Authors - T. Sruthi, Martha Sheshikala Abstract - Cloud computing has brought significant benefits to healthcare systems, especially when it comes to improving data access and storage efficiency. However, it also rais-es major concerns around privacy and security, particularly for Electronic Health Records (EHRs). In this paper, we propose a new framework designed to protect EHRs in cloud environments. The framework uses a combination of symmetric and asymmetric encryption techniques to ensure that data stays private during both storage and transmission. It also includes advanced access control measures that regulate who can access the data based on established policies. Our tests show that the framework successfully reduces the risk of unauthorized access while keeping system performance intact. These results highlight the importance of using strong privacy protections to safeguard sensitive health data in cloud-based healthcare applications. This research builds on Dutta et al.'s (2023) work, which explored hybrid encryption techniques to improve the security of health data in the cloud.
Authors - Sanasam Bimol, Mutum Indrakumar Meetei Abstract - In higher education, knowledge management (KM) is important for encouraging innovation, helping students learn better, and making university management more effective. However, traditional ways of handling knowledge face problems like too much information, poor systems for finding content, and not enough personalized learning experiences. Using artificial intelligence (AI) can help by automating data handling, making information easier to retrieve and more accurate and offering customized learning paths. This paper looks at how AI can be used in KM within higher education, focusing on its ability to improve the creation, sharing, organization, and decision-making processes of knowledge. It also examines issues like the AI infrastructure needed for KM, compares different approaches, discusses strategies for development, and addresses the challenges and ethical questions that come with using AI in educational settings.
Authors - Praveen Savarapu, Shankar Lingam. M Abstract - Insider threats pose significant risks to organizational cybersecurity, often arising from complex human behaviors that traditional detection systems struggle to identify (Smith & Johnson, 2023). This study proposes a novel, cross-disciplinary approach in-tegrating artificial intelligence (AI) and behavioral analytics to enhance insider threat detection. By combining machine learning techniques with cognitive science principles, the framework captures nuanced behavioral patterns and psychological indicators that precede malicious insider activities (Lee et al., 2022). This work contributes to advancing proactive risk mitigation strategies by bridging technical cybersecurity defenses with human behavioral insights, for both researchers and practitioners.
Authors - Mark Anthony A. Gavino, Rafaela Medilane A. Gonzales, Queenie Marie E. Manilag, Angeline G. Reyes, Rey Mark C. Sadoy, Lester P. Acoba Abstract - With the rapid advancement of technology, traditional manual accounting methods are increasingly being replaced by Electronic Accounting Systems (EAS) to improve organizational efficiency and decision-making. This study aimed to examine the perceived effectiveness of EAS and its impact on the productivity of accounting personnel in Makati City. Utilizing a quantitative-descriptive research design, data were collected from 152 finance officers and accountants with at least six months of experience using EAS through a structured Likert-scale questionnaire. The study assessed EAS effectiveness across five dimensions—speed, accuracy, security, reliability of information, and decision-making—and evaluated productivity based on motivation, satisfaction, performance, absenteeism, and turnover. Using partial least squares-structural equation modeling (PLS-SEM), results revealed a strong and statistically significant relationship between perceived effectiveness and productivity. Reliability and validity analyses confirmed the robustness of the measurement model. The findings indicate that effective EAS use significantly enhances both system performance and employee productivity. The study concludes that investing in well-implemented EAS can lead to improved efficiency and workforce outcomes. Practical recommendations include providing continuous user training, choosing secure and adaptable systems, integrating EAS into business strategies, and regularly evaluating system impact.
Authors - Payel Das, Uditaa K, Hariprasad Abstract - Artificial Intelligence (AI) is redefining healthcare through enhanced diagnostics, personalized interventions, and preventive monitoring. Privacy-preserving AI-enabled health applications—leveraging technologies such as federated learning and differential privacy—hold the potential to protect sensitive health data while delivering actionable insights. Yet, adoption among Generation Z (Gen Z) remains inconsistent, hindered by complex and interlinked barriers. This study identifies and models ten critical barriers—Awareness Deficit, Data Misuse Anxiety, AI Credibility Doubt, Surveillance Concern, UX–UI Friction, Digital Health Knowledge Gap, Regulatory Ambiguity, Human Displacement Fear, Perceived Health Irrelevance, and Eco–Ethical Concerns—through a survey of 142 Gen Z respondents in India. Using Interpretive Structural Modeling (ISM) and MICMAC analysis, the research reveals a hierarchical structure were foundational drivers cascade into immediate deterrents, demonstrating that isolated interventions may be insufficient. By integrating Protection Motivation Theory (PMT) and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the study offers a dual lens capturing both resistance (security–privacy dimension) and adoption (utility–experience dimension) dynamics. Findings provide actionable guidance for developers, policy-makers, educators, and healthcare providers to address root causes, bridge knowledge gaps, enhance trust, and align solutions with Gen Z’s values, fostering informed and sustained adoption.
