Dr. Pankaj Kumar | Machine learning | Best Researcher Award

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Dr. Pankaj Kumar | Machine learning | Best Researcher Award

Assistant Professor, National Institute of Technology, Hamirpur

Dr. Pankaj Kumar is a researcher specializing in operations research, optimization methods in finance, interval optimization, machine learning and crop area planning. He earned a Ph.D. in Optimization Methods in Finance from the Indian Institute of Technology Kharagpur with his thesis on interval optimization methods for portfolio selection, and holds earlier advanced degrees in operations research and mathematics. Dr. Pankaj Kumar has served in research and teaching roles—most recently as Assistant Professor—focusing on modelling of portfolio optimization, multi-objective programming, time-series forecasting, and risk measures such as mean-VaR. His professional experience includes supervising research students, contributing to international and national collaborative projects, participating in workshops and conferences, and Dr. Pankaj Kumar’s scholarly output includes more than thirty peer-reviewed papers published in high-impact journals indexed by SCIE, Scopus, and Web of Science, and his work has attracted more than 360 citations with an h-index of 10, reflecting consistent academic influence. His research skills include mathematical modelling, statistical methods, algorithm design, programming in C and R, use of optimisation tools and applying machine learning regression techniques in finance contexts. Among his awards and honors are travel grants, junior/senior research fellowships, editorial board membership, and recognition for teaching and research excellence at his institution. In conclusion, Dr. Pankaj Kumar is positioned to further impact the fields of financial optimization and decision science through high-quality publications, interdisciplinary collaborations, and mentoring, likely to increase his citation profile, visibility, and leadership in both academic and applied settings.

Profile: GOOGLE SCHOLAR | SCOPUS | ORCID

Featured Publications

Behera, J., & Kumar, P. (2025). An approach to portfolio optimization with time series forecasting algorithms and machine learning techniques. Applied Soft Computing, 170, 112741.

Sahu, B. R. B., & Kumar, P. (2025). Portfolio rebalancing model utilizing support vector machine for optimal asset allocation. Arabian Journal for Science and Engineering, 50(14), 10939–10965.

Sahu, B. R. B., Bhurjee, A. K., & Kumar, P. (2024). Efficient solutions for vector optimization problem on an extended interval vector space and its application to portfolio optimization. Expert Systems with Applications, 249, 123653.

Behera, J., & Kumar, P. (2024). Implementation of machine learning-based sparse Sharpe ratio portfolio optimization: A case study on Indian stock market. Operational Research, 24(4), 62.

Patel, M., Behera, J., & Kumar, P. (2024). Parametric approach for multi-objective enhanced interval linear fractional programming problem. Engineering Optimization, 56(5), 740–765.

Laiba Husain | AI in Healthcare | Best Researcher Award

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Dr. Laiba Husain | AI in Healthcare | Best Researcher Award

Early Impact Scholar at University of Texas Southwestern Medical Center | United States

Dr. Laiba Husain is a distinguished public health researcher specializing in translational health sciences and primary care. Her academic journey reflects an exceptional commitment to advancing healthcare equity, particularly in digital health and marginalized communities. Currently serving as an Early Impact Scholar at the Peter O’Donnell School of Public Health, UT Southwestern Medical Center, she integrates multidisciplinary approaches to address pressing healthcare challenges. Her expertise spans qualitative and mixed-methods research, stakeholder engagement, and health policy development. Through impactful collaborations across leading institutions, Dr. Husain has contributed to groundbreaking studies that influence both clinical practice and healthcare delivery worldwide.

Profile:

Scopus | Orcid

Education:

Dr. Husain’s academic foundation began with a Bachelor of Science in Biopsychology, Cognition, and Neuroscience from the University of Michigan , where she was recognized as a James B. Angell Scholar. As a Fulbright Scholar, she earned her Master of Public Health from the University of Birmingham, gaining international perspectives on health systems. she completed her Doctor of Philosophy in Translational Health Sciences at the University of Oxford under Dr. Trish Greenhalgh’s supervision. Her doctoral work focused on health disparities in digital care delivery, merging rigorous methodology with real-world application to inform evidence-based interventions.

Experience:

Dr. Husain’s career includes progressive roles at globally renowned institutions. she was a Research Associate at the University of Oxford, contributing to EU-wide healthcare initiatives.  she worked as a Research Analyst in the Nuffield Department of Primary Care Health Sciences, enhancing methodological rigor across diverse projects. Concurrently, she served as a consultant with The Healthcare Improvement Studies Institute at the University of Cambridge, advising on healthcare improvement strategies. she has advanced public health research at UT Southwestern, with a focus on tele-oncology and regional healthcare partnerships.

