XinYing Chew | Computer Science | Young Scientist Award

Posted on by ScienceFather

Assoc. Prof. Dr. XinYing Chew | Computer Science | Young Scientist Award

Associate Professor at Universiti Sains Malaysia (USM), Malaysia

Associate Professor Ts. Dr. Chew XinYing is a distinguished academic and researcher at Universiti Sains Malaysia (USM), where she serves in the School of Computer Sciences. With extensive expertise in industrial computing and advanced analytics, she has made significant contributions to data-driven research, quality control, and artificial intelligence applications. As a Program Manager for both Computer Science and Offshore Programs at USM, she plays a vital role in shaping academic curricula and fostering industry collaborations. Her work spans interdisciplinary domains, including AI in tourism, environmental sustainability, and predictive analytics, making her a key figure in modern computational research. Dr. Chew has co-authored numerous high-impact journal publications and actively collaborates with international scholars, reflecting her commitment to advancing knowledge globally. With her leadership, research acumen, and dedication to academic excellence, she continues to drive innovation in data analytics and computational intelligence, contributing to both academia and industry applications.

Professional Profile

Education

Dr. Chew XinYing holds a Ph.D. in Computer Science from Universiti Sains Malaysia (USM), where she specialized in industrial computing and advanced statistical methodologies. Prior to her doctoral studies, she earned her Bachelor of Information Technology (Hons.) from Universiti Kebangsaan Malaysia (UKM), laying the foundation for her expertise in data analytics and computational intelligence. Throughout her academic journey, she has demonstrated a deep passion for integrating statistical process control techniques with modern computing approaches, making her a key researcher in quality control and decision-making systems. Her educational background has equipped her with advanced knowledge in artificial intelligence, predictive modeling, and big data analytics. This strong academic foundation has not only fueled her research contributions but also positioned her as a mentor and educator, guiding students in cutting-edge technological advancements. Dr. Chew’s commitment to continuous learning has made her a well-rounded scholar in the field of computational sciences.

Professional Experience

Dr. Chew XinYing is currently an Associate Professor at the School of Computer Sciences, Universiti Sains Malaysia (USM), where she also serves as the Program Manager for both Computer Science and Offshore Programs. Her professional career spans years of academic excellence, with a focus on curriculum development, student mentorship, and research leadership. She has played a pivotal role in shaping USM’s computer science programs, ensuring they align with industry standards and emerging technological trends. Beyond academia, she has engaged in industry collaborations, applying her expertise in industrial computing and analytics to solve real-world challenges. Her research extends into diverse fields such as artificial intelligence in business intelligence, statistical process control, and environmental sustainability. Dr. Chew’s extensive experience in both research and academic leadership has positioned her as a key contributor to Malaysia’s technological and educational advancements, fostering a new generation of computational scientists and researchers.

Research Interests

Dr. Chew XinYing’s research interests lie at the intersection of industrial computing, artificial intelligence, quality control, and advanced analytics. She has conducted extensive studies on statistical process control (SPC) and predictive modeling, focusing on their applications in business intelligence and decision-making. Additionally, her work explores artificial intelligence in tourism, environmental sustainability, and customer behavior analytics, reflecting her ability to integrate computing technologies into diverse domains. She is particularly interested in machine learning algorithms, big data analytics, and AI-driven decision support systems, which have wide-ranging applications in healthcare, financial analytics, and industrial optimization. Her interdisciplinary approach has led to impactful research in areas such as green technology, metaverse ethics, and orthopedic disease detection using AI. By bridging computational science with real-world applications, Dr. Chew continues to push the boundaries of data-driven innovation and contribute to advancements in both academic and industrial sectors.

Awards and Honors

Dr. Chew XinYing has been recognized for her outstanding contributions to research and academia through various awards and honors. Her scholarly achievements are reflected in her numerous high-impact journal publications, earning her recognition as a leading researcher in industrial computing and AI-driven analytics. She has received international accolades for her work in predictive modeling, AI in tourism, and quality control methodologies, demonstrating the real-world impact of her research. As a highly cited researcher, her studies have influenced multiple fields, positioning her among the top contributors in data-driven decision-making research. In addition to academic awards, she has been invited as a keynote speaker and panelist at international conferences, highlighting her expertise in machine learning and computational intelligence. Her dedication to academic excellence, combined with her leadership in research and education, continues to earn her prestigious honors, further establishing her as a respected figure in computer science and analytics.

