Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics Green Engineering and Technology Series

Data science revolves around two giants: Big Data analytics and Deep Learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of Big Data as well as Deep Learning systems.

This book discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and decision-making. It also covers numerous applications in healthcare, education, communication, media, and entertainment. Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics offers innovative platforms for integrating Big Data and Deep Learning and presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval.

FEATURES

• Provides insight into the skill set that leverages one?s strength to act as a good data analyst
• Discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and help in decision-making
• Covers numerous potential applications in healthcare, education, communication, media, and entertainment
• Offers innovative platforms for integrating Big Data and Deep Learning
• Presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval from Big Data

This book is aimed at industry professionals, academics, research scholars, system modelers, and simulation experts.

1. A Study on Big Data and Artificial Intelligence Techniques in Agricultural Sector 2. Deep Learning Models for Object Detection in Self-Driven Cars 3. Deep Learning for Analyzing the Data on Object Detection and Recognition 4. Emerging Applications of Deep Learning 5. Emerging Trend and Research Issues in Deep Learning with Cloud Computing 6. An Investigation of Deep Learning 7. A Study and Comparative Analysis of Various Use Cases of NLP Using Sequential Transfer Learning Techniques 8. Deep Learning for Medical Dataset Classification Based on Convolutional Neural Networks 9. Deep Learning in Medical Image Classification 10 A Comparative Review of the Role of Deep Learning in Medical Image Processing

R. Sujatha completed the Ph.D. degree at Vellore Institute of Technology, in 2017 in the area of data mining. She received her M.E. degree in computer science from Anna University in 2009 with university ninth rank and done Master of Financial Management from Pondicherry University in 2005. She received her B.E. degree in computer science from Madras University, in 2001. Has 17 years of teaching experience and has been serving as an associate professor in the School of Information Technology and Engineering in Vellore Institute of Technology, Vellore. Organized and attended a number of workshops and faculty development programs. She actively involves herself in the growth of the institute by contributing to various committees at both academic and administrative levels. She used to guide projects for undergraduate and postgraduate students. Currently guides doctoral students. She gives technical talks in colleges for the symposium and various sessions. She acts as an advisory, editorial member, and technical committee member in conferences conducted in other educational institutions and in-house too. She has published a book titled software project management for college students. Also has published research articles in reputed high impact journals. The institution of Green Engineers awarded IGEN women achiever 2021 in future computing category. Interested to explore different places and visit the same to know about the culture and people of various areas. She is interested in learning upcoming things and gets herself acquainted with the student’s level. Her areas of research interest include Data Mining, Machine Learning, Software Engineering, Soft Computing, Big Data, Deep Learning, and Blockchain.

S. L. Aarthy completed the Ph.D. degree at Vellore Institute of Technology, in 2018 in the area of medical image processing. She received her M.E. degree in computer science from Anna University in 2010. She received her B.E. degree in computer sc