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 >

The rapid accumulation of large-scale, long-term meteorological data presents unprecedented opportunities for data-driven weather modeling and high-resolution numerical weather prediction. While various deep learning techniques—such as Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs)—have been explored for weather forecasting, the complex spatial dependencies within historical meteorological data, particularly dynamic spatial correlations, remain insufficiently addressed. To tackle this challenge, we propose a Dynamic Spatio-Temporal Fusion Graph Network (DSTFGN), a novel module that integrates multivariate time-series analysis with graph-... More >

The growing role of citation relations in identifying research impact has spurred much investigation on assessing the most cited papers and their roles within datasets. Due to the richness of the CORA dataset, this study selects highly cited papers and measures the results of node classification, as well as the H-index of research articles. Besides, it explores the correlations and robustness with regard to the nodes by computing their chances and studying their connections. To these ends, linear transformation was utilized for mapping low-level node features to high-level, and the Graph Attention Networks (GAT) for node classification. The study was able to find highly cited papers and com... More >

Artificial intelligence (AI) has become a critical tool for risk management across industries such as insurance, healthcare, business, and finance. It enables risk quantification, improves predictive accuracy, and supports decision-making in dynamic and uncertain environments. This paper examines models, methods, and frameworks for AI-based risk assessment, while addressing concerns of ethics, regulation, and explainability. Key technologies, including machine learning, deep learning, and reinforcement learning, are highlighted for their ability to transform traditional approaches by enhancing prediction, optimization, and decision processes. The second part focuses on AI-driven risk modelin... More >

Real-time communication between autonomous vehicles, infrastructure, and their environment has facilitated the Internet of Vehicles (IoVs). Although this connectivity provides vehicular networks with significant benefits, it also introduces severe security threats, such as message injection attacks, particularly due to the heavy reliance on the Controller Area Network (CAN) protocol, which is inherently vulnerable. Electronic Control Units (ECUs) become primary targets for these attacks, leading to unsafe vehicle behaviors. To address these challenges, an Intrusion Detection System (IDS) based on deep learning architectures, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and... More >