Wah / Berry / Mohamed Data Science and Emerging Technologies

Proceedings of DaSET 2022
1. Auflage 2023
ISBN: 978-981-99-0741-0
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 165, 548 Seiten, eBook

Reihe: Lecture Notes on Data Engineering and Communications Technologies

The book presents selected papers from International Conference on Data Science and Emerging Technologies (DaSET 2022), held online at UNITAR International University, Malaysia, during December 20–21, 2022. This book aims to present current research and applications of data science and emerging technologies. The deployment of data science and emerging technology contributes to the achievement of the Sustainable Development Goals for social inclusion, environmental sustainability, and economic prosperity. Data science and emerging technologies such as artificial intelligence and blockchain are useful for various domains such as marketing, health care, finance, banking, environmental, and agriculture.  An important grand challenge in data science is to determine how developments in computational and social-behavioral sciences can be combined to improve well-being, emergency response, sustainability, and civic engagement in a well-informed, data-driven society.  The topics ofthis book include, but not limited to: artificial intelligence, big data technology, machine and deep learning, data mining, optimization algorithms, blockchain, Internet of Things (IoT), cloud computing, computer vision, cybersecurity, augmented and virtual reality, cryptography, and statistical learning.

Zielgruppe

Research

Autoren/Hrsg.

Wah, Yap Bee

Berry, Michael W.

Mohamed, Azlinah

Al-Jumeily, Dhiya

Inhaltsverzeichnis

Part I: Artificial Intelligence.- Extractive Text Summarization Using Syntactic Sub- Graph Models.- Analysis of Big Five Personality Factors to determine the Appropriate Type of Career using the C4.5 Algorithm.- Predicting Disaster Type from Social Media Imagery via Deep Neural Networks Directed by Visual Attention.- Dissemination Management for Official Statistics Using Artificial Intelligence-Based Media Monitoring.- Part II: Computational Vision.- A Naive but Effective Post-Processing Approach for Dark Channel Prior (DCP).- COVID-19 Face Mask Classification Using Deep Learning.- Gender Classification Using Transfer Learning and Fine-Tuning.- Multi-Language Recognition Translator by Using the Convolutional Neural Network (CNN) Algorithm and Optical Character Recognition (OCR).- Autonomous Driving Through Road Segmentation Based on Computer Vision Techniques.- Part III: Cybersecurity.- Phishing Attack Types and Mitigation: A Survey.- A Review of Privacy Protection Methods for Smart Homes Against Wireless Snooping Attack.- Development of Graph-Based Knowledge on Ransom-Ware Attacks Using Twitter Data.- Part IV: Big Data Analytics.- BigMDHealth: Supporting Multidimensional Big Data Management and Analytics over Big Healthcare Data via Effective and Efficient Multidimensional Aggregate Queries over Key-Value Stores (Prof Alredo's paper).- Design and Implementation of Data Warehouse Solution at Kumpulan Wang Persaraan (KWAP).- Consumer Behavior Prediction During Covid-19 Pandemic Conditions Using Sentiment Analytics.- Big Data Application on Prediction of HDD Manufacturing Process Performance.- Visualising Economic Situation Through Malaysia Economic Recovery Dashboard (MERD).- Part V: Machine/Deep Learning.- Lung Nodules Classification Using Convolutional Neural Network with Transfer Learning.- Plant Growth Phase Classification Using Deep Neural Network.- The Implementation of Genetic Algorithm-Ensemble Learning on QSAR Study of Diacylglycerol Acyltransferase-1(DGAT1)Inhibitors as Anti-Diabetes.- Classification of Exercise Game Data for Rehabilitation Using Machine Learning Algorithms.- SDDLA: A New Architecture for Secured Decentralized Distributed Learning.- Gated Memory Unit: A Novel Recurrent Neural Network Architecture for Sequential Analysis.- Multi-Class Classification for Breast Cancer with High Dimensional Microarray Data Using Machine Learning Classifier.- Predicting Risks of Late Delivery to Online Shopping Customers Using Machine Learning Techniques.- Quora Insincere Questions Classification Using Attention Based Model.- Suicide Ideation Detection: A Comparative Study of Sequential and Transformer Hybrid Algorithms.- Well Log Data Preparation and Effective Utilization of Drilling Parameters Using Data Science Based Approaches.- Deep Learning-Based Approach for Classifying the Severity of Metal Corrosion Using SEM Images.- Insurance Risk Prediction Using Machine Learning.- Loan Default Forecasting Using StackNet.- Part VI: Statistical Learning.- Neural Network Autoregressive Model for Forecasting Malaysia Under-5 Mortality.- Robustness of Support Vector Regression and Random Forest Models: A Simulation Study.- The Impact of Restrictions Community Activities on COVID-19 Transmission: A Case Study in Sumatra Island, Indonesia.- Predicting Internet Usage for Digital Finance Services: Multitarget Classification Using Vector Generalized Additive Model with SMOTE-NC.- Part VII: Text Mining and Classification.- Identifying Topic Modeling Technique in Evaluating Textual Datasets.- Y-X-Y Encoding for Identifying Types of Sentence Similarity.- Evaluation of Extractive and Abstract Methods in Text Summarization

Über Autor(innen)

