Proceedings of International Conference on Big Data, Machine Learning and their Applications: ICBMA 2019

Proceedings of ICBMA 2019: Big Data and Machine Learning in Practice

This volume collects 34 peer-reviewed papers from the International Conference on Big Data, Machine Learning and Their Applications (ICBMA 2019). Edited by Shailesh Tiwari, Erma Suryani, Andrew Keong Ng, K. K. Mishra, and Nitin Singh, the proceedings appear in Springer’s Lecture Notes in Networks and Systems series (Volume 150). The contributions span theoretical advances and applied case studies, reflecting how data-driven methods are being used to solve engineering, medical, agricultural, and security problems.

What the Volume Covers

Across its pages, the proceedings bring together work on big data analytics, machine learning algorithms, deep learning architectures, and their practical deployment. You will find studies on:

• Network intrusion detection and cybersecurity
• Solar irradiance forecasting and agricultural IoT
• Medical record classification, glaucoma detection, and diabetes risk prediction
• Self-driving car behavior cloning and chatbot performance
• Plant disease detection and smart street lighting
• Blockchain for pharmaceutical supply chains and cloud forensics

These topics illustrate the breadth of the conference, from core algorithmic research to system-level applications.

Research That Bridges Theory and Application

The papers are not isolated experiments. Many address how machine learning can be integrated into real-world workflows, such as reducing precision in deep neural networks, improving debonding quantification with meta-learning, or managing data in IoT environments. The mix of review articles and original studies makes the volume useful for understanding both current methods and emerging challenges.

Who Will Find It Useful

This proceedings is intended for researchers, academics, and practitioners working in computer science, data science, and related engineering fields. Graduate students looking for a snapshot of active research areas will also find relevant material. Because it is a collection of conference papers rather than a tutorial textbook, readers should have some familiarity with machine learning and data analysis concepts.

Series Context

Published by Springer Nature Singapore in 2021, the book is part of the Lecture Notes in Networks and Systems series. The series is indexed by Scopus, INSPEC, WTI Frankfurt eG, zbMATH, and SCImago, and volumes are submitted for consideration in Web of Science. This placement reflects the volume’s focus on networks, systems, and computational intelligence.

About the Editors

The editors bring together expertise from institutions in India, Indonesia, and Singapore. Their combined backgrounds in computer science, information systems, and electrical engineering shaped the selection of papers and the structure of the volume.

For readers who need a focused record of recent advances in big data and machine learning applications, this ICBMA 2019 proceedings offers a substantial, peer-reviewed collection in a single digital volume.