Abu Sarwar Zamani | AI in Healthcare | Best Researcher Award

Dr. Abu Sarwar Zamani | AI in Healthcare | Best Researcher Award 

Asst. Professor | Prince Sattam bin Abdulaziz University | Saudi Arabia

Dr. Abu Sarwar Zamani is a dedicated and disciplined academic and research professional with over 15 years of experience. Specializing in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Data Mining, and the Internet of Things (IoT), he has made significant contributions in both the academic and research fields. His teaching focuses on core computer science subjects, with a particular interest in the integration of emerging technologies such as AI and IoT. His professional journey reflects a passion for knowledge sharing and innovative research, contributing to scientific advancements in computer science and technology. Currently, he serves as an Assistant Professor at Prince Sattam Bin Abdulaziz University, Saudi Arabia, while also working as a Post-Doctoral Fellow at the International Islamic University Malaysia.

Profile

Scholar

Education

Dr. Zamani’s academic foundation includes a Ph.D. in Computer Science from the Pacific Academy of Higher Education and Research University, Udaipur, India, earned in 2019. Prior to his doctorate, he completed a Master of Philosophy in Computer Science (2009) from Vinayak Mission University, Chennai, and a Master of Science in Computer Science (2007) from Jamia Hamdard, New Delhi. His undergraduate studies were in Computer Applications at MCRP, Bhopal, India (2002). This extensive academic background, paired with his continuous pursuit of knowledge, has laid the foundation for his research contributions and teaching success.

Experience

Dr. Zamani has held various academic positions throughout his career. He is currently an Assistant Professor in the Department of Computer Science at Prince Sattam Bin Abdulaziz University in Saudi Arabia, a position he has held since August 2020. In addition to his teaching role, Dr. Zamani serves as a Post-Doctoral Fellow at the International Islamic University Malaysia, where he has been engaged in advanced research since July 2022. Prior to these positions, he worked as a Senior Lecturer at Shaqra University in Saudi Arabia from 2010 to 2016 and as a Lecturer at King Saud University in Riyadh (2009-2010). His academic career began as a Lecturer at Ibne Seena Pharmacy College in India (2007-2009). Over the years, Dr. Zamani has contributed significantly to both the academic and administrative frameworks of these institutions, including curriculum development and research committees.

Research Interests

Dr. Zamani’s research interests lie primarily in AI, ML, Deep Learning, Data Mining, IoT, and their applications in various domains. His work focuses on leveraging machine learning techniques to develop predictive models for healthcare, cybersecurity, and educational services. He has also researched IoT-based systems, contributing to advancements in real-time data analytics for improved decision-making and optimization of resources. His research has garnered attention in areas like automated disease detection, smart health monitoring, and the design of secure and efficient systems for IoT networks.

Awards

Dr. Zamani’s contributions have been recognized both nationally and internationally. He has been granted three international patents from India and Australia, further solidifying his standing as an innovator in the fields of machine learning and IoT-based systems. His patents cover key areas such as machine learning-based prediction systems for heart disease and systems for improving educational services. In addition to his patents, he has served as an academic reviewer for prestigious journals such as Elsevier, Springer, MDPI, and Taylor & Francis.

Publications

Dr. Zamani has published more than 100 papers in SCI, PubMed, and Scopus-indexed journals, as well as two conference papers. Some of his significant publications include:

Zamani, A. S., et al. “Implementation of machine learning techniques with big data and IoT to create effective prediction models for health informatics.” Biomedical Signal Processing and Control, 2024, Elsevier, DOI: 10.1016/j.bspc.2024.106247.

Zamani, A. S., et al. “The Prediction of Sleep Quality using Wearable-assisted Smart Health Monitoring System based on Statistical Data.” Journal of King Saud University-Science, 2023, Elsevier, DOI: 10.1016/j.jksus.2023.102927.

Zamani, A. S., et al. “Machine Learning Techniques for Automated and Early Detection of Brain Tumor.” International Journal of Next-Generation Computing, 2022, Perpetual Innovation, DOI: 10.47164/ijngc.v13i3.711.

Zamani, A. S., et al. “Cloud Network Design and Requirements for the Virtualization System for IoT Networks.” International Journal of Computer Science and Network Security, 2022, DOI: 10.22937/IJCSNS.2022.22.11.101.

