This study evaluates four image enhancement techniques---Bilateral Filtering, Contrast Limited Adaptive Histogram Equalization (CLAHE), Wavelet Denoising, and Super-Resolution---with respect to their impact on Convolutional Neural Network (CNN)-based body part classification from X-ray images. Image quality is quantified using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) across three datasets: LEG, CT Scan, and Chest X-ray. Bilateral Filtering consistently delivers superior performance to the other enhancement methods on all three datasets. On the LEG dataset, Bilateral Filtering achieved a PSNR of 51.78 and an SSIM of 0.99918, outperforming CLAHE, which yielded a... 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 drones and their control servers and to ensure efficient communication. The propos... 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 key representative works 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 spati... More >