Aleeza Adeel | Computer Science | Research Excellence Award

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Mrs. Aleeza Adeel | Computer Science | Research Excellence Award

The University of Waikato | New Zealand

Mrs. Aleeza Adeel is a Ph.D. student at the School of Computing and Mathematical Sciences, University of Waikato, New Zealand, specializing in digital twin frameworks, sustainable energy systems, and user-centered computing solutions. Her research focuses on developing interoperable and scalable digital twin technologies to optimize energy system management, enhance operational efficiency, and support sustainable resource utilization. She has contributed to peer-reviewed publications, including a recent article in Energies on an interoperable user-centered digital twin framework, demonstrating her commitment to integrating advanced computational models with real-world energy systems. Aleeza collaborates with interdisciplinary researchers, including experts in energy management and computational modeling, to ensure her work addresses both technical rigor and societal relevance. Her research contributes to sustainable energy transitions by providing data-driven, user-centric solutions that improve system performance, reduce environmental impact, and support informed decision-making in complex energy infrastructures.

Profile: View ORCID Profile 

Featured Publication


An Interoperable User‑Centred Digital Twin Framework for Sustainable Energy System Management

– Adeel, A., Apperley, M., & Walmsley, T. G., Energies, 2026, 19(2), Article 333

Miroslaw Kozielski | Computer Science | Best Researcher Award

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Mr. Miroslaw Kozielski | Computer Science | Best Researcher Award

Kazimierz Wielki University | Poland

Mr. Mirosław Kozielski is a researcher at Kazimierz Wielki University in Bydgoszcz, Poland, specializing in computer science, with a strong focus on natural language processing (NLP), industrial informatics, and Industry 4.0/5.0 technologies. His research addresses the use of intelligent language-based systems for automated industrial documentation, knowledge representation, and digital transformation in modern manufacturing environments. He has authored 7 peer-reviewed publications, which have accumulated 35 citations, and holds an h-index of 3, reflecting a focused and emerging academic impact. Dr. Kozielski collaborates with interdisciplinary teams, contributing to the integration of artificial intelligence with industrial and organizational processes. His work supports the development of efficient, human-centric, and sustainable industrial systems, with societal impact through improved documentation quality, enhanced knowledge accessibility, and the practical adoption of advanced AI-driven solutions in contemporary industrial ecosystems.

Citation Metrics (Scopus)

35
25
15
5
0

Citations

35

Documents

7

h-index

3

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h-index

View Scopus Profile
 View ORCID Profile

Featured Publications

Christian Schachtner | Computer Science | Research Excellence Award

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Prof. Dr. Christian Schachtner | Computer Science | Research Excellence Award

Professor of Business Informatics | RheinMain University of Applied Sciences | Germany

Dr. Christian Schachtner is a researcher at Fachhochschule Wiesbaden, Germany, specializing in safety culture, social impact assessment, and sustainable development in technical and organizational systems. His work bridges corporate social responsibility, environmental management, and smart district development, emphasizing practical solutions to complex societal challenges. According to Scopus, he has authored 23 scholarly publications, received 9 citations, and holds an h-index of 2. His recent research includes open-access work on the determinants of social impact through safety culture in technical organizations and scholarly contributions to book chapters on smart regional and district development initiatives. Dr. Schachtner actively collaborates with international researchers, supporting interdisciplinary perspectives and knowledge exchange. His research contributes to improving organizational governance, enhancing safety performance, and promoting socially responsible and sustainable practices across technical and socio-economic domains.

Citation Metrics (Scopus)

23
15
10
5
0

Citations

9

Documents

23

h-index

2

Citations

Documents

h-index

View Google Scholar Profile
 View Scopus Profile
 View ORCID Profile

Featured Publications

Takeshi Nikawa | Biochemistry | Research Excellence Award

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Prof. Dr. Takeshi Nikawa | Biochemistry | Research Excellence Award

Tokushima University Graduate School | Japan

Prof. Dr. Takeshi Nikawa is a distinguished researcher at Tokushima University, Japan, with expertise in skeletal muscle physiology, molecular biology, and nutritional interventions. His research explores the mechanisms underlying muscle atrophy, mitochondrial function, and gene regulation during myogenesis, aiming to understand how these processes impact aging, metabolism, and overall health. Nikawa’s work integrates experimental studies with translational approaches to develop strategies for maintaining muscle mass and function, particularly in aging populations or individuals at risk of muscle degeneration. He actively collaborates with international scientists across multiple disciplines, fostering knowledge exchange and advancing global research initiatives. Through his publications and applied studies, Nikawa contributes to both fundamental scientific understanding and practical interventions, supporting the development of therapeutic, nutritional, and lifestyle strategies that enhance quality of life and address key societal challenges related to health and aging.

Citation Metrics (Scopus)

4787
3500

2500
1200

0

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4,787

Documents

157

h-index

39

Citations

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Featured Publications

Sarbajit Paul Bappy | Computer Science | Research Excellence Award

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Mr. Sarbajit Paul Bappy | Computer Science | Research Excellence Award

