The rapid expansion of digital technologies has transformed the collection, processing, and sharing of personal data, creating unprecedented opportunities for innovation while raising serious concerns regarding privacy, cybersecurity, and government surveillance. As cyber threats, terrorism, and cross-border data flows continue to increase, governments have strengthened surveillance mechanisms to protect national security. However, these measures often create tensions with the fundamental right to privacy, making it essential to establish an appropriate legal balance between individual freedoms and collective security. This review paper critically examines the evolving relationship between privacy and national security through an analysis of contemporary data protection laws and international legal frameworks. Adopting a doctrinal and qualitative review methodology, the study analyses secondary sources, including legislation, judicial decisions, policy documents, and scholarly literature. The paper comparatively evaluates major legal frameworks, including the European Union's General Data Protection Regulation (GDPR), India's Digital Personal Data Protection Act, 2023, the United States' sector-specific privacy model, and the United Kingdom's data protection regime. The review highlights the strengths and limitations of these legal systems in addressing surveillance, government access to personal data, and individual privacy rights. It further identifies emerging challenges arising from artificial intelligence, biometric surveillance, cross-border data transfers, and digital governance. The study concludes that neither privacy nor national security should be treated as absolute rights; instead, both must be harmonized through transparent legislation, proportional restrictions, judicial oversight, and institutional accountability. A balanced, rights-based legal framework supported by international cooperation and adaptive regulatory mechanisms is essential for protecting individual liberties while enabling governments to respond effectively to evolving security threats in the digital era.
Yogarajsingh R. Bais· Zenodo (CERN European Organi...· 0 citations
India’s entertainment economy increasingly depends upon digital circulation, platform licensing and data-driven distribution. Music is consumed through streaming services, films move rapidly from theatres to online platforms, and over-the-top services commission, acquire and exploit content across multiple territories. These developments have expanded audiences and created new markets, but they have also exposed structural weaknesses in the protection and administration of intellectual property rights. This article examines emerging copyright and related-rights issues affecting music, cinema and OTT platforms in India. It adopts a doctrinal and analytical method, relying on the Copyright Act, 1957, the Copyright (Amendment) Act, 2012, the Information Technology Act, 2000, the Cinematograph Act, 1952 as amended in 2023, relevant rules, international instruments and judicial decisions. The article argues that the principal difficulty is not the absence of rights, but the fragmentation of ownership, licensing and enforcement. A single song or film may contain separate rights in lyrics, composition, sound recording, screenplay, performance, artwork and the audiovisual work itself. Digital exploitation further divides these rights by territory, language, duration, device, subscription model and mode of communication. Disputes therefore arise over royalty sharing, statutory licensing, online streaming, platform liability, piracy, synthetic performances, voice cloning and the commercial use of celebrity identity. Indian courts have responded through intermediary-liability principles, dynamic injunctions and personality-rights remedies, yet the legal position remains uneven in relation to generative artificial intelligence, transparent royalty accounting and cross-platform licensing. The article recommends clearer digital licensing standards, auditable royalty systems, stronger collective management, proportionate notice-and-action procedures, technologically informed anti-piracy remedies and a consent-based framework for artificial intelligence uses of voice, likeness and performance. A coherent approach must protect creators and performers without imposing indiscriminate liability on legitimate intermediaries. Such balance is necessary to sustain cultural production, promote lawful digital markets and preserve public access to diverse entertainment in India.
