Afeez Soladoye | Machine learning | Young Scientist Award

Published on 13/06/202513/06/2025 by NESIN Awards

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🏆

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

Hafiz Khan | Machine Learning | Best Researcher Award

Published on 11/03/202511/03/2025 by NESIN 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