Iliyas Karim Khan | Statistics | Best Researcher Award

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Mr. Iliyas Karim Khan | Statistics | Best Researcher Award

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

Research Interests🔬
  • Machine Learning
  • Statistical Modeling
  • Forecasting
  • Big Data Analysis
  • Cluster Optimization Algorithms

Author Metrics

Iliyas has published several high-impact journal articles in Q1 journals, including Egyptian Informatics Journal and AIMS Mathematics, with notable contributions to the advancement of clustering algorithms and data science techniques. His research work has garnered significant recognition within the academic community.

Awards and Honors
  • Publication Recognition Achievement 2024, Universiti Teknologi PETRONAS, Malaysia
  • Acknowledged for outstanding contributions to statistical analysis and machine learning applications
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.

Mohammad Ali Saniee Monfared | Robustness | Lifetime Achievement Award

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Assoc. Prof. Dr. Mohammad Ali Saniee Monfared | Robustness | Lifetime Achievement Award

Associate Professor at Alzahra University, Iranđź“–

Dr. Mohammadali Saniee Monfared is a distinguished academic and industry expert with over 20 years of experience across diverse sectors, including manufacturing, automotive, electronics, and cosmetics. His expertise lies in reliability engineering, maintenance planning, and predictive analytics, with a strong focus on turning complex engineering challenges into structured statistical models validated through machine learning techniques. In addition to his extensive industrial background, Dr. Monfared has held esteemed teaching positions at top universities such as Sharif University of Technology, Amirkabir Polytechnic University, K.N. Toosi University, and Alzahra University, where he has guided graduate and undergraduate students across multiple engineering disciplines.

Profile

Scopus Profile

Orcid Profile

Google Scholar Profile

Education Background🎓

Dr. Mohammadali Saniee Monfared holds a Ph.D. in Manufacturing and Mechanical Engineering from the University of Birmingham, UK (1997), where he developed advanced methodologies in reliability and system analysis. He earned his first M.Sc. in Industrial Engineering & Operations Research from Sharif University of Technology, Iran (1991), gaining expertise in systems optimization and decision-making models. He further pursued a second M.Sc. in Systems Engineering at the University of Regina, Canada (1994), specializing in system-level design and analysis. This strong academic foundation equipped Dr. Monfared with multidisciplinary knowledge and skills to address complex engineering challenges across industries.

Professional Experience🌱

Dr. Monfared brings over 20 years of professional experience spanning diverse industries, including General Tire and Rubber Manufacturing (8 years), automotive (2 years), electronics manufacturing (2 years), and cosmetic and soap manufacturing (2 years). His industrial work involved solving challenging engineering problems, optimizing production systems, and enhancing operational efficiencies. Notably, his expertise in reliability engineering and predictive analytics has enabled industries to improve system performance, mitigate risks, and ensure process safety. Alongside his industry roles, Dr. Monfared has actively collaborated with organizations, including Iran’s National Gas Company and municipal authorities, on projects such as multi-stakeholder risk assessments, robust maintenance planning, and network vulnerability analyses. His dual experience in academia and industry uniquely positions him to deliver innovative, real-world solutions to complex engineering problems.

Research Interests🔬

Dr. Monfared’s research focuses on:

  • Reliability Engineering
  • Maintenance Planning
  • Complex Networks and System Vulnerability Analysis
  • Predictive Analytics and Machine Learning Applications

Author Metrics

Dr. Monfared has authored impactful papers in renowned journals such as Reliability Engineering & System Safety, Physica A, and Soft Computing. Notable works include:

  • “Topology and vulnerability of the Iranian power grid” (Physica A).
  • “Investigating conflicts in blood supply chains at emergencies” (Soft Computing).
  • “Reliability analysis and optimization of road networks” (Reliability Engineering and System Safety).

His work has garnered significant recognition for its innovative, data-driven solutions addressing real-world challenges in reliability and risk engineering.

Expertise and Skills

  • Data Science and Machine Learning: Neural Networks, Support Vector Machines (SVM)
  • Mathematical Programming
  • Statistical Time Series Analysis
  • State-Space Modeling: Kalman Filters
Publications Top Notes đź“„

1. Network DEA: An Application to Analysis of Academic Performance

  • Authors: M.A. Monfared Saniee, M. Safi
  • Journal: Journal of Industrial Engineering International
  • Volume/Issue: 9 (1), Page 15
  • Year: 2013
  • Citations: 73
  • Summary: This paper applies Network Data Envelopment Analysis (DEA) to evaluate and compare academic performance, providing a systematic approach to assess efficiency in educational settings.

2. Topology and Vulnerability of the Iranian Power Grid

  • Authors: M.A.S. Monfared, M. Jalili, Z. Alipour
  • Journal: Physica A: Statistical Mechanics and its Applications
  • Volume/Issue: 406, Pages 24–33
  • Year: 2014
  • Citations: 55
  • Summary: The study examines the topology of the Iranian power grid using complex network theory, analyzing its vulnerability and critical nodes to improve resilience against disruptions.

3. A Complex Network Theory Approach for Optimizing Contamination Warning Sensor Location in Water Distribution Networks

  • Authors: R. Nazempour, M.A.S. Monfared, E. Zio
  • Journal: International Journal of Disaster Risk Reduction
  • Volume/Issue: 30, Pages 225–234
  • Year: 2018
  • Citations: 43
  • Summary: This research optimizes sensor placement in water distribution networks using complex network theory, enhancing contamination warning systems to mitigate disaster risks.

4. Comparing Topological and Reliability-Based Vulnerability Analysis of Iran Power Transmission Network

  • Authors: Z. Alipour, M.A.S. Monfared, E. Zio
  • Journal: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
  • Year: 2014
  • Citations: 35
  • Summary: The paper compares topological and reliability-based methods for analyzing the vulnerability of Iran’s power transmission network, identifying critical areas to improve reliability.

5. Controlling the Multi-Electron Dynamics in the High Harmonic Spectrum from N2O Molecule Using TDDFT

  • Authors: M. Monfared, E. Irani, R. Sadighi-Bonabi
  • Journal: The Journal of Chemical Physics
  • Volume/Issue: 148 (23)
  • Year: 2018
  • Citations: 33
  • Summary: This study utilizes time-dependent density functional theory (TDDFT) to investigate multi-electron dynamics in high harmonic generation from N2O molecules, offering insights into electron control mechanisms.

Conclusion

Assoc. Prof. Dr. Mohammad Ali Saniee Monfared is highly deserving of the Lifetime Achievement Award due to his exemplary career in both academia and industry. His ability to address real-world engineering problems through a combination of theoretical innovation and practical application has made a substantial impact in the fields of reliability engineering, complex networks, and predictive analytics.

His work in optimizing systems (power grids, water networks, and production systems) demonstrates critical contributions to improving societal resilience and operational efficiency. With continued emphasis on global collaborations and leadership in emerging research areas, Dr. Monfared’s influence will undoubtedly expand, solidifying his legacy as a leader in reliability and network analysis.