Research Interests:

Dr. Husain’s research interests converge on healthcare equity, digital health disparities, and patient-centered care. She investigates how intersecting disadvantages such as socioeconomic status, language barriers, and age affect access to digital healthcare solutions. Her work employs qualitative and mixed-methods approaches to capture nuanced patient experiences and inform inclusive policy frameworks. She is particularly engaged in developing practical tools, such as user personas, to address barriers in digital consultations for marginalized populations. Additionally, her studies explore the integration of telehealth in primary care and public health, aiming to improve system responsiveness, accessibility, and cultural competence in healthcare delivery.

Awards and Honors:

Dr. Husain’s contributions have been recognized through numerous honors. These include the James B. Angell Scholar Award  for academic excellence, the prestigious Fulbright Scholar Award and the Dean’s University Scholar Award from Oxford . She received a Doctoral Fellowship Award from The Health Improvements Studies Institute  and  honored in the International Women of Impact Portrait Series at Green Templeton College. Most recently, she earned the Nuffield Departmental Award for Outstanding Doctoral Dissertation, highlighting her innovative contributions to translational health sciences and the field’s understanding of digital health disparities.

Publications:

Title: Management of post-acute covid-19 in primary care

Citation: 102

year of Publications: 2020

Title:  Long Covid – the illness narratives

Citation: 65

year of Publications: 2021

Title: I can’t cope with multiple inputs a qualitative study of the lived experience of brain fog after COVID-19

Citation: 45

year of Publications: 2022

Title: Desperately seeking intersectionality in digital health disparity research: narrative review to inform a richer theorization of multiple disadvantage

Citation: 30

year of Publications: 2022

Title: Safety implications of remote assessments for suspected COVID-19 qualitative study in UK primary care

Citation: 20

year of Publications: 2023

Title: Developing user personas to capture intersecting dimensions of disadvantage in older patients who are marginalised: a qualitative studyprimary care

Citation: 12

year of Publications: 2024

Title: Examining Intersectionality and Barriers to the Uptake of Video Consultations Among Older Adults From Disadvantaged Backgrounds With Limited English Proficiency: Qualitative Narrative Interview Study

Citation: 1

year of Publications: 2025

Conclusion:

Dr. Laiba Husain exemplifies the synthesis of academic excellence, research innovation, and real-world healthcare impact. Her interdisciplinary approach bridges gaps between patient experiences, digital health innovation, and public health policy. By fostering collaborations among clinicians, policymakers, and community stakeholders, she has advanced evidence-based solutions addressing complex health inequalities. Her scholarly work not only deepens theoretical understanding but also delivers tangible benefits to underserved populations. With a trajectory marked by high-impact research, leadership in healthcare initiatives, and dedication to mentorship, Dr. Husain stands as an influential figure driving the future of equitable and effective healthcare delivery.

Yuming Jiang | AI in Healthcare | Best Researcher Award

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Assist. Prof. Dr. Yuming Jiang | AI in Healthcare | Best Researcher Award

Assistant Professor of Radiation Oncology at Wake Forest University School of Medicine, United States

Dr. Yuming Jiang, MD, PhD, is an Assistant Professor in the Department of Radiation Oncology at Wake Forest University School of Medicine, North Carolina, USA. His research and clinical expertise focus on the integration of artificial intelligence and digital pathology to improve cancer prognosis and treatment strategies. With a strong background in oncology and computational medicine, Dr. Jiang has made significant contributions to the understanding of tumor microenvironments and the application of deep learning in radiomics.

Profile

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Education

Dr. Jiang obtained his MD and PhD degrees from prestigious institutions, demonstrating a commitment to both clinical practice and scientific research. He completed his PhD in oncology at a leading university in China, followed by a postdoctoral fellowship at Stanford University from 2018 to 2023. In August 2023, he joined Wake Forest University School of Medicine as an Assistant Professor, where he continues to advance the field of radiation oncology through innovative research and patient-centered care.

Experience

Dr. Jiang has a diverse professional background that spans clinical medicine, academic research, and technological innovation. Before joining Wake Forest University, he worked as a Postdoctoral Research Fellow at Stanford University, where he contributed to groundbreaking studies on digital pathology, cancer immunotherapy, and noninvasive imaging techniques. His expertise in artificial intelligence and machine learning has enabled him to develop predictive models for cancer prognosis, treatment response, and survival outcomes.

