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Creative Technology Home Introduction Chair Department Departments Artifical Intelligence (AI) Data Sciece (DS) Software Engineering (SE) Program Details PLO's PEO's Program Offered Artifical Intelligence Data Science Software Engineeering Academic Calender Research Groups Industrial Advisory Board
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Dr. Faridoon Khan
Assistant Professor
Contact Information
  [email protected]
  --

Education Detail
PhD (Econometrics) Pakistan Institute of Development Economics, Islamabad, Pakistan
MPhil (Statistics) Quaid-e-Azam University Islamabad
MSc (Statistics) University of Peshawar, Peshawar
BSc (--) Govt. Degree College Peshawar

1. Sarwar, J., Khan, S. A., Azmat, M., & Khan, F. (2024). An Application of Hybrid Bagging-Boosting Decision Trees Ensemble Model for Riverine Flood Susceptibility Mapping and Regional Risk Delineation. Water Resources Management, 1-31. (Impact Factor: 3.9) 2. Khan, F., & Albalawi, O. (2024). Analysis of Fat Big Data Using Factor Models and Penalization Techniques: A Monte Carlo Simulation and Application. Axioms, 13(7), 418. (Impact Factor: 1.9) 3. Iftikhar, H., Khan, F., Rodrigues, P. C., Alharbi, A. A., & Allohibi, J. (2025). Forecasting of Inflation Based on Univariate and Multivariate Time Series Models: An Empirical Application. Mathematics, 13(7), 1121. (Impact Factor 2.3) 4. Odhah, O. H., Alshanbari, H. M., Ahmad, Z., Khan, F., & El-Bagoury, A. A. A. H. (2024). A new family of distributions using a trigonometric function: Properties and applications in the healthcare sector. Heliyon, 10(9). 5. Sarwar, J., Khan, S. A., Azmat, M., & Khan, F. (2024). A comparative analysis of feature selection models for spatial analysis of floods using hybrid metaheuristic and machine learning models. Environmental Science and Pollution Research, 1-20. (Impact Factor: 5.8) 6. Cao, H., Khan, F. U., Shaikh, P. A., Khan, F., Zehri, M., & Yousaf, H. (2024). Does Oil Price Shock Drive Inflation? Evidence from G20 Countries. Journal of the Knowledge Economy, 1-21. (Impact Factor: 3.3) 7. Khan, F., Muhammadullah, S., Sharif, A., & Lee, C. C. (2023). The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models. Energy Economics, 107269. (Impact Factor: 12.8) 8. Gul, A., Sandhu, A. J., Farooq, M., Adil, M., Hassan, Y., & Khan, F. (2023). Half logistic-truncated exponential distribution: Characteristics and applications. Plos one, 18(11), e0285992. Impact Factor: 3.7) 9. Khan, F., Ahmad, Z., Khosa, S. K., Alomair, M. A., Alomair, A. M., & khalid Alsharidi, A. (2023). A New Modification of the Flexible Weibull Distribution Based on Power Transformation: Monte Carlo Simulation and Applications. Heliyon. (Impact Factor: 4) 10. Alshanbari, H. M., Ahmad, Z., Khan, F., Khosa, S. K., Ilyas, M., & El-Bagoury, A. A. A. H. (2023). Univariate and multivariate analyses of the asset returns using new statistical models and penalized regression techniques. AIMS Mathematics, 8(8), 19477-19503. (Impact Factor: 2.2) 11. Khan, F. U., Khan, F., & Shaikh, P. A. (2023). Forecasting returns volatility of cryptocurrency by applying various deep learning algorithms. Future Business Journal, 9(1), 25. (ESCI)12. Alshanbari, H. M., Odhah, O. H., Ahmad, Z., Khan, F., & El-Bagoury, A. A. A. H. (2023). A New Probability Distribution: Model, Theory and Analyzing the Recovery Time Data. Axioms, 12(5), 477. (Impact Factor: 2) 13. Mobeen, H., Khan, S. A., Khan, F., & Younis, N. (2022). Modeling Volatility Spillover and Role of Volatility Spillover Effect to Improve Forecasting Performance of GARCH Models Based on Varying Distributions. Kashmir Economic Review, 31(2). (HEC approved Y Category) 