Suraya Masrom, Universiti Teknologi MARA, Malaysia

Associate Professor Ts. Dr Suraya Masrom is the head of Machine Learning and Interactive Visualization (MaLIV) Research Group at Universiti Teknologi MARA (UiTM) Perak Branch. She is the chief editor of Mathematical Sciences and Informatics Journal of Universiti Teknologi MARA Press. She received her Ph.D. in Information Technology and Quantitative Science from UiTM in 2015. She started her career in the information technology industry as an Associate Network Engineer at Ramgate Systems Sdn. Bhd (a subsidiary of DRB-HICOM) in June 1996 after receiving her bachelor’s degree in computer science from Universiti Teknologi Malaysia (UTM) in Mac 1996. She started her career as a lecturer at UTM after receiving her master’s degree in computer science from Universiti Putra Malaysia in 2001. She transferred to the Universiti Teknologi MARA (UiTM), Seri Iskandar, Perak, Malaysia, in 2004. She is an active researcher in the meta-heuristics search approach, machine learning, and educational technology. She can be contacted through email.

(Online Talk) Speech Title: Genetic Programming in Automated Machine Learning
Abstract: The challenge of implementing machine learning (ML) models in various domains lies not just in the complexity of algorithms but also in the intricacies of feature selection, algorithm choice, and hyper-parameter tuning. This is where Automated Machine Learning (AML) steps in, offering a transformative approach to surmount these hurdles. AML simplifies the end-to-end process of developing ML models, making it accessible to both experts and novices alike, and significantly reducing the time and expertise required to deploy effective solutions.  One of the most promising techniques within AML is Genetic Programming (GP), a type of evolutionary algorithm that mimics the process of natural selection to iteratively find the best solutions. GP can be effectively used in AML to optimize machine learning pipelines. This optimization process involves automatically selecting the best features, choosing the most suitable algorithms, and tuning hyper-parameters in a way that is significantly more efficient and less prone to human error than traditional methods. GP-based AML does not rely on brute-force or exhaustive search. Instead, it evolves over generations, gradually improving solutions by combining and mutating them to find the most effective configurations. This approach can suggest the best feature selection strategies, identify the most appropriate algorithms, and determine the optimal settings for hyper-parameters, leading to enhanced model performance and accuracy. The application of GP in AML has shown promising results across a wide range of domains, from healthcare and finance to environmental modeling and beyond. By leveraging the power of GP, AML is not only making ML more accessible but also pushing the boundaries of what can be achieved, enabling the development of more sophisticated, efficient, and robust models. This evolutionary approach represents a significant leap forward in the quest to democratize and streamline the ML development process, highlighting the vast potential of combining genetic algorithms with automated machine learning techniques.

 

 

Anand Nayyar, Duy Tan University, Vietnam

Dr. Anand Nayyar received Ph.D (Computer Science) from Desh Bhagat University in 2017 in the area of Wireless Sensor Networks, Swarm Intelligence and Network Simulation. He is currently working in School of Computer Science-Duy Tan University, Da Nang, Vietnam as Professor, Scientist, Vice-Chairman (Research) and Director- IoT and Intelligent Systems Lab. A Certified Professional with 125+ Professional certifications from CISCO, Microsoft, Amazon, EC-Council, Oracle, Google, Beingcert, EXIN, GAQM, Cyberoam and many more. Published more than 175+ Research Papers in various High-Quality ISI-SCI/SCIE/SSCI Impact Factor- Q1, Q2, Q3,Q4 Journals cum Scopus/ESCI indexed Journals, 70+ Papers in International Conferences indexed with Springer, IEEE and ACM Digital Library, 40+ Book Chapters in various SCOPUS/WEB OF SCIENCE Indexed Books with Springer, CRC Press, Wiley, IET, Elsevier with Citations: 10000+, H-Index: 53 and I-Index: 190. Member of more than 60+ Associations as Senior and Life Member. He has authored/co-authored cum Edited 50+ Books of Computer Science. Associated with more than 600+ International Conferences as Programme Committee/Chair/Advisory Board/Review Board member. He has 18 Australian Patents, 7 German Patents, 4 Japanese Patents, 33 Indian Design cum Utility Patents, 8 UK Patents, 1 USA Patent, 3 Indian Copyrights and 2 Canadian Copyrights to his credit in the area of Wireless Communications, Artificial Intelligence, Cloud Computing, IoT, Healthcare, Drones, Robotics and Image Processing. Awarded 43 Awards for Teaching and Research—Young Scientist, Best Scientist, Best Senior Scientist, Asia Top 50 Academicians and Researchers, Young Researcher Award, Outstanding Researcher Award, Excellence in Teaching, Best Senior Scientist Award, DTU Best Professor and Researcher Award- 2019, 2020-2021, 2022, Distinguished Scientist Award by National University of Singapore, Obada Prize 2023 and many more. He is listed in Top 2% Scientists as per Stanford University (2020, 2021, 2022) , Ad Index (Rank No:1 Duy Tan University, Rank No:1 Computer Science in Viet Nam) and Listed on Research.com (Top Scientist of Computer Science in Viet Nam- National Ranking: 2; D-Index: 31). He is acting as Associate Editor for Wireless Networks (Springer), Computer Communications (Elsevier), International Journal of Sensor Networks (IJSNET) (Inderscience), Frontiers in Computer Science, PeerJ Computer Science, Human Centric Computing and Information Sciences (HCIS), Tech Science Press- IASC, IET-Quantum Communications, IET Wireless Sensor Systems, IET Networks, IJDST, IJISP, IJCINI, IJGC, IJSIR. He is acting as Managing Editor of IGI-Global Journal, USA titled “International Journal of Knowledge and Systems Science (IJKSS)” and Editor-in-Chief of IGI-Global, USA Journal titled “International Journal of Smart Vehicles and Smart Transportation (IJSVST)”. He has reviewed more than 2500+ Articles for diverse Web of Science and Scopus Indexed Journals. He is currently researching in the area of Wireless Sensor Networks, Internet of Things, Swarm Intelligence, Cloud Computing, Artificial Intelligence, Drones, Blockchain, Cyber Security, Healthcare Informatics, Big Data and Wireless Communications.

Title: AI in Education: Exploring ChatGPT in Education and Future Perspectives

Abstract:  As artificial intelligence (AI) continues to revolutionize various industries, its integration into education has garnered significant attention for its potential to enhance learning experiences and outcomes. This talk will delve into the role of AI, particularly focusing on ChatGPT, in education and will provide insights into its current applications and future prospects. The presentation will commence with an overview of AI's growing influence in education, highlighting its capacity to personalize learning, automate administrative tasks, and facilitate adaptive learning environments. ChatGPT, an advanced natural language processing model, will be introduced as a versatile tool capable of simulating human-like conversations and offering tailored educational support. The talk will then explore the diverse applications of ChatGPT in education, ranging from virtual tutoring systems to automated essay grading and intelligent educational assistants. Through real-world examples and case studies, attendees will gain a comprehensive understanding of how ChatGPT can be leveraged to address various educational challenges and cater to individualized learning needs.