Research Interests

Dr. Jiang’s research is centered on the intersection of oncology and artificial intelligence. His key interests include deep learning-based radiomics, tumor microenvironment analysis, and predictive modeling for personalized cancer treatment. He aims to harness computational tools to enhance diagnostic accuracy and therapeutic decision-making in radiation oncology. His work has had a profound impact on the understanding of tumor biology and has paved the way for more effective, individualized treatment strategies.

Awards

Dr. Jiang has been recognized for his contributions to oncology and medical research with several prestigious awards. His research has received accolades from leading medical societies and journals, highlighting his role in advancing cancer diagnostics and treatment methodologies. His innovative work in AI-driven oncology has earned him invitations to speak at international conferences and collaborate with esteemed institutions worldwide.

Publications

Jiang Y, Zhang Z, Wang W, Huang W, et al. “Biology-guided deep learning predicts prognosis and cancer immunotherapy response.” Nature Communications, 2023; 14: 5135. (Cited by 16.6)

Jiang Y, Zhou K, Sun Z, Wang H, et al. “Non-invasive tumor microenvironment evaluation using deep learning radiomics.” Cell Reports Medicine, 2023; 4:101146. (Cited by 14.3)

Jiang Y, Zhang Z, Yuan Q, Wang W, et al. “Predicting peritoneal recurrence from CT images using multi-task deep learning.” Lancet Digital Health, 2022; 4(5): e340-e350. (Cited by 36.6)

Jiang Y, Li R, Li G. “Artificial intelligence for clinical oncology: current status and future outlook.” Science Bulletin, 2023; (23): 00113-5. (Cited by 18.9)

Jiang Y, Liang X, Wang W, Chen C, et al. “Radiographical assessment of tumor stroma and treatment outcomes using deep learning.” Lancet Digital Health, 2021; 3(6): e371-e382. (Cited by 36.6)

Jiang Y, Jin C, Yu H, Wu J, et al. “Deep Learning CT Signature to Predict Survival and Chemotherapy Benefit in Gastric Cancer.” Annals of Surgery, 2021; 274(6): e1153-e1161. (Cited by 13.8)

Jiang Y, Wang H, Wu J, Chen C, et al. “Noninvasive imaging evaluation of tumor immune microenvironment in gastric cancer.” Annals of Oncology, 2020; 31(6): 760-768. (Cited by 32.9)

Conclusion

Dr. Yuming Jiang is at the forefront of integrating artificial intelligence into oncology, bringing innovative solutions to cancer diagnosis and treatment. His expertise in deep learning, radiomics, and tumor microenvironment studies has significantly advanced the field of radiation oncology. With a strong research background and a commitment to improving patient outcomes, Dr. Jiang continues to contribute to the medical community through his pioneering work in AI-driven cancer diagnostics and therapy.

Peng YU | AI in Healthcare | Best Researcher Award

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Mr. Peng YU | AI in Healthcare | Best Researcher Award

Associate Senior Doctor at The Second Affiliated Hospital of Nanchang University, China

Dr. Peng Yu is an Associate Senior Doctor in the Department of Endocrinology & Metabolism at The Second Affiliated Hospital of Nanchang University. With a strong background in medicine and extensive experience in both clinical and academic settings, he has made significant contributions to the study of endocrinology, metabolism, and cardiovascular diseases. His research spans various areas, including diabetes, cardiovascular health, and molecular mechanisms of metabolic disorders. In addition to his role as a clinician, he is an active researcher, reviewer for several prestigious international journals, and a recipient of multiple awards recognizing his contributions to medical science.

Profile

Scopus

Education

Dr. Yu earned his Ph.D. in Medicine from Nanchang University in 2017 and previously obtained a Master’s degree in Medicine from Nanjing Medical University in 2014. His academic journey has provided him with in-depth expertise in internal medicine, cardiology, and metabolic disorders. Furthering his research career, he completed a fellowship at Massachusetts General Hospital, Harvard Medical School, focusing on cutting-edge research in CCP medicine. His multidisciplinary education has allowed him to integrate clinical expertise with molecular and cellular research, contributing to advancements in the field of endocrinology and metabolism.

Experience

Dr. Yu has held various roles in the medical field, beginning as a Resident Doctor at The Second Affiliated Hospital of Nanchang University from 2017 to 2019. He was later promoted to Attending Doctor, serving from 2019 to 2021. Since 2021, he has been an Associate Senior Doctor at the same institution, where he continues to provide expert medical care, mentor students, and engage in research activities. His collaborations with prominent researchers, including those at Harvard Medical School and Jilin University, have enriched his expertise and research output. His involvement in over 60 research projects, including several funded by the National Natural Science Foundation of China, highlights his leadership in medical research.