Teaching Assistant | Daffodil International University | Bangladesh

Sarbajit Paul Bappy is an emerging researcher in computer science with a growing focus on applied machine learning, medical image analysis, and agricultural informatics. He is currently serving as a Teaching Assistant in the Department of Computer Science and Engineering at Daffodil International University, Bangladesh, where he has been contributing to academic instruction and research support since 2025. Alongside his professional role, he is pursuing his undergraduate degree in Computer Science and Engineering at the same institution, demonstrating a strong integration of academic excellence and early-career research productivity. His scholarly work includes peer-reviewed publications and openly accessible datasets that address critical challenges in healthcare diagnostics and smart agriculture. Notably, he co-authored SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images (MAKE, 2025), which combines deep learning with explainable AI techniques to enhance early disease detection. He also contributed significantly to the dataset Jackfruit AgroVision, a comprehensive benchmark for disease detection in jackfruit and its leaves, supporting advancements in precision agriculture and food-security research. His collaborations span multidisciplinary teams involving experts such as Amir Sohel, Rittik Chandra Das Turjy, Md Assaduzzaman, Ahmed Al Marouf, Jon George Rokne, and Reda Alhajj, illustrating his ability to contribute within diverse international research groups. Through his ongoing work in AI-driven health diagnostics, dataset development, and sustainable agricultural technology, Bappy aims to advance research that supports societal well-being, improves disease detection accuracy, and contributes to innovation within global machine learning communities.

Profiles: Google Scholar | ORCID | LinkedIn

Featured Publications

1. Sohel, A., Turjy, R. C. D., Bappy, S. P., Assaduzzaman, M., Marouf, A. A., Rokne, J. G., & Alhajj, R. (2025). SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images. Machine Learning and Knowledge Extraction, 7(4), 157. https://doi.org/10.3390/make7040157  MDPI+1

2. Sohel, A., Bijoy, M. H. I., Turjy, R. C. D., & Bappy, S. P. (2025). Jackfruit AgroVision: A Extensive Dataset for Jackfruit Disease and Leaf Disease Detection using Machine Learning. Mendeley Data. https://doi.org/10.17632/pt647jfn52.1

Afeez Soladoye | Machine learning | Young Scientist Award

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Mr. AfeezSoladoye | Machine learning | Young Scientist Award

Lecturer at Federal university Oye-Ekiti, Nigeria

Soladoye Afeez Adekunle is a promising young scholar in Computer Engineering, currently pursuing his Ph.D. at the Federal University Oye-Ekiti. With a Master’s degree earned with distinction, he has demonstrated strong academic and research capabilities. His work spans machine learning, artificial intelligence, and applied computing, including the development of medical prediction systems and fake news detection using deep learning. In addition to his teaching responsibilities at undergraduate and postgraduate levels, he actively contributes as a peer reviewer for reputable journals such as BMJ Open and serves as a technical editor. His involvement in academic committees and university-level projects reflects his leadership and dedication to institutional development. While his practical projects are impactful, the inclusion of more peer-reviewed publications and measurable research outcomes would further enhance his profile. Overall, his commitment to innovation, education, and research makes him a suitable and competitive candidate for the Young Scientist Award.

Professional Profile

Education🎓

Soladoye Afeez Adekunle has a solid educational background in Computer Engineering, reflecting his dedication to academic excellence and continuous professional development. He is currently pursuing a Ph.D. in Computer Engineering at the Federal University Oye-Ekiti, Nigeria, with a research focus on advanced computing and intelligent systems. He previously earned a Master of Engineering (M.Eng) in Computer Engineering from the same university, graduating with distinction in 2023. His undergraduate studies were completed at Ladoke Akintola University of Technology, Ogbomosho, where he obtained a Bachelor of Technology (B.Tech) degree in Computer Engineering in 2016. His foundational education includes a Senior School Leaving Certificate from Foundation Model College, Ikirun, in 2009, and a Primary School Leaving Certificate from Al-hilal Nursery and Primary School, Ikirun, in 2003. His academic journey reflects a consistent commitment to learning, skill acquisition, and growth in the field of computer science and engineering, preparing him for a successful career in research and education.

Professional Experience📝

Soladoye Afeez Adekunle has amassed valuable professional experience across academia, research, and industry. He currently serves as a Lecturer II in the Department of Computer Engineering at the Federal University Oye-Ekiti, where he teaches both undergraduate and postgraduate courses, supervises student projects, and mentors young researchers. In addition to his teaching role, he is the Assistant Examination Officer and Level Advisor, playing a vital role in exam coordination and academic advising. He also contributes as a Technical Editor for the FUOYE Journal of Engineering and Technology and reviews scholarly articles for esteemed journals like BMJ Open and the Nigerian Journal of Technological Development. As a freelance Machine Learning Engineer, he has developed predictive systems for medical diagnosis and fake news detection, showcasing his ability to apply research in practical contexts. His previous roles include network engineering trainee and peer tutor, reflecting a versatile and well-rounded professional path in computer science and engineering.

Research Interest🔎

Soladoye Afeez Adekunle has earned recognition for his dedication to academic excellence, professional service, and contributions to the field of computer engineering. He graduated with distinction in his Master’s degree in Computer Engineering from the Federal University Oye-Ekiti, a testament to his academic strength and commitment to excellence. He has also been entrusted with key roles within the university, such as Assistant Examination Officer, Level Advisor, and member of several strategic committees, including the Artificial Intelligence Committee and departmental accreditation teams. These roles highlight the trust placed in him by his peers and institutional leadership. Additionally, his active involvement as a reviewer for respected international and national journals such as BMJ Open and the Nigerian Journal of Technological Development reflects recognition of his scholarly competence and critical thinking. Although formal awards are not explicitly listed, his growing responsibilities, editorial roles, and consistent academic performance collectively reflect a strong professional honor and recognition within his academic community.

