Alladoumbaye Ngueilbaye | Data Science | Best Researcher Award

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Dr. Alladoumbaye Ngueilbaye | Data Science | Best Researcher Award

Associate Researcher at Shenzhen University, China

Dr. Alladoumbaye Ngueilbaye is an accomplished researcher in the field of Computer Science, currently serving as an Associate Researcher at the National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, China. His expertise spans Big Data Computing, Machine Learning, Approximate Computing, Data Mining, and Bioinformatics. With over 20 peer-reviewed publications in high-impact journals such as IEEE Transactions on Big Data, Information Sciences, and Applied Soft Computing, Dr. Ngueilbaye has made significant contributions to scalable data processing and AI applications. He also holds editorial responsibilities and is an active member of the International Artificial Intelligence Committee (IAIC). With a strong international academic foundation and a focus on high-performance systems, he is recognized as a global contributor to research in intelligent systems and computational science. His multidisciplinary knowledge, research leadership, and commitment to advancing science in emerging regions make him an exceptional candidate for prestigious academic recognition.

Professional Profile 

Google Scholar | Scopus Profile | ORCID Profile

Education

Dr. Ngueilbaye completed his Ph.D. in Computer Science and Technology at the prestigious Harbin Institute of Technology, China (2017–2021), where he also obtained a Master’s degree in 2016. His academic journey reflects a strong international perspective, beginning with a Bachelor’s degree in Computer Science from Ahmadu Bello University, Nigeria (2006–2010). He further enhanced his educational background with multiple professional diplomas in Data Processing, Computer Maintenance, and Business Management. These include certifications from ALISON University (Ireland) and various institutes in Nigeria. His education not only focused on core computer science principles but also emphasized applied mathematics, entrepreneurship, and scientific communication—skills crucial for multidisciplinary innovation. With exposure to global programs such as the One Belt One Road initiative and participation in international summer schools, Dr. Ngueilbaye’s educational background is both diverse and tailored for excellence in advanced research, cross-cultural academic exchange, and applied computing innovation.

Professional Experience

Dr. Ngueilbaye has held multiple roles that reflect both academic excellence and professional versatility. Since June 2022, he has been an Associate Researcher at Shenzhen University, China, contributing to major projects in Big Data and AI. His earlier positions include roles as an IT Manager, Support Supervisor, and Engineer at organizations in Chad and Nigeria, such as Huawei Technologies and Clinique LA PROVIDENCE. Additionally, he has served as a teacher and instructor, emphasizing his commitment to education and knowledge dissemination. These experiences have equipped him with a deep understanding of both research and industry, enabling him to lead and collaborate across sectors. His professional trajectory reflects a rare blend of technical expertise, leadership, and international engagement. The diversity of his roles, ranging from infrastructure-level engineering to high-end computational research, enables him to bridge gaps between academic theories and real-world applications effectively.

Research Interest

Dr. Ngueilbaye’s research interests are centered around Big Data Analytics, Machine Learning, Deep Learning, Data Quality Management, Bioinformatics, and Approximate Computing. He explores scalable solutions for processing massive, distributed datasets and focuses on improving algorithms for data clustering, recommendation systems, and time series classification. His work also addresses challenges in resource-constrained environments, with innovations such as multi-sample approximate computing for distributed systems. Furthermore, he is passionate about applying AI in conservation and public health, as seen in his contributions to elephant monitoring systems and COVID-19 data quality models. His interest in hybrid AI techniques and neural architectures positions him at the forefront of intelligent data analysis. By integrating fundamental computing concepts with practical problem-solving, Dr. Ngueilbaye contributes meaningfully to global advancements in both academic and applied data science.

Award and Honor

Dr. Ngueilbaye has received multiple prestigious scholarships and recognitions throughout his academic journey. He was awarded the Chinese Government Scholarship twice—once for his Master’s and again for his Ph.D.—highlighting his academic excellence and international competitiveness. He received the UNESCO Great Wall Scholarship and was named one of the Outstanding Doctoral Students for the “Perception of China” initiative. His honors include prizes for Outstanding Students and Excellence in Academic Performance, awarded during his graduate studies. These accolades reflect a consistent track record of merit and dedication. Beyond academic honors, he has been invited to participate in elite conferences such as the AAAI Summer Symposium and various doctoral innovation forums. These recognitions validate his contributions to scientific research and his potential as a future leader in technology and innovation.

