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 >

The process of assigning grammatical categories, such as ``Noun'' and ``Verb,'' to every word in a text corpus is known as part-of-speech (POS) tagging. This technique is widely used in applications like sentiment analysis, machine translation, and other linguistic and computational tasks. However, the unique features of the Pashto language and its limited resources present significant challenges for POS tagging. This study explores the critical role of POS tagging in the Pashto language by employing six popular deep-learning and machine-learning techniques. Experimental results demonstrate machine learning methods' effectiveness in capturing Pashto text's grammatical patterns. The evaluatio... More >

The Metaverse represents a virtual realm that is progressively supplanting the digital world, offering a unified, immersive, and enduring 3D virtual space. Its potential for transforming various aspects of human life is immense, spanning work, leisure, and everyday activities. It delves into the essential elements of the metaverse, including its prerequisites, structure, standards, challenges, and potential solutions. Bitcoin and the rise of NFTs also attract attention to the blockchain ecosystem. This increased focus on blockchain prompted discussions about the metaverse. Mark Zuckerberg, the CEO of Facebook, announced the company's transformation into a metaverse-focused entity and its ren... More >

Stance detection identifies a text’s position or attitude toward a given subject. A major challenge in Roman Urdu is the lack of a publicly available dataset for political stance detection. To address this gap, we constructed a high-quality dataset of 8,374 political tweets and comments using the Twitter API, annotated with stance labels: agree, disagree, and unrelated. The dataset captures diverse political viewpoints and user interactions. For feature representation, we employed TF-IDF due to its effectiveness in handling high-dimensional, context-sensitive Roman Urdu text. Several machine learning classifiers were evaluated, with Random Forest achieving the highest accuracy of 95%. Addi... More >

Across the globe, heart diseases rank as the top cause of death, with their incidence steadily rising. However, early detection before a cardiac event (e.g., cardiac arrest) remains a significant challenge. Although the healthcare sector possesses extensive data on heart disease, the effective use of this data for timely detection is essential to protect from such events. This paper proposes an innovative approach using fuzzy logic (FL), convolutional neural network (CNN) models, and feature selection to more accurately assess the risk of heart attacks. Our study also emphasizes the importance of data preprocessing, including data transformation, cleaning, and normalization, to facilitate th... More >

The Internet of Vehicles (IoVs) is an emerging technology that enhances transportation systems by enabling interactions between vehicles, infrastructure, and other entities. Securing IoV networks from cyber threats like eavesdropping, data tampering, and intrusions is a major challenge. This research presents a Blockchain-Enabled Secure Authentication Protocol for IoVs (BESA-IOV), which leverages blockchain’s decentralized and tamper-resistant nature for secure communication in vehicular networks. By utilizing ECC-based lightweight cryptography and blockchain-based public key management, it ensures strong authentication, confidentiality, and integrity. The results show that BESA-IOV signif... More >

This paper introduces ViTDroid, a novel hybrid model that combines Vision Transformers (ViTs) and recurrent neural networks (RNNs) to enhance Android and IoT malware detection. ViTDroid addresses critical challenges by leveraging ViTs to capture global spatial dependencies and RNNs (LSTM and GRU) to model temporal patterns, enabling comprehensive analysis of complex malware behaviors. Additionally, the model integrates explainability tools, such as LIME and SHAP, to enhance transparency and trustworthiness, essential for real-world cybersecurity applications. The study evaluates ViTDroid's performance against conventional models, including RNN, LSTM, and GRU, using accuracy, precision, recal... More >

In the therapy of Coronavirus, the drug target is a demanding task to find novel medicine. A bunch of pharmaceutics procedures are employed to recognize these mutual actions. But they are exhausting and high-priced. Keeping this in view, computational procedures are widely approached to determine the mutual action of the medicine and their respective proteins. Many scientists have applied ML approaches to deduce attributes from simplified molecular-input line systems (for medicine) and protein sequences. Such approaches dropped the proteins' chemical, physical, and structural characteristics and the respective medicine. Our job is to undertake deep learning approaches to detect coronavirus e... More >

This paper introduces a novel method for controlling a class of nonlinear non-affine systems with fractional-order dynamics, using an adaptive fuzzy technique. By incorporating a novel fractional update law in the design procedure, the controller can effectively suppress chaotic behaviour and smoothly track desired trajectories. The proposed method offers key advantages such as robustness against uncertainties, fast error convergence to the neighbourhood of zero, and satisfactory disturbance rejection performance. To demonstrate the capabilities of the proposed fractional controller, simulation results were conducted using Python on a fractional order Arneodo chaotic system. The results high... More >

I am pleased to introduce a new Transactions focusing on the rapidly evolving field of Advanced Computing and Systems. This journal is intended to serve as a platform for cutting-edge research and technological advancements that have the potential to reshape industries through state-of-the-art computing methodologies. The goal is to foster interdisciplinary collaboration among researchers, practitioners, and industry leaders, facilitating the advancement of computing systems and exploring their impact on real-world applications. Through this publication, I aim to contribute to the academic discourse and help drive innovation in this critical domain. More >