Enabling Ai Applications In Data Science

This book provides a detailed overview of the latest developments and applications in the field of artificial intelligence and data science. AI applications have achieved great accuracy and performance with the help of developments in data processing and storage. It has also gained strength through the amount and quality of data which is the main nucleus of data science. This book aims to provide the latest research findings in the field of artificial intelligence with data science.

This book provides a detailed overview of the latest developments and applications in the field of artificial intelligence and data science. AI applications have achieved great accuracy and performance with the help of developments in data processing and storage. It has also gained strength through the amount and quality of data which is the main nucleus of data science. This book aims to provide the latest research findings in the field of artificial intelligence with data science.

This book gathers selected high-quality research papers presented at Arab Conference for Emerging Technologies 2020 organized virtually in Cairo during 21–23 June 2020. This book emphasizes the role and recent developments in the field of emerging technologies and artificial intelligence, and related technologies with a special focus on sustainable development in the Arab world. The book targets high-quality scientific research papers with applications, including theory, practical, prototypes, new ideas, case studies and surveys which cover machine learning applications in data science.

This book covers the topic of data science in a comprehensive manner and synthesizes both fundamental and advanced topics of a research area that has now reached its maturity. The book starts with the basic concepts of data science. It highlights the types of data and their use and importance, followed by a discussion on a wide range of applications of data science and widely used techniques in data science. Key Features • Provides an internationally respected collection of scientific research methods, technologies and applications in the area of data science. • Presents predictive outcomes by applying data science techniques to real-life applications. • Provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. • Gives the reader a variety of intelligent applications that can be designed using data science and its allied fields. The book is aimed primarily at advanced undergraduates and graduates studying machine learning and data science. Researchers and professionals will also find this book useful.

This book provides an overview of the emerging smart connected world, and discusses the roles and the usage of underlying semantic computing and Internet-of-Things (IoT) technologies. The book comprises ten chapters overall, grouped in two parts. Part I “Smart Connected World: Overview and Technologies” consists of seven chapters and provides a holistic overview of the smart connected world and its supporting tools and technologies. Part II “Applications and Case Studies” consists of three chapters that describe applications and case studies in manufacturing, smart cities, health, and more. Each chapter is self-contained and can be read independently; taken together, readers get a bigger picture of the technological and application landscape of the smart connected world. This book is of interest for researchers, lecturers, and practitioners in Semantic Web, IoT and related fields. It can serve as a reference for instructors and students taking courses in hybrid computing getting abreast of cutting edge and future directions of a connected ecosystem. It will also benefit industry professionals like software engineers or data scientists, by providing a synergy between Web technologies and applications. This book covers the most important topics on the emerging field of the smart connected world. The contributions from leading active researchers and practitioners in the field are thought provoking and can help in learning and further research. The book is a valuable resource that will benefit academics and industry. It will lead to further research and advancement of the field. Bharat K. Bhargava, Professor of Computer Science, Purdue University, United States

THE DIGITAL AGRICULTURAL REVOLUTION The book integrates computational intelligence, applied artificial intelligence, and modern agricultural practices and will appeal to scientists, agriculturists, and those in plant and crop science management. There is a need for synergy between the application of modern scientific innovation in the area of artificial intelligence and agriculture, considering the major challenges from climate change consequences viz. rising temperatures, erratic rainfall patterns, the emergence of new crop pests, drought, flood, etc. This volume reports on high-quality research (theory and practice including prototype & conceptualization of ideas, frameworks, real-world applications, policy, standards, psychological concerns, case studies, and critical surveys) on recent advances toward the realization of the digital agriculture revolution as a result of the convergence of different disruptive technologies. The book touches upon the following topics which have contributed to revolutionizing agricultural practices. Applications of Artificial Intelligence in Agriculture (AI models and architectures, system design, real-world applications of AI, machine learning and deep learning in the agriculture domain, integration & coordination of systems and issues & challenges). IoT and Big Data Analytics Applications in Agriculture (theory & architecture and the use of various types of sensors in optimizing agriculture resources and final product, benefits in real-time for crop acreage estimation, monitoring & control of agricultural produce). Robotics & Automation in Agriculture Systems (Automation challenges, need and recent developments and real case studies). Intelligent and Innovative Smart Agriculture Applications (use of hybrid intelligence in better crop health and management). Privacy, Security, and Trust in Digital Agriculture (government framework & policy papers). Open Problems, Challenges, and Future Trends. Audience Researchers in computer science, artificial intelligence, electronics engineering, agriculture automation, crop management, and science.

This reference text presents the usage of artificial intelligence in healthcare and discusses the challenges and solutions of using advanced techniques like wearable technologies and image processing in the sector. Features: Focuses on the use of artificial intelligence (AI) in healthcare with issues, applications, and prospects Presents the application of artificial intelligence in medical imaging, fractionalization of early lung tumour detection using a low intricacy approach, etc Discusses an artificial intelligence perspective on wearable technology Analyses cardiac dynamics and assessment of arrhythmia by classifying heartbeat using electrocardiogram (ECG) Elaborates machine learning models for early diagnosis of depressive mental affliction This book serves as a reference for students and researchers analyzing healthcare data. It can also be used by graduate and post graduate students as an elective course.