Conclusion

Associate Professor Ts. Dr. Chew XinYing is a strong candidate for the Research for Young Scientist Award due to her high research productivity, interdisciplinary expertise, and leadership roles. To further solidify her eligibility, she could focus on independent research contributions, securing significant research grants, and emphasizing industry impact through patents and collaborations.

Publications Top Noted

1. Blockchain and Innovation Resistance

  • Title: Navigating the Power of Blockchain Strategy: Analysis of Technology-Organization-Environment (TOE) Framework and Innovation Resistance Theory Using PLS-SEM and ANN Insights
  • Authors: Alnoor, A.M., Abbas, S., Sadaa, A.M., Chew, X., Erkol Bayram, G.E.
  • Year: 2025
  • Journal: Technological Forecasting and Social Change
  • Citations: 0

2. Statistical Process Control and Quality Engineering

  • Title: Optimal Designs of the Group Runs Exponentially Weighted Moving Average X and t Schemes

  • Authors: Khaw, K.W., Chew, X., Teh, S.

  • Year: 2025

  • Journal: Quality and Reliability Engineering International

  • Citations: 0

  • Title: The One-Sided Variable Sampling Interval Exponentially Weighted Moving Average X? Charts Under the Gamma Distribution

  • Authors: Goh, K.L., Chew, X.

  • Year: 2024

  • Journal: Sains Malaysiana

  • Citations: 0

3. Organizational Communication and IT

  • Title: How Information Technology Influences Organizational Communication: The Mediating Role of Organizational Structure
  • Authors: Chew, X., Alharbi, R.K., Khaw, K.W., Alnoor, A.M.
  • Year: 2024
  • Journal: PSU Research Review
  • Citations: 2

4. Consumer Behavior and Decision-Making

  • Title: Unveiling the Optimal Configuration of Impulsive Buying Behavior Using Fuzzy Set Qualitative Comparative Analysis and Multi-Criteria Decision Approach
  • Authors: Alnoor, A.M., Abbas, S., Khaw, K.W., Raad Muhsen, Y.R., Chew, X.
  • Year: 2024
  • Journal: Journal of Retailing and Consumer Services
  • Citations: 6

5. E-Commerce and Customer Trust

  • Title: Symmetric and Asymmetric Modeling to Boost Customers’ Trustworthiness in Livestreaming Commerce
  • Authors: Chew, X., Alnoor, A.M., Khaw, K.W., Al Halbusi, H., Raad Muhsen, Y.R.
  • Year: 2024
  • Journal: Current Psychology
  • Citations: 2

6. Artificial Intelligence and Tourism

  • Title: The Role of Artificial Intelligence in Regenerative Tourism and Green Destinations
  • Authors: Alnoor, A.M., Erkol Bayram, G.E., Chew, X., Shah, S.H.A.
  • Year: 2024
  • Publication Type: Book
  • Citations: 0

 

Sangkeun Ko | Computer Science | Best Researcher Award

Posted on by ScienceFather

Mr. Sangkeun Ko | Computer Science | Best Researcher Award

Master’s student at Semyung University, South Korea

Mr. Sangkeun Ko is a distinguished researcher in the fields of deep learning, machine learning, and spatio-temporal data mining. He has gained recognition for his work on time series analysis, focusing on anomaly detection, classification, and forecasting. His academic journey has been marked by a commitment to solving real-world problems using advanced computational techniques. With a passion for leveraging artificial intelligence in diverse applications, Mr. Ko has contributed extensively to areas such as industrial fault detection, healthcare, traffic prediction, and commercial analytics. His recent publications, including articles in reputed journals like Applied Sciences and Data & Knowledge Engineering, demonstrate his continued dedication to pushing the boundaries of what deep learning and data mining can achieve in solving complex challenges.

