Skin diseases affect millions worldwide. Early detection is key to preventing disfigurement, lifelong disability, or death. Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance, and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks (CNNs). We frame skin lesion recognition as graph-based reasoning and, to ensure fair evaluation and avoid data leakage, adopt a strict lesion-level partitioning strategy. Each image is first over-segmented using SLIC (Simple Linear Iterative Clustering) to produce perceptually homogeneous superpixels. These superpixels form the nodes of a region-adjacency graph whose edges encode spatial continuity. Node attributes are 1280-dimensional embeddings extracted with a lightweight yet expressive EfficientNet-B0 backbone, providing strong representational power at modest computational cost. The resulting graphs are processed by a five-layer Graph Attention Network (GAT) that learns to weight inter-node relationships dynamically and aggregates multi-hop context before classifying lesions into seven classes with a log-softmax output. Extensive experiments on the DermaMNIST benchmark show the proposed pipeline achieves 88.35% accuracy and 98.04% AUC, outperforming contemporary CNNs, AutoML approaches, and alternative graph neural networks. An ablation study indicates EfficientNet-B0 produces superior node descriptors compared with ResNet-18 and DenseNet, and that roughly five GAT layers strike a good balance between being too shallow and over-deep while avoiding oversmoothing. The method requires no data augmentation or external metadata, making it a drop-in upgrade for clinical computer-aided diagnosis systems.

The Internet of Things (IoT) and cloud computing have significantly contributed to the development of smart cities, enabling real-time monitoring, intelligent decision-making, and efficient resource management. These systems, particularly in IoT networks, rely on numerous interconnected devices that handle time-sensitive data for critical applications. In related approaches, trusted communication and reliable device interaction have been overlooked, thereby lowering security when sharing sensitive IoT data. Moreover, it incurs additional energy consumption and overhead while addressing potential threats in the dynamic environment. In this research, an Artificial Intelligence (AI) recommended fault-tolerant framework is proposed that leverages blockchain technology, aiming to enhance device trustworthiness and ensure data privacy. In addition, the intelligence of the proposed framework enables more authentic and authorized device involvement in data routing, thereby enabling seamless transmission in smart cities integrated with lightweight computing. To evaluate dynamic network conditions, the proposed framework offers a timely decision-making system to ensure robust delivery of IoT-assisted services. Using simulations, the efficacy of the proposed framework is validated by comparing it with existing approaches across various network metrics, demonstrating remarkable performance while achieving energy efficiency and optimizing network resources.

Problem: The integration of Artificial Intelligence (AI) into cybersecurity, while enhancing threat detection, is hampered by the “black box” nature of complex models, eroding trust, accountability, and regulatory compliance. Explainable AI (XAI) aims to resolve this opacity but introduces a critical new vulnerability: the adversarial exploitation of model explanations themselves. Gap: Current research lacks a comprehensive synthesis of this dual role of XAI in cybersecurity—as both a tool for transparency and a potential attack vector. There is a pressing need to systematically analyze the trade-offs between interpretability and security, evaluate defense mechanisms, and outline a path for developing robust, next-generation XAI frameworks. Solution: This review provides a systematic examination of XAI techniques (e.g., SHAP, LIME, Grad-CAM) and their applications in intrusion detection, malware analysis, and fraud prevention. It critically evaluates the security risks posed by XAI, including model inversion and explanation-guided evasion attacks, and assesses corresponding defense strategies such as adversarially robust training, differential privacy, and secure-XAI deployment patterns. Contribution: The primary contributions of this work are: (1) a comparative analysis of XAI methods tailored for cybersecurity contexts; (2) an identification of the critical trade-off between model interpretability and security robustness; (3) a synthesis of defense mechanisms to mitigate XAI-specific vulnerabilities; and (4) a forward-looking perspective proposing future research directions, including quantum-safe XAI, hybrid neuro-symbolic models, and the integration of XAI into Zero Trust Architectures. This review serves as a foundational resource for developing transparent, trustworthy, and resilient AI-driven cybersecurity systems.

Wireless technologies and the Internet of Things (IoT) are being extensively utilized for advanced development in traditional communication systems. This evolution lowers the cost of the extensive use of sensors, changing the way devices interact and communicate in dynamic and uncertain situations. Such a constantly evolving environment presents enormous challenges to preserving a secure and lightweight IoT system. Therefore, it leads to the design of effective and trusted routing to support sustainable smart cities. This research study proposed a Genetic Algorithm sentiment-enhanced secured optimization model, which combines big data analytics and analysis rules to evaluate user feedback. The sentiment analysis is utilized to assess the perception of network performance, allowing the classification of device behavior as positive, neutral, or negative. By integrating sentiment-driven insights, the IoT network adjusts the system configurations to enhance the performance using network behaviour in terms of latency, reliability, fault tolerance, and sentiment score. Accordingly to the analysis, the proposed model categorizes the behavior of devices as positive, neutral, or negative, facilitating real-time monitoring for crucial applications. Experimental results revealed a significant improvement in the proposed model for threat prevention and network efficiency, demonstrating its resilience for real-time IoT applications.

Software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification, which affect the testing process. Therefore, it is difficult to identify all faults in software. As requirement changes continuously, it increases the irrelevancy and redundancy during testing. Due to these challenges; fault detection capability decreases and there arises a need to improve the testing process, which is based on changes in requirements specification. In this research, we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based environment. The research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change analysis. Then compute the similarity of requirements through case-based reasoning, which predicted the requirements for reuse and restricted to error-based requirements. Afterward, the apriori algorithm mapped out requirement frequency to select relevant test cases based on frequently reused or not reused test cases to increase the fault detection rate. Furthermore, the proposed model was evaluated by conducting experiments. The results showed that requirement redundancy and irrelevancy improved due to semantic analysis, which correctly predicted the requirements, increasing the fault detection rate and resulting in high user satisfaction. The predicted requirements are mapped into test cases, increasing the fault detection rate after changes to achieve higher user satisfaction. Therefore, the model improves the redundancy and irrelevancy of requirements by more than 90% compared to other clustering methods and the analytical hierarchical process, achieving an 80% fault detection rate at an earlier stage. Hence, it provides guidelines for practitioners and researchers in the modern era. In the future, we will provide the working prototype of this model for proof of concept.