Award and Honor🏆

Soladoye Afeez Adekunle has earned recognition for his dedication to academic excellence, professional service, and contributions to the field of computer engineering. He graduated with distinction in his Master’s degree in Computer Engineering from the Federal University Oye-Ekiti, a testament to his academic strength and commitment to excellence. He has also been entrusted with key roles within the university, such as Assistant Examination Officer, Level Advisor, and member of several strategic committees, including the Artificial Intelligence Committee and departmental accreditation teams. These roles highlight the trust placed in him by his peers and institutional leadership. Additionally, his active involvement as a reviewer for respected international and national journals such as BMJ Open and the Nigerian Journal of Technological Development reflects recognition of his scholarly competence and critical thinking. Although formal awards are not explicitly listed, his growing responsibilities, editorial roles, and consistent academic performance collectively reflect a strong professional honor and recognition within his academic community.

Research Skill🔬

Soladoye Afeez Adekunle possesses a diverse and practical set of research skills that align with cutting-edge developments in computer engineering and artificial intelligence. His expertise includes data analysis, machine learning model development, deep learning, and natural language processing. He has applied these skills in various impactful projects such as medical prediction systems for cancer and stroke, fake news detection, and object measurement using computer vision techniques. Adept at data preprocessing, model training, performance evaluation, and algorithm optimization, he ensures high-quality and accurate research outcomes. He is also skilled in using tools and frameworks such as Python, TensorFlow, Keras, and MATLAB for simulation and modeling. His experience in peer reviewing academic journals and formatting manuscripts further demonstrates his understanding of scientific writing and research ethics. Soladoye’s ability to merge academic research with practical application, along with his commitment to innovation, positions him as a capable and forward-thinking researcher in the technology domain.

Conclusion💡

Soladoye, Afeez Adekunle presents a strong case for the Young Scientist Award, especially in the areas of emerging technologies, machine learning, and applied computing. His academic excellence, teaching versatility, peer-review contributions, and practical ML project development demonstrate his passion and potential.

Publications Top Noted✍️

  • Title: IMPACT OF SOCIAL MEDIA ON POLICE BRUTALITY AWARENESS IN NIGERIA

    • Authors: OJOA, SOLADOYE Afeez A.

    • Year: 2020

    • Citations: 24

  • Title: Detection of Cervical Cancer Using Deep Transfer Learning

    • Authors: B.A. Omodunbi, A.A. Soladoye, A.O. Esan, N.S. Okomba, T.G.O.O.M. Ojelabi

    • Year: 2024

    • Citations: 4*

  • Title: Optimizing Stroke Prediction Using Gated Recurrent Unit and Feature Selection in Sub-Saharan Africa

    • Authors: A.A. Soladoye, D.B. Olawade, I.A. Adeyanju, O.M. Akpa, N. Aderinto, et al.

    • Year: 2025

    • Citations: 2

  • Title: E-learning: Significance on Federal Unity Schools Students’ in Nigeria Amidst COVID-19 Lockdown

    • Authors: A.A. Soladoye

    • Year: 2020

    • Citations: 2

  • Title: Development of a Medical Condition Prediction Model Using Natural Language Processing with K-Nearest Neighbour

    • Authors: B.A. Omodunbi, A.A. Soladoye, N.S. Okomba, M.O. Ayinla, C.S. Odeyemi

    • Year: [Year not specified]

    • Citations: 2*

  • Title: Smart Hospitality: Leveraging Technological Advances to Enhance Customer Satisfaction

    • Authors: O.O. Osadare, O.N. Akande, A.A. Soladoye, P.O. Sobowale

    • Year: 2024

    • Citations: 1

  • Title: Internet of Things (IoT) Based Remote Surveillance Camera for Supervision of Examinations

    • Authors: C. Segun Odeyemi, B.A. Omodunbi, O.M. Olaniyan, A.A. Soladoye

    • Year: 2024

    • Citations: 1

  • Title: Prediction of Customer Satisfaction in Airline Hospitality Services for Improved Service Delivery Using Support Vector Machine

    • Authors: A.A. Sobowale, O.O. Osadare, A.A. Soladoye, P.O. Sobowale

    • Year: 2024

    • Citations: 1

  • Title: Development of an Interactive Android-Based Ayo-Olopon Game

    • Authors: E.Y. Bolaji Abigail Omodunbi, Afeez Adekunle Soladoye, Opeyemi Asaolu

    • Year: 2023

    • Citations: 1

Xiaoyun Gong | Intelligent Diagnosis | Best Researcher Award

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Prof. Dr. Xiaoyun Gong  | Intelligent Diagnosis | Best Researcher Award

Department head at Zhengzhou University of Light Industry, China

Prof. Dr. Gong Xiaoyun, a faculty member at Zhengzhou University of Light Industry, is a specialist in rotating machinery fault diagnosis and mechanical vibration signal processing—critical areas within mechanical and electrical engineering. Her academic role and focused research demonstrate strong technical expertise with potential industrial impact, particularly in predictive maintenance and system reliability. However, to strengthen her candidacy for the Best Researcher Award, additional evidence of academic output is needed. Key areas for improvement include detailing her publication record, citation metrics, involvement in major research projects or funding, and participation in international academic collaborations or conferences. Further contributions such as student mentorship, journal reviewing, or leadership roles in academic committees would also enhance her profile. While her background shows promise, incorporating these elements would significantly elevate her competitiveness for the award. With a more comprehensive portfolio, Prof. Gong would be a compelling nominee for recognition as an outstanding researcher in her field.

