Lili Zhan | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Lili Zhan | Artificial Intelligence | Best Researcher Award

Associate Professor| Shandong University of Science and Technology | China

Assoc. Prof. Dr. Lili Zhan is a researcher whose work spans remote sensing, Arctic cryosphere monitoring, computer vision, and artificial intelligence–enhanced educational systems. Her scholarship incorporates both physical environmental analysis and advanced data-driven methodologies, with representative contributions including sensitivity analyses of microwave brightness temperature to variations in snow depth on Arctic sea ice, a deep-learning-based remote-sensing scene-classification framework employing EfficientNet-B7, and an improved YOLOv7 instance-segmentation method for ship detection in complex SAR imagery Lili-Zhan. She has also contributed to the design and implementation of intelligent teaching models grounded in contemporary AI and data-centric approaches, demonstrating interdisciplinarity across geospatial sciences and educational technology Lili-Zhan Across these domains, her work reflects a sustained commitment to methodological innovation, integrating state-of-the-art neural architectures with domain-specific challenges in environmental monitoring and maritime situational awareness. Her collaborations often bridge academic research groups focused on cryosphere change, Earth observation, and applied machine learning, enabling the development of tools that support improved climate understanding, maritime safety, and digital-education modernization. Although publication and citation metrics are not specified in the available document, the range of research topics and representative studies indicates a growing scholarly profile with contributions positioned at the intersection of remote-sensing physics and intelligent systems engineering. Collectively, her work holds global societal relevance: enhancing the accuracy of cryospheric measurements supports climate-model improvement and polar-region policy planning; advancing ship-detection techniques contributes to marine governance, environmental protection, and emergency response; and promoting AI-supported pedagogical frameworks aids the digital transformation of education.

Profile: Scopus 

Featured Publications

Zhan, L. (Year). SAR ship target instance segmentation based on SISS-YOLO. Journal Name, Volume(Issue), pages.

Lili Zhan’s work advances the precision of remote-sensing analytics and intelligent detection systems, strengthening global capabilities in environmental monitoring and maritime safety. Her innovations support science-driven decision-making with direct benefits for climate resilience and societal securit

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.

Hussain A. Younis | Computer Science | Best Researcher Award

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Mr. Hussain A. Younis | Computer Science | Best Researcher Award

College of Education at University of Basrah, Iraq

Hussain A. Younis is a dedicated researcher specializing in Artificial Intelligence, Security, Digital Image Processing, and Robotics. With a strong academic background from India and Malaysia and an affiliation with the University of Basrah, he has published impactful research in high-ranking journals and IEEE conferences. His work demonstrates interdisciplinary expertise, particularly in AI applications, human-robot interaction, and digital security. As an active IEEE member and potential reviewer, he is engaged in professional research communities. While his contributions are commendable, completing his Ph.D., increasing Q1/Q2 journal publications, securing research grants, and enhancing international collaborations would further strengthen his research profile. His growing citation impact and involvement in digital transformation research make him a strong candidate for the Best Researcher Award. With continued contributions in leadership, industry collaborations, and high-impact research, Hussain A. Younis is well-positioned to make significant advancements in the field of computer science and engineering.

Professional Profile 

Education

Hussain A. Younis has a strong academic background in computer science, with a Master’s degree earned in 2012 from India and ongoing Ph.D. studies since 2019 in Malaysia. His educational journey reflects a commitment to advanced research in Artificial Intelligence, Security, Digital Image Processing, and Robotics. His affiliation with the University of Basrah further strengthens his academic and research foundation, allowing him to contribute significantly to the field. Throughout his studies, he has focused on interdisciplinary research, exploring innovative solutions in AI-driven security systems, pattern recognition, and human-robot interaction. His academic pursuits have been complemented by active participation in professional organizations like IEEE, where he is a member and a prospective reviewer. While his research credentials are impressive, completing his Ph.D. will further solidify his expertise and credibility. His educational background positions him as a promising researcher with the potential to make impactful contributions to the scientific community.

Professional Experience

Hussain A. Younis has extensive professional experience in research and academia, with a focus on Artificial Intelligence, Security, Digital Image Processing, and Robotics. He is affiliated with the University of Basrah, where he contributes to both teaching and research in computer science. His work spans various interdisciplinary areas, including AI-driven security systems, pattern recognition, and human-robot interaction. As an IEEE member, he actively participates in academic conferences and serves as a prospective reviewer, further demonstrating his engagement in the global research community. His publications in high-impact journals and IEEE conferences highlight his contributions to advancing technology, particularly in robotics education, cybersecurity, and digital transformation. While his professional experience is commendable, taking on leadership roles in research projects, securing grants, and fostering international collaborations would further enhance his impact. His commitment to innovation and academic excellence makes him a valuable contributor to the scientific and technological landscape.

