This book explores the practical use of data science in AI, healthcare, sustainability, and security. It covers key topics like predictive modelling, deep learning, and natural language processing, offering a mix of theory and hands-on applications.
Data-Driven Applications for Emerging Technologies explores the practical use of data science in AI, healthcare, sustainability, and security. It covers key topics like predictive modelling, deep learning, and natural language processing, offering a mix of theory and hands-on applications. The book highlights how data-driven techniques can improve decision-making, optimize processes, and solve real-world problems.
Each chapter includes research contributions from academics and industry professionals, making the content both relevant and accessible. Readers will find practical insights into applying machine learning frameworks, data preprocessing techniques, and emerging technologies across different domains.
Designed for researchers, professionals, and students, this book provides a solid foundation in data-driven methods without being overly technical. Whether you’re looking to enhance your understanding of AI and machine learning or apply data science in real-world scenarios, this book serves as a useful and practical resource.
1. Towards Deep Autoencoder for Recommendation System Using Implicit
Feedback.
2. Intelligent Learning Behavior Analysis for Student Based on
Fuzzy Agent Model.
3. Quantifying Shifts in Word Contexts from Social Media
Data.
4. BanglaOngko: A New Dataset for Accurate Bengali Mathematical
Expression Detection Utilizing YOLOv8 Architecture.
5. Pandemic Prediction
and Prevention in Bangladesh by Data Mining Approach.
6. Impacts of passenger
request trends on ride-sharing system performance.
7. Protein Structure
Prediction Using Feature Selective Support Vector Machines.
8. Sentiment
Analysis of Bangla Text Using Transformer Based Model.
9. DDOS Detection
Using Machine Learning.
10. Extracting Clinically Relevant Phrases from
Patient Notes using BERT and Multi-Teacher Knowledge Distillation.
11. An
Approach to Ensure Public Safety Using Masked Face Recognition.
12. Improving
the Efficiency of Waste Management with a Residual Network-Based Framework.
13. Designing the Most Eco-Friendly Spatial Landscape and Natural Environment
Using A* Path Finding Algorithms.
14. Rainwater Harvesting and Reducing
Biological Threat of Utilizing.
15. Yoga Pose Classification Using Transfer
Learning.
16. Bornomala: A CNN Ensemble approach for Bangla Sign Language
Detection.
17. Fuzzy-based Model for Perceived Value and Customer
Satisfaction in Fine Dining.
18. An Improved Ensemble Model for Intent
Classification of Bangla Chatbot.
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. Ahmed Wasif Reza, a Professor at East West University, has been driving research and innovation for over two decades in AI, Machine Learning, Robotics, and Wireless Communications. Beyond teaching, he plays a key role in quality assurance and accreditation, ensuring excellence in engineering education. With over 250 published papers, his work spans Green Computing, Brain-Computer Interfaces, and the Internet of Things, making a lasting impact in both academia and industry.
Dr. Aminul Haque, a professor and associate head at Daffodil International University, completed his B.Sc. at Shahjalal University of Science and Technology and earned his Ph.D. from MONASH University. He has been actively involved in developing a national curriculum for big data and data science, while also contributing to AI, machine learning, and IoT research. Beyond academics, he plays a key role in quality assurance, coordinates the Future DIU program, and leads innovation labs focused on emerging technologies.