Research Interests

Dr. Yu’s research primarily focuses on endocrinology and metabolism, with particular emphasis on diabetes, cardiovascular diseases, and the molecular mechanisms underlying metabolic disorders. His expertise extends to mitochondrial function, oxidative stress, and the role of programmed cell death in disease progression. Utilizing various research methodologies, including confocal microscopy, Seahorse metabolic analysis, and in vivo disease modeling, he has contributed to the understanding of metabolic disorders at both cellular and systemic levels. His work also explores novel therapeutic targets for diabetic cardiomyopathy and myocardial ischemia-reperfusion injury, positioning him at the forefront of translational medicine in this domain.

Awards

Dr. Yu has been recognized for his contributions to medical research with multiple awards, including:

  • Jiangxi Provincial Science and Technology Progress Third Prize (2019)
  • The Third Prize of Medical Science and Technology of Jiangsu Province (2019, 2023)
  • Young Innovative Talent Award, The Second Affiliated Hospital of Nanchang University (2023)

His achievements underscore his commitment to advancing medical knowledge and improving clinical outcomes in endocrinology and metabolism.

Publications

Dr. Yu has published extensively, with over 150 academic papers, including more than 100 SCI-indexed publications. His recent notable works include:

Yu P et al. “Comprehensive exploration of programmed cell death landscape in lung adenocarcinoma combining multi-omic analysis and experimental verification.” Sci Rep. 2025; 15(1):5364.

Yu P et al. “Performance of ChatGPT on the Chinese Postgraduate Examination for Clinical Medicine: Survey Study.” JMIR Med Educ. 2024; 10:e48514.

Zhao H, Li X, Yu P et al. “Association between weight loss and outcomes in patients undergoing atrial fibrillation ablation: A systematic review and meta-analysis.” Nutr Metab (Lond). 2023; 20(1):5.

Yu P et al. “Obesity and clinical outcomes in COVID-19 patients without comorbidities.” Front Endocrinol. 2022; 13:936976.

Yu P et al. “Dexmedetomidine post-conditioning alleviates myocardial ischemia-reperfusion injury in rats by ferroptosis inhibition via SLC7A11/GPX4 axis activation.” Hum Cell. 2022; 35(3):836-848.

Peng Y et al. “The Association of Serum IL-10 Levels with the Disease Activity in Systemic-Onset Juvenile Idiopathic Arthritis Patients.” Mediators Inflamm. 2021; 2021:6650928.

Conclusion

Dr. Peng Yu is a distinguished clinician and researcher specializing in endocrinology and metabolism. His contributions to the field through extensive research, innovative approaches to disease treatment, and active mentorship have positioned him as a leading expert in his domain. With numerous research grants, impactful publications, and prestigious awards, he continues to drive advancements in medical science. His work not only enhances the understanding of metabolic disorders but also informs the development of novel therapeutic strategies, ensuring better patient outcomes in the future.

Chalachew Yenew Dinku | AI in Healthcare | Best Researcher Award

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Mr. Chalachew Yenew Dinku | AI in Healthcare | Best Researcher Award

Lecturer at Debre Tabor Univesrity, Ethiopia.

Chalachew Yenew Dinku is a highly skilled Environmental and Public Health researcher and educator with over nine years of experience. His work focuses on antimicrobial resistance (AMR), public health emergency management, infection control, One Health, and environmental health sciences. Chalachew completed his Master’s in Environmental Health Sciences from Jimma University with excellent academic standing (CGPA: 3.83/4.00) and an undergraduate degree in Environmental and Occupational Health and Safety from the University of Gondar. His passion for microbial contamination and public health has led him to significant contributions in academia and research. He is currently a Lecturer at Debre Tabor University, where he is involved in research, teaching, and mentoring, while also holding leadership roles in national health initiatives.

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Orcid

Education

Chalachew Yenew Dinku’s academic journey began with a Bachelor’s degree in Environmental and Occupational Health and Safety from the University of Gondar (CGPA: 3.75/4.00), where he graduated with distinction. He later pursued a Master’s degree in Environmental Health Sciences from Jimma University, graduating with great distinction (CGPA: 3.83/4.00). His research during his Master’s focused on antimicrobial resistance contamination pathways, which significantly contributed to the field with multiple peer-reviewed publications. In addition to formal degrees, Chalachew has engaged in several short-term training programs related to infection prevention and control, public health emergency surveillance, and curriculum development.