Professional Profile 

Education🎓

Prof. Dr. Gong Xiaoyun holds a Ph.D. in a specialized field related to mechanical and electrical engineering, which forms the foundation of her academic and research career. Her advanced education has equipped her with in-depth knowledge in areas such as rotating machinery fault diagnosis and mechanical vibration signal processing—fields that require a strong grounding in engineering principles, mathematics, and data analysis. Although specific details about the universities attended, thesis focus, or academic distinctions are not provided, her current position as a professor at Zhengzhou University of Light Industry indicates a solid academic background and extensive training at the postgraduate level. Her educational journey has likely included rigorous coursework, research projects, and contributions to scientific literature, which have prepared her for a career in both teaching and research. To further strengthen her academic profile, detailed information about her degrees, institutions, and academic achievements would provide clearer insight into the depth and scope of her educational qualifications.

Professional Experience📝

Prof. Dr. Gong Xiaoyun has built a strong professional career as a faculty member at the Mechanical and Electrical Engineering Institute of Zhengzhou University of Light Industry. Her expertise lies in rotating machinery fault diagnosis and mechanical vibration signal processing—technical areas with significant industrial applications in equipment maintenance and system reliability. As a professor, she is likely involved in teaching undergraduate and postgraduate courses, supervising student research, and contributing to the academic development of her department. Her professional experience includes not only academic instruction but also active research in mechanical systems diagnostics, suggesting a blend of theoretical knowledge and practical application. While specific details about previous positions, industrial collaborations, or leadership roles are not provided, her current status indicates years of experience in academia and research. Expanding on her participation in funded projects, consultancy work, or contributions to academic conferences would further highlight the depth of her professional accomplishments and impact in the engineering field.

Research Interest🔎

Prof. Dr. Gong Xiaoyun’s research interests focus on rotating machinery fault diagnosis and mechanical vibration signal processing—two critical areas within mechanical and electrical engineering. Her work aims to improve the reliability, safety, and efficiency of mechanical systems by developing advanced diagnostic techniques for identifying faults in rotating machinery. This involves analyzing vibration signals, applying signal processing methods, and possibly integrating intelligent algorithms to detect anomalies and predict failures. Her research has significant implications for industrial applications such as manufacturing, energy, and transportation, where predictive maintenance and early fault detection are essential. By exploring how mechanical vibrations reveal the health and performance of machines, she contributes to the advancement of condition monitoring systems and operational safety. Although more detailed examples of her methodologies, tools used, or interdisciplinary applications would enhance the clarity of her focus, her specialization suggests a valuable contribution to both academic research and practical engineering problem-solving in this domain.

Award and Honor🏆

Prof. Dr. Gong Xiaoyun has established herself as a dedicated academic and researcher at Zhengzhou University of Light Industry, and while specific awards and honors are not listed in the available information, her position as a professor suggests a strong record of academic recognition and professional achievement. It is likely that she has received internal university commendations, research excellence awards, or recognition for her contributions to teaching and mentoring students in the field of mechanical and electrical engineering. Her work in rotating machinery fault diagnosis and vibration signal processing positions her well for honors related to innovation and applied engineering research. To strengthen her profile for major awards such as the Best Researcher Award, it would be beneficial to include details of any national or international honors, competitive research grants received, keynote speaker invitations, or notable academic accolades. Documented recognition would further validate her impact and leadership in her area of specialization.

Research Skill🔬

Prof. Dr. Gong Xiaoyun demonstrates strong research skills in the specialized areas of rotating machinery fault diagnosis and mechanical vibration signal processing. Her expertise includes the ability to analyze complex mechanical systems by interpreting vibration signals to identify and predict faults, a skill that requires proficiency in signal processing techniques, data analysis, and mechanical engineering principles. She likely utilizes advanced tools and software for monitoring and diagnosing mechanical health, combining theoretical knowledge with practical applications. Her research skills also involve designing experiments, developing diagnostic algorithms, and validating results through testing and simulation. Additionally, her role as a professor suggests experience in guiding student research projects, collaborating with colleagues, and possibly managing research teams. These skills enable her to contribute to innovations in predictive maintenance and machinery reliability, making her research both academically rigorous and industrially relevant. Further documentation of published research and funded projects would highlight the full extent of her research capabilities.

Conclusion💡

Prof. Dr. Gong Xiaoyun shows promising qualifications for the Best Researcher Award based on her specialized expertise and institutional role. However, for a competitive nomination, her candidacy would benefit greatly from the inclusion of measurable research outputs, such as:

  • A comprehensive list of publications and citations,

  • Evidence of research leadership or project funding,

  • Recognition from the academic community at national or international levels.

Publications Top Noted✍️

  1. IGFT-MHCNN: An intelligent diagnostic model for motor compound faults based decoupling and denoising of multi-source vibration signals

    • Authors: Gong Xiaoyun, Zhi Zeheng, Gao Yiyuan, Du Wenliao

    • Year: 2025

    • Citations: 1

  2. Multiscale Dynamic Weight-Based Mixed Convolutional Neural Network for Fault Diagnosis of Rotating Machinery

    • Authors: Du Wenliao, Yang Lingkai, Gong Xiaoyun, Liu Jie, Wang Hongchao

    • Year: 2025

  3. A fault diagnosis method for key transmission components of rotating machinery based on SAM-1DCNN-BiLSTM temporal and spatial feature extraction

    • Authors: Du Wenliao, Niu Xinchuang, Wang Hongchao, Li Ansheng, Li Chuan

    • Year: 2025

  4. Dual-loss nonlinear independent component estimation for augmenting explainable vibration samples of rotating machinery faults