Madhuri V. Sarwade· Zenodo (CERN European Organi...· 0 citations
The rapid expansion of digital technologies has transformed the collection, processing, and sharing of personal data, creating unprecedented opportunities for innovation while raising serious concerns regarding privacy, cybersecurity, and government surveillance. As cyber threats, terrorism, and cross-border data flows continue to increase, governments have strengthened surveillance mechanisms to protect national security. However, these measures often create tensions with the fundamental right to privacy, making it essential to establish an appropriate legal balance between individual freedoms and collective security. This review paper critically examines the evolving relationship between privacy and national security through an analysis of contemporary data protection laws and international legal frameworks. Adopting a doctrinal and qualitative review methodology, the study analyses secondary sources, including legislation, judicial decisions, policy documents, and scholarly literature. The paper comparatively evaluates major legal frameworks, including the European Union's General Data Protection Regulation (GDPR), India's Digital Personal Data Protection Act, 2023, the United States' sector-specific privacy model, and the United Kingdom's data protection regime. The review highlights the strengths and limitations of these legal systems in addressing surveillance, government access to personal data, and individual privacy rights. It further identifies emerging challenges arising from artificial intelligence, biometric surveillance, cross-border data transfers, and digital governance. The study concludes that neither privacy nor national security should be treated as absolute rights; instead, both must be harmonized through transparent legislation, proportional restrictions, judicial oversight, and institutional accountability. A balanced, rights-based legal framework supported by international cooperation and adaptive regulatory mechanisms is essential for protecting individual liberties while enabling governments to respond effectively to evolving security threats in the digital era.
Yogarajsingh R. Bais· Zenodo (CERN European Organi...· 0 citations
India’s entertainment economy increasingly depends upon digital circulation, platform licensing and data-driven distribution. Music is consumed through streaming services, films move rapidly from theatres to online platforms, and over-the-top services commission, acquire and exploit content across multiple territories. These developments have expanded audiences and created new markets, but they have also exposed structural weaknesses in the protection and administration of intellectual property rights. This article examines emerging copyright and related-rights issues affecting music, cinema and OTT platforms in India. It adopts a doctrinal and analytical method, relying on the Copyright Act, 1957, the Copyright (Amendment) Act, 2012, the Information Technology Act, 2000, the Cinematograph Act, 1952 as amended in 2023, relevant rules, international instruments and judicial decisions. The article argues that the principal difficulty is not the absence of rights, but the fragmentation of ownership, licensing and enforcement. A single song or film may contain separate rights in lyrics, composition, sound recording, screenplay, performance, artwork and the audiovisual work itself. Digital exploitation further divides these rights by territory, language, duration, device, subscription model and mode of communication. Disputes therefore arise over royalty sharing, statutory licensing, online streaming, platform liability, piracy, synthetic performances, voice cloning and the commercial use of celebrity identity. Indian courts have responded through intermediary-liability principles, dynamic injunctions and personality-rights remedies, yet the legal position remains uneven in relation to generative artificial intelligence, transparent royalty accounting and cross-platform licensing. The article recommends clearer digital licensing standards, auditable royalty systems, stronger collective management, proportionate notice-and-action procedures, technologically informed anti-piracy remedies and a consent-based framework for artificial intelligence uses of voice, likeness and performance. A coherent approach must protect creators and performers without imposing indiscriminate liability on legitimate intermediaries. Such balance is necessary to sustain cultural production, promote lawful digital markets and preserve public access to diverse entertainment in India.
Madhuri V. Sarwade· Zenodo (CERN European Organi...· 0 citations
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India’s entertainment economy increasingly depends upon digital circulation, platform licensing and data-driven distribution. Music is consumed through streaming services, films move rapidly from theatres to online platforms, and over-the-top services commission, acquire and exploit content across multiple territories. These developments have expanded audiences and created new markets, but they have also exposed structural weaknesses in the protection and administration of intellectual property rights. This article examines emerging copyright and related-rights issues affecting music, cinema and OTT platforms in India. It adopts a doctrinal and analytical method, relying on the Copyright Act, 1957, the Copyright (Amendment) Act, 2012, the Information Technology Act, 2000, the Cinematograph Act, 1952 as amended in 2023, relevant rules, international instruments and judicial decisions. The article argues that the principal difficulty is not the absence of rights, but the fragmentation of ownership, licensing and enforcement. A single song or film may contain separate rights in lyrics, composition, sound recording, screenplay, performance, artwork and the audiovisual work itself. Digital exploitation further divides these rights by territory, language, duration, device, subscription model and mode of communication. Disputes therefore arise over royalty sharing, statutory licensing, online streaming, platform liability, piracy, synthetic performances, voice cloning and the commercial use of celebrity identity. Indian courts have responded through intermediary-liability principles, dynamic injunctions and personality-rights remedies, yet the legal position remains uneven in relation to generative artificial intelligence, transparent royalty accounting and cross-platform licensing. The article recommends clearer digital licensing standards, auditable royalty systems, stronger collective management, proportionate notice-and-action procedures, technologically informed anti-piracy remedies and a consent-based framework for artificial intelligence uses of voice, likeness and performance. A coherent approach must protect creators and performers without imposing indiscriminate liability on legitimate intermediaries. Such balance is necessary to sustain cultural production, promote lawful digital markets and preserve public access to diverse entertainment in India.