Research Skill

Dr. Ngueilbaye possesses advanced skills in Big Data system architecture, AI model development, and approximate computing. His hands-on expertise spans Spark-based basket analysis, graph neural networks, hybrid deep learning models, and Bayesian inference techniques. He has developed innovative solutions for challenges like missing data imputation, contextual data quality issues, and long-tailed recognition in machine learning. His technical stack includes tools for distributed computing, Python-based AI frameworks, and tools for data visualization and evaluation. Dr. Ngueilbaye is also experienced in research design, scientific writing, and collaborative software development. His consistent presence in SCI-indexed journals and IEEE publications speaks to his methodological rigor, peer recognition, and commitment to reproducible science. These skills, coupled with his ability to work across disciplines and geographies, make him a valuable contributor to any forward-looking research initiative.

Publications Top Noted

  • Ngueilbaye A., Wang H., Mahamat D.A., Junaidu S.B. (2021)
    “Modulo 9 Model-Based Learning for Missing Data Imputation”

    • Journal: Applied Soft Computing 103, 107167

    • Citations: 38

  • Mahmud M.S., Huang J.Z., Ruby R., Ngueilbaye A., Wu K. (2023)
    “Approximate Clustering Ensemble Method for Big Data”

    • Journal: IEEE Transactions on Big Data

    • Citations: 29

  • Khan M., Wang H., Ngueilbaye A., Elfatyany A. (2023)
    “End-to-End Multivariate Time Series Classification via Hybrid Deep Learning Architectures”

    • Journal: Personal and Ubiquitous Computing 27 (2), 177–191

    • Citations: 27

  • Al Sibahee M.A., Abduljabbar Z.A., Ngueilbaye A., Luo C., Li J., Huang Y., et al. (2024)
    “Blockchain-Based Authentication Schemes in Smart Environments: A Systematic Literature Review”

    • Journal: IEEE Internet of Things Journal 11 (21), 34774–34796

    • Citations: 16

  • Sun X., Ngueilbaye A., Luo K., Cai Y., Wu D., Huang J.Z. (2024)
    “A Scalable and Flexible Basket Analysis System for Big Transaction Data in Spark”

    • Journal: Information Processing & Management 61 (2), 103577

    • Citations: 12

  • Ngueilbaye A., Wang H., Mahamat D.A., Elgendy I.A. (2021)
    “SDLER: Stacked Dedupe Learning for Entity Resolution in Big Data Era”

    • Journal: The Journal of Supercomputing 77 (10), 10959–10983

    • Citations: 12

  • Khan M., Wang H., Ngueilbaye A. (2021)
    “Attention-Based Deep Gated Fully Convolutional End-to-End Architectures for Time Series Classification”

    • Journal: Neural Processing Letters 53 (3), 1995–2028

    • Citations: 11

  • Ngueilbaye A., Lei L., Wang H. (2016)
    “Comparative Study of Data Mining Techniques on Heart Disease Prediction System: A Case Study for the Republic of Chad”

    • Journal: International Journal of Science and Research 5 (5), 1564–1571

    • Citations: 7

  • Elahi E., Anwar S., Al-kfairy M., Rodrigues J.J.P.C., Ngueilbaye A., Halim Z., et al. (2025)
    “Graph Attention-Based Neural Collaborative Filtering for Item-Specific Recommendation System Using Knowledge Graph”

    • Journal: Expert Systems with Applications 266, 126133

    • Citations: 6

  • Ngueilbaye A., Huang J.Z., Khan M., Wang H. (2023)
    “Data Quality Model for Assessing Public COVID-19 Big Datasets”

    • Journal: The Journal of Supercomputing 79 (17), 19574–19606

    • Citations: 6

  • Ngueilbaye A., Wang H., Khan M., Mahamat D.A. (2021)
    RETRACTED ARTICLE: “Adoption of Human Metabolic Processes as Data Quality Based Models”

    • Journal: The Journal of Supercomputing 77 (2), 1779–1817

    • Citations: 6

Conclusion

Dr. Alladoumbaye Ngueilbaye is a highly deserving candidate for the Best Researcher Award, given his consistent scholarly contributions, multi-country collaborations, and impactful research in areas vital to modern computing and AI. His efforts in bridging academic work between developing and developed nations and promoting cutting-edge research in scalable computing, data science, and AI demonstrate a unique blend of technical depth and global relevance. With continued support and recognition, he is well-positioned to become a global leader in big data systems and AI-driven innovation, contributing not only to academia but also to society through intelligent systems and knowledge dissemination.

