Summary

Ubaid Ullah is a graduate with a Bachelor's in Statistics, having completed his degree at the University of Peshawar, Pakistan, in 2023. His research interests Data Analysis, Machine Learning, Deep learning, Statistical Modeling, and Economic Forecasting. Ubaid has contributed to various research projects, including studies on predicting fertility outcomes and economic trends economic variables influencing Foreign Direct Investment (FDI) in Pakistan using advanced machine learning techniques. Currently, Ubaid works as a Research Assistant, applying machine learning and statistical tools in various research projects. His technical expertise includes Python, TensorFlow/Kera’s, R, SPSS, SQL, and various machine learning methods.

Edited Journals

ICCK Contributions

Articles 1 Edited 0
Open Access | Research Article | 07 November 2025
Innovative Machine Learning Approaches for Evaluating Climate Change Vulnerabilities of SMEs
by
Ubaid Ullah
,
Alamgir Khalil
,
Abdur Rehman
,
Sami Ullah
,
Sehran Hassan
,
Muhammad Sani
ICCK Transactions on Advanced Computing and Systems | Volume 1, Issue 4: 275-290, 2025 | DOI: 10.62762/TACS.2025.395911
Abstract
This paper examines the vulnerability of Small and Medium-sized Enterprises (SMEs) exposed to evolving climate changes in Pakistan, specifically the impacts of extreme weather events, including floods and drought. The earlier literature illustrates that SMEs are affected by climate-related risks, but the current study takes the discussion further by implementing machine learning algorithms to measure the vulnerabilities of SMEs more objectively. A mixed-methods design was used to combine surveys with machine-learning techniques. PyCaret was employed to tune instruments such as Logistic Regression (LR), Random Forest (RF), ordered logistic regression, LightGBM, ADA Boost, SVM, KNN, GBC, and N... More >
Graphical Abstract
Innovative Machine Learning Approaches for Evaluating Climate Change Vulnerabilities of SMEs