Madhuri V. Sarwade· Zenodo (CERN European Organi...· 0 citations
India’s entertainment economy increasingly depends upon digital circulation, platform licensing and data-driven distribution. Music is consumed through streaming services, films move rapidly from theatres to online platforms, and over-the-top services commission, acquire and exploit content across multiple territories. These developments have expanded audiences and created new markets, but they have also exposed structural weaknesses in the protection and administration of intellectual property rights. This article examines emerging copyright and related-rights issues affecting music, cinema and OTT platforms in India. It adopts a doctrinal and analytical method, relying on the Copyright Act, 1957, the Copyright (Amendment) Act, 2012, the Information Technology Act, 2000, the Cinematograph Act, 1952 as amended in 2023, relevant rules, international instruments and judicial decisions. The article argues that the principal difficulty is not the absence of rights, but the fragmentation of ownership, licensing and enforcement. A single song or film may contain separate rights in lyrics, composition, sound recording, screenplay, performance, artwork and the audiovisual work itself. Digital exploitation further divides these rights by territory, language, duration, device, subscription model and mode of communication. Disputes therefore arise over royalty sharing, statutory licensing, online streaming, platform liability, piracy, synthetic performances, voice cloning and the commercial use of celebrity identity. Indian courts have responded through intermediary-liability principles, dynamic injunctions and personality-rights remedies, yet the legal position remains uneven in relation to generative artificial intelligence, transparent royalty accounting and cross-platform licensing. The article recommends clearer digital licensing standards, auditable royalty systems, stronger collective management, proportionate notice-and-action procedures, technologically informed anti-piracy remedies and a consent-based framework for artificial intelligence uses of voice, likeness and performance. A coherent approach must protect creators and performers without imposing indiscriminate liability on legitimate intermediaries. Such balance is necessary to sustain cultural production, promote lawful digital markets and preserve public access to diverse entertainment in India.
Madhuri V. Sarwade· Zenodo (CERN European Organi...· 0 citations
The marine sector is vital to international trade, and the dependability of cargo ships is necessary to maintain continuous logistics operations. Conventional maintenance methods frequently lead to unforeseen equipment malfunctions, higher operating expenses, and cargo delivery delays. As a result, predictive maintenance based on artificial intelligence (AI) has become a cutting-edge technology that permits early fault identification, real-time monitoring, and optimal maintenance scheduling. The current study, "Predictive Maintenance of Cargo Vessels Using AI: Implications for Logistics Reliability," looks at how AI-based predictive maintenance affects logistics reliability while assessing the impact of operational performance and repair difficulties. The study used a quantitative research approach, and a structured questionnaire with a five-point Likert scale was used to gather primary data from 100 respondents. Respondents with experience in predictive maintenance and cargo vessel operations were chosen using a convenience sample technique. Descriptive statistics, skewness and kurtosis analysis, Cronbach's alpha reliability analysis, correlation analysis, regression analysis, mediation analysis, and structural equation modelling (SEM) were all used in the analysis of the gathered data using R programming. The results showed that all variables had adequate normality, the measuring tool had good to exceptional reliability, and artificial intelligence had a strong positive correlation with logistical reliability. By enhancing operational performance and resolving maintenance issues, AI-based predictive maintenance has a favourable direct and indirect impact on logistics dependability, according to regression and mediation analyses. In order to increase maintenance efficiency, decrease unplanned vessel downtime, and boost logistical reliability, the report recommends that shipping companies invest in AI-driven predictive maintenance solutions, IoT-enabled sensors, worker training, and strong digital infrastructure. The study concludes that AI-enabled predictive maintenance is a successful tactic for boosting operational effectiveness, guaranteeing on-time cargo delivery, cutting maintenance costs, and enhancing the competitiveness and sustainability of the maritime logistics sector.