14. Alshanbari, H. M., Iftikhar, H., Khan, F., Rind, M., Ahmad, Z., & El-Bagoury, A. A. A. H. (2023). On the Implementation of the Artificial Neural Network Approach for Forecasting Different Healthcare Events. Diagnostics, 13(7), 1310. (Impact Factor: 3.6) 15. Iftikhar, H., Khan, M., Khan, Z., Khan, F., Alshanbari, H. M., & Ahmad, Z. (2023). A Comparative Analysis of Machine Learning Models: A Case Study in Predicting Chronic Kidney Disease. Sustainability, 15(3), 2754. (Impact Factor: 3.9) 16. Eliwa, M. S., Ahmad, Z., Alabdulhadi, M. H., & Khan, F. A New Beta Power Generator for Continuous Random Variable: Features and Inference to Model Asymmetric Data. (HEC Approved Y Category)17. Bantan, R. A., Ahmad, Z., Khan, F., Elgarhy, M., Almaspoor, Z., Hamedani, G. G., ... & Gemeay, A. M. (2023). Predictive modeling of the COVID-19 data using a new version of the flexible Weibull model and machine leaning techniques. Math. Biosci. Eng, 20, 2847-2873. (Impact Factor: 2.6) 118. Zhou, Y., Ahmad, Z., Almaspoor, Z., Khan, F., Tag-Eldin, E., Iqbal, Z., & El-Morshedy, M. (2023). On the implementation of a new version of the Weibull distribution and machine learning approach to model the COVID-19 data. Mathematical biosciences and engineering: MBE, 20(1), 337-364. (Impact Factor: 2.6) 19. Wang, Y., Ahmad, Z., Khan, F., Alnagar, D. K., Alsuhabi, H., Alkhairy, I., & Yusuf, M. (2023). Analysis of cryptocurrency exchange rates vs USA dollars using a new Dagum model. Alexandria Engineering Journal, 64, 645-658. (Impact Factor: 6.8) 20. Ahmad, Z., Almaspoor, Z., Khan, F., Alhazmi, S. E., El-Morshedy, M., Ababneh, O. Y., & Al-Omari, A. I. (2022). On fitting and forecasting the log-returns of cryptocurrency exchange rates using a new logistic model and machine learning algorithms. AI MS Math, 7, 18031-18049. (Impact Factor: 2.2)21. Wang, F., Ahmad, Z., Khan, F., Hussam, E., El-Bagoury, A. A. H., & Alshanbari, H. M. (2022). A new statistical distribution with applications to sports and health sciences. Alexandria Engineering Journal, 61(12), 9661-9671. (Impact Factor: 6.8) 22. Khan, F. U., Rafique, A., Ullah, E., & Khan, F. (2022). Revisiting the relationship between remittances and CO2 emissions by applying a novel dynamic simulated ARDL: empirical evidence from G-20 economies. Environmental Science and Pollution Research, 29(47), 71190-71207. (Impact Factor: 5.8) 23. Ahmad, Z., Almaspoor, Z., Khan, F., & El-Morshedy, M. (2022). On predictive modeling using a new flexible Weibull distribution and machine learning approach: Analyzing the COVID-19 data. Mathematics, 10(11), 1792. (Impact Factor: 2.4) 24. Ali, A., Salman, A., Amjad, F., & Khan, F. (2022). Growth Dynamics of Sulfur Emissions in the Case of SAARC Countries. Perspectives on Global Development and Technology, 21(1), 100-117. (ESCI) 25. Khan, F., Urooj, A., Khan, S. A., Khosa, S. K., Muhammadullah, S., & Almaspoor, Z. (2022). Evaluating the performance of feature selection methods using huge big data: a Monte Carlo simulation approach. Mathematical Problems in Engineering, 2022, 1-10. (Impact Factor: 1.43) 26. Khan, F., Urooj, A., Khan, S. A., Khosa, S. K., Muhammadullah, S., & Almaspoor, Z. (2022). Evaluating the performance of feature selection methods using huge big data: a Monte Carlo simulation approach. Mathematical Problems in Engineering, 2022, 1-10. (Impact Factor: 2.2) 27. Muhammadullah, S., Urooj, A., Khan, F., Alshahrani, M. N., Alqawba, M., & Al-Marzouki, S. (2022). Comparison of weighted lag adaptive LASSO with Autometrics for covariate selection and forecasting using time-series data. Complexity, 2022, 1-10. (Impact Factor: 2.3) 28. Khan, F., Urooj, A., & Muhammadullah, S. (2021). An ARIMA-ANN hybrid model for monthly gold