Data Driven Applications for Industry 4.0 and Beyond
Published on 1. December 2025
304 pages
978-1-040-42463-6 (ISBN)
Description
The increasing reliance on automation and data-driven decision-making is transforming industries. As technology advances, the need for more intelligent and efficient systems is growing. This book explores how data-driven approaches are being applied in various fields to solve real-world challenges.
With contributions from researchers and professionals, the chapters discuss practical applications of modern computational techniques. Topics range from optimizing industrial processes to improving predictive systems in different sectors. The book also emphasizes the importance of responsible and interpretable technology to ensure fairness and transparency.
This book is a valuable resource for students, researchers, and professionals looking to understand the evolving role of data in industry. It provides insights into emerging trends and encourages further exploration in the field of intelligent systems and automation.
With contributions from researchers and professionals, the chapters discuss practical applications of modern computational techniques. Topics range from optimizing industrial processes to improving predictive systems in different sectors. The book also emphasizes the importance of responsible and interpretable technology to ensure fairness and transparency.
This book is a valuable resource for students, researchers, and professionals looking to understand the evolving role of data in industry. It provides insights into emerging trends and encourages further exploration in the field of intelligent systems and automation.
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Product notice
Reflowable
Illustrations
4 Tables, color; 56 Tables, black and white; 96 Line drawings, color; 27 Line drawings, black and white; 14 Halftones, color; 110 Illustrations, color; 27 Illustrations, black and white
File size
33,10 MB
ISBN-13
978-1-040-42463-6 (9781040424636)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Nazmul Siddique | Mohammad Shamsul Arefin | K.M. Azharul Hasan
Data Driven Applications for Industry 4.0 and Beyond
Book
approx. 12/2025
1st Edition
Chapman & Hall/CRC
€164.50
Not yet published
Persons
Dr. Nazmul Siddique is a researcher at the School of Computing, Engineering, and Intelligent Systems, Ulster University. He has published over 170 research papers and several books on cybernetics and computational intelligence. His editorial roles in top journals highlight his academic influence and contributions.
Dr. Mohammad Shamsul Arefin is a professor at the Department of CSE, CUET, and Dean of Electrical and Computer Engineering. He has over 170 publications in journals and conferences on data mining, distributed computing, and machine learning. His leadership has significantly fostered research growth and academic excellence in many aspects.
Dr. K. M. Azharul Hasan, Professor, Department of CSE, KUET, has served as the Head of CSE and Dean of EEE, making significant contributions to engineering education in Bangladesh. He earned the University Gold Medal at Khulna University, completed his M.Eng. at AIT, Thailand, and obtained a Ph.D. from the University of Fukui, Japan, specializing in flexible data warehouse design. Currently, his research focuses on Big Data and Natural Language Processing, driving advancements in data management, computational intelligence, and AI.
Dr. M Shamim Kaiser is a professor and Chairman at the Institute of Information Technology, Jahangirnagar University. He has authored over 100 research papers on machine learning, cyber security, and cognitive radio networks. His leadership at IIT has driven academic and research excellence in ICT.
Dr. Mohammad Shamsul Arefin is a professor at the Department of CSE, CUET, and Dean of Electrical and Computer Engineering. He has over 170 publications in journals and conferences on data mining, distributed computing, and machine learning. His leadership has significantly fostered research growth and academic excellence in many aspects.
Dr. K. M. Azharul Hasan, Professor, Department of CSE, KUET, has served as the Head of CSE and Dean of EEE, making significant contributions to engineering education in Bangladesh. He earned the University Gold Medal at Khulna University, completed his M.Eng. at AIT, Thailand, and obtained a Ph.D. from the University of Fukui, Japan, specializing in flexible data warehouse design. Currently, his research focuses on Big Data and Natural Language Processing, driving advancements in data management, computational intelligence, and AI.
Dr. M Shamim Kaiser is a professor and Chairman at the Institute of Information Technology, Jahangirnagar University. He has authored over 100 research papers on machine learning, cyber security, and cognitive radio networks. His leadership at IIT has driven academic and research excellence in ICT.
Content
1. A System that Analyzes Bengali Text on Facebook Posts Using Machine Learning to Spot Suspicious Content. 2. A Reversible Transformer Based Bangla Conversational Agent. 3. URL Based Website Classification Using Deep Learning and Word Based Multiple N-gram Models. 4. BN-HTRd: A Benchmark Dataset for Document Level Offline Bangla Handwritten Text Recognition (HTR) and Line Segmentation. 5. RiceNet: Accurate Classification of Rice Varieties using Convolutional Neural Networks. 6. Vehicle Name Plate Detection and Blurring from social media images using image processing and Deep learning. 7. DCNN-SMD: A Deep Convolutional Neural Network Model to Diagnosis, Prognosis, and Characterise Sperm Morphology. 8. Muslim Salat Gesture Recognition Framework: Integrating Deep Transfer Learning and Machine Learning. 9. BHSGR-Net: A Light-Weight Convolutional Neural Architecture for Recognition of Bengali Hand Sign Gestures. 10. Can Machine Learning Help Identify Suicidal Tweets? An Ensemble Classifier Approach. 11. An Interpretable Systematic Review of Machine Learning Models for Predictive Maintenance of Aircraft Engine. 12. Multichannel Attention Networks with Ensembled Transfer Learning to Recognize Bangla Handwritten Charecter. 13. Development of a Deep Learning Classification Model for Improved Rainfall Prediction in Ireland. 14. Defect Detection of Casting Products Using Deep Learning: A Method Based on Convolutional Neural Networks. 15. OLD-TL: Offensive Language Detection in Gaming Live Stream using Transfer Learning. 16. Predicting Stress in Bangladeshi University Students: A LIMEInterpretable Machine Learning Approach. 17. Early detection of system failure using machine learning techniques. 18. Churn Prediction Using Machine Learning in the Tours and Travel Industry.
