Rajesh Shardanand Prasad

Cybersecurity is the core where protection is provided to digital resources like servers, Data centers and Networks. Adoption of Artificial Intelligence (AI) driven cybersecurity strengthens the digital ecosystem. Web Sites and Social media are engines in the modern digital world where organizations, customers and users are facing challenges due to theft of data, money, identity and cyber attacks. Cyber crimes like tampering of digital assets need to be detected with tools and techniques that should be automated. Autonomous Systems in various sectors try to prevent crimes through Anti malware, Antivirus, Firewalls, Encryption and Multi stage authorization. This chapter provides analytical study of various tools, research work carried out with AI techniques. Critical analysis of challenges in unified security frameworks, Models as well as an explainable AI framework for Cybersecurity presented in paper. Quantum Computing capabilities to crack algorithms and decryption of data. Quantum-safe cybersecurity explored along with challenges for the digital world that would arise in future.

The emerging 6G technologies will bring billions of IoT sensors which are going to demand little radio spectrum. The resulting high level of connectivity poses some novel problems, among them network congestion, and privacy: because energy-consumption data might be shared unintentionally, it provides insight into personal behavioral patterns. In addition, the disproportionate access to highly developed infrastructure can contribute to the need to continue the digital divide between urban and rural areas. This paper presents a decentralized infrastructure combining federated learning and Hyperledger Fabric to deal with these issues. To implement it, PyTorch is used to perform distributed learning tasks, and MATLAB is used to generate the synthetic spectrum traces that reflect the real-world CBRS conditions based on the NTIA field measurements between 2021 and 2024. These traces were validated by Kolmogorov–Smirnov test ( p = 0.87). The framework outperforms conventional centralized DSS benchmarks while complying with 3GPP Release 18 transparency and ETSI ESG principles. The framework is a modular, open-source system with sharding that scales horizontally to over 5,000 nodes. The proposed solution successfully addresses the scarcity of the spectrum with the use of cooperative resource sharing through congested urban vehicle-to-everything (V2X) networks, remote solar-powered microgrids, and so on. The findings verify that the next generation 6G networks are capable of not only providing extremely high data rates but also improved privacy and fair connectivity in different environments.

Investigation and prediction of defects in software is one of the important solutions to ensure software quality and reliability. Machine learning algorithms are used across a wide array of fields to solve real-world problems by building large, complex models. Many researchers have made significant contributions by developing predictive models for software defects using statistical and machine-learning approaches. But only a few frameworks have discussed the issue of building a universal software defect prediction model. Most existing models have been trained on limited datasets, which results in good performance on the training data but poor performance on unseen data. These limitations have motivated researchers to explore and develop more generalised and universal models for software defect prediction. Moreover, the growing complexity of contemporary software systems. Such limitations have encouraged researchers to investigate and build more generalised and universal models for software defect prediction. In addition, the increasing complexity of modern software systems and the rapid growth of software repositories have driven a demand for intelligent prediction techniques capable of handling heterogeneous data. Research is being conducted to investigate advanced machine learning and deep learning methods, including ensemble learning and transfer learning, to enhance prediction accuracy and adaptability across different software projects. These approaches aim to reduce development and maintenance costs and increase the overall reliability and performance of software products.

Background: Early breast cancer detection remains central to improving clinical outcomes, yet conventional screening pathways, particularly mammography, have recognized limitations in sensitivity, specificity, and performance in dense breast tissue. Circulating microRNAs (miRNAs) have emerged as promising minimally invasive biomarkers, while artificial intelligence and machine learning (AI/ML) offer powerful tools for identifying diagnostically relevant multi-marker patterns within complex biomarker datasets. This systematic review and meta-analysis evaluated the diagnostic performance of AI/ML-based circulating miRNA signatures for early breast cancer detection. Methods: A systematic search of PubMed/MEDLINE, Scopus, and Web of Science Core Collection was conducted from database inception to 31 December 2025. Studies were eligible if they were original human investigations evaluating circulating miRNAs using an AI/ML-based diagnostic model for breast cancer detection and reporting extractable diagnostic performance metrics. Study selection followed PRISMA 2020 and PRISMA-DTA guidance. Methodological quality was assessed using QUADAS-2. Pooled sensitivity and specificity were synthesized using a bivariate random-effects model, and overall diagnostic performance was summarized using a hierarchical summary receiver operating characteristic framework. Results: Seven studies met the inclusion criteria for qualitative synthesis, with eligible studies contributing to the quantitative analysis depending on data availability. Across the pooled analysis, AI/ML-based circulating miRNA models demonstrated good overall diagnostic performance, with a pooled AUC of 0.905 (95% CI: 0.890–0.921), pooled sensitivity of 81.3% (95% CI: 76.8%–85.2%), and pooled specificity of 87.0% (95% CI: 82.4%–90.7%). Heterogeneity was moderate for AUC (I² = 42.3%) and sensitivity (I² = 38.7%) and low for specificity (I² = 28.4%). Risk-of-bias assessment showed overall low-to-moderate methodological concern, with patient selection representing the most variable domain. Deeks’ funnel plot asymmetry test showed no significant evidence of publication bias (p = 0.34). Conclusions: AI/ML-based circulating miRNA signatures show promising diagnostic accuracy for early breast cancer detection and may have value as non-invasive adjunctive tools within imaging-supported diagnostic pathways. However, the evidence base remains limited by methodological heterogeneity, variable validation rigor, and the predominance of retrospective case-control designs. Prospective, standardized, and externally validated studies are needed before routine clinical implementation can be justified.

Assistive Technology (AT) is essential in improving the quality of life for elderly individuals, particularly by facilitating their ability to age in place while preserving their independence and safety. This paper examines the various forms of AT, such as mobility aids, sensory enhancements, cognitive support devices, and health monitoring systems, and their effectiveness in addressing the challenges encountered by older adults. Furthermore, it reviews emergency response systems and personalized technologies, while also addressing the obstacles to AT adoption, including financial constraints, accessibility issues, and reluctance to embrace new technologies. Ethical aspects, including privacy, data security, and the need to balance technological support with human interaction, are also discussed. These findings aim to offer a thorough understanding of the current landscape of assistive technologies and their potential to aid the aging population.

AI has the potential to fundamentally transform healthcare delivery and revolutionize the sector with applications such as precision diagnosis, personalized treatment plans, remote patient monitoring, and drug discovery. The chapter commences with a brief introduction and historical overview of AI. The second segment focuses on the current status of AI in healthcare. The third segment delves into its legal and ethical considerations, while the fourth addresses the challenges that must be overcome for successful AI integration. Lastly, future trends in AI implementation within healthcare are discussed. Resolving these implementation barriers requires a collaborative effort among clinicians, technologists, regulators, and policymakers.

The most influential research was studied that spans over the domains from Authorship attribution and stylometry. The reference material contributes robust classifiers with reasonable array of feature extraction techniques, such as Dirichlet–multinomial change point regression to extract the progress of inscription elegance with time, comprising plodding variations in stylishness as the author ages and unexpected vicissitudes. This paper presents quantifiable evaluation of the research in terms of year-wise research output, diversity of applications, nature of collaboration, characteristics of highly productive techniques and the benchmark of performance criteria by eminent high impact researchers. The outcomes of this study can by deployed for dialectology analysis and corpus linguistics, stylistics, natural language processing, classification, and literary and historical analysis, forensic analysis etc.