Volume 13 | Issue 10 | October-2025

 Abstract

Healthcare databases are increasingly becoming prime targets for cyberattacks and ransomware, leading to severe threats to patient privacy and hospital functionality. Traditional Intrusion Detection Systems (IDS) often fall short in detecting sophisticated attacks due to high false-positive rates, limited adaptability, and inability to detect novel threats. This research paper explores the growing necessity of integrating Artificial Intelligence (AI) into IDS to enhance cybersecurity in healthcare environments. By leveraging adaptive learning and intelligent decision-making, AI-powered IDS can efficiently identify anomalies, predict potential breaches, and minimize false alarms. This study highlights the limitations of traditional IDS, the potential of AI-driven solutions, and their role in safeguarding patient data. The ultimate goal is to emphasize that AI-powered IDS is not just an optional upgrade but an essential security measure in modern healthcare systems.

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 Keywords

Healthcare, Cybersecurity, Intrusion Detection System, Artificial Intelligence, Ransomware, Data Privacy

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

??