems. Using deep learning and LLMs to transform cybersecurity is essential for addressing both their potential and the challenges that come with their adoption. Revolutionizing Cybersecurity With Deep Learning and Large Language Models explores the intersection of AI, cybersecurity, deep learning, and LLMs, and the potential of these technologies in safeguarding the digital world. It examines real-world applications, ethical challenges, and new technological advancements. This book covers topics such as artificial intelligence, cybersecurity, and threat detection, and is a useful resource for academicians, researchers, security professionals, computer engineers, and data scientists.