Editor: Brojo Kishore Mishra

Series Title: Advances in Computing Communications and Informatics

Data Science and Interdisciplinary Research: Recent Trends and Applications

Volume 5

eBook: US $49 Special Offer (PDF + Printed Copy): US $83
Printed Copy: US $59
Library License: US $196
ISSN: 2737-5722 (Print)
ISSN: 2737-5730 (Online)
ISBN: 978-981-5079-01-2 (Print)
ISBN: 978-981-5079-00-5 (Online)
Year of Publication: 2023
DOI: 10.2174/97898150790051230501


Data Science and Interdisciplinary Research: Recent Trends and Applications is a compelling edited volume that offers a comprehensive exploration of the latest advancements in data science and interdisciplinary research. Through a collection of 10 insightful chapters, this book showcases diverse models of machine learning, communications, signal processing, and data analysis, illustrating their relevance in various fields.

The book highlights many use cases for recommendation systems:

Key Themes:

  • - Advanced Rainfall Prediction: Presents a machine learning model designed to tackle the challenging task of predicting rainfall across multiple countries, showcasing its potential to enhance weather forecasting.
  • - Efficient Cloud Data Clustering: Explains a novel computational approach for clustering large-scale cloud data, addressing the scalability of cloud computing and data analysis.
  • - Secure In-Vehicle Communication: Explores the critical topic of secure communication in in-vehicle networks, emphasizing message authentication and data integrity.
  • - Smart Irrigation 4.0: Details a decision model designed for smart irrigation, integrating agricultural sensor data reliability analysis to optimize water usage in precision agriculture.
  • - Smart Electricity Monitoring: Highlights machine learning-based smart electricity monitoring and fault detection systems, contributing to the development of smart cities.
  • - Enhanced Learning Environments: Investigates the effectiveness of mobile learning in higher education, shedding light on the role of technology in shaping modern learning environments.
  • - Coastal Socio-Economy Study: Presents a case study on the socio-economic conditions of coastal fishing communities, offering insights into the livelihoods and challenges they face.
  • - Signal Noise Removal: Shows filtering techniques for removing noise from ECG signals, enhancing the accuracy of medical data analysis and diagnosis.
  • - Deep Learning in Biomedical Research: Explores deep learning techniques for biomedical research, particularly in the realm of gene identification using Next Generation Sequencing (NGS) data.
  • - Medical Diagnosis through Machine Learning: Concludes with a chapter on breast cancer detection using machine learning concepts, demonstrating the potential of AI-driven diagnostics.

This volume bridges the gap between data science and interdisciplinary research, making it a valuable resource for researchers, academics, and professionals seeking to leverage cutting-edge technologies for transformative applications.

Audience: Researchers, academics, and professionals seeking to leverage cutting-edge technologies for transformative applications.


Data science has recently gained much attention for a number of reasons, Big Data is the most significant among them. Scientists (from almost all disciplines including physics, chemistry, biology, and sociology, among others) and engineers (from all fields including civil, environmental, chemical, and mechanical, among others) are faced with challenges posed by data volume, variety, and velocity, or Big Data.

The book contains quantitative research, case studies, conceptual papers, and model papers, review papers, theoretical backing, etc. This book will cover data science and its application to interdisciplinary science.

This book will prove valuable for graduate students, researchers, academicians, and professionals in information science, business, health, planning, manufacturing, and other areas who are interested in exploring the ever-expanding research on Data Science.

Chapter-01 provides a detailed survey and comparative analysis of various methodologies in the prediction of rainfall over multiple countries.

Chapter -02 focuses on applying clustering for gaining the benefits of evolutionary computation to process large-scale data and based on optimality, the performance of the datasets can be measured.

Chapter-05 presents an investigation of the data obtained from IoT sensors and observed that a huge amount of work can be done in the reliability analysis of the data from the sensors deployed in the agricultural fields.

Chapter-06 says that - Smart devices have rapidly started intruding our lifestyles with the technological promotion of the Internet of Things. One of the most used smart devices is the electric meter. Urban areas witness power theft as well as un-proportionate billing, both incurring tremendous losses to the respective exchequers. We thought that if a system may be designed which can predict power utilization and also classify the current usage, it would be beneficial to both the service providers as well as the consumers. Equipped with such thoughts, thorough research was conducted to monitor electric consumption and fault detection in the devices.

Chapter-08 focused on undertaking a quick analysis of socio-economic conditions. Information on the aforementioned parameters was gathered in order to get insight into the research area's socio-economic profile.

In the end, we thank the contributory authors, reviewers and my family members for their support. Special thanks to Prof. (Dr.) Pradeep Kumar Singh for his best support as a Book Series Editor. The editors are also thankful to all members of Bentham Science Publication house.

Brojo Kishore Mishra
Department of Computer Science & Engineering
NIST Institute of Science and Technology (Autonomous)
Institute Park, Pallur Hills, Golanthara
Berhampur-761008, Odisha, India