Mohsin Hasan | Management science and engineering | Best Researcher Award

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Mr . Mohsin Hasan | Management science and engineering | Best Researcher Award

Student at Nanjing University of Aeronautics and Astronautics , China

Mohsin Hasan is a dedicated and impactful researcher currently pursuing a PhD in Management Science and Engineering at Nanjing University of Aeronautics and Astronautics, China. His research focuses on epileptic seizure prediction using advanced machine learning techniques, including LSTM, SHAP, and deep neural networks, addressing a critical healthcare challenge. With publications in top-tier SCIE-indexed journals such as Engineering Applications of Artificial Intelligence and Annals of Operations Research, he demonstrates strong academic rigor and innovation. Mohsin possesses expertise in Python programming, big data analysis, and research writing, supported by a multi-disciplinary academic background in sociology. He has also actively contributed to community health initiatives in Pakistan, reflecting a blend of technical and social impact. While improved English proficiency and expanded international collaboration could enhance his profile, his current achievements make him a strong candidate for the Best Researcher Award, showcasing both research excellence and real-world relevance.

Professional Profile

Education🎓

Mohsin Hasan has a diverse and interdisciplinary educational background that bridges social sciences and engineering. He is currently pursuing a PhD in Management Science and Engineering at Nanjing University of Aeronautics and Astronautics in China, with a research focus on epileptic seizure prediction using machine learning and deep learning techniques. Prior to his doctoral studies, he completed an M.S. in Rural Sociology from the University of Agriculture Faisalabad and a Master’s degree in Sociology from the University of Sargodha, Pakistan. His academic journey began with a Bachelor of Arts from Government College University Faisalabad, followed by intermediate studies at Government Islamia College Chiniot and matriculation at Government High School Chak No. 152 JB Chiniot. Throughout his education, Mohsin has developed strong skills in Python programming, big data analysis, and research writing, positioning him to apply advanced technological solutions to both social and engineering problems, particularly in healthcare and community development.

Professional Experience📝

Mohsin Hasan has a well-rounded professional background that spans academic research and community development. Currently, he is engaged in cutting-edge research as a PhD scholar, working on epileptic seizure prediction using machine learning, with multiple SCIE-indexed publications to his name. His earlier professional experience includes various social outreach and coordination roles across Pakistan. As a Social Outreach Worker with UNODC, he led awareness campaigns and community mobilization for drug addiction treatment. He also served as Supervisor for the Sehat Sahulat Insaaf Card project with RCDP, managing field staff and overseeing healthcare card distribution. As a Dosti Coordinator with Muslim Hands International, he trained teachers and encouraged school enrollment and student participation in extracurricular activities. Additionally, he worked as an Assistant Constituency Coordinator for the FAFEN Election Project, monitoring electoral processes and data collection. His experience demonstrates a strong blend of technical expertise, leadership, and community-oriented service.

Research Interest🔎

Mohsin Hasan’s research interests lie at the intersection of artificial intelligence, healthcare, and data science, with a strong focus on real-world applications that enhance human well-being. His primary area of interest is the prediction and classification of epileptic seizures using advanced machine learning and deep learning techniques, including Long Short-Term Memory (LSTM), Kolmogorov Arnold Network Theorem, SHAP-driven feature analysis, and attention-based neural networks. He is particularly passionate about leveraging electroencephalography (EEG) data to develop interpretable and accurate models for early seizure detection. His research also extends to reliability engineering, operational research, and the integration of AI in medical diagnostics. With a background in sociology and rural development, Mohsin brings a unique, human-centered approach to technological innovation, aiming to bridge the gap between data-driven solutions and community health challenges. His interdisciplinary perspective fuels his commitment to creating scalable, impactful tools for healthcare and beyond, particularly in under-resourced and developing contexts.

