Authors: Malini R. De Silva

Abstract: The increasing complexity of regulatory requirements and the growing volume of enterprise data have made IT compliance auditing a challenging process for organizations. Traditional audit methods, reliant on static rule sets and manual inspection, often struggle to maintain accuracy and timeliness in dynamic digital environments. Cognitive analytics, integrating artificial intelligence, machine learning, and natural language processing, has emerged as a transformative approach to enhance audit precision and adaptability. By simulating human reasoning and contextual understanding, cognitive systems can analyze structured and unstructured data, interpret compliance documentation, and detect anomalies with greater accuracy. These systems not only automate evidence collection but also prioritize high-risk areas using predictive insights, thereby improving overall compliance efficiency. Furthermore, cognitive analytics enables continuous learning from historical audit outcomes, refining its models to adapt to evolving regulations. As a result, IT audit processes become more proactive, data-driven, and transparent. This paper reviews how cognitive analytics reshapes compliance auditing, improving reliability, speed, and accuracy while reducing operational overhead. The integration of cognitive intelligence represents a paradigm shift toward intelligent, adaptive, and autonomous compliance ecosystems capable of ensuring governance integrity in modern IT infrastructures.

DOI: http://doi.org/10.5281/zenodo.17880764

Published by: vikaspatanker

Authors: Anura J. Perera

Abstract: Artificial intelligence (AI) has revolutionized post-incident investigations by introducing automation, precision, and predictive intelligence into digital forensic processes. Traditional forensic techniques, dependent on manual log examination and human expertise, often struggle to handle the enormous data volumes and complex digital trails generated in modern cyber incidents. AI-based forensic systems leverage machine learning, natural language processing, and data mining to extract, analyze, and correlate evidence at unprecedented speed and accuracy. These systems can automatically detect anomalies, reconstruct attack timelines, and identify threat actors by learning from historical data patterns. The review explores the evolution of digital forensics, the integration of AI-driven tools, and the measurable improvements in investigation efficiency. It also highlights challenges such as model transparency, bias, explainability, and legal admissibility of AI-generated evidence. Moreover, it discusses emerging research opportunities, including explainable AI, blockchain-enabled evidence validation, and collaborative forensics through federated learning. By synthesizing academic research and industrial applications, this paper emphasizes that AI-based forensic systems are not merely tools but strategic enablers of resilience in cyber incident response. They represent a paradigm shift toward intelligent, adaptive, and self-learning investigation frameworks, crucial for addressing the growing sophistication of digital threats in enterprise and law enforcement environments.

DOI: http://doi.org/10.5281/zenodo.17880644

Published by: vikaspatanker

Authors: Sonam D. Wangchuk

Abstract: Container orchestration systems have become the foundation of modern microservice architectures, enabling automated deployment, scaling, and management of containerized workloads. As organizations shift to cloud-native environments, the security of orchestration platforms such as Kubernetes, Docker Swarm, and OpenShift has emerged as a critical determinant of overall system reliability. Orchestration security ensures that applications maintain consistent performance, resilience, and integrity even in the face of evolving cyber threats. However, inadequate security controls within orchestration layers such as unprotected APIs, misconfigured network policies, and unencrypted communications can lead to severe vulnerabilities that disrupt service continuity. This review explores the intricate relationship between container orchestration security and microservices reliability. It analyzes the evolution of orchestration systems, common security challenges, and the impact of security mechanisms on microservices performance and stability. Additionally, it discusses best practices and emerging frameworks that integrate security and automation to ensure sustained reliability. The paper concludes by emphasizing the need for adaptive, intelligent orchestration mechanisms that integrate security and reliability by design, ensuring robust and scalable cloud-native infrastructures capable of withstanding future operational and security challenges.

