Mr. Bilal Ahmad | Computational Metallurgy | Research Excellence Award

University of Johannesburg | South Africa

Mr. Bilal Ahmad demonstrates emerging excellence in data science and artificial intelligence, with scholarly focus on machine learning and deep learning applications for complex, real-world problems. Research contributions emphasize predictive analytics and intelligent modeling, including peer-reviewed work on epidemic outbreak analysis using advanced computational techniques. The research reflects methodological soundness, interdisciplinary relevance, and alignment with current global challenges in data-driven systems. According to the Scopus profile, the researcher has 1 indexed publication, 2 total citations, and an h-index of 1, indicating early academic visibility and growing research impact. These contributions highlight strong potential for continued advancement and research excellence.

Citation Metrics ( Google Scholar )

Citations
2

Documents
1

h-index
1

Dr. Aamir Saghir | Statistical Process Control | Best Researcher Award

Associate professor at Mirpur University of Science and Technology | Pakistan

Dr. Aamir Saghir, Associate Professor at Mirpur University of Science and Technology (MUST), Pakistan, is recognized for his outstanding work in statistical quality control, probability modeling, and industrial data analytics. His research primarily focuses on developing flexible, robust, and Bayesian control charts for process monitoring in both univariate and multivariate environments. With 49 publications, 567 citations, and an h-index of 15 (Scopus), Dr. Saghir’s contributions are well-established in the international scientific community. He has published extensively in high-impact journals, including Computers & Industrial Engineering, Quality and Reliability Engineering International, Communications in Statistics, and Applied and Computational Mathematics. His collaborative research extends globally, involving advanced modeling for reliability analysis, environmental statistics, and the integration of machine learning with statistical monitoring. His co-authored book, “Introduction to Statistical Process Control” (John Wiley & Sons, 2020), serves as a reference for modern control chart design and applications. Dr. Saghir’s scholarly output demonstrates a balance of theoretical innovation and practical relevance, influencing diverse fields such as manufacturing, environmental studies, and data-driven decision-making. Through supervision of postgraduate students and contributions to academic boards, he has fostered research excellence in Pakistan and abroad. His sustained research impact and leadership affirm his suitability for the Best Researcher Award.

Profiles : Scopus | ORCID | Google Scholar

Featured Publications

Saghir, A., Khadim, A., & Lin, Z. (2017). The Maxwell length-biased distribution: Properties and estimation.Journal of Statistical Theory and Practice, 11(1), 26–40. Citation: 24

Saghir, A., Ahmad, L., Aslam, M., & Jun, C. H. (2019). A EWMA control chart based on an auxiliary variable and repetitive sampling for monitoring process location. Communications in Statistics–Simulation and Computation, 48(7), 2034–2045. Citation: 23

Ahmad, I., Abbas, A., Saghir, A., & Fawad, M. (2016). Finding probability distributions for annual daily maximum rainfall in Pakistan using linear moments and variants. Polish Journal of Environmental Studies, 25(3), 925–937. Citation: 23

Saghir, A. (2015). Phase-I design scheme for $Xˉ\bar{X}$ -chart based on posterior distribution. Communications in Statistics–Theory and Methods, 44(3), 644–655. Citation: 22

Hu, X. L., Zhang, S. Y., Zhang, J., & Saghir, A. (2023). Efficient CUSUM control charts for monitoring the multivariate coefficient of variation. Computers & Industrial Engineering, 179, 109159. Citation: 15