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 >

The research evaluates different image enhancement approaches regarding their impact on deep learning algorithms which detect body regions in X-ray scans. We analyze how Bilateral Filtering as well as Contrast Limited Adaptive Histogram Equalization (CLAHE) and Wavelet Denoising and Super-Resolution influence X-ray image quality which subsequently impacts Convolutional Neural Networks (CNNs) classification results. The evaluation demonstrates Bilateral Filtering delivers superior performance than other enhancement processes according to PSNR and SSIM evaluations on LEG, CTScan and Chest X-ray datasets. The experimental results for the LEG dataset demonstrated Bilateral Filtering produced a h... More >

The Internet of Vehicles (IoV) has greatly influenced transportation by allowing autonomous vehicles to interact and communicate with other cars as well as with the surrounding traffic system. Even so, being interconnected comes with risks in terms of cyber attacks, for example, by injecting messages or fooling sensors through CAN systems. The study, consequently, suggests an Intrusion Detection System (IDS) that uses Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and RoBERTa, to properly detect and handle these cyber threats. To solve the problem of unbalanced data, we use R... More >

Roman Urdu sentiment analysis faces significant challenges due to transliteration inconsistencies, informal language usage, and the lack of labeled datasets. This study proposes a novel framework that addresses these challenges by combining advanced data preprocessing techniques and data augmentation strategies such as synonym replacement, back-translation, and random word insertion. These methods enhance dataset diversity, improving the model’s generalization ability. A rich Roman Urdu dataset was collected from diverse sources, including social media platforms (Facebook, Twitter, YouTube), blogs, forums, and e-commerce sites, to capture a wide range of user opinions. Three deep learning... More >

Brain tumors pose a serious threat to global health, making accurate and early detection essential for effective treatment planning. While Magnetic Resonance Imaging (MRI) is widely used for diagnosis, manual interpretation is time-consuming and subject to error. This has prompted increasing use of deep learning for automated tumor classification. We propose a novel framework based on the Swin Transformer V2 architecture for classifying brain tumors in MRI scans into glioma, meningioma, pituitary tumor, and non-tumor categories. The design introduces two core innovations: a Dual-Branch Down-sampling module and an Enhanced Attention Mechanism, which improve multi-scale feature representation... More >

The concept of an Internet of Drones (IoD) is becoming increasingly important in various domains, including surveillance and logistics. Effective communication between the interconnected systems is the essence of the Internet of Drones, however, due to the resource constraints of drones and the dynamic nature of the operating environment, security of communication within IoD networks is indeed the top priority. Considering these challenges on the part of IoD communication, a novel Hyperelliptic Curve Cryptography (HECC)-based authentication protocol is proposed in this paper to secure the data exchange between two drones and to ensure efficient communication. The proposed HECC protocol is co... More >

Traffic flow prediction is a critical component of Intelligent Transportation Systems (ITS) and smart city infrastructures. This survey paper provides a comprehensive analysis of recent advancements in deep learning-based approaches for traffic flow prediction, focusing on spatiotemporal correlations and attention mechanisms. We systematically review five seminal papers that propose innovative neural network architectures including DHSTNet, Att-DHSTNet, and ASTMGCNet for citywide traffic prediction. Our survey examines their methodologies, key contributions, experimental results, and comparative performance. We organize the discussion around three main themes: (1) modeling dynamic spatiotemp... More >