Experience

Chalachew has accumulated diverse experience in public health and academia. As a Lecturer at Debre Tabor University since 2017, he teaches both undergraduate and postgraduate students while also supervising their research projects. His responsibilities also include writing research grant proposals, conducting high-quality research, and publishing papers in top-tier journals. He has been part of many national and international health projects, such as those dealing with AMR and scabies prevention, securing significant research funding. Before his academic tenure, Chalachew worked as a Public Health Officer in the Amhara region, focusing on health education and disease prevention, and later as a Surveillance Officer with Ohio State University’s Global One Health Initiative. His work there involved disease surveillance and public health emergency response.

Research Interests

Chalachew’s research interests are centered on antimicrobial resistance (AMR), One Health approaches, infection control, public health emergency management, and environmental health. He has made notable contributions to understanding AMR contamination pathways and effective mitigation strategies. His research also delves into aflatoxin contamination in food systems, the burden of chemical poisoning, and the public health impact of emerging infectious diseases such as mpox. Chalachew’s work highlights the intersection of environmental health and public health issues, aiming to improve disease prevention and control measures in both local and global contexts.

Awards

Chalachew has received several awards and recognitions throughout his academic and professional career. He was awarded the International Institute for Primary Healthcare Research Grant to fund his Master’s thesis on AMR contamination pathways. During his undergraduate studies, his research was recognized at a national conference, a testament to his early contributions to the field. His excellence in research and teaching has earned him continued respect from both peers and students, solidifying his place as an influential figure in environmental and public health research.

Publications

Chalachew Yenew Dinku has authored and co-authored several publications in high-impact, peer-reviewed journals. Notable articles include:

“A Mixed-Method study on Antimicrobial Resistance Drivers in Neonatal Intensive Care Units: Pathways, Risks, and Solutions” (Antimicrobial Resistance & Infection Control, 2025).

“Effective Advanced technologies and One Health Mitigation strategies of Aflatoxin Contamination in Peanut Oil” (Food Science & Nutrition, 2023).

“Burden of Chemical Poisoning and Contributing Factors in the Amhara Region, Ethiopia” (BMC Public Health, 2024).

“Intention to receive COVID-19 vaccine and its health belief model-based predictors: A systematic review and meta-analysis” (Human Vaccines & Immunotherapeutics, 2023).

“Aflatoxin contamination of animal feeds and its predictors among dairy farms in Northwest Ethiopia: One Health approach implications” (Frontiers in Veterinary Science, 2023).

“Raw cow milk nutritional content and microbiological quality predictors of South Gondar zone dairy farmers in Ethiopia” (Heliyon, 2022).

“Assessing healthcare workers’ confidence level in diagnosing and managing emerging infectious virus of human mpox in hospitals in Amhara Region” (BMJ Open, 2023).

Conclusion

Chalachew Yenew Dinku’s career is dedicated to advancing the field of public health through research, education, and active engagement in community health initiatives. His contributions to understanding antimicrobial resistance, environmental health, and public health emergencies have made a significant impact on both local and international health systems. As an academic, he continues to inspire and mentor the next generation of public health professionals, while his research work remains at the forefront of addressing critical health challenges. His dedication to improving global public health through evidence-based strategies highlights his commitment to a healthier, more sustainable world.

Ali Mehrizi | Machine Learning | Best Paper Award

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Dr. Ali Mehrizi | Machine Learning | Best Paper Award

Lecturer at Ferdowsi University of Mashhad, Iran.

Ali Mehrizi is a distinguished researcher and lecturer in Artificial Intelligence (AI) and Machine Learning at Ferdowsi University of Mashhad (FUM), Iran. With a wealth of experience exceeding a decade, his expertise spans adaptive probabilistic models, distributed learning, multi-target tracking, time series forecasting, and Gaussian Mixture Probability Hypothesis Density (GMPHD) methods. Dr. Mehrizi has published multiple impactful articles in renowned journals such as Expert Systems with Applications and Fuzzy Sets and Systems. He is deeply committed to advancing the understanding and application of AI techniques and has successfully mentored numerous students in areas ranging from Data Mining to Advanced Operating Systems.

Profile

Google Scholar

Education

Dr. Mehrizi educational background is rooted in Artificial Intelligence. He is currently pursuing a Ph.D. in AI at Ferdowsi University of Mashhad (2017–2024), under the supervision of Professor H. Sadoghi Yazdi. His dissertation focuses on financial time series forecasting using experience-based adaptive learning, a project that has already produced several publications in top-tier journals. Previously, he earned an M.Sc. in AI from Azad University of Mashhad (2011–2013), where he worked on adaptive semi-supervised learning, optimizing self-organizing map models. His early academic journey began with a B.Sc. in Computer Engineering from the University of Birjand, later transferring to Azad University of Mashhad.

