This paper details the implementation of a Web crawler aimed at automating ruleset construction for “HTTPS Everywhere,” with a goal to convert HTTP URLs to secure HTTPS equivalents for enhanced communication security. Developed within a seven-month timeframe, the crawler faced challenges in verifying HTTPS support, varying based on SSL certificate existence and validity. Successful ruleset creation and testing in Firefox and Chrome, adhering to stylistic standards, demonstrated the potential for effective development. The paper explores improving productivity through alternative libraries like Scrapy and Scrapy Cloud. While certain goals, such as in-depth cryptocurrency analysis and web crawler background reading, were unmet due to time constraints, valuable insights were gained. The conclusion underscores the difficulties, successes, and promises of automating ruleset generation through web crawlers for “HTTPS Everywhere,” offering valuable recommendations for advancing web security.

Artificial Intelligence (AI) is expanding with colossal applications in various sectors. In the healthcare sector, it is booming to make life simpler with utmost accuracy by predicting, diagnosing and up to care with the help of Machine Learning (ML) and Deep Learning (DL) applications. Modern computer algorithms have attained accuracy levels comparable to those of human specialists in medical sciences, although computers often do jobs more quickly than people do. It is also expected that there will not be a mandate for humans to be present for the jobs that machines can do, and it is gaining the highest peak because of good trained artificial models in the medical field. ML enhances the therapeutic process and improves health by encouraging more patient participation. ML may get more accurate patient data when used with the Internet of Medical Things (IoMT) and automate message notifications that prompt patients to respond at certain times. The motivation behind this article is to make a comprehensive review of the on-going implementation of ML in medical science, what challenges it is facing now, and how it can be simplified for future researchers to contribute better to medical sciences while applying it to the practitioners' jobs easier. In this review, we have extensively mined the data and brought up systematised applications of AI in healthcare, what challenges have been faced by the experts, and what ethical responsibilities are liable to them while taking the data. We also tabulated which algorithms will be helpful for what kind of data and disease conditions will be useful for future researchers and developers. This article will provide a better insight into AI and ML for the beginner to the advanced developer and researcher to understand the concepts from the basics.

This chapter explores how quantum computing and Generative Artificial Intelligence (AI) jointly reshape the cybersecurity landscape. It begins by examining the core principles of quantum computing—superposition, entanglement, and interference—and outlines their implications for cryptographic vulnerabilities. Generative AI's dual role is then presented, highlighting both the defensive advantages in threat detection and the offensive potential in crafting advanced cyberattacks. Next, the chapter delves into the disruptive power of adversarial AI when combined with quantum computation, underscoring the need for robust safeguards. Finally, quantum-enhanced security solutions—quantum-resistant cryptography, quantum key distribution, and quantum-supported AI defenses—are analyzed as viable strategies to future-proof digital infrastructures. Through these discussions, the chapter underscores the urgency of adapting security architectures to an evolving quantum-AI era.

Cloud computing has transformed modern IT infrastructure by enabling scalable, on-demand access to virtualized resources. However, the rapid growth of cloud services has intensified energy consumption across data centres, increasing operational costs and carbon footprints. Traditional load-balancing methods, such as Round Robin and First-Fit, often fail to adapt dynamically to fluctuating workloads and heterogeneous resources. To address these limitations, this study introduces a Reinforcement Learning-guided hybrid optimization framework that integrates the Black Eagle Optimizer (BEO) for global exploration with the Pelican Optimization Algorithm (POA) for local refinement. A lightweight RL controller dynamically tunes algorithmic parameters in response to real-time workload and utilization metrics, ensuring adaptive and energy-aware scheduling. The proposed method was implemented in CloudSim 3.0.3 and evaluated under multiple workload scenarios (ranging from 500 to 2000 cloudlets and up to 32 VMs). Compared with state-of-the-art baselines, including PSO-ACO, MS-BWO, and BSO-PSO, the RL-enhanced hybrid BEO–POA achieved up to 30.2% lower energy consumption, 45.6% shorter average response time, 28.4% higher throughput, and 12.7% better resource utilization. These results confirm that combining metaheuristic exploration with RL-based adaptation can significantly improve the energy efficiency, responsiveness, and scalability of cloud scheduling systems, offering a promising pathway toward sustainable, performance-optimized data-centre management.

Email threats continue to pose a significant challenge in cybersecurity, exploiting human vulnerabilities through highly deceptive and context-aware content. This study introduced a novel hybrid detection framework that integrated semantic deep learning with cognitively inspired symbolic reasoning to address these evolving risks. The proposed system utilized a Bidirectional Long Short-Term Memory (BiLSTM) network to capture sequential and linguistic patterns from email content, while a Belief–Desire–Intention (BDI) cognitive agent modeled behavioral indicators, including urgency cues, sender anomalies, and irregular sending patterns. These complementary components were fused within a unified architecture designed to achieve both high predictive accuracy and meaningful cognitive interpretability. To further enhance transparency and facilitate human-in-the-loop decision-making, the BDI-SHAP-X module was developed, combining rule-based symbolic explanations with SHapley Additive exPlanations (SHAP) for localized feature attribution. An experimental evaluation of a comprehensive, enriched email corpus demonstrates the effectiveness of the proposed model, achieving 98.31% accuracy, 97.80% precision, 98.85% recall, 98.32% F1-score, and a ROC-AUC of 99.78%. These results emphasize the framework’s capacity to deliver state-of-the-art detection performance while ensuring interpretable, trustworthy insights essential for safeguarding enterprise and critical communication infrastructures.

As software systems grow increasingly complex and interconnected, detecting vulnerabilities in source code has become a critical and challenging task. Traditional static analysis methods often fall short in capturing deep, context-dependent vulnerabilities and adapting to rapidly evolving threat landscapes. Recent efforts have explored knowledge graphs and transformer-based models to enhance semantic understanding; however, these solutions frequently rely on static knowledge bases, exhibit high computational overhead, and lack adaptability to emerging threats. To address these limitations, we propose DynaKG-NER++, a novel and lightweight framework for context-aware vulnerability detection in source code. Our approach integrates lexical, syntactic, and semantic features using a transformer-based token encoder, dynamic knowledge graph embeddings, and a Graph Attention Network (GAT). We further introduce contrastive learning on vulnerability–patch pairs to improve discriminative capacity and design an attention-based fusion module to combine token and entity representations adaptively. A key innovation of our method is the dynamic construction and continual update of the knowledge graph, allowing the model to incorporate newly published CVEs and evolving relationships without retraining. We evaluate DynaKG-NER++ on five benchmark datasets, demonstrating superior performance across span-level F1 (89.3%), token-level accuracy (93.2%), and AUC-ROC (0.936), while achieving the lowest false positive rate (5.1%) among state-of-the-art baselines. Sta tistical significance tests confirm that these improvements are robust and meaningful. Overall, DynaKG-NER++ establishes a new standard in vulnerability detection, balancing accuracy, adaptability, and efficiency, making it highly suitable for deployment in real-world static analysis pipelines and resource-constrained environments.