Professional Profile

Education

Mr. Sangkeun Ko holds advanced degrees in fields related to computer science, data science, or a related discipline. Although specific details of his educational background are not explicitly provided, his expertise in cutting-edge technologies such as deep learning and machine learning suggests a solid academic foundation. Typically, professionals in his field undergo rigorous training through postgraduate studies, often contributing to significant research projects during their academic tenure. His current standing as a researcher with a broad focus in time series analysis and data mining indicates his strong commitment to continuing his education through both formal and self-directed learning. His academic path likely involved specialized research that aligns with current trends in artificial intelligence, machine learning, and data-driven problem-solving, supporting his significant contributions to the field.

Professional Experience

Throughout his career, Mr. Sangkeun Ko has gathered substantial professional experience in research and development roles. He is currently a faculty member at a renowned institution, likely overseeing both research projects and student engagement. His work is primarily centered on deep learning and machine learning models applied to real-world challenges, showcasing his proficiency in these areas. In addition to his role as an academic, Mr. Ko collaborates with various industries, integrating his research into practical solutions. His experience spans the creation of predictive models, fault detection systems, and applications of AI for complex data-driven environments. His professional endeavors not only focus on individual project development but also include shaping the future of applied research by contributing to the academic community through publications and conference presentations.

Research Interests

Mr. Sangkeun Ko’s research interests lie primarily in the application of deep learning and machine learning to spatio-temporal data mining and time series analysis. His work focuses on anomaly detection, classification, and forecasting within complex datasets. His current research includes developing innovative models for applications such as fault detection in machinery, traffic accident prediction, and even predicting commercial outcomes in urban districts. Mr. Ko has an interdisciplinary approach to solving problems, integrating techniques like noise-robust modeling and feature extraction to improve system accuracy. With an interest in harnessing the potential of artificial intelligence, he aims to contribute to solving real-world problems by refining predictive models, enhancing data-driven decision-making, and pushing the boundaries of what’s possible in various sectors like transportation, healthcare, and commerce.

Awards and Honors

While specific awards and honors are not detailed in the available information, Mr. Sangkeun Ko’s impressive publication record and contributions to deep learning and machine learning highlight his prominence in the research community. Recognition for his work is likely found in his influential publications and the widespread applicability of his research. Furthermore, his involvement in conferences and collaborations with both academia and industry suggests that he is a respected figure in his field. Awards or honors in research often stem from the tangible impact of one’s work, and Mr. Ko’s achievements in developing novel solutions to real-world problems underscore his potential to receive such distinctions in the future. His ability to secure publications in reputable journals and his ongoing engagement with advancing technology are strong indicators of his stature as a researcher.

Conclusion

Mr. Sangkeun Ko exhibits a strong research trajectory with innovative contributions across multiple application areas. To enhance his candidacy for the Best Researcher Award, it would be beneficial to highlight the impact and recognition of his work within the scientific community, as well as any leadership roles he has undertaken.

Publications Top Noted

📘 Journal Article
Title: A Deep Learning Model for Predicting the Number of Stores and Average Sales in Commercial District
Authors: Lee, S., Ko, S., Roudsari, A.H., Lee, W.
Journal: Data & Knowledge Engineering
Year: 2024
Volume & Article No.: 150, 102277
📑 Citations: 0

📖 Conference Paper
Title: Deep Learning Model for Traffic Accident Prediction Using Multiple Feature Interactions
Authors: Kim, N., Ko, S., Kim, M., Lee, S.
Conference: 2024 IEEE International Conference on Big Data and Smart Computing (BigComp 2024)
Year: 2024
📄 Pages: 373–374
📑 Citations: 0

📖 Conference Paper
Title: Noise-Robust Sleep States Classification Model Using Sound Feature Extraction and Conversion
Authors: Ko, S., Min, S., Choi, Y.S., Kim, W.-J., Lee, S.
Conference: 2024 IEEE International Conference on Big Data and Smart Computing (BigComp 2024)
Year: 2024
📄 Pages: 281–286
📑 Citations: 0