Data Science and Interdisciplinary Research: Recent Trends and Applications

Pradeep Kumar Singh, Bharat Bhargava, Wei-Chiang Hong

• Publisher: Bentham Science Publishers
• Year: 2000
• ISBN:
  9789815079005
• Topic: Computer Science
• Subtopic: Data Mining
• Index: Computer Science

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. 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.

Table of contents

• Welcome
• Table of Content
• Title
• BENTHAM SCIENCE PUBLISHERS LTD.
• PREFACE
• List of Contributors
• A Comprehensive Study and Analysis on Prediction of Rainfall Across Multiple Countries using Machine Learning
• A Novel Approach for Clustering Large-scale Cloud Data using Computational Mechanism
• Secure Communication Over In-Vehicle Network Using Message Authentication
• A Decision Model for Reliability Analysis of Agricultural Sensor Data for Smart Irrigation 4.0
• Machine Learning based Smart Electricity Monitoring & Fault Detection for Smart City 4.0 Ecosystem
• Investigating the Effectiveness of Mobile Learning in Higher Education
• Socio-Economy of Coastal Fishing Community of Southern Coast of Odisha: A Case Study
• Filtering Techniques for Removing Noise From ECG Signals
• Deep Learning Techniques for Biomedical Research and Significant Gene Identification using Next Generation Sequencing (NGS) Data: - A Review
• Breast Cancer Detection Using Machine Learning Concepts