    • Authors: Gong Xiaoyun, Hao Mengxuan, Li Chuan, Du Wenliao, Pu Zhiqiang

    • Year: 2024

    • Citations: 4

Xiang Li | Computer Science | Best Researcher Award

Published on by Global Scholar Awards

Ms. Xiang Li | Computer Science | Best Researcher Award

PHD candidate at University of Chinese Academy of Sciences, China

Xiang Li, a Ph.D. candidate at the University of Chinese Academy of Sciences, demonstrates exceptional potential for the Best Researcher Award. With a solid academic foundation—ranking in the top 5–7% throughout his studies—he has excelled in areas such as deep learning, stochastic processes, and pattern recognition. His research focuses on cross-domain few-shot learning, addressing real-world challenges like medical lesion detection and remote sensing scene classification. He has published in the prestigious Knowledge-Based Systems journal and submitted another to IEEE Transactions on Geoscience and Remote Sensing. Xiang has also earned accolades, including the Second Prize in the National Mathematical Modeling Competition and a top-tier finish in the Huawei Software Elite Challenge. His future interests in class-incremental learning and prompt tuning highlight a clear vision for impactful research. Overall, Xiang Li’s innovative contributions, academic excellence, and commitment to advancing AI technologies make him a strong and deserving candidate for this recognition.

Professional Profile 

Education

Xiang Li has demonstrated outstanding academic performance throughout his educational journey. He earned his Bachelor’s degree in Information and Computer Science from Shandong University, graduating in July 2021 with an impressive GPA of 91.73/100, placing him in the top 7.46% of his class. His coursework included high-level subjects such as Mathematical Statistics, Operations Research, and Advanced Algebra, in which he consistently achieved top scores. Following this, he was admitted to the University of Chinese Academy of Sciences, where he completed foundational Ph.D. training from September 2021 to July 2022, ranking in the top 5% with a GPA of 87.13/100. His advanced studies covered critical areas like Matrix Analysis, Deep Learning, and Pattern Recognition. Currently, he is conducting doctoral research at the Institute of Optics and Electronics, Chinese Academy of Sciences, focusing on cross-domain few-shot learning. His educational background reflects strong technical competence and a solid foundation for innovative research.

Professional Experience

Xiang Li has accumulated valuable professional research experience during his Ph.D. studies at the Institute of Optics and Electronics, Chinese Academy of Sciences. His primary research focuses on cross-domain few-shot learning, a vital area in artificial intelligence that addresses challenges in data-scarce environments. He has led and contributed to key projects, including the development of a dynamic representation enhancement framework to improve model generalization across different domains, and the fine-tuning of general pre-trained models for few-shot remote sensing scene classification. In addition to research, Xiang has actively participated in national competitions, winning third prize in the Huawei Software Elite Challenge for designing a traffic scheduling plan and contributing to infrared small target detection strategies in another competition. These experiences highlight his strong technical problem-solving skills, teamwork, and ability to apply theoretical knowledge to real-world challenges. His professional work reflects both depth and versatility, positioning him as a highly capable and innovative young researcher.

Research Interest

Xiang Li’s research interests lie at the forefront of artificial intelligence, with a strong focus on cross-domain few-shot learning, computer vision, and representation learning. He is particularly interested in developing algorithms that enable models to perform effectively in data-scarce scenarios, addressing the challenges posed by domain shifts and limited labeled data. His current work involves enhancing the representational capacity of models to learn diverse and meaningful features across domains, with applications in medical image analysis and remote sensing. Xiang is also exploring techniques for fine-tuning general pre-trained models to adapt to new tasks without extensive retraining. Looking ahead, he is keen on advancing research in few-shot class-incremental learning, where models continuously adapt to new classes with minimal data, and in prompt tuning for vision-language pre-trained models, which integrates natural language processing with visual recognition. His interests reflect a forward-thinking approach to building intelligent systems capable of learning efficiently and generalizing across tasks.

Award and Honor

Xiang Li has received several prestigious awards and honors in recognition of his academic excellence and research capabilities. During his undergraduate and doctoral studies, he was consistently awarded scholarships from both Shandong University and the University of Chinese Academy of Sciences, reflecting his outstanding academic performance and dedication. In June 2022, he was named a Merit Student at the University of Chinese Academy of Sciences, an honor reserved for top-performing students. His strong analytical and problem-solving skills were further recognized in national competitions, where he earned the Second Prize in the National College Students’ Mathematical Modeling Competition in 2019. Additionally, he played a key role in a team that won third prize in the Huawei Software Elite Challenge, a highly competitive event involving over 300 teams. These honors highlight his ability to excel both academically and practically, reinforcing his position as a promising and accomplished young researcher in the field of computer science.

Research skill

Xiang Li possesses a strong set of research skills that make him a capable and innovative scholar in the field of artificial intelligence and computer vision. His expertise spans advanced areas such as cross-domain few-shot learning, deep learning, and representation learning. He demonstrates exceptional analytical abilities, evident in his design and implementation of dynamic representation frameworks to enhance model generalization across diverse domains. Xiang is proficient in applying theoretical concepts to practical problems, as seen in his work on fine-tuning pre-trained models for remote sensing scene classification. His skill set includes programming, algorithm development, statistical analysis, and critical thinking, which he has effectively applied in both solo research and collaborative projects. Furthermore, his ability to publish in top-tier journals, such as Knowledge-Based Systems, reflects his competence in scientific writing, experimental design, and result interpretation. These research skills enable him to tackle complex challenges and contribute meaningfully to the advancement of intelligent systems.

Conclusion

Xiang Li is a highly promising young researcher with a solid academic foundation, well-defined research focus, and impactful contributions in the field of computer vision and machine learning. His achievements in cross-domain few-shot learning, publication in a top-tier journal, and award-winning competition experience clearly demonstrate excellence in research and innovation.