DOI: http://doi.org/10.5281/zenodo.17880571

Published by: vikaspatanker

Authors: Keshav M. Rana

Abstract: Cloud-native observability platforms have revolutionized how organizations understand, measure, and improve service performance in distributed computing environments. Unlike traditional monitoring tools that focus on static metrics, observability provides a holistic, data-driven view of system behavior through the collection and correlation of metrics, logs, and traces. In dynamic environments powered by containers, microservices, and Kubernetes orchestration, such platforms enable real-time insights into performance bottlenecks, latency variations, and service dependencies. This comprehensive visibility helps teams identify root causes, optimize system efficiency, and enhance user experience. However, observability in cloud-native systems also introduces challenges, including data volume management, complex instrumentation, and high computational costs. Modern observability platforms address these issues through automation, AI-driven analytics, and scalable data architectures capable of handling multidimensional telemetry. This review explores the evolution, architecture, and influence of observability platforms on service performance visibility, highlighting their role in proactive fault detection, system resilience, and decision-making efficiency. It also examines the challenges and future directions shaping the next generation of observability frameworks that promise self-optimizing and predictive performance management in cloud-native ecosystems.

DOI: http://doi.org/10.5281/zenodo.17879382

Published by: vikaspatanker

Authors: Sandhya R. Bista

Abstract: Generative Artificial Intelligence (AI) has rapidly become a transformative yet paradoxical force in the domain of cybersecurity. Its dual-edged nature capable of both fortifying defenses and amplifying cyber threats has redefined the way organizations approach malware detection, prevention, and response. Traditional cybersecurity models, which rely heavily on signature-based detection and heuristic methods, are increasingly inadequate against polymorphic, evasive, and zero-day malware variants. These conventional systems lack the adaptive capacity to counter attackers who continuously modify malicious code to escape static defense algorithms. In contrast, generative AI introduces a new paradigm in which defense systems evolve dynamically, learning from both real and simulated threats to anticipate and neutralize future attacks before they occur. Generative AI models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based architectures have been instrumental in driving this evolution. GANs, for instance, can simulate sophisticated attack patterns, enabling security systems to train against artificially generated malware samples that replicate real-world adversarial behavior. Similarly, transformer-based models enhance contextual awareness and anomaly detection by processing vast streams of network, behavioral, and endpoint data in real time. This fusion of generative modeling and adaptive learning fosters proactive defense strategies capable of identifying subtle deviations indicative of malicious intent long before damage is inflicted.

DOI: http://doi.org/10.5281/zenodo.17879335

Published by: vikaspatanker

Authors: Rohan C. Shrestha

Abstract: Phishing attacks have become a critical cybersecurity threat in the digital era, targeting individuals, businesses, and organizations to obtain sensitive information such as login credentials, financial data, and personal identification details. The sophistication of modern phishing attacks has evolved beyond simple spam emails, encompassing spear phishing, clone phishing, smishing, and vishing, making detection increasingly difficult. Traditional detection mechanisms, including rule-based systems, blacklists, and heuristic approaches, often fail to detect new or obfuscated attacks and are prone to high false-positive rates, which can compromise security operations. Deep learning (DL), a subset of artificial intelligence, offers promising solutions to these challenges through its ability to automatically extract complex features, learn non-linear relationships, and detect patterns that are imperceptible to human analysts or conventional machine learning algorithms. This review examines the application of deep learning in enhancing phishing detection mechanisms, focusing on architectures such as Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and autoencoders. The discussion highlights how these models improve detection accuracy, adaptability, and resilience against evolving phishing strategies. Furthermore, the review explores the utilization of diverse datasets, challenges in computational requirements and adversarial robustness, and the role of hybrid and ensemble models in optimizing performance. Finally, future directions, including explainable AI, multi-modal detection systems, and adaptive reinforcement learning frameworks, are addressed. Overall, deep learning provides a transformative approach to phishing detection, offering enhanced efficiency, robustness, and proactive threat mitigation, while opening avenues for continued research into intelligent, adaptive cybersecurity solutions.

