Bincy Baburaj Kaluvilla | Machine Learning | Best Researcher Award

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Dr. Bincy Baburaj Kaluvilla | Machine Learning | Best Researcher Award

Head of Academics | Learners University College | United Arab Emirates

Dr. Bincy B. Kaluvilla is an accomplished academic and researcher specializing in sustainable finance, investment management, and hospitality education, with a particular emphasis on integrating environmental, social, and governance (ESG) principles into financial and hospitality frameworks. She currently serves as Head of Academics and Partnerships at Learners University College, UAE, and previously worked as Assistant Professor and Undergraduate Program Manager at the Emirates Academy of Hospitality Management, where she played a central role in program leadership, faculty coordination, and industry collaboration. Holding a Ph.D. in Accounting from the University of Leicester, an M.Res in Accounting and Finance from the University of Glasgow, and professional recognition as a Fellow of the Higher Education Academy (UK) and CPA Australia, Dr. Kaluvilla combines strong academic foundations with practical insight. Her research encompasses real estate finance, green finance, ESG reporting, and digital transformation in hospitality, contributing over fifteen peer-reviewed publications and book chapters in leading journals such as Frontiers in Computer Science, Asia Pacific Journal of Tourism Research, and Library Hi Tech News, with growing citation impact across Scopus and Web of Science databases. She has authored chapters for major publishers including Springer Nature, Emerald, IGI Global, and Apple Academic Press, addressing emerging issues in sustainable investment, digital currencies, and responsible finance. Her academic influence extends globally through conference presentations at EuroCHRIE in Vienna, GHLS in Dubai, and IPoE in the UAE. Beyond research, she has led significant corporate training initiatives with the Jumeirah Group, Omran Group, and the UAE Ministry of Foreign Affairs, advancing professional development and gender empowerment within the hospitality industry. Through her research, teaching, and leadership, Dr. Kaluvilla continues to advance global understanding of sustainable finance and investment practices, fostering stronger links between academia, industry, and community development.

Featured Publication

Fahad, Z., Kaluvilla, B. B., & Mulla, T. (2024). Embracing the new era: Artificial intelligence and its multifaceted impact on the hospitality industry. Journal of Open Innovation: Technology, Market, and Complexity, 10(4), 100390.

Ghazanfar, U., Kaluvilla, B. B., & Zahidi, F. (2023). The post-COVID emergence of dark kitchens: A qualitative analysis of acceptance and the advantages and challenges. Research in Hospitality Management, 13(1), 23–30.

Kaluvilla, B. B. (2024). Cultural preservation through technology in UAE libraries. Library Hi Tech News, 41(8), 6–9.

Kalarikkal, S. A., Thamilvannan, G., & Kaluvilla, B. B. (2024). Enhancing access to missionary archives: The role of digital libraries and online repositories. Library Hi Tech News.

Kaluvilla, B. B., Mulla, T., Zahidi, F., & Wondirad, A. (2024). Driving sustainable choices through understanding consumer behaviour and underlying factors that influence the purchasing intention of refurbished furniture. SSRN Electronic Journal.

Md. Habibullah Shakib | Machine Learning | Best Researcher Award

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Mr. Md. Habibullah Shakib | Machine Learning | Best Researcher Award

Researcher| World University of Bangladesh| Bangladesh

Mr. Md. Habibullah Shakib is an emerging researcher and analyst from Bangladesh with over 3.5 years of research experience in artificial intelligence, supervised and deep learning, genetic AI, and foundation models. He holds a Bachelor of Science in Computer Science and Engineering from the World University of Bangladesh and a Diploma in Computer Technology from the National Polytechnic Institute. His research focuses on developing intelligent and secure computing systems, with significant contributions to Android malware detection, federated learning, autonomous systems, and IoT-based smart home automation. Among his key projects are the Active Federated YOLOR Model for enhancing autonomous vehicle safety, deep learning and genetic AI approaches for Android malware detection, and the integration of Conformer, Active Learning, and Federated Learning models for encrypted malware traffic detection. His ongoing work on Autonomous Generative AI for Android malware detection reflects his interest in advancing cutting-edge AI-driven cybersecurity solutions. Recognized for his scholarly engagement, he received a Certificate of Reviewing from the Information Processing and Management journal (Elsevier, 2024). He has built a growing academic presence with profiles on Google Scholar, ORCID, SSRN, GitHub, and the AD Scientific Index. Fluent in Bangla and English, he combines strong analytical and organizational skills with a commitment to innovation and teamwork. Through his dedication to ethical AI development, quantitative data analysis, and research collaboration, Md. Habibullah Shakib aims to contribute globally to the progress of intelligent systems, data-driven decision-making, and digital security for sustainable technological advancement.