price forecasting: empirical evidence from Pakistan. Pakistan Econ Rev, 4(1), 61-75. (HEC Approved Y Category) 29. Shah, B. A., Shareef, M., & Khan, F. (2021). Factors affecting credit risk in Islamic banks. The Discourse, 7(2), 1-15. (HEC Approved Y Category) 30. Khan, F., Urooj, A., Khan, S. A., Alsubie, A., Almaspoor, Z., & Muhammadullah, S. (2021). Comparing the forecast performance of advanced statistical and machine learning techniques using huge big data: evidence from Monte Carlo experiments. Complexity, 2021, 1-11. (Impact Factor: 2.3)31. Iftikhar, H., Khan, F., Torres Armas, E. A., Rodrigues, P. C., & López-Gonzales, J. L. (2025). A novel hybrid framework for forecasting stock indices based on the nonlinear time series models. Computational Statistics, 1-24. (Impact Factor: 1)32. Iftikhar, H., Khan, F., Rodrigues, P. C., Alharbi, A. A., & Allohibi, J. (2025). Forecasting of Inflation Based on Univariate and Multivariate Time Series Models: An Empirical Application. Mathematics, 13(7), 1121. (Impact Factor: 2.3)33. Khan, F., Urooj, A., Ullah, K., Alnssyan, B., & Almaspoor, Z. (2021). A comparison of Autometrics and penalization techniques under various error distributions: evidence from Monte Carlo simulation. Complexity, 2021, 1-8. (Impact Factor: 2.3)34. Peng, Z., Khan, F. U., Khan, F., Shaikh, P. A., Yonghong, D., Ullah, I., & Ullah, F. (2021). An application of hybrid models for weekly stock market index prediction: Empirical evidence from SAARC countries. Complexity, 2021, 1-10. (Impact Factor: 2.3)35. Muhammadullah, S., Urooj, A., & Khan, F. (2021). A revisit of the unemployment rate, interest rate, GDP growth and Inflation of Pakistan: Whether Structural break or unit root?. iRASD Journal of Economics, 3(2), 80-92. (HEC Approved Y Category)36. Rasheed, Z., Hussain, B., Shahzad, Q., & Khan, F. (2021). Role of Socio-economic Constraints in Female Education in Pakistan: A Binary Outcome Approach. Multicultural Education, 7(4). (HEC Approved Y Category)37. Khan, F., Khan, F. U., & Urooj, A. (2020). Factors Affecting Economic Growth: A Comparative Analysis of Democratic and Non-Democratic Eras of Pakistan. iRASD Journal of Economics, 2(2), 61-71. (HEC Approved Y Category)38. Ullah, I., Rehman, A., Khan, F. U., Shah, M. H., & Khan, F. (2020). Nexus between trade, CO2 emissions, renewable energy, and health expenditure in Pakistan. The International journal of health planning and management, 35(4), 818-831. (Impact Factor: 2.7)39. Khan, F. U., Ahmad, M. N., Rehman, A., Khan, F., & Khan, A. Modeling Exchange Rate Volatility in Pakistani Context The Application of Ardl Bounds Test Approach.40. Evaluating the Performance of Factor Models Against Autometrics and MachineLearning Tools Under Various Asymmetric Distributions: Simulations and Application (Under Review, Impact Factor: 2.5)41. A Novel Sparse VAR for Modelling and Forecasting Key Macroeconomic Variables of USA (Revised, Impact Factor: 2.8)42. Unmasking Return and Volatility Connectedness between Oil Market and Stock Markets of the G20 Countries (Under Review, Impact Factor: 1.9)43. An Application of Novel Machine Learning Model for Predicting the Real Estate Prices (With Editor, Impact Factor: 3.4)44. An Application of Integrated Statistical Model and Machine Learning Ensemble Model for Spatial Measurement of Land-use Transition Impacts on Hydrologic Response (Under Review, Impact Factor: 3.9)45. AI-Driven Hybrid Model for High Precision Weather Forecasting (Under Review, Impact Factor: 3.9)

Research Interest
Time Series Forecasting,
Classical and Bayesian Machine Learning,
Deep Learning for Sequential Data,
Fintech,
Energy Economics,
Climate Modeling.
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