Authors - Vijay Makwana Abstract - The rapid integration of artificial intelligence (AI) into business necessitates a curricular shift in management education toward practical AI literacy. This study addresses the gap between the availability of AI tools and students' ability to leverage them effectively. It evaluates a structured prompt engineering intervention designed to improve undergraduate business students' AI self-efficacy, foundational AI knowledge, and practical prompting skills. Using a mixed-methods, pre-test/post-test design, the study included 78 undergraduate business administration students at a university in Gujarat, India. Participants engaged in a workshop that introduced advanced prompting strategies for business applications. Data were collected via a validated AI Self-Efficacy Scale, an AI Knowledge Assessment, and a Prompt Engineering Skill Rubric. The findings show statistically significant improvements in AI knowledge and self-efficacy, corroborated by qualitative evidence of enhanced prompt sophistication. The results underscore the intervention's efficacy in cultivating essential AI competencies. This research offers a replicable pedagogical framework for integrating prompt engineering into business curricula, providing key insights for educators seeking to prepare future professionals for an AI-driven land-scape.
Authors - Chau Huy Thuan, Nguyen Dac Thanh Phuoc, Bui Chau Sao, Le Phu Thanh, Tong Van Dinh, Hung Nguyen-Huu, Vinh Dinh Nguyen Abstract - The widespread use of facial masks poses a major challenge for face recognition systems, often leading to drastic performance degradation. This paper presents a systematic, multi-stage framework to build a highly robust face recognition model against such occlusions. We perform extensive ablation experiments by gradually introducing three main enhancements over a strong Triplet Loss baseline: (1) Mask-Aware Sampling to explicitly learn cross-mask invariances; (2) a Spatial Attention module to adaptively focus on un-occluded facial regions; and (3) the state-of-the-art ArcFace loss to maximize the embedding's discrimination power. Extensive experiments demonstrate that our final model not only achieves an outstanding F1-score exceeding 97% across all verification scenarios (unmasked-unmasked, masked-masked, and cross-mask) but, more critically, exhibits exceptional robustness, reducing the performance variance between the easiest and hardest scenarios from 5.9% in the baseline to a mere 2.0%. Furthermore, we evaluate our models also on the Labeled Faces in the Wild (LFW) which is a standard face benchmark and our final model achieved an efficient improvement of +8.9% accuracy improvement over the baseline in the general case of LFW, demonstrating that our model acts effectively in general when it may not be necessarily occlusive situation as well.
Authors - Sarvani Kocherlakota, Lavanya Goura, Poojitha Panchakarla, Thupakula Kushwanth, Jangala Venkata Shanmukha Abstract - The impact of artificial intelligence in digital marketing has emerged as a prominent research topic in recent decades, with numerous findings published in leading international journals. To investigate its current research landscape and emerging trends, we conduct a bibliometric analysis by using R software 4.5.0, and VOSviewer to identify the research trend, emerging topics, collaborations among countries, authors, documents and the contributing academic journals during the study period. Lecture Notes in Networks and Systems journal is having the highest publications and Journal of Operations Management is having the most cited document. Sharma A is the most contributing author to the literature. India is the most contributing country with around 12.5% of total publications. By research gap analysis, the future direction in the domain of artificial intelligence in digital marketing can be green marketing, ethical AI, personalization, automation and prediction.
Authors - Nandgopan R, Pavan Shankar R, Santanu Mandal Abstract - This research investigates the interconnected factors of financial literacy and digitised investment behaviour of Generation Z inhabiting the financial ecosystem in India that is increasingly becoming digitised. The study examines nine significant drivers through the lens of a cross-sectional survey involving 115 respondents aged 18–27 years, including financial knowledge, trust in digital finance platforms, digital literacy, social media and peer effect, perceived financial risk, consumer experience and design of the application, financial socialization by parents, awareness of regulations, and the perception of uncertainty in the economy. ISM is used for the identification of hierarchal relationships between all the variables, whereas MICMAC analysis is used for classifying the variables on the basis of driving power and dependence power. Our results indicate that regulatory awareness, perception of economic uncertainty, and financial socialization by parents are the exogenous high-driving determinants of the intermediate enablers of financial literacy and digital literacy, and the intermediate determinants of the dependent factors like trust, perceived risk, and user experience. This hierarchy demonstrates that policy and practice must build on higher-ordered enablers before approaching determinants of behaviours at lower order levels. The study contributes theoretically by integrating the Theory of Planned Behaviour and Technology Acceptance Model, and offers practical recommendations for policymakers, educators, and fintech designers to foster sustainable financial literacy and digital investment participation among Gen Z.
Authors - Liladhar P. Dhake, Jayashree Katti, Sapana Kolambe Abstract - Website defacement attacks have been a significant threat to both private and public organizations’ websites and web portals. Such attacks can have severe repercussions for website owners, disrupting website operations and tarnishing their reputation, potentially resulting in substantial financial losses. With our approach, we examined SVM, which is a type of machine learning for detecting website defacement. Our approach applied machine learning methods to develop classifiers that distinguish web pages into normal and attacked classes. Moreover, we collected a large number of features from the website's content and metadata to train and test the algorithms. This method is applicable to both static and dynamic websites; through training, it can learn to adjust to a wide range of page types. The use of an algorithm from machine learning to obtain results has shown that our approach achieves very high detection accuracy with a very low rate of false positives. Additionally, it should be noted that our approach does not require it to depend on massive computational capabilities.
