Maedeh GholamAzad | Mathematics | Best Researcher Award

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Dr. Maedeh GholamAzad | Mathematics | Best Researcher Award

Postdoctoral Researcher at University of Kurdistan, Iran

Dr. Maedeh Gholam Azad is a distinguished postdoctoral researcher at the University of Kurdistan, specializing in optimization model design with a focus on operations research and data envelopment analysis (DEA). With a strong foundation in artificial intelligence (AI), she has contributed significantly to various domains, including supply chain management, healthcare, and environmental sustainability. Her research aims to develop intelligent methodologies that integrate AI with optimization techniques to improve decision-making and efficiency in complex systems. Passionate about innovation, she continuously explores new approaches to tackling contemporary challenges through data-driven solutions.

Profile

Scopus

Education

Dr. Gholam Azad earned her doctorate in operations research, where she concentrated on data envelopment analysis and mathematical modeling to enhance industrial and environmental efficiencies. Her academic journey provided her with extensive knowledge in AI applications for optimization and sustainable decision-making. Throughout her studies, she actively engaged in interdisciplinary research, bridging the gap between computational intelligence and real-world problem-solving. Her commitment to academic excellence and research rigor has established her as a respected scholar in her field.

Experience

With extensive experience in research and academia, Dr. Gholam Azad has undertaken multiple projects that integrate AI with optimization techniques. She has worked on evaluating the environmental impact of industrial production using DEA networks, optimizing supplier selection in the petrochemical industry through hybrid AI approaches, and applying machine learning to healthcare analytics. Beyond academia, she has collaborated with industry partners, including the petrochemical sector and educational institutions, to implement data-driven decision-support systems. Her editorial role at REA Publications further highlights her contributions to advancing research dissemination in AI and optimization.

Research Interests

Dr. Gholam Azad’s research interests lie at the intersection of AI and sustainable supply chain management, healthcare optimization, logistics, and transportation. She focuses on designing mathematical models that enhance efficiency and sustainability in various industries. Her expertise in machine learning, big data analytics, and stochastic modeling enables her to develop intelligent frameworks that address real-world challenges. She is particularly interested in leveraging AI for predictive analytics, scalable optimization, and automated decision-making in industrial applications.

Awards

Dr. Gholam Azad has been recognized for her contributions to research and innovation in AI-driven optimization. Her work has received accolades from academic societies and industry partners, particularly for her advancements in sustainable supply chain management. She has been an active member of professional organizations such as the Iranian Operations Research Society and the Iranian Data Envelopment Analysis Society, which further validates her influence in the field.

Publications

“Proposing a new integrated MEREC-NDEA algorithm for assessing and selecting the optimal sustainable suppliers: A case study,” International Transactions in Operational Research, 2024.

“Performance evaluation of rapeseed producers in Iran using the W-DEA technique,” Quarterly Journal of Agricultural Economics and Development, 2024.

“Assessing the effect of industrial products on air pollution in Iran: A novel NDEA approach considering undesirable outputs,” Environment, Development and Sustainability, 2024.

“Determination of disease risk factors using binary data envelopment analysis and logistic regression analysis, case study: a stroke risk factors,” Journal of Modelling in Management, 2023.

“Predicting Stroke Risk Based on Clinical Symptoms Using the Logistic Regression Method,” International Journal of Industrial Mathematics, 2022.

“Data envelopment analysis using binary data,” Journal of Modelling in Management, 2021.

“Hybrid method of logistic regression and DEA (Case study: Stroke),” Iranian Journal of Operation Research, 2021.

Conclusion

Dr. Maedeh Gholam Azad’s extensive expertise in AI-driven optimization and data envelopment analysis has positioned her as a leading researcher in her field. Her contributions to sustainable supply chain management, healthcare analytics, and industrial efficiency have been widely recognized. Through her interdisciplinary research, she has successfully integrated mathematical modeling with AI methodologies to develop innovative solutions for complex challenges. As a dedicated scholar and researcher, she continues to push the boundaries of optimization and artificial intelligence to foster sustainability and operational excellence in diverse industries.

Muhammad Qiyas | Mathematics | Best Researcher Award

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Assist. Prof. Dr. Muhammad Qiyas | Mathematics | Best Researcher Award

Assistant Professor at Riphah International University Faisalabad, Pakistan

Muhammad Qiyas is an accomplished academic and researcher in the field of mathematics, specializing in fuzzy decision-making, aggregation operators, and multi-criteria decision analysis. With a strong passion for problem-solving and knowledge dissemination, he has contributed significantly to academia through teaching, research, and publishing in high-impact journals. His career spans various educational institutions, where he has played a pivotal role in mentoring students, supervising research projects, and advancing mathematical research. His dedication to continuous learning and interdisciplinary collaboration has established him as a leading expert in his domain.