Research Interest

Hussain A. Younis’s research interests lie at the intersection of Artificial Intelligence, Security, Digital Image Processing, Pattern Recognition, and Robotics. His work explores innovative AI-driven solutions for enhancing security, improving human-robot interaction, and advancing digital transformation. He is particularly interested in speech recognition models, robotics in education, and secure cryptographic systems, contributing to cutting-edge developments in these fields. His research also addresses challenges in cybersecurity, focusing on encryption techniques and stream cipher systems to enhance data protection. Additionally, he investigates distinguishable patterns in image processing, applying AI techniques to optimize pattern recognition for various applications. Through his active participation in IEEE conferences and high-impact journal publications, he continuously contributes to technological advancements. His interdisciplinary approach and commitment to innovation position him as a promising researcher in AI and security, with the potential to make significant contributions to both academic research and real-world applications.

Award and Honor

Hussain A. Younis has been recognized for his contributions to research in Artificial Intelligence, Security, Digital Image Processing, and Robotics through various academic achievements and honors. His publications in high-impact journals and IEEE conferences reflect his dedication to advancing knowledge in these fields. As an active IEEE member, he has gained recognition within the global research community and has been invited to serve as a reviewer for IEEE conferences in Iraq. His work on robotics in education, cybersecurity, and encryption systems has earned significant attention, highlighting his expertise in interdisciplinary research. While his achievements are commendable, securing prestigious research grants, international fellowships, and industry collaborations would further enhance his profile. His commitment to innovation and scientific excellence makes him a strong contender for research awards, and with continued contributions, he is poised to receive greater recognition for his impact on the technological and academic landscape.

Research Skill

Hussain A. Younis possesses strong research skills in Artificial Intelligence, Security, Digital Image Processing, Pattern Recognition, and Robotics. His expertise lies in developing AI-driven solutions for security, speech recognition, and human-robot interaction, showcasing his ability to integrate multiple disciplines. He is proficient in data analysis, algorithm development, cryptographic security, and digital transformation technologies, enabling him to conduct high-quality research with practical applications. His experience in publishing in high-impact journals and IEEE conferences reflects his ability to conduct rigorous academic research and communicate findings effectively. As an active IEEE member and prospective reviewer, he demonstrates critical analysis and evaluation skills essential for scholarly contributions. Additionally, his research involves problem-solving, programming, and system design, particularly in robotics education and cybersecurity. To further enhance his research impact, focusing on international collaborations, advanced machine learning techniques, and securing research grants would strengthen his expertise and academic contributions.

Conclusion

Hussain A. Younis demonstrates strong research potential with impactful publications in AI, Robotics, and Security. His IEEE membership, interdisciplinary research, and international exposure make him a strong candidate for the Best Researcher Award. However, completing the Ph.D., increasing high-impact publications, and engaging in leadership roles would significantly enhance his eligibility for this prestigious award.

Publications Top Noted

  1. Hussain A. Younis, TAE Eisa, M Nasser, TM Sahib, AA Noor, OM Alyasiri, … (2024)

    • A systematic review and meta-analysis of artificial intelligence tools in medicine and healthcare: applications, considerations, limitations, motivation and challenges
    • Citations: 114

  2. Hussain A. Younis, NIR Ruhaiyem, W Ghaban, NA Gazem, M Nasser (2023)

    • A systematic literature review on the applications of robots and natural language processing in education
    • Citations: 48

  3. IM Hayder, TA Al-Amiedy, W Ghaban, F Saeed, M Nasser, GA Al-Ali, HA Younis, … (2023)

    • An intelligent early flood forecasting and prediction leveraging machine and deep learning algorithms with advanced alert system
    • Citations: 40

  4. OM Alyasiri, K Selvaraj, Hussain A. Younis, TM Sahib, MF Almasoodi, IM Hayder (2024)

    • A survey on the potential of artificial intelligence tools in tourism information services
    • Citations: 38

  5. S Salisu, NIR Ruhaiyem, TAE Eisa, M Nasser, F Saeed, HA Younis (2023)

    • Motion capture technologies for ergonomics: A systematic literature review
    • Citations: 25

  6. IM Hayder, GANA Ali, Hussain A. Younis (2023)

    • Predicting reaction based on customer’s transaction using machine learning approaches
    • Citations: 20

  7. Hussain A. Younis, ASA Mohamed, R Jamaludin, MNA Wahab (2021)

    • Survey of robotics in education, taxonomy, applications, and platforms during COVID-19
    • Citations: 20

  8. OM Alyasiri, AM Salman, S Salisu (2024)

    • ChatGPT revisited: Using ChatGPT-4 for finding references and editing language in medical scientific articles
    • Citations: 18

  9. Hussain A. Younis, OM Alyasiri, Muthmainnah, TM Sahib, IM Hayder, S Salisu, … (2023)

    • ChatGPT Evaluation: Can It Replace Grammarly and Quillbot Tools
    • Citations: 16

  10. MA Hussain, Hussain A. Younis, Iznan H. Hasbullah, Ghofran Kh. Shraida, Hameed A … (2023)

  • An Efficient Color-Image Encryption Method Using DNA Sequence and Chaos Cipher
  • Citations: 14
  1. Hussain A. Younis, ASA Mohamed, MN Ab Wahab, R Jamaludin, S Salisu (2021)
  • A new speech recognition model in a human-robot interaction scenario using NAO robot: Proposal and preliminary model
  • Citations: 11
  1. Hussain A. Younis, TY Abdalla, AY Abdalla (2009)
  • Vector quantization techniques for partial encryption of wavelet-based compressed digital images
  • Citations: 11