M. A. Shakila Banu, Pazila Sara. R. S, A.S. Minhaj Begum· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel AI–Digital Twin framework for smart academic library management by integrating Artificial Intelligence (AI), Digital Twin technology, the Internet of Things (IoT), cloud computing, and RFID systems into a unified intelligent ecosystem. The proposed framework enables real-time monitoring, predictive analytics, resource optimization, personalized user services and evidence-based decision-making while improving collection management, space utilization, security and operational efficiency. It also addresses key implementation challenges, including data privacy, cybersecurity, interoperability, infrastructure and workforce readiness. By presenting a scalable and sustainable model, the study contributes to the advancement of Library and Information Science and provides a strategic roadmap for developing intelligent, user-centric and future-ready academic libraries in the era of digital transformation
Sachin Dattatraya Patil· Zenodo (CERN European Organi...· 0 citations
Abstract The marine sector is vital to international trade, and the dependability of cargo ships is necessary to maintain continuous logistics operations. Conventional maintenance methods frequently lead to unforeseen equipment malfunctions, higher operating expenses, and cargo delivery delays. As a result, predictive maintenance based on artificial intelligence (AI) has become a cutting-edge technology that permits early fault identification, real-time monitoring, and optimal maintenance scheduling. The current study, "Predictive Maintenance of Cargo Vessels Using AI: Implications for Logistics Reliability," looks at how AI-based predictive maintenance affects logistics reliability while assessing the impact of operational performance and repair difficulties. The study used a quantitative research approach, and a structured questionnaire with a five-point Likert scale was used to gather primary data from 100 respondents. Respondents with experience in predictive maintenance and cargo vessel operations were chosen using a convenience sample technique. Descriptive statistics, skewness and kurtosis analysis, Cronbach's alpha reliability analysis, correlation analysis, regression analysis, mediation analysis, and structural equation modelling (SEM) were all used in the analysis of the gathered data using R programming. The results showed that all variables had adequate normality, the measuring tool had good to exceptional reliability, and artificial intelligence had a strong positive correlation with logistical reliability. By enhancing operational performance and resolving maintenance issues, AI-based predictive maintenance has a favourable direct and indirect impact on logistics dependability, according to regression and mediation analyses. In order to increase maintenance efficiency, decrease unplanned vessel downtime, and boost logistical reliability, the report recommends that shipping companies invest in AI-driven predictive maintenance solutions, IoT-enabled sensors, worker training, and strong digital infrastructure. The study concludes that AI-enabled predictive maintenance is a successful tactic for boosting operational effectiveness, guaranteeing on-time cargo delivery, cutting maintenance costs, and enhancing the competitiveness and sustainability of the maritime logistics sector.
M. A. Shakila Banu, Pazila Sara. R. S, A.S. Minhaj Begum· Zenodo (CERN European Organi...· 0 citations
Abstract This paper proposes a novel AI–Digital Twin framework for smart academic library management by integrating Artificial Intelligence (AI), Digital Twin technology, the Internet of Things (IoT), cloud computing, and RFID systems into a unified intelligent ecosystem. The proposed framework enables real-time monitoring, predictive analytics, resource optimization, personalized user services and evidence-based decision-making while improving collection management, space utilization, security and operational efficiency. It also addresses key implementation challenges, including data privacy, cybersecurity, interoperability, infrastructure and workforce readiness. By presenting a scalable and sustainable model, the study contributes to the advancement of Library and Information Science and provides a strategic roadmap for developing intelligent, user-centric and future-ready academic libraries in the era of digital transformation.
