Data Driven Applications for Emerging Technologies

1. Auflage 2025
ISBN: 978-1-032-99693-6
Verlag: Taylor & Francis Ltd

Buch, Englisch, 296 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 453 g

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.

Zielgruppe

Academic and Postgraduate

Autoren/Hrsg.

Reza, Ahmed Wasif

Siddique, Nazmul

Arefin, Mohammad Shamsul

Haque, S. M. Aminul

Fachgebiete

• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Mathematik | Informatik EDV | Informatik Informatik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Datenbankprogrammierung
• Mathematik | Informatik EDV | Informatik Technische Informatik

Inhaltsverzeichnis

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.

Über Autor(innen)

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.