Teaching Assistance at Universiti Teknologi Petronas Malaysia, Malaysiađź“–
Iliyas Karim Khan is a dedicated researcher and educator with a strong background in statistics and data science. He is currently pursuing his Ph.D. at Universiti Teknologi PETRONAS, Malaysia, focusing on advanced statistical modeling and machine learning applications. With extensive teaching experience spanning over 8 years in various academic institutions, he has contributed significantly to the field through research and publications. His work primarily revolves around clustering algorithms, data analysis, and predictive modeling.
Profile
Scopus Profile
Google Scholar Profile
Education Background🎓
- Ph.D. in Statistics (2024), Universiti Teknologi PETRONAS, Malaysia
- M.Phil. in Statistics (2016), Peshawar University, KPK, Pakistan
- M.Sc. in Statistics (2014), Peshawar University, KPK, Pakistan
- B.Sc. in Statistics (2012), SBBU Sheringhal, Upper Dir, Pakistan
- B.Ed. (2015), SBBU Sheringhal, Upper Dir, Pakistan
- F.Sc. in Engineering (2010), BISE Peshawar, Pakistan
- S.S.C. in Science (2008), BISE KPK, Peshawar, Pakistan
Professional Experience🌱
Iliyas has accumulated diverse teaching and research experience in both national and international institutions. He has served as a lecturer and subject specialist at GHSS Bang Chitral, Pakistan, and Abbottabad University of Science and Technology, contributing to curriculum development and student mentorship. Additionally, he has gained international teaching experience as a Teaching Assistant at Universiti Teknologi PETRONAS, Malaysia. His professional expertise extends to statistical analysis, machine learning, and forecasting, with hands-on experience in tools such as Python, SPSS, and Minitab
- Machine Learning
- Statistical Modeling
- Forecasting
- Big Data Analysis
- Cluster Optimization Algorithms
Author Metrics
Publications Top Notes đź“„
1. Determining the Optimal Number of Clusters by Enhanced Gap Statistic in K-mean Algorithm
- Authors: I.K. Khan, H.B. Daud, N.B. Zainuddin, R. Sokkalingam, M. Farooq, M.E. Baig, et al.
- Journal: Egyptian Informatics Journal
- Volume: 27, Article 100504
- Year: 2024
- Citations: 3
- Abstract: This study introduces an enhanced gap statistic method to determine the optimal number of clusters in the K-means clustering algorithm. The approach addresses common challenges in cluster analysis, improving the reliability and efficiency of the algorithm.
- Impact: Provides an effective method to enhance clustering performance in various data-driven applications.
2. Numerical Solution of Heat Equation using Modified Cubic B-spline Collocation Method
- Authors: M. Iqbal, N. Zainuddin, H. Daud, R. Kanan, R. Jusoh, A. Ullah, I.K. Khan
- Journal: Journal of Advanced Research in Numerical Heat Transfer
- Volume: 20, Issue 1, Pages 23-35
- Year: 2024
- Citations: 2
- Abstract: The paper presents a numerical solution to the heat equation using a modified cubic B-spline collocation method. The proposed method enhances accuracy and computational efficiency compared to conventional techniques.
- Impact: Contributes to the advancement of numerical modeling in heat transfer applications.
3. Addressing Limitations of the K-means Clustering Algorithm: Outliers, Non-spherical Data, and Optimal Cluster Selection
- Authors: Iliyas Karim Khan, Abdussamad, Abdul Museeb, Inayat Agha
- Journal: AIMS Mathematics
- Volume: 9, Pages 25070-25097
- Year: 2024
- Citations: 2
- Abstract: This paper critically examines the limitations of the K-means clustering algorithm, proposing novel solutions to handle outliers, non-spherical data, and optimal cluster determination.
- Impact: Enhances the applicability of clustering techniques in complex real-world datasets.
4. Numerical Solution by Kernelized Rank Order Distance (KROD) for Non-Spherical Data Conversion to Spherical Data
- Authors: I.K. Khan, H.B. Daud, R. Sokkalingam, N.B. Zainuddin, A. Abdussamad, et al.
- Journal: AIP Conference Proceedings
- Volume: 3123, Issue 1
- Year: 2024
- Citations: 1
- Abstract: The study introduces the Kernelized Rank Order Distance (KROD) method to convert non-spherical data to spherical data, improving the performance of traditional clustering algorithms.
- Impact: Provides a novel solution for handling data distribution challenges in clustering applications.
5. A Mini Review of the State-of-the-Art Development in Oil Recovery Under the Influence of Geometries in Nanoflood
- Authors: M. Zafar, H. Sakidin, A. Hussain, M. Sheremet, I. Dzulkarnain, R. Safdar, et al.
- Journal: Journal of Advanced Research in Micro and Nano Engineering
- Volume: 26, Issue 1, Pages 83-101
- Year: 2024
- Abstract: This review paper explores recent advancements in oil recovery techniques using nanotechnology, emphasizing the influence of geometries on the efficiency of nanoflooding processes.
- Impact: Provides critical insights for improving oil recovery processes using nanomaterials.
Conclusion
Iliyas Karim Khan is a highly deserving candidate for the Best Researcher Award due to his impressive academic credentials, impactful research contributions, and dedication to the field of statistics and data science. His work on clustering algorithms and machine learning applications offers innovative solutions to critical challenges in data analysis.
To further strengthen his profile, he should focus on expanding his research network, leading high-value projects, and enhancing his presence in industry-oriented applications. With continued efforts, Iliyas is poised to make even greater contributions to the field and emerge as a thought leader in statistical modeling and data science.