Award and Honor🏆

Mohsin Hasan has earned recognition for his dedication to academic excellence and impactful research, positioning him as a strong candidate for prestigious honors. His most notable achievement is his contribution to high-impact, SCIE-indexed journals such as Engineering Applications of Artificial Intelligence and Annals of Operations Research, where his research on epileptic seizure prediction has gained international attention. In addition to academic publications, Mohsin has been involved in global policy discussions and training sessions, including regional dialogues hosted by the Asian Institute of Technology and certification courses by the World Health Organization on emerging health threats and COVID-19 response. His ability to translate complex data science techniques into meaningful healthcare solutions reflects both innovation and social commitment. These accomplishments highlight his exceptional talent, work ethic, and relevance in critical global issues. Such recognition not only underscores his scholarly contributions but also establishes him as a deserving candidate for awards celebrating research excellence and societal impact.

Research Skill🔬

Mohsin Hasan possesses a comprehensive set of research skills that enable him to conduct advanced, data-driven investigations with real-world impact. He is highly proficient in Python programming and well-versed in tools such as Jupyter Notebook, PyCharm, and Google Colab, which he utilizes for building and testing machine learning models. His core expertise lies in deep learning, particularly in applying algorithms like Long Short-Term Memory (LSTM), 1D-ResNet, and attention mechanisms for medical data analysis, especially EEG-based epileptic seizure prediction. Mohsin is skilled in big data analytics, neural network development, and SHAP-based model interpretation, which enhances the transparency and usability of AI models. Additionally, he is experienced in academic research writing, LaTeX formatting, and data visualization using software like Edraw Max and Visio. His ability to integrate technical depth with scientific communication, along with a strong foundation in statistical methods and real-time problem-solving, marks him as a capable and innovative researcher.

Conclusion💡

✅ Yes, Mohsin Hasan is a strong and deserving candidate for the Best Researcher Award.

His profile demonstrates a rare and valuable combination of technical AI research, medical applications, and community-level engagement. His high-quality publications, technical skills, and international academic involvement position him as a rising researcher with significant impact potential.

Publications Top Noted✍

  • Title: Long Short-Term Memory and Kolmogorov Arnold Network Theorem for Epileptic Seizure Prediction

  • Authors: Mohsin Hasan, Xufeng Zhao, Wenjuan Wu, Jiafei Dai, Xudong Gu, Asia Noreen

  • Year: 2025

  • Journal: Engineering Applications of Artificial Intelligence

  • Volume and Issue: Volume 154

  • Pages: Article 110757

  • Publisher: Elsevier

  • Indexing: SCIE

  • Citation Format (APA Style):
    Hasan, M., Zhao, X., Wu, W., Dai, J., Gu, X., & Noreen, A. (2025). Long Short-Term Memory and Kolmogorov Arnold Network Theorem for epileptic seizure prediction. Engineering Applications of Artificial Intelligence, 154, 110757. https://doi.org/10.1016/j.engappai.2025.110757 (DOI placeholder if needed)

 

Fengyu Liu | Computer Science | Best Researcher Award

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Dr. Fengyu Liu | Computer Science | Best Researcher Award

PhD candidate at Southeast University, China

Fengyu Liu is a dedicated researcher specializing in deep learning, integrated navigation, intelligent unmanned systems, multi-sensor fusion, and SLAM (Simultaneous Localization and Mapping). He has authored 10 academic papers, including 5 SCI-indexed Q1 journal articles, and has contributed significantly to the fields of robotics and sensor technology. With 5 domestic invention patents and 1 PCT patent, his work demonstrates a strong focus on innovation. He has received numerous awards, including the National Scholarship and the Southeast University ‘Zhishan’ Scholarship, and has won four national and provincial-level first prizes in student competitions. He actively participates in academic conferences and serves as a reviewer for IEEE TIM, IEEE Sensor Journal, and MST journals. His research contributions and leadership in the academic community make him a promising figure in the field of intelligent navigation and robotics.

Professional Profile

Education

Fengyu Liu earned his B.S. degree in Electronic Science and Technology from the School of Instrument and Electronics, North University of China, in 2020. Currently, he is pursuing a Ph.D. in Instrument Science and Technology at the School of Instrument Science and Engineering, Southeast University, Nanjing, China. His doctoral research focuses on deep learning-driven navigation, SLAM, and multi-sensor fusion for intelligent unmanned systems. Throughout his academic journey, he has been recognized for his outstanding performance, receiving prestigious scholarships and awards for academic excellence and research contributions.