Featured Publication

Shakib, M. (2023). Android malware detection approach based on genetic AI, CNN, RNN, LSTM, GRU, and active learning. SSRN. Cited by: 1

Shakib, M. H., Yeasin, M., Rahman, M. H., Rahman, K. M., Hossain, S., & Mahi, F. F. (2025). Active learning model used for Android malware detection. Machine Learning with Applications, 100680. Cited by: 8

Shakib, M. D. H. (2024). Android malware detection using transformer and encoder models. SSRN. Cited by: 5

Shakib, M. H. (2024). Comparing conformer, genetic artificial intelligence conformer, and active learning conformer approaches for encrypted Android malware traffic detection. SSRN. Cited by: 4

Rana Ghazali | Data Science | Best Researcher Award

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Dr. Rana Ghazali | Data Science | Best Researcher Award

Researcher |McMaster University | Iran

Dr. Rana Ghazali focuses on advancing intelligent computing systems through the integration of machine learning, reinforcement learning, and large language models to optimize big data and distributed computing environments. Her work bridges the domains of cloud computing, Hadoop-based systems, and intelligent caching to enhance computational performance and resource utilization in large-scale data frameworks. She has contributed to innovative algorithms such as CLQLMRS and H-SVM-LRU for improving cache locality and intelligent cache replacement in MapReduce job scheduling, combining machine learning with distributed system optimization. Rana’s research also extends to the design and analysis of routing protocols in mobile ad hoc networks, leveraging bio-inspired algorithms such as the Ant Colony Optimization method to improve network efficiency. Her current exploration includes the application of reinforcement learning in scheduling and performance enhancement for distributed computing platforms, with additional attention to emerging paradigms like edge, fog, and serverless computing. As a researcher affiliated with the Resource Allocation and Stochastic Systems Lab at McMaster University, she contributes to cutting-edge discussions on adaptive data management, cyber and network security, and intelligent system design. Rana’s expertise further encompasses data analytics, large language models, and the intersection of artificial intelligence with real-world computing challenges. She has served as a reviewer for leading international journals including Elsevier and Wiley publications and has participated in academic collaborations that explore deep learning and resource optimization in distributed architectures. Her research endeavors consistently emphasize scalable, secure, and intelligent computational systems that advance the performance of modern data-intensive applications. Rana Ghazali has 13 citations, 2 documents, and an h-index of 2.

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Ghazali, R., Down, D. G. (2025). Smart data prefetching using KNN to improve Hadoop performance. EAI Endorsed Transactions on Scalable Information Systems, 12(3). Cited by 1

Ghazali, R., Adabi, S., Rezaee, A., Down, D. G., & Movaghar, A. (2023). Hadoop-oriented SVM-LRU (H-SVM-LRU): An intelligent cache replacement algorithm to improve MapReduce performance. arXiv preprint arXiv:2309.16471. Cited by 2

Ghazali, R., Adabi, S., Rezaee, A., Down, D. G., & Movaghar, A. (2022). CLQLMRS: Improving cache locality in MapReduce job scheduler using Q-learning. Journal of Cloud Computing, 9. Cited by 9

Ghazali, R., Adabi, S., Down, D. G., & Movaghar, A. (2021). A classification of Hadoop job schedulers based on performance optimization approaches. Cluster Computing, 24(4), 3381–3403. Cited by 11

Ghazali, R., Down, D. G. (2025). A systematic overview of caching mechanisms to improve Hadoop performance. Concurrency and Computation: Practice and Experience, 37(25–26), e70337.

Ye Tao | Machine Learning | Best Researcher Award

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Dr. Ye Tao | Machine Learning | Best Researcher Award

