Data Analytics, Information Retrieval, Artificial Intelligence, Machine Learning, Knowledge Discovery, Text Mining, Natural Language Processing
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Rayner Alfred is an Associate Professor of Computer Science at the Faculty of Computing and Informatics, Universiti Malaysia Sabah in Malaysia. He leads and defines projects around knowledge discovery and information retrieval that focuses on building smarter mechanism that enables knowledge discovery in structured and unstructured data. His work addresses the challenges related to big data problem: How can we create and apply smarter collaborative knowledge discovery technologies that bridge the structured and unstructured data mining and cope with the big data problem.
Rayner completed his PhD in 2008 looking at intelligent techniques to model and optimize the dynamic and distributed processes of knowledge discovery for structured and unstructured data. He holds a PhD degree in Computer Science from York University (United Kingdom), a Master degree in Computer Science from Western Michigan University, Kalamazoo (USA) and a Computer Science degree from Polytechnic University of Brooklyn, New York (USA) where he was the recipient of the Myron M. Rosenthal Academic Achievement Award for the outstanding academic achievement in Computer Science in 1994. He has authored and co-authored more than 85 journals/book chapters and conference papers, editorials, and served on the program and organizing committees of numerous national and international conferences and workshops.
Rayner is currently a member of IEEE, a Certified Software Tester (CTFL) from the International Software Testing Qualifications Board (ISTQB) and also a certified IBM DB2 Academic Associate (IBM DB2 AA). He leads the Knowledge Discovery and Machine Learning (KDML-UMS) research group in UMS and he has lead several projects related to knowledge discovery and machine learning on Big Data. Some of the projects include Semantic Multi-Agent For Knowledge Sharing (Indigenous Ensemble Semantic Model for Knowledge-Based Society), Developing An Evolutionary-Based Ensemble Classifier Framework for Learning Big Relational Data, Developing a genetic-based hierarchical agglomerative clustering technique for parallel clustering of bilingual corpora based on reduced terms, Enhancing Document Clustering By Integrating Semantic Background Knowledge and Syntactic Features Into the BOW Representation and the Fundamental Study on an Evolutionary Based Features Construction Methods for Data Summarization Approach to Predict Survival Factors of Coral Reefs in Malaysia, to name a few.