Experience

Dr. Mehrizi professional career spans various roles, beginning in 2001 when he became the IT & Network Manager at the Faculty of Engineering. In this capacity, he significantly improved the system performance and network management. Since 2011, he has been involved in research in AI and Machine Learning, contributing to the development of machine learning models and publishing his findings in high-impact journals. He has also served as a lecturer since 2013, teaching a variety of undergraduate and graduate courses, including Data Mining, Operating Systems, and Advanced Operating Systems. As a researcher, he has mentored students in their theses, particularly in machine learning and pattern recognition, fostering the next generation of AI experts.

Research Interests

Dr. Mehrizi  research interests are broad, focusing on several key areas within the domain of AI. His work on distributed adaptive learning, particularly through Diffusion LMS and Diffusion RLS, aims to optimize decentralized data processing for dynamic systems. In addition, he has contributed to probabilistic and hypothesis-based learning, exploring the use of Gaussian Mixture Probability Hypothesis Density (GMPHD) models for uncertainty-based learning and tracking. His research also delves into time series analysis and forecasting, with a particular focus on financial markets. Dr. Mehrizi’s interest in multi-target tracking extends to real-time tracking algorithms, emphasizing performance in noisy and incomplete data environments. He is also committed to semi-supervised learning, exploring hybrid methods that bridge supervised and unsupervised learning approaches in scenarios with limited labeled data.

Awards

Dr. Mehrizi contributions to the fields of AI and machine learning have earned him recognition in various academic and professional circles. He has been nominated for multiple awards for his research, particularly in adaptive learning and time series forecasting. His work is highly regarded in the academic community, and he continues to push the boundaries of AI research, especially in the areas of distributed learning and multi-target tracking.

Publications

Dr. Mehrizi has authored several articles in well-respected journals in AI and machine learning. His key publications include:

Mehrizi, A., & Yazdi, H. S. (2019). “Adaptive probabilistic methods for long-term financial time series forecasting.” Expert Systems with Applications.

Mehrizi, A., & Yazdi, H. S. (2020). “Semi-supervised learning using GSOM for adaptive classification.” Fuzzy Sets and Systems.

Mehrizi, A. (2022). “Distributed adaptive learning for dynamic systems using Diffusion LMS and RLS.” Emerging Markets Finance and Trade.

Mehrizi, A., & Yazdi, H. S. (2021). “Gaussian Mixture Probability Hypothesis Density for multi-target tracking.” Journal of Machine Learning Research.

These publications have been cited extensively by various researchers in the fields of machine learning, AI, and financial forecasting, underscoring Dr. Mehrizi’s significant impact on the academic community.

Conclusion

Dr. Ali Mehrizi is a leading researcher and educator in the field of Artificial Intelligence and Machine Learning, with a deep commitment to advancing these fields through his innovative research. His extensive academic background and his practical experience in both teaching and real-world applications have made him an invaluable asset to Ferdowsi University of Mashhad. With a strong focus on adaptive learning, probabilistic models, and time series forecasting, Dr. Mehrizi continues to contribute to the evolution of AI. His work not only shapes academic research but also provides vital insights into practical AI solutions for industries like finance and engineering. As a mentor and educator, he remains dedicated to shaping the future of AI professionals and researchers.

Abdultaofeek Abayomi | Machine Learning | Best Researcher Award

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Dr. Abdultaofeek Abayomi | Machine Learning | Best Researcher Award

Researcher at Walter Sisulu University, South Africa

ABDULTAOFEEK ABAYOMI, Ph.D., is a distinguished academic and researcher with a rich career in Information Technology and Computer Science. He holds a Ph.D. from Durban University of Technology, South Africa, and has been an influential figure in various educational institutions, including Mangosuthu University of Technology, where he served as a Postdoctoral Research Fellow and Lecturer. His extensive experience spans roles in teaching, research, and industry, with a specific focus on ICT, machine learning, and telecommunications. Dr. Abayomi’s contributions extend beyond academia, having held positions in major banks and IT firms, where he influenced projects in system analysis, IT infrastructure, and banking operations.

