XinYing Chew | Computer Science | Young Scientist Award

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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

 

Ritu Tanwar | Artificial intelligence | Best Researcher Award

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Ms. Ritu Tanwar | Artificial intelligence | Best Researcher Award

Research Scholar, NIT Uttarakhand, India

Ms. Ritu Tanwar is a dedicated Research Scholar at the National Institute of Technology, Uttarakhand, India, specializing in stress and emotion recognition through advanced machine learning techniques. Her innovative research harnesses deep learning and artificial intelligence to interpret physiological signals, contributing significantly to the field of affective computing. Ritu’s academic journey and teaching roles underline her commitment to advancing both theoretical and practical aspects of her research.

Profile

Scopus

Research for “Best Researcher Award” for Ms. Ritu Tanwar

Strengths for the Award

Ms. Ritu Tanwar, currently pursuing her PhD at the National Institute of Technology, Uttarakhand, has demonstrated exceptional strengths in her field of research. Her primary area of focus—stress and emotion recognition through physiological signals—highlights her deep engagement with cutting-edge technology and data analysis. Ritu’s work utilizes advanced techniques in deep learning and machine learning to address significant challenges in affective state recognition.

Innovative Research Contributions: Ritu’s research integrates multimodal physiological signals to enhance stress recognition, showcasing her ability to develop and implement novel frameworks. Her attention-based hybrid deep learning models for wearable stress recognition, published in prestigious journals like Engineering Applications of Artificial Intelligence and Computers and Electrical Engineering, underline her proficiency in blending theory with practical application.

High-Impact Publications: Her publications in high-impact journals and conferences, including Computers in Biology and Medicine and the International Conference on Artificial Intelligence, reflect the substantial impact of her work on the field. Her innovative models, such as the CNN-LSTM based stress recognition system, are well-received and contribute to advancing the state of the art in affective computing.

Diverse Expertise: Ritu’s skill set spans various domains, from deep learning and artificial intelligence to data analysis and signal processing. Her ability to apply these skills effectively in her research demonstrates a well-rounded expertise that is crucial for a leading researcher.

Areas for Improvement

While Ms. Tanwar’s achievements are commendable, there are areas where she could further enhance her profile:

Broader Research Collaboration: Expanding her collaborative network with researchers from diverse fields could provide new insights and foster interdisciplinary approaches. Engaging in more collaborative projects could also increase the visibility and applicability of her research outcomes.

Broadened Publication Scope: Although Ritu has published extensively, diversifying her publication portfolio to include more interdisciplinary journals or higher-impact venues could further amplify the reach and influence of her research.

Enhanced Outreach: Increasing her participation in academic and industry conferences, workshops, and seminars could boost her professional network and provide more platforms to showcase her research. Additionally, contributing to review articles or special issues in her field could enhance her visibility as a thought leader.

Education 🎓

Ms. Tanwar is currently pursuing a PhD in Electronics Engineering at the National Institute of Technology, Uttarakhand, India, focusing on developing a deep learning framework for affective state recognition using multimodal physiological signals (April 2021-present). She earned her M.Tech. in Electronics & Communication Engineering from the University Institute of Engineering & Technology, Kurukshetra, India, with a thesis on emotion recognition from audio signals (July 2018). Her foundational B.Tech. in Electronics & Communication Engineering was also completed at the same institute (July 2013).

Experience 💼

Ms. Tanwar has a robust academic background, having worked as a Teaching Assistant at the National Institute of Technology, Uttarakhand, where she taught courses on Microcontroller and Interfacing, Digital Signal Processing, and Speech & Image Processing. Her research experience includes contributions as an Assistant/Associate Supervisor for undergraduate students and active participation in administrative and outreach activities, including her roles as Session Coordinator and Reviewer for the IC2E3 IEEE Conference.

Research Interests 🔬

Ms. Tanwar’s research interests are centered around stress and emotion recognition, physiological signals, and advanced data analysis techniques. She specializes in applying deep learning, machine learning, and artificial intelligence to improve the accuracy and applicability of affective state recognition systems.

Awards 🏆

Senior Research Fellow Scholarship (2021-present): Awarded for her exceptional research capabilities and contributions to her field.

Publication Recognition: Her work has been accepted and recognized in leading journals and conferences, reflecting her significant contributions to the field of artificial intelligence and machine learning.

Publications Top Notes

Tanwar, R., Phukan, O. C., Singh, G., Pal, P. K., & Tiwari, S. (2024). Attention based hybrid deep learning model for wearable based stress recognition. Engineering Applications of Artificial Intelligence, 127, 107391.

Tanwar, R., Singh, G., & Pal, P. K. (2024). A Hybrid Transposed Attention Based Deep Learning Model for Wearable and Explainable Stress Recognition. Computers and Electrical Engineering (Accepted).

Tanwar, R., Singh, G., & Pal, P. K. (2024). Explainable Artificial Intelligence System For Stress Recognition Using Multimodal Physiological Signals. Computers in Biology and Medicine (under review).

Tanwar, R., Singh, G., & Pal, P. K. (2024). Stress-Wed: Stress recognition autoencoder using Wearables Data. In Second International Conference on Artificial Intelligence: Towards Sustainable Intelligence. Springer (Accepted).

Conclusion

Ms. Ritu Tanwar’s research on stress and emotion recognition using physiological signals is both innovative and impactful, making her a strong candidate for the “Best Researcher Award.” Her contributions to deep learning and machine learning in affective computing are significant, and her academic and teaching experiences add to her profile as a dedicated and knowledgeable researcher. By addressing areas for improvement, such as expanding collaboration and publication scope, Ritu can further strengthen her position as a leading researcher in her field. Her ongoing research promises to make substantial contributions to both theoretical and applied aspects of artificial intelligence and emotion recognition.