Professional Experience

During his undergraduate studies, Fengyu Liu served as the Chair of the Embedded Laboratory at the Innovation Elite Research Institute, where he led multiple student research projects. He has been actively involved in presenting at international conferences, including the 2023 International Conference on Robotics, Control, and Vision Engineering (Tokyo) and the China-Russia “Navigation and Motion Control” Youth Forum (2024, Nanjing). His research findings have been published in top-tier journals, and he has contributed as a reviewer for leading IEEE journals. His expertise in SLAM, sensor fusion, and AI-driven navigation technologies has led to patents and real-world applications, making him a key contributor to the advancement of autonomous systems and intelligent robotics.

Research Interests

Fengyu Liu’s research focuses on deep learning, integrated navigation, intelligent unmanned systems, multi-sensor fusion, and simultaneous localization and mapping (SLAM). His work explores advanced sensor fusion techniques, including the integration of LiDAR, cameras, inertial measurement units (IMUs), and deep learning models to enhance navigation accuracy and autonomy in complex environments. He is particularly interested in developing robust localization algorithms for dynamic and unstructured environments, with applications in robotics, autonomous vehicles, and aerospace navigation. His contributions to AI-driven SLAM and vision-based perception systems aim to improve real-time mapping, object recognition, and motion estimation for next-generation autonomous systems.

Awards and Honors

Fengyu Liu has received multiple prestigious awards, including the National Scholarship and the Southeast University ‘Zhishan’ Scholarship, recognizing his academic excellence. He has won four first prizes at national and provincial-level university student competitions, demonstrating his problem-solving skills and technical expertise. His research has also been recognized at academic conferences, earning him the Outstanding Paper Award at the 2022 Science and Technology Workers Seminar of the Chinese Society of Inertial Technology. His participation in international research forums, such as the China-Russia “Navigation and Motion Control” Youth Forum (2024, Nanjing), further highlights his growing impact in the field.

Research Skills

Fengyu Liu possesses a diverse skill set in deep learning, computer vision, and multi-sensor data fusion, particularly for robotics and autonomous navigation. He is proficient in developing AI-based SLAM algorithms, sensor calibration techniques, and real-time embedded system implementations. His expertise extends to software tools and programming languages, including Python, MATLAB, C++, TensorFlow, and PyTorch, which he utilizes for machine learning and signal processing applications. He has hands-on experience with robotic perception systems, LiDAR-based mapping, and inertial navigation technologies, contributing to multiple high-impact research projects. Additionally, his role as a peer reviewer for IEEE TIM, IEEE Sensor Journal, and MST journals reflects his strong analytical and critical evaluation skills in cutting-edge research.

Conclusion

Fengyu Liu is a highly promising young researcher with strong academic contributions, patents, and international recognition. His research in SLAM, deep learning, and multi-sensor fusion aligns with cutting-edge advancements in robotics and AI. His leadership roles, awards, and editorial responsibilities further strengthen his profile.

For the Best Researcher Award, he is a strong candidate, but additional international collaborations, funded research projects, and industry partnerships could further enhance his competitiveness for top-tier global research awards.

Publications Top Noted

  • Confidence Factor Based Robust Localization Algorithm with Visual-Inertial-LiDAR Fusion in Underground Space

  • LiDAR-Aided Visual-Inertial Odometry Using Line and Plane Features for Ground Vehicles

    • Authors: Jianfeng Wu, Xianghong Cheng, Fengyu Liu, Xingbang Tang, Wengdong Gu
    • Year: 2025
    • DOI: 10.1109/TVT.2025.3527472

  • Spatial Feature Recognition and Layout Method Based on Improved CenterNet and LSTM Frameworks

  • Transformer-Based Local-to-Global LiDAR-Camera Targetless Calibration With Multiple Constraints

  • Spacecraft-DS: A Spacecraft Dataset for Key Components Detection and Segmentation via Hardware-in-the-Loop Capture

  • A Visual SLAM Method Assisted by IMU and Deep Learning in Indoor Dynamic Blurred Scenes

  • A Spatial Layout Method Based on Feature Encoding and GA-BiLSTM

  • Combination of Iterated Cubature Kalman Filter and Neural Networks for GPS/INS During GPS Outages

    • Authors: Fengyu Liu, Xiaohong Sun, Yufeng Xiong, Huang Haoqian, Xiao-ting Guo, Yu Zhang, Chong Shen
    • Year: 2019
    • DOI: 10.1063/1.5094559