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Education

Dr. Abayomi’s academic journey began with a B.Sc. in Computer Science from the University of Ilorin, Nigeria, where he graduated with a Second Class Upper Division. This was followed by a Master’s in Technology (Computer Science) and an MBA from the Federal University of Technology, Akure, Nigeria. He then pursued a Ph.D. in Information Technology at Durban University of Technology, South Africa, where his doctoral research explored real-time tracking of individuals in distress situations using physiological signals, a significant contribution to the field of IT and human-centered computing.

Experience

Dr. Abayomi’s professional career spans teaching, research, and leadership roles in the technology sector. He has lectured and conducted research at various universities, including Durban University of Technology and Mangosuthu University of Technology in South Africa. Additionally, he has worked as a system analyst and instructor for IT certifications such as MCSE and MCSA at JIT Solutions in Akure, Nigeria. His career in the banking sector includes roles as a Profit Centre Manager and ICT System Administrator at United Bank for Africa Plc., where he contributed to improving operational efficiency and implementing IT solutions. Dr. Abayomi has also been involved in research projects aimed at addressing pressing issues in ICT and society, particularly focusing on the intersection of technology and human needs.

Research Interests

Dr. Abayomi’s research interests lie at the convergence of Information Technology, machine learning, and network systems. His work has explored deep learning, cognitive radio networks, spectrum sensing, and software-defined networks. He is particularly interested in the application of artificial intelligence to solve real-world problems, such as dynamic spectrum access and health insurance prediction. Dr. Abayomi’s research aims to improve the way technology interacts with human and environmental factors, making significant contributions to both academic and applied research.

Awards

Dr. Abayomi has received numerous accolades in recognition of his academic and research excellence. He was honored with the Research Award for Most Productive Postdoctoral Research Fellow in 2022 at Mangosuthu University of Technology, South Africa. He has also been an active participant in international conferences, serving as a session chair for various events such as the 22nd International Conference on Hybrid Intelligent Systems in 2022 and the 13th International Conference on Soft Computing and Pattern Recognition in 2021. His contributions to research are further exemplified by his involvement in winning the South African National Research Foundation’s Infrastructure Bridging Funding in 2016.

Publications

Dr. Abayomi’s scholarly work is well-regarded in academic circles, with several impactful publications in peer-reviewed journals. His notable publications include:

Ukpong, U.C., Idowu-Bismark, O., Adetiba, E., Kala, J.R., Owolabi, E., Oshin, O., Abayomi, A., Dare, O.E. (2025). “Deep reinforcement learning agents for dynamic spectrum access in television whitespace cognitive radio networks.” Scientific African, 27, e02523.

Dare, O.E., Okokpujie, K., Adetiba, E., Idowu-Bismark, O., Abayomi, A., Kala, R.J., Owolabi, E., Ukpong, U.C. (2024). “Development of a Conditional Generative Adversarial Network Model for Television Spectrum Radio Environment Mapping.” IEEE Access, 12, 197632-197644.

Mavundla, K., Thakur, S., Adetiba, E., Abayomi, A. (2024). “Predicting Cross-Selling Health Insurance Products Using Machine-Learning Techniques.” Journal of Computer Information Systems.

Adetiba, E., Uzoatuegwu, P.C., Ifijeh, A.H., Abayomi, A., Obiyemi, O. (2024). “NomadicBTS-2: A Network-in-a-Box with Software-Defined Radio and Web Based App for Multiband Cellular Communication.” International Journal of Computing and Digital Systems, 15(1), 1-16.

Aroba, O.J., Abayomi, A. (2023). “An Implementation of SAP Enterprise Resource Planning – A Case Study of the South African Revenue Services and Taxation Sectors.” Cogent Social Sciences.

These publications reflect his diverse research interests and his significant impact on fields ranging from telecommunications to machine learning and health technology.

Conclusion

Dr. Abayomi’s academic and professional journey is a testament to his dedication to advancing knowledge in Information Technology and its application to solving societal challenges. His work has influenced both the academic community and industry practices, particularly in the areas of cognitive radio networks, machine learning, and ICT solutions for societal development. His numerous accolades and impactful publications underscore his standing as a leading researcher in his field, and his continued contributions promise further advancements in the intersection of technology and human development.

Neeraj Thakur | AI in Healthcare | Best Researcher Award

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Dr. Neeraj Thakur | AI in Healthcare | Best Researcher Award

Postdoctoral Fellow at University of Oklahoma Health Sciences, United States

Dr. Neeraj S. Thakur, currently a Postdoctoral Fellow in the Department of Pharmaceutical Sciences at the University of Oklahoma Health Sciences Center (OUHSC), is a highly motivated researcher specializing in drug delivery systems, theranostic platforms, and AI-based approaches for developing novel diagnostics and treatments. With over a decade of international research experience spanning the USA, Europe, and Asia, Dr. Thakur has led multiple projects related to nanomaterial design, formulation development, and the synthesis of advanced drug delivery systems for treating diseases like cancer and infections. His expertise extends to both small molecules and large biomolecules, utilizing cutting-edge technologies such as polymeric and lipid nanoparticles. He has an extensive publication record with 29 papers, five patents, six book chapters, and significant contributions to the field.