Profile

Scopus

Education

Muhammad Qiyas holds a Ph.D. in Mathematics from Abdul Wali Khan University Mardan, Pakistan, completed in 2020. His doctoral research focused on “Aggregation Operators on Linguistic Picture Fuzzy Sets and their Applications in Decision Making Problems,” showcasing his expertise in mathematical modeling and decision science. Prior to that, he obtained an MS in Mathematics from Mohi-Ud-Din Islamic University, Islamabad, Pakistan, where he explored the domain of Orthodox Γ-Semigroup. He completed his Bachelor’s in Mathematics from the University of Malakand, KP, Pakistan, with a project on “Rule of Prime Numbers in Cryptography,” demonstrating his early interest in computational mathematics.

Professional Experience

Muhammad Qiyas is currently serving as an Assistant Professor at Riphah International University Faisalabad Campus, Pakistan. In this role, he is responsible for delivering high-quality instruction in mathematics, supervising graduate research projects, and contributing to the university’s research output. Additionally, he holds a Research Fellow position at Universiti Sultan Zainal Abidin, Malaysia, where he collaborates on cutting-edge research in mathematical decision-making. His previous experience includes active participation in international conferences, editorial responsibilities in academic journals, and contributions to mathematical research communities worldwide.

Research Interests

Muhammad Qiyas specializes in fuzzy set theory, aggregation operators, decision-making models, and multi-criteria decision analysis. His research primarily focuses on developing novel mathematical techniques to enhance decision support systems. His work on picture fuzzy sets, spherical fuzzy aggregation, and hybrid decision-making models has significantly contributed to advancing computational intelligence and applied mathematics. He actively explores new approaches to uncertainty modeling and their applications in various real-world problems, including supply chain management, engineering decision-making, and artificial intelligence.

Awards and Recognitions

Muhammad Qiyas has been recognized for his contributions to mathematical research and education. His work has been cited in numerous high-impact journals, showcasing the influence of his research on the global academic community. He has also been invited as a speaker at prestigious mathematics conferences, demonstrating his expertise and thought leadership in fuzzy decision-making and aggregation operators. His scholarly achievements have earned him nominations for awards in academia and research excellence.

Publications

Zeng, S., Qiyas, M., Arif, M., & Mahmood, T. (2019). Extended version of linguistic picture fuzzy TOPSIS method and its applications in enterprise resource planning systems. Mathematical Problems in Engineering.

Qiyas, M., Abdullah, S., Ashraf, S., & Abdullah, L. (2019). Linguistic picture fuzzy Dombi aggregation operators and their application in multiple attribute group decision-making problems. Mathematics, 7(8), 764.

Khan, A.A., Ashraf, S., Abdullah, S., Qiyas, M., Luo, J., & Khan, S.U. (2019). Pythagorean fuzzy Dombi aggregation operators and their application in decision support systems. Symmetry, 11(3), 383.

Khan, A.A., Qiyas, M., Abdullah, S., Luo, J., & Bano, M. (2019). Analysis of robot selection based on 2-tuple picture fuzzy linguistic aggregation operators. Mathematics, 7(10), 1000.

Jin, H., Ashraf, S., Abdullah, S., Qiyas, M., Bano, M., & Zeng, S. (2019). Linguistic spherical fuzzy aggregation operators and their applications in multi-attribute decision-making problems. Mathematics, 7(5), 413.

Ashraf, S., Abdullah, S., Aslam, M., Qiyas, M., & Kutbi, M.A. (2019). Spherical fuzzy sets and its representation of spherical fuzzy t-norms and t-conorms. Journal of Intelligent & Fuzzy Systems, 36(6), 6089-6102.

Ashraf, S., Abdullah, S., Qiyas, M., & Khan, A. (2019). Picture fuzzy grey approach for decision problems with unknown weight information. Journal of Biostatistics and Biometric Applications, 4(1).

Conclusion

Muhammad Qiyas has made significant contributions to the field of mathematics through his research, teaching, and publications. His work in fuzzy set theory and decision-making models has provided valuable insights into complex mathematical and computational problems. With a strong academic background, extensive research experience, and a commitment to advancing mathematical sciences, he continues to influence the field through innovation and collaboration. His future endeavors will likely further enhance the application of mathematical methodologies in decision science and computational intelligence.