An edited book is a compilation of academic chapters produced by several authors to share their perspectives and experiences on a certain subject. The chapters of the edited book, which are written by many writers, go through a peer-reviewing procedure to confirm their quality. The volume editor next manages the uniformity of style and material flow. The chapters in the edited book should be the author's own unpublished original works. If they are carefully planned and edited well, edited volumes can be excellent books. A successful edited book is more than just a longer version of a journal issue with a similar theme; it is also a complete intellectual undertaking that should be treated as such from the outset.

An edited book (2nd Volume) is a compilation of academic chapters produced by several authors to share their perspectives and experiences on a certain subject. The chapters of the edited book, which are written by many writers, go through a peer-reviewing procedure to confirm their quality. The volume editor next manages the uniformity of style and material flow. The chapters in the edited book should be the author's own unpublished original works. If they are carefully planned and edited well, edited volumes can be excellent books. A successful edited book is more than just a longer version of a journal issue with a similar theme; it is also a complete intellectual undertaking that should be treated as such from the outset.

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.

This book is based on columns and tutorials published in the Bulletin of the European Association for Theoretical Computer Science (EATCS) during the period 2000-2003. It presents many of the most active current research lines in theoretical computer science. The material appears in two volumes, “Algorithms and Complexity” and “Formal Models and Semantics”, reflecting the traditional division of the field.The list of contributors includes many of the well-known researchers in theoretical computer science. Most of the articles are reader-friendly and do not presuppose much knowledge of the area in question. Therefore, the book constitutes very suitable supplementary reading material for various courses and seminars in computer science.

Proceedings of the International Conference on Interdisciplinary Research in Electronics and Instrumentation Engineering 2015 (ICIREIE)