Profile

Scopus

Education

Dr. Thakur earned his Ph.D. in Pharmaceutical Technology (Biotechnology) from the National Institute of Pharmaceutical Education and Research (NIPER), Mohali, India, where his research focused on the development of nanoparticle-based fluorescent probes for biomedical applications. He also holds an M.Tech in Pharmaceutical Technology (Biotechnology) from NIPER, Mohali, where he was the class topper, and a B.Pharm degree from Shri G.S. Institute of Technology and Science, Indore. Additionally, he is pursuing a Certificate in Data Science and Machine Learning from the Massachusetts Institute of Technology (MIT).

Experience

Dr. Thakur has held significant roles in various research institutions. As a Postdoctoral Fellow at OUHSC, he established research laboratories and led projects on nanomaterial development for drug delivery systems targeting multiple applications, including inner ear and ocular delivery. Prior to this, he worked at the University of Geneva, Switzerland, where he developed hybrid micelle formulations for transdermal delivery and designed iontophoretic devices. He also contributed to the Center of Innovative and Applied Bioprocessing (CIAB), India, where he led the development of topical drug delivery systems and was involved in several patent filings. These experiences have honed his skills in formulation and analytical development, laboratory management, and industrial collaboration.

Research Interests

Dr. Thakur’s research interests focus on the development of advanced drug delivery systems, particularly using nanomaterials such as polymeric and lipid nanoparticles. His work aims to improve the therapeutic efficacy of drugs by enhancing their delivery to specific sites in the body, such as the inner ear, ocular tissues, and tumors. He is also dedicated to the integration of AI-based approaches for optimizing drug delivery design, ensuring more precise treatments for conditions like cancer and ototoxicity. His passion extends to the creation of theranostic platforms for early disease diagnosis and personalized medicine.

Awards

Throughout his career, Dr. Thakur has received numerous accolades for his contributions to pharmaceutical sciences. Notable among them are the John B. Bruce Scholarship Award (2024), the Best Abstract Award at AAPS Pharm360 (2023), and the prestigious Swiss Government Excellence Scholarship (2020-2021). His earlier achievements include the DST-INSPIRE and CSIR Research Fellowships, which are awarded to the top 1% of students in India. These recognitions underscore his impact on the scientific community and his leadership in advancing pharmaceutical research.

Publications

Dr. Thakur has authored 29 peer-reviewed papers, with some of his recent work focused on innovative drug delivery systems. Key publications include:

Thakur, N.S., et al., “Crosslinked Hybrid Nanoparticle Embedded in Thermogel For Sustained Co-Delivery to Inner Ear,” Journal of Nanobiotechnology, 2024.

Thakur, N.S., et al., “Progress and Promise of Photoresponsive Nanocarriers for Precision Drug Delivery in Cancer,” Journal of Photochemistry & Photobiology C: Photochemistry Reviews, 2024.

Thakur, N.S., et al., “Dual Stimuli-Responsive and Sustained Drug Delivery Nanosensogel for Prevention of Cisplatin-Induced Ototoxicity,” Journal of Controlled Release, 2024.

Paul, S., et al., “Co-Administration of Chemo-Phototherapeutic Loaded Lignin Nanoarchitecture for Skin Cancer and Bacterial Infections,” ACS Applied Nano Materials, 2024.

Kumar, S., et al., “Bioengineered Multi-Walled Carbon Nanotubes Based Biosensors and Applications,” Sensors and Diagnostics, 2023. These works, which cover nanomedicine, drug delivery, and nanomaterial innovation, have significantly contributed to the field of pharmaceutical sciences and have been widely cited.

Conclusion

Dr. Neeraj S. Thakur’s multifaceted career highlights his dedication to advancing pharmaceutical research through innovation in nanotechnology and drug delivery systems. His profound knowledge in both academic and industrial settings, along with his leadership in driving collaborative research projects, continues to position him as a key figure in his field. With a focus on precision medicine, AI-based design, and the development of targeted therapies, Dr. Thakur is making remarkable strides toward improving patient outcomes and advancing the future of drug delivery and diagnostics.