Publications Top Noted

  • Title: RSGPT: A remote sensing vision language model and benchmark
    Authors: Y. Hu, Yuan; J. Yuan, Jianlong; C. Wen, Congcong; Y. Liu, Yu; X. Li, Xiang
    Year: 2025

  • Title: Uni3DL: A Unified Model for 3D Vision-Language Understanding
    Authors: X. Li, Xiang; J. Ding, Jian; Z. Chen, Zhaoyang; M. Elhoseiny, Mohamed
    Year: 2025 (Conference Paper)

  • Title: 3D Shape Contrastive Representation Learning With Adversarial Examples
    Authors: C. Wen, Congcong; X. Li, Xiang; H. Huang, Hao; Y.S. Liu, Yu Shen; Y. Fang, Yi
    Year: 2025
    Journal: IEEE Transactions on Multimedia
    Citations: 4

  • Title: Learning general features to bridge the cross-domain gaps in few-shot learning
    Authors: X. Li, Xiang; H. Luo, Hui; G. Zhou, Gaofan; M. Li, Meihui; Y. Liu, Yunfeng
    Year: 2024
    Journal: Knowledge-Based Systems
    Citations: 1

Hafiz Khan | Machine Learning | Best Researcher Award

Published on by Global Scholar Awards

Prof. Dr. Hafiz Khan | Machine Learning | Best Researcher Award

Professor at Texas Tech University Health Sciences Center, United States

Dr. Hafiz M. R. Khan is a Full Professor of Biostatistics at Texas Tech University Health Sciences Center, with an extensive academic and research background. He holds a Ph.D. in Statistics from the University of Western Ontario and has postdoctoral training in Bioinformatics. His career spans multiple institutions, including Florida International University and the University of Medicine & Dentistry of New Jersey. Dr. Khan has held leadership roles such as Associate Chair and Director of Outcome Measures, contributing significantly to academic committees and research initiatives. He has published extensively in peer-reviewed journals, focusing on biostatistics, public health, and cognitive impairment research. His strengths for the Best Researcher Award include a strong publication record, leadership in academia, and interdisciplinary collaboration. Areas for improvement may include further engagement in international research projects. Overall, his contributions to biostatistics and public health research make him a strong candidate for the Best Researcher Award.

Professional Profile 

Education

Dr. Hafiz M. R. Khan has a strong educational background in statistics and biostatistics. He earned his Ph.D. in Statistics from the University of Western Ontario, Canada, where he specialized in statistical methodologies and their applications in health sciences. To further enhance his expertise, he completed postdoctoral training in Bioinformatics, gaining advanced knowledge in computational biology and data analysis. His academic journey also includes a Master’s and Bachelor’s degree in Statistics, which provided him with a solid foundation in quantitative analysis and research methods. Throughout his education, Dr. Khan focused on interdisciplinary applications of statistics, particularly in public health, epidemiology, and biomedical sciences. His strong academic credentials have enabled him to contribute significantly to research, teaching, and mentoring students in biostatistics and public health. His education has played a pivotal role in shaping his career, allowing him to bridge the gap between statistical theory and real-world health applications.

Professional Experience

Dr. Hafiz M. R. Khan has an extensive professional background in statistics, biostatistics, and public health research. He has held various academic and research positions, contributing significantly to statistical methodologies in biomedical and epidemiological studies. As a professor and researcher, he has taught biostatistics, data analysis, and public health courses at reputable institutions, mentoring numerous students and professionals. His expertise extends to consulting for healthcare organizations and research institutions, where he applies statistical models to solve complex health-related problems. Dr. Khan has also collaborated on interdisciplinary projects involving bioinformatics, machine learning, and predictive analytics in healthcare. His professional journey includes publishing high-impact research papers, serving as a peer reviewer for scientific journals, and participating in international conferences. His work has been instrumental in advancing statistical applications in medicine and public health, bridging the gap between theoretical research and practical implementation in real-world health challenges.

Research Interest

Dr. Hafiz M. R. Khan’s research interests lie at the intersection of biostatistics, epidemiology, and public health, with a strong focus on statistical modeling, predictive analytics, and machine learning applications in healthcare. He is particularly interested in developing advanced statistical methodologies to analyze complex biomedical data, improve disease prediction models, and enhance public health decision-making. His work explores the integration of statistical techniques with bioinformatics to study genetic influences on diseases and health outcomes. Additionally, he investigates the application of artificial intelligence in medical research, aiming to optimize diagnostic accuracy and treatment effectiveness. Dr. Khan is also passionate about global health issues, including infectious disease surveillance, health disparities, and aging populations. Through interdisciplinary collaborations, he strives to bridge the gap between statistical theory and real-world healthcare applications, contributing to innovative solutions that enhance patient care, policy-making, and public health interventions worldwide.

Award and Honor

Dr. Hafiz M. R. Khan has received numerous awards and honors in recognition of his outstanding contributions to biostatistics, public health, and epidemiology. He has been honored with prestigious research grants and fellowships from esteemed institutions, highlighting his excellence in statistical modeling and healthcare analytics. His groundbreaking work has earned him accolades such as the Best Researcher Award and Excellence in Public Health Research recognition. Dr. Khan has been invited as a keynote speaker at international conferences and has received distinguished scholar awards for his impactful publications. His dedication to academic excellence has also been acknowledged through teaching awards, mentoring recognitions, and leadership roles in professional organizations. Additionally, he has been recognized for his contributions to global health initiatives, demonstrating his commitment to improving healthcare outcomes. These awards and honors underscore his influence in the field and his continuous efforts to advance research, education, and policy in health sciences.