DOI: http://doi.org/10.5281/zenodo.17879281

Published by: vikaspatanker

Authors: Rahul Jaiswar, Abhishek Kumar Sharma, Meena Rai

Abstract: The present study focuses on the morphological identification of dominant liverwort genera in the Kumaon hills of Uttarakhand, India, with particular emphasis on sporophyte characters for accu-rate taxonomic resolution. Field investigations were conducted in the Jageshwar region of Almora district and its adjoining areas up to Jageshwar Dham, a moist temperate zone characterized by mixed broad-leaved forests, shaded rock surfaces, and anthropogenically influenced temple com-plexes. These varied habitats form a mosaic of microenvironments favourable for the establish-ment of thalloid liverworts. During the survey, members of the families Aytoniaceae, Marchantiaceae, and Targioniaceae were recorded across soil, rock, and wall substrates. The liv-erwort flora documented comprised five species belonging to the genera Plagiochasma, Targio-nia, and Marchantia. Species of Plagiochasma and Targionia formed extensive patches on ex-posed to semi-shaded soil and rocky slopes, whereas Marchantia species were predominant in persistently moist, partially shaded habitats. These distributional patterns indicate clear ecological preferences among the dominant taxa within the study area. Overall, the investigation highlights the rich representation of complex thalloid liverworts in the Jageshwar landscape and underscores the significance of habitat heterogeneity in shaping bryophytic diversity in the mid-altitude Ku-maon Himalaya.

DOI: http://doi.org/10.5281/zenodo.17879162

Published by: vikaspatanker

Authors: Jai Gupta, Shreya Upadhyaya, Dr. H S Guruprasad

Abstract: This paper introduces a smart shoe that helps people move around inside buildings using gentle vibrations. Instead of relying on GPS or online maps, it tracks steps and direction with the phone’s built-in motion sensors. The method used is called pedestrian dead reckoning, which figures out position based on movement patterns. A matching app made with Flutter holds custom digital floor plans for different places indoors. Users can plan paths or get guided directions straight from their phone. Commands are sent wirelessly to small computers in each shoe using Wi-Fi signals. These tiny controllers then turn on one of two vibrating pads per foot – indicating turns or when they’ve reached the spot. The setup offers a complete, standalone way to navigate – ideal for indoor demos, restricted areas, or studies helping people with vision loss. Tests show it guides users step by step with precision while giving steady touch-based alerts on the go, proving that wearable navigation using only PDR can work reliably, no outside systems needed.

 

Published by: vikaspatanker

Authors: Aqsa Khalid

Abstract: Sustainability has emerged as a central driver of strategic transformation within the beauty and lifestyle segment of the fast-moving consumer goods (FMCG) industry. The expression “Green is the New White” captures the sector’s movement away from conventional, resource-intensive production practices toward environmentally responsible, ethically governed, and transparently communicated business models. Drawing on secondary data from international sustainability frameworks, peer-reviewed studies, market intelligence reports, and corporate disclosures, this research employs thematic analysis to identify three dominant patterns: Sustainable Product and Packaging Innovation, the Growth of Green Consumerism, and Regulatory–Reputational Pressures. The findings demonstrate that sustainability now underpins brand reputation, competitive advantage, and long-term sectoral resilience. The study concludes that beauty and lifestyle FMCG companies must embed environmental stewardship throughout the value chain to remain relevant in an evolving global marketplace.

Published by: vikaspatanker

Authors: Suraj Kumar, Mr. Vaibhav Singh Sekhawat

Abstract: The automation of anatomical and pathological region identification in clinical imaging has become a cornerstone of modern diagnostics. This review presents a systematic exploration of machine learning paradigms—from classical statistical models to cutting-edge foundation architectures—and their role in transforming segmentation accuracy, speed, and generalizability. We dissect foundational techniques such as kernel-based classifiers, ensemble tree models, and probabilistic graphical frameworks, contrasting them with deep learning systems including convolutional, recurrent, and transformer-based networks. Performance metrics from 2022–2025 benchmarks are synthesized across MRI, CT, ultrasound, and pathology datasets. We address persistent barriers—annotation scarcity, class imbalance, domain shift, and computational overhead—and evaluate mitigation strategies like transfer learning, synthetic data generation, and prompt-driven inference. A dedicated section introduces 2020–2025 breakthroughs: vision transformers, large-scale pre-trained models (e.g., MedSAM), diffusion-based synthesis, and hybrid neuro-symbolic systems. The convergence of these innovations signals a paradigm shift toward universal, data-efficient, and clinically deployable segmentation.

Published by: vikaspatanker