PhD Student | China University of Petroleum, Beijing| China

Dr Ye Tao is a dedicated researcher focusing on sedimentology, unconventional oil and gas exploration, and the integration of artificial intelligence into geological studies. His work emphasizes fine characterization and sweet spot evaluation of shale gas reservoirs, tectonic evolution, sedimentary system reconstruction, and deepwater hydrocarbon accumulation models. Ye Tao has served as principal investigator and key researcher on multiple funded projects, including studies on shale reservoir heterogeneity in the Wufeng–Longmaxi Formations, tectonic evolution of the North Uscult Basin, and migration and accumulation mechanisms in the Guyana Basin. His expertise spans seismic data interpretation, fracture classification, mechanical modeling, and stress field simulation, contributing to accurate prediction of reservoir sweet spots and caprock sealing capacity. Ye Tao has actively published in peer-reviewed journals, presenting significant contributions such as deep learning-aided shale reservoir analysis, isotope-based sea-level reconstructions, and machine learning-based carbonate fossil recognition. His interdisciplinary approach bridges geology with computer vision and artificial intelligence, providing innovative methodologies for improving exploration accuracy. Ye Tao has been awarded multiple national and institutional prizes, including first prizes at China University of Petroleum’s Graduate Academic Forum and the National Doctoral Student Academic Forum, showcasing his academic excellence and leadership. His skillset includes seismic processing, petrographic thin section analysis, carbon and oxygen isotope testing, and restoration of paleoenvironments, enabling comprehensive understanding of sedimentary processes. By applying deep learning techniques to geological data, Ye Tao is contributing to next-generation exploration strategies that enhance prediction of hydrocarbon distribution and optimize resource development. His work demonstrates strong potential for advancing both theoretical sedimentology and applied petroleum exploration, making significant impact on energy resource evaluation and development strategies in complex geological settings.

Profile:  ORCID
Featured Publication

Tao, Y., Bao, Z., & Ma, F. (2025). Analyzing key controlling factors of shale reservoir heterogeneity in “thin” stratigraphic settings: A deep learning-aided case study of the Wufeng-Longmaxi Formations, Fuyan Syncline, Northern Guizhou. Applied Computing and Geosciences, 100293.

Tao, Y., Bao, Z., Yu, J., & Li, Y. (2025). The petrophysical characteristics and controlling factors of the Wufeng Formation–Longmaxi Formation shale reservoirs in the Fuyan Syncline, Northern Guizhou. Geological Journal.

Tao, Y., Gao, D., He, Y., Ngia, N. R., Wang, M., Sun, C., Huang, X., & Wu, J. (2023). Carbon and oxygen isotopes of the Lianglitage Formation in the Tazhong area, Tarim Basin: Implications for sea-level changes and palaeomarine conditions. Geological Journal, 58, 967–980.

Tao, Y., He, Y., Zhao, Z., Wu, D., & Deng, Q. (2023). Sealing of oil-gas reservoir caprock: Destruction of shale caprock by micro-fractures. Frontiers in Earth Science, 10, 1065875.

Ahsan Ali | Machine Learning | Best Researcher Award

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Mr. Ahsan Ali | Machine Learning | Best Researcher Award

PhD Student at Tianjin University | Pakistan

Overall, Ahsan Ali emerges as a promising young researcher whose academic journey reflects both excellence and commitment to advancing the field of electrical power engineering. With a strong foundation laid through his master’s and bachelor’s degrees, he has already demonstrated the ability to translate theoretical knowledge into practical solutions. His expertise covers deep learning-based power quality disturbance classification, fault diagnosis in converters, power system protection, and renewable energy integration—areas that are of great importance in the current era of smart grids and sustainable power technologies. Beyond his academic pursuits, Ahsan has also gained valuable industrial exposure in sugar mills, cement factories, and large-scale power plants, which has enriched his applied perspective and problem-solving abilities. Furthermore, his active participation in IEEE activities, seminars, and conferences highlights his growing leadership potential. With sustained research productivity, strong collaborations, and a focus on impactful publications, Ahsan is well-prepared to become a leading figure in his domain.

Professional Profile

 Scopus 

Education

Ahsan Ali completed his Master’s degree in Electrical Power Engineering from Quaid-e-Awam University of Engineering, Science and Technology, Pakistan, with a strong academic record His master’s research was focused on the classification of power quality disturbances using advanced deep learning methods. The study addressed the increasing importance of reliable power system operation in modern electrical networks and explored the integration of Discrete Wavelet Transform and Multi-Resolution Analysis with one-dimensional convolutional neural networks. This work aimed to improve the accuracy of identifying and classifying disturbances such as sags, swells, harmonics, and transients that affect system reliability. He also earned a Bachelor of Electrical Engineering degree from the same institution. His undergraduate project involved modeling and simulating under-frequency relays for generator protection using MATLAB and Simulink, providing him with practical expertise in system reliability.