Research Skill

Dr. Hafiz M. R. Khan possesses exceptional research skills in biostatistics, public health, and epidemiology, enabling him to conduct advanced statistical analyses and develop innovative models for healthcare studies. His expertise includes data analysis, predictive modeling, machine learning applications in health research, and designing population-based studies. He has a strong command of statistical software such as R, SPSS, SAS, and STATA, which he utilizes to interpret complex datasets effectively. Dr. Khan excels in systematic reviews, meta-analysis, and quantitative research methodologies, ensuring rigorous scientific inquiry and evidence-based conclusions. His ability to synthesize large datasets and extract meaningful insights has contributed significantly to policy recommendations and healthcare improvements. Additionally, his collaborative approach to interdisciplinary research allows him to work seamlessly with experts from diverse fields. His critical thinking, problem-solving abilities, and meticulous research design skills make him a valuable contributor to advancing public health, epidemiology, and statistical sciences.

Conclusion

Dr. Hafiz M. R. Khan is a highly qualified candidate for the Best Researcher Award due to his extensive contributions to academia, research, and public health. His leadership roles, mentoring, and commitment to advancing Biostatistics make him a strong contender. However, enhancing visibility of research impact, citations, international collaborations, and applied innovations could further strengthen his application.

Publications Top Noted

  • Title: Metabolic syndrome in aboriginal Canadians: prevalence and genetic associations
    Authors: RL Pollex, AJG Hanley, B Zinman, SB Harris, HMR Khan, RA Hegele
    Year: 2006
    Citations: 145

  • Title: Differences between carotid wall morphological phenotypes measured by ultrasound in one, two and three dimensions
    Authors: K Al-Shali, AA House, AJG Hanley, HMR Khan, SB Harris, …
    Year: 2005
    Citations: 142

  • Title: Genetic Variation in PPARG Encoding Peroxisome Proliferator-Activated Receptor γ Associated With Carotid Atherosclerosis
    Authors: KZ Al-Shali, AA House, AJG Hanley, HMR Khan, SB Harris, B Zinman, …
    Year: 2004
    Citations: 123

  • Title: Guillain–Barré syndrome after Gardasil vaccination: data from vaccine adverse event reporting system 2006–2009
    Authors: N Souayah, PA Michas-Martin, A Nasar, N Krivitskaya, HA Yacoub, …
    Year: 2011
    Citations: 120

  • Title: Type 2 diabetes and its correlates among adults in Bangladesh: a population-based study
    Authors: MAB Chowdhury, MJ Uddin, HMR Khan, MR Haque
    Year: 2015
    Citations: 110

  • Title: Physical therapists’ attitudes, knowledge, and practice approaches regarding people who are obese
    Authors: S Sack, DR Radler, KK Mairella, R Touger-Decker, H Khan
    Year: 2009
    Citations: 78

  • Title: Trends in outcomes and hospitalization costs for traumatic brain injury in adult patients in the United States
    Authors: K Farhad, HMR Khan, AB Ji, HA Yacoub, AI Qureshi, N Souayah
    Year: 2013
    Citations: 56

  • Title: Predictive inference from a two-parameter Rayleigh life model given a doubly censored sample
    Authors: HMR Khan, SB Provost, A Singh
    Year: 2010
    Citations: 49

  • Title: Optimizing RNA extraction yield from whole blood for microarray gene expression analysis
    Authors: J Wang, JF Robinson, HMR Khan, DE Carter, J McKinney, BA Miskie, …
    Year: 2004
    Citations: 48

  • Title: Secondhand smoke exposure reduction intervention in Chinese households of young children: a randomized controlled trial
    Authors: AS Abdullah, F Hua, H Khan, X Xia, Q Bing, K Tarang, JP Winickoff
    Year: 2015
    Citations: 45

  • Title: Statistical machine learning approaches to liver disease prediction
    Authors: F Mostafa, E Hasan, M Williamson, H Khan
    Year: 2021
    Citations: 40

  • Title: The safety profile of home infusion of intravenous immunoglobulin in patients with neuroimmunologic disorders
    Authors: N Souayah, A Hasan, HMR Khan, HA Yacoub, M Jafri
    Year: 2011
    Citations: 34

  • Title: Tumor-infiltrating lymphocytes (TILs) as a biomarker of abscopal effect of cryoablation in breast cancer: A pilot study
    Authors: SY Khan, MW Melkus, F Rasha, M Castro, V Chu, L Brandi, H Khan, …
    Year: 2022
    Citations: 31

  • Title: Vulnerability prioritization, root cause analysis, and mitigation of secure data analytic framework implemented with MongoDB on Singularity Linux containers
    Authors: AM Dissanayaka, S Mengel, L Gittner, H Khan
    Year: 2020
    Citations: 31

  • Title: Colorectal cancer screening use among insured adults: Is out-of-pocket cost a barrier to routine screening?
    Authors: A Perisetti, H Khan, NE George, R Yendala, A Rafiq, S Blakely, …
    Year: 2018
    Citations: 31

Naeem Ullah | Computer Science | Best Researcher Award

Published on by Global Scholar Awards

Mr. Naeem Ullah | Computer Science | Best Researcher Award

PhD Student at Software Engineering Research Group (SERG-UOM) University of Malakand, Pakistan

Mr. Naeem Ullah is a dedicated academic and researcher currently pursuing a PhD in Computer Science, with a focus on cybersecurity challenges in vehicle-to-vehicle communication from a software engineering perspective. Holding a strong academic record with a CGPA of 3.75/4.00, he has presented his research at international forums, such as the 2nd Annual International Workshop on Software Engineering, where he shared his Multivocal Literature Review (MLR) protocol on cybersecurity culture. Mr. Ullah has also received recognition for his teaching excellence, earning the Best Teacher Award in 2018. His work experience includes roles as a lecturer at the University Model College KPK, part-time tutor at Allama Iqbal Open University, and facilitator for continuous professional development programs for teachers. His research, currently under review, addresses crucial cybersecurity issues in vehicle-to-vehicle communications. Mr. Ullah’s commitment to furthering his knowledge is evident through multiple certifications in data science, networking, and cybersecurity.

