Anna Crisci | Statistics | Best Research Article Award

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Prof. Anna Crisci | Statistics | Best Research Article Award

University of Naples Federico II | Italy

Prof. Anna Crisci is a Research Associate in Statistics at the Department of Economics, Management, and Institutions of the University of Naples Federico II. She earned her PhD in Economics in 2011 at the Second University of Naples (now the University of Campania “Luigi Vanvitelli”), with a dissertation on estimation methods for structural equation models applied to innovation performance in manufacturing firms. Over the years, she has built extensive teaching experience in statistics and market research across leading Italian universities, including Federico II University of Naples, the University of Campania “Luigi Vanvitelli,” and Pegaso Telematic University, delivering both core and supplementary courses at undergraduate and postgraduate levels. She has been actively engaged in organizing and chairing sessions at prestigious international conferences, serving on scientific and organizing committees such as CLADAG 2025, ENBIS 2017, and IRSYSC 2019. Her research contributions and expertise have been recognized with the National Scientific Qualification for Associate Professor (13/D1 – Statistics), editorial board memberships for journals such as Mathematical and Computational Applications (MDPI) and Journal of Applied Quantitative Methods, and guest editorship of special issues on advanced numerical methods. A committed reviewer for high-impact journals including SEPS, Quality & Quantity, and Social Indicators Research, she is also an active member of the Italian Statistical Society and its “Statistics for the Evaluation and Quality of Services” research group. Prof. Crisci’s scholarly activity reflects a strong commitment to advancing statistical methodologies for applied research in economics, management, and social sciences.

Profiles: Orcid ID

Featured Publications

"Decomposition of the Main Effects and Interaction term by using Orthogonal Polynomials in Multiple Non Symmetrical Correspondence Analysis."

"Weighted log ratio analysis by means of Poisson factor models: a case study to evaluate the quality of the public services offered to the citizens."

"The confidence ellipses in decomposition Multiple Non- Symmetrical Correspondence Analysis."

"Estimation methods for the Structural Equation Models: Maximum Likelihood, Partial Least Squares and Generalized Maximum Entropy."

"Permutation Test for group comparison in PLS-Modeling."

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, Malaysia📖

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.

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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 📄

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.