Zamani, A. S., et al. “Towards Applicability of Information Communication Technologies in Automated Disease Detection.” International Journal of Next-Generation Computing, 2022, Perpetual Innovation, DOI: 10.47164/ijngc.v13i3.705.

Akhtar, M. M., Zamani, A. S., et al. “Stock Market Prediction Based on Statistical Data Using Machine Learning Algorithm.” Journal of King Saud University-Science, 2022, Elsevier, DOI: 10.1016/j.jksus.2022.101940.

Prasad, V. D. P., Zamani, A. S., et al. “Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis.” Security and Communication Networks, 2022, Hindawi, DOI: 10.1155/2022/1918379.

Conclusion

Dr. Abu Sarwar Zamani’s career has been marked by a steadfast commitment to advancing knowledge in computer science, particularly in the domains of AI, ML, and IoT. His extensive experience in both teaching and research has made him a key figure in these fields, with numerous published works and patents to his name. As a dedicated educator and researcher, Dr. Zamani continues to make valuable contributions to the academic community and industry, with a focus on developing innovative solutions for healthcare, cybersecurity, and education. His work exemplifies the intersection of technology and human well-being, ensuring that his research has a lasting impact on society.

leilei pei | AI in Healthcare | Best Researcher Award

Prof. leilei pei | AI in Healthcare | Best Researcher Award

Professor | Xi’an Jiaotong University | China

Professor Leilei Pei is a prominent academic specializing in epidemiology and biostatistics. Currently serving as the Vice Director of the Department of Epidemiology and Health Statistics at the School of Public Health, Xi’an Jiaotong University, he is a leader in disease prediction modeling and life-cycle health promotion strategies. With over 50 scholarly publications, including contributions as the first or corresponding author, his work has significantly impacted public health methodologies. As a mentor to 29 graduate students and an editor of academic texts, Professor Pei exemplifies excellence in research, education, and professional service.

Profile

Orcid

Education

Professor Leilei Pei completed rigorous training in biostatistics and epidemiology, culminating in advanced degrees that laid the foundation for his research career. His education emphasized the integration of statistical methodologies with public health applications, equipping him to address complex health challenges. This educational background has enabled him to innovate in areas such as disease modeling and intervention strategies, contributing to advancements in public health.

Experience

With extensive experience in academic research and leadership, Professor Pei has played a pivotal role in multiple high-impact projects, including those funded by the National Natural Science Foundation of China. He has held key administrative positions at Xi’an Jiaotong University and collaborated on significant national and international initiatives. His expertise extends to serving as a reviewer for prestigious grants and participating in professional associations that shape public health policies.

Research Interests

Professor Pei’s research interests focus on the prevention and control of birth defects, nutritional epidemiology, and advanced statistical methods for longitudinal data analysis. He is particularly skilled in developing early warning models for congenital diseases and optimizing intervention strategies using hidden Markov models. These interests align with his commitment to improving population health through data-driven insights and innovative methodologies.

Awards

Professor Pei has been recognized for his contributions to public health research, including leading projects on congenital heart disease and myopia prevention. His innovative methodologies have earned acclaim from both academic and professional communities, establishing him as a leading figure in his field.

Publications

The Contribution of the Underlying Factors to Socioeconomic Inequalities in Obesity: A Life Course Perspective (2024, International Journal of Public Health)

    • Cited by: Articles focusing on life-course health disparities.

Life-Course Social Disparities in Body Mass Index Trajectories Across Adulthood (2023, BMC Public Health)

    • Cited by: Studies on social determinants of health.

Associations Between Trajectories of Cardiovascular Risk Factor Change and Cognitive Impairment (2023, Frontiers in Aging Neuroscience)

    • Cited by: Research on cardiovascular and neurological health intersections.

Effects of Potential Risk Factors on Cardiometabolic Multimorbidity Among the Elders in China (2022, Frontiers in Cardiovascular Medicine)

    • Cited by: Multimorbidity and aging studies.

The Association of Folic Acid, Iron Nutrition During Pregnancy and Congenital Heart Disease (2022, Nutrients)

    • Cited by: Nutritional epidemiology research.

Conclusion

Professor Leilei Pei’s career is a testament to his dedication to public health and biostatistics. Through ground breaking research, mentorship, and active participation in professional communities, he has contributed to improving health outcomes on both national and global scales. His work exemplifies the integration of innovative statistical approaches with real-world health challenges, making a lasting impact on the field.