Professional Profile 

Education

Mr. Naeem Ullah has a strong educational background in Computer Science. He is currently pursuing a PhD in Computer Science with a focus on cybersecurity challenges in vehicle-to-vehicle communication, maintaining an impressive CGPA of 3.75/4.00. His research aims to develop a mitigation model for cybersecurity issues in connected vehicle systems, reflecting his deep engagement with current technological challenges. Mr. Ullah completed his Master’s degree in Computer Science in 2019, achieving a CGPA of 3.7/4.00, with his thesis titled Software Development Process Improvement Model for Small Pakistani Software Development Companies. He also holds a Bachelor’s degree in Computer Science from 2014, with a CGPA of 3.62/4.00. His final year project, Auction Management System, showcased his ability to apply practical solutions to real-world problems. Mr. Ullah’s academic journey is marked by consistent excellence and a strong commitment to advancing his expertise in the field of computer science.

Professional Experience

Mr. Naeem Ullah has accumulated diverse professional experience in both academic and research roles. He has served as a Lecturer in Computer Science at the University Model College KPK, Peshawar, Pakistan, where he taught and mentored students in various computer science subjects. In addition, he has worked as a part-time tutor for Allama Iqbal Open University, Islamabad, since 2022, focusing on Information and Communication Technologies (ICT). Mr. Ullah has also contributed to teacher development programs, serving as a facilitator for the Continuous Professional Development (CPD) of Primary School Teachers (PSTs) through the Provincial Institute of Teacher Education (PITE) in KPK. His role as a part-time researcher at the Department of Computer Science and IT at the University of Malakand further underscores his involvement in academic research. Earlier in his career, he worked as a Secondary School Teacher at the Elementary and Secondary Education Department, KPK. His experiences reflect a blend of teaching, research, and educational development.

Research Interest

Mr. Naeem Ullah’s research interests primarily focus on cybersecurity, particularly in the context of emerging technologies such as vehicle-to-vehicle (V2V) communication. His PhD research investigates cybersecurity challenges and proposes mitigation models for securing V2V communication systems from a software engineering perspective. This area of research is highly relevant due to the increasing integration of connected vehicles and the need for secure communication protocols to protect sensitive data. Additionally, Mr. Ullah is interested in software engineering, with a particular emphasis on improving software development processes for small software companies in Pakistan, as demonstrated in his Master’s thesis. He has also contributed to the field of cybersecurity culture through his work on a Multivocal Literature Review (MLR) protocol, which identifies cybersecurity challenges and best practices in V2V communication. His research endeavors aim to address critical issues in both cybersecurity and software engineering, contributing to the development of safer, more efficient technologies.

Award and Honor

Mr. Naeem Ullah has received notable recognition for his academic and professional achievements. In 2022, he presented his Multivocal Literature Review (MLR) Protocol at the 2nd Annual International Workshop on Software Engineering (WSE-2022), organized by the Software Engineering Research Group at the University of Malakand. This presentation, focused on Cybersecurity Culture, showcased his expertise and contribution to the field of cybersecurity. Additionally, Mr. Ullah earned the prestigious Best Teacher Award from the Director of Elementary and Secondary Education, KPK, Pakistan, in 2018. This recognition highlights his excellence in teaching and his commitment to fostering the growth and development of his students. These awards and honors reflect Mr. Ullah’s dedication to advancing both his academic research and educational practices, demonstrating his commitment to the fields of computer science and cybersecurity while contributing positively to the educational community.

Conclusion

Naeem Ullah is a promising candidate for the Best Researcher Award, with a solid academic record, a focused and impactful research topic, and a commitment to both education and professional development. His strengths lie in his dedication to advancing cybersecurity research in emerging technologies like vehicle-to-vehicle communication and his capacity for leadership in educational initiatives. To further enhance his candidacy, Naeem could focus on increasing his research output, expanding his research scope, and engaging more in international collaborations to elevate the impact of his work.

Publications Top Noted

  • Title: Solutions to Cybersecurity Challenges in Secure Vehicle-to-Vehicle Communications: A Multivocal Literature Review
    Authors: Naeem Ullah, S.U. Khan, M. Niazi, A.A. Khan, J.A. Nasir
    Journal: Information and Software Technology
    Year: 2025
    Volume: 179
    Article ID: 107639
    Citations: 0
  • Title: Challenges and Their Practices in Adoption of Hybrid Cloud Computing: An Analytical Hierarchy Approach
    Authors: S.U. Khan, H.U. Khan, Naeem Ullah, R.A. Khan
    Journal: Security and Communication Networks
    Year: 2021
    Article ID: 1024139
    Citations: 2
  • Title: Internet of Things for Healthcare Using Effects of Mobile Computing: A Systematic Literature Review
    Authors: S. Nazir, Y. Ali, Naeem Ullah, I. García-Magariño
    Journal: Wireless Communications and Mobile Computing
    Year: 2019
    Article ID: 5931315
    Citations: 138
  • Title: Practices for Clients in the Adoption of Hybrid Cloud
    Authors: S.U. Khan, Naeem Ullah
    Journal: Proceedings of the Pakistan Academy of Sciences: Part A
    Year: 2017
    Volume: 54(1A)
    Pages: 13–32
    Citations: 3