Professor Yap Bee Wah is currently Director of Research and Consultancy Centre of UNITAR International University, Malaysia.  She was formerly Faculty Member of the Centre of Statistical and Decision Science Studies at Faculty of Computer and Mathematical Sciences (FSKM), Universiti Teknologi MARA. She was also Head of Advanced Analytics Engineering Centre (AAEC), a research center of excellence in Faculty of Computer and Mathematical Sciences (2016-2020). In February 2021, AAEC became a Centre of Excellence in UiTM with the name Institute of Big Data Analytics and Artificial Intelligence (IBDAAI). She has supervised 15 Ph.D. students. She is Active Researcher and has published papers in ISI journals such as Expert Systems with Applications Journal of Statistical Computation and Simulation, Communications in Statistics-Computation and Simulation and Journal of Clinical and Translational Endocrinology, and also in Scopus-indexed journals. She was Conference Chair of the International Conference on Soft Computing in Data Science (2015-2019 and 2021). She was also one of the editors of the SCDS conference proceedings published in Springer CCIS series. She was Guest Editor of Applied Soft Computing (Q1) journal and Pertanika Journal of Social Science and Humanities Special Issue (2016). She is also one of the editors of the book titled “Supervised and Unsupervised Learning for Data Science” published by Springer Nature Switzerland AG 2020. This book is in collaboration with Prof. Michael W. Berry, University of Tennessee, USA, and Prof. Azlinah Mohamed, Universiti Teknologi MARA. Professor Michael W. Berry is Co-author and Editor of fifteen books covering topics in scientific computing, information retrieval, text/data mining, and data science. His most recent book entitled “Supervised and Unsupervised Learning for Data Science” was published in 2019 by Springer International Publishing.  He is Co-editor of the Soft Computing in DataScience volumes from 2015-2019 published by Springer and is Co-author of popular books published by Society for Industrial and Applied Mathematics (SIAM): Understanding Search Engines: Mathematical Modeling and Text Retrieval, Second Edition, and Computational Information Retrieval. He has published over 115 refereed journal and conference publications. He has organized numerous workshops on Text Mining and was Conference Co-chair of the 2003 SIAM Third International Conference on Data Mining in San Francisco, CA. He was also Program Co-chair of the 2004 SIAM Fourth International Conference on Data Mining in Orlando, Florida, and is currently Honorary Co-chair of the International Conference on Soft Computing in Data Science (SCDS) series (2015-present). He is Member of SIAM, ACM, MAA, ASEE, and the IEEE Computer Society and is on the editorial board of Foundations of Data Science (AIMS) and the SIAM Journal on Matrix Analysis and Applications (SIAM).  Professor Berry is also Certified Program Evaluator for the Computing Accreditation Commission (CAC) of the Accreditation Board for Engineering and Technology, Inc. (ABET). Professor Dr. Azlinah Mohamed holds the title of Full Professor at the Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti of Teknologi MARA (UiTM), Shah Alam, Malaysia. She has a strong managerial background and a series of industrial linkages. She is also one of the editors of the Soft Computing in Data Science, SCDS (2015-2019 and 2021) conference proceedings published in Springer CCIS series. She is also one of the editors of the book titled “Supervised and Unsupervised Learning for Data Science” published by Springer Nature Switzerland AG 2020. This book is in collaboration with Prof. Michael W. Berry, University of Tennessee, USA, and Prof. Yap Bee Wah, Universiti Teknologi MARA. Her current research interests are in the areas of Big Data, Soft Computing, Artificial Intelligence, and Web-based Decision Support Systems using intelligent agents in electronic government applications. She has good strategic appreciation and vision with a proven track record in supporting business and industry needs and highly focused with a consistent track record of successful and relevant academic programs with time and budget. Her research is well communicated in a series of conferences, journals, and high-impact journals indexed in ISI or Scopus.  Dr. Dhiya Al-Jumeily OBE is Professor of Artificial Intelligence and President of eSystems Engineering Society. He has extensive research interests covering a wide variety of interdisciplinary perspectives concerning the theory and practice of applied artificial intelligence in medicine, human biology, environment, intelligent community, and health care. He has published well over 300 peer-reviewed scientific international publications, 17 books, and 17 book chapters in multidisciplinary research areas including: machine learning, neural networks, signal prediction, telecommunication fraud detection, AI-based clinical decision-making, medical knowledge engineering, human–machine interaction, intelligent medical information systems, sensors and robotics, and wearable and intelligent devices and instruments. But his current research passion is decision support systems for self-management of health and medicine. Dhiya has successfully supervised over 20 Ph.D. students’ studies and has been External Examiner to various UK and international universities for undergraduate programs, postgraduate programs, and research degrees. He has been actively involved as Member of editorial board and review committee for a number of peer-reviewed international journals and acts as Program Committee Member or as General Chair for a number of international conferences. Dhiya is also successful Entrepreneur. He is Head of enterprise for the Faculty of Engineering and Technology. He has been awarded various commercial and research grants, nationally and internationally, over £7.5M from Overseas Research and Educational Partners, UK, through British Council and directly from industry with portfolio of various Knowledge Transfer Programs between academia and industry. He has a large number of international contacts and leads or participates in several international committees in his research fields. Dhiya has one patent and coordinated over 10 projects at national and international levels.