Experience

Ahsan Ali has developed a professional career in the field of electrical power systems through roles that combined technical responsibilities and applied industry learning. He worked as an Assistant Electrical Engineer at Khairpur Sugar Mills, where he supported the engineering team in resolving power disturbances, implementing protection schemes, and managing distribution systems. In a similar role at Rohri Cement Factory, he assisted in project planning and power management activities while ensuring smooth plant operations. He also gained valuable industrial training during internships at Zorlu Enerji Pakistan, where he observed wind turbine operations and grid station management, TNB Liberty Power Plant, where he studied combined cycle operations and turbine performance, and Jamshoro Power Company, where he familiarized himself with the functioning of large-scale thermal units. These experiences helped him build a strong foundation in energy production, distribution, and system reliability, combining both theoretical and practical aspects of electrical engineering in real environments.

Skills

Ahsan Ali possesses a wide range of technical and analytical skills that complement his academic and professional background in electrical engineering. He has advanced proficiency in MATLAB and Simulink for modeling, simulation, and analysis of power systems, as well as strong competence in programmable logic controller programming for industrial automation and protective arrangements. His expertise covers power system analysis, electrical distribution engineering, fault protection, renewable energy integration, and the design and control of electrical machines and drives. He has applied these skills in both academic research and industrial practice, focusing on optimizing system performance and ensuring reliability. Ahsan has also acquired certifications in advanced courses, including power system analysis, electrical distribution system engineering, and MATLAB applications. He completed specialized training in Typhoon HIL, gaining experience in power quality testing and power flow modeling. In addition, he has explored fields such as freelancing, WordPress, and graphic design to diversify his professional capabilities.

Research Focus

Ahsan Ali’s research focus centers on power system reliability and advanced diagnostic methods for modern electrical networks. His interests include fault diagnosis of high-power electronic converters, stability analysis, and the integration of renewable energy systems into existing grids. He has also worked extensively on the classification of power quality disturbances through the application of deep learning algorithms, which represents a significant contribution to intelligent power system monitoring. His publications highlight his dedication to advancing the field, with studies on PQD detection techniques, microgrid design for seaport operations, and classification models for system optimization. His research reflects a balance between theoretical development and applied engineering, addressing the challenges posed by distributed generation, energy transitions, and increasing demand for sustainable technologies. Through his projects, Ahsan has emphasized the importance of integrating artificial intelligence and machine learning into power systems to enhance fault detection, predictive maintenance, and operational decision-making.

Awards 

Ahsan Ali has earned recognition for his academic excellence, research contributions, and active participation in professional activities. He has received certificates of appreciation for organizing technical events and webinars, including recognition for his performance during the COVID-19 period, when he contributed to academic engagement through virtual platforms. He participated in poster competitions on power system fault diagnosis and was acknowledged by the IEEE QUEST Chapter for his contributions. His involvement in seminars and workshops includes presenting research on power quality disturbances classification and generator protection at national and institutional conferences, where he shared findings with peers and faculty. He has also attended multiple training programs and short courses related to industrial safety, renewable progress, technical writing, and research management. These experiences have strengthened his academic and professional profile. As an associate member of IEEE, Ahsan has demonstrated his commitment to professional growth and engagement with the global engineering community.

Publication Top Notes

Title: Comprehensive review of power quality disturbance detection and classification techniques
Journal: Computers and Electrical Engineering, Vol. 126, Article 110512

Title: Design and Analysis of Seaport Microgrid with Ship Loads
Journal: Proceedings of IEEE China International Youth Conference on Electrical Engineering (CIYCEE), Wuhan, China

Title: Power Quality Disturbances (PQDs) Classification Analyzed Based on Deep Learning Technique
Journal: Journal of Computing and Biomedical Informatics, Vol. 4, Issue 1

Title: Comparative Analysis of the PWM and SPWM on Three-Phase Inverter through Different Loads and Frequencies
Journal: Journal of Computing and Biomedical Informatics, Vol. 4, Issue 2, pp. 204–220

Conclusion

Ahsan Ali is a highly suitable and deserving candidate for the Best Researcher Award in Electrical Power Engineering, given the scope and relevance of his contributions. His research consistently bridges theoretical frameworks with real-world applications, particularly in areas such as power system reliability, renewable energy, and advanced control methods. These contributions underscore his ability to design innovative solutions that can enhance system stability and sustainability. Although there remains room for growth in terms of expanding his global research impact, securing patents, and publishing in more high-impact journals, his current record already reflects a blend of academic excellence and professional dedication. His consistent engagement with international conferences and reputed journals highlights his growing presence in the research community. With his career trajectory, it is evident that he embodies the qualities of an emerging researcher whose work contributes not only to scientific advancement but also to practical technological development, making him an ideal award recipient.