Sachin Dattatraya Patil· Zenodo (CERN European Organi...· 0 citations
Wireless networks are undergoing a paradigm shift with the advent of 6G, in which artificial intelligence (AI) is becoming a necessity at the physical layer for tasks like spectrum sensing, interference mitigation, and adaptive resource allocation. The Open Radio Access Network (O-RAN) architecture enables this transition by disaggregating network functions and pushing intelligence to the edge. However, deploying deep learning (DL) models on resource-constrained O-RAN Radio Units introduces a critical challenge: balancing the computational demands of AI with the stringent latency, fronthaul, and hardware constraints of real-time wireless systems. Current cloud-optimised AI architectures, designed for centralised data centres with abundant compute, fail to meet these edge requirements. Furthermore, the dynamic nature of wireless channels and the need for distributed coordination across multiple nodes complicate practical deployment. While theoretical advances in edge AI exist, a gap remains between isolated machine learning models and their system-level integration in real O-RAN environments. This dissertation bridges the gap by introducing System-Aware Edge Intelligence, a design paradigm that jointly optimises deep learning models alongside the physical and architectural constraints of O-RAN. Validated through synthetic datasets, software defined radio experimentation, over-the-air measurements, and hardware profiling on commercial off-the-shelf (COTS) platforms, this work provides empirical evidence for the advantages of a system-aware approach aligned with emerging industry standards. To systematically address these challenges, the thesis adopts a bottom-up approach, progressing from individual edge node optimisation to network-wide distributed coordination. First, focusing on the individual edge nodes, the dissertation investigates how physical-layer DL models can dynamically adapt their computational complexity to varying signal conditions. To achieve this, it introduces Width-Wise Early Exiting (WWEE), an adaptive inference framework that scales its active parameter count based on instantaneous signal complexity. By integrating selective classification, WWEE can reliably identify and reject uncertain, low-SNR signals. This capability to abstain from processing unreliable data reduces the average computational load while preserving overall classification quality, demonstrating how algorithmic adaptivity can successfully align with strict node-level hardware limitations. Second, at the system level, the thesis explores how unique O-RAN functional splits can be exploited to enable efficient inference. It introduces OSIRIS, a representation-aware split inference architecture aligned with O-RAN Split 7-2x. OSIRIS sequentially evaluates multi-domain representations (time, frequency, and Channel State Information) and dynamically routes computation based on intermediate confidence. This co-design of inference pipelines with system-level functional splits enables sub-millisecond physical-layer inference on standard COTS hardware. Third, the work leverages collaboration between different edge devices by distributing physical-layer deep learning across cell-free O-RAN topologies. By evaluating centralised, hybrid, and fully distributed inference paradigms, the research demonstrates that local feature extraction and soft decision fusion can maintain competitive accuracy relative to centralised baselines. Crucially, this collaborative approach mitigates limitations of single-node sensing (e.g., coverage gaps, interference blind spots) and enables the dynamic reallocation of compute resources across the network, all without overwhelming fronthaul capacity. Finally, to make this distributed collaboration practically viable, the dissertation addresses the critical need for low-overhead physical-layer coordination. It introduces localised machine learning frameworks to maintain temporal synchronisation and verify spatial event matching, ensuring that geographically separated nodes observe the exact same physical transmission. By shifting the coordination burden from continuous network signalling to predictive local computation, these solutions reduce over-the-air synchronisation overhead and enable fronthaul compression, thereby facilitating efficient ad hoc coordination. Overall, this dissertation explores structural methodologies for integrating AI into 6G networks more efficiently, suggesting a transition from treating machine learning as an isolated external tool to co-designing it with the wireless system itself. By addressing adaptive computation, representation-aware inference, distributed collaboration, and practical coordination in a unified framework, this work establishes a practical foundation for scalable, AI-native wireless systems tailored for the extreme edge.
A semantic attention-driven retrieval framework based on a lightweight Meta-Domain Adaptive Segmentation Network (MDA-SN) with an adaptive data normalization strategy to enhance infection detection in cross-dataset analysis and achieves real-time execution.
Muhammad Owais, Taimur Hassan, Naqash Afzal et al.· Scientific Reports· 1 citation
