Navdeep Bohra

Early detection of Alzheimer’s Disease (AD), particularly at the Mild Cognitive Impairment (MCI) stage, is essential for effective intervention and disease management. Traditional deep learning models often struggle to simultaneously capture both local and global brain connectivity patterns from functional Magnetic Resonance Imaging (fMRI) data. To address these challenges, we propose the Hierarchical Spatio-Temporal Multi-Neighbour Aggregation Graph Convolutional Network (Hi-STMNA-GCN), a novel deep learning framework designed for robust AD classification. The Hi-STMNA-GCN model operates at two levels: at the Region Level, it employs multi-neighbour aggregation graph convolutional networks to comprehensively capture spatial dependencies by aggregating information from neighbouring nodes across multiple scales, while Gated Recurrent Units (GRU) effectively model the temporal dynamics of brain activity. At the Population Level, a hypergraph-based module is introduced to learn complex inter-subject relationships, enhancing the global discriminative power of the model. Additionally, a Masked Relation Learning (MRL) mechanism is integrated to prune redundant connections, ensuring that the most informative brain region interactions are preserved. Extensive experiments demonstrate that Hi-STMNA-GCN significantly outperforms existing methods, achieving classification accuracies of 96% (AD vs. Cognitively Normal (CN)), 94.8% (AD vs. MCI), and 98% (MCI vs. CN). These results highlight the model’s potential for early, reliable AD detection and its applicability in advancing neuroimaging-based diagnostic tools.

Advancements in intelligent vehicular networks and computing systems have created new possibilities for innovative approaches that enhance traffic safety, comfort, and transportation performance. Machine Learning (ML) has become widely employed for boosting conventional data-driven methodologies in various scientific study domains. The integration of a Vehicle-to-Everything (V2X) system with ML enables the acquisition of knowledge from multiple places, enhances the operator’s awareness, and predicts future crashes to prevent them. The information serves multiple functions, such as determining the most efficient route, increasing the driver’s knowledge, forecasting movement strategy to avoid risky circumstances, and eventually improving user convenience, security, and overall highway experiences. This article thoroughly examines Artificial Intelligence (AI) and ML methods that are now investigated through different study endeavors in vehicular ad hoc networks (VANETs). Furthermore, it examines the benefits and drawbacks accompanying such intelligent methods in the context of the VANETs system and simulation tools. Ultimately, this study pinpoints prospective domains for vehicular network development that can utilize the capabilities of AI and ML.

Skin cancer is one of the most common types of cancer, and early identification is key to better patient outcomes. An optimized YOLOv8, method of detecting skin cancer utilizing the CLEO (Chimpanzee Leader Election Optimization) optimizer to enhance performance is presented in this paper. YOLOv8 incorporates the CLEO optimizer to fine-tune the model's hyperparameters, enhancing the model's efficacy and precision to identify skin lesions. By leveraging YOLOv8's advanced object detection capabilities and CLEO's optimization strengths, this approach offers a highly effective solution in real-time, for automated skin cancer screening, potentially aiding dermatologists in early diagnosis and treatment planning. Extensive experimentation on publicly available ISIC (International Skin Imaging Collaboration), skin cancer dataset demonstrates that the proposed model improves the precision, accuracy, recall, and mAP (mean Average Precision) rates by 2.1%, 2.1%, 6.5%, and 1.1% as compared with the unoptimized algorithm. Further, YOLOv8 integrated with CLEO optimizer attains superior detection accuracy and quicker inference times in comparison to the other cutting-edge methods for skin cancer detection. The analysis can set the stage for practical use.

The Word Internet of Things (IoT) wherever will support billions of gadgets, people, and organizations to interconnect and trade data and important data. Security, Protection and Privacy are significant inquiries for IoT applications and still face some urgent troubles. Today, Internet availability is generally accessible, and the expense of association is radically diminishing. The number of gadgets associated with the web and inherent sensors continues to increment, just as security and protection concerns are expanding. In our particular paper, we will discuss the Internet of things in detail and its security & privacy in terms of Architecture of IoT, security concerns in terms of different phases of IoT systems. Suggesting some security characteristics that are very much essential for building the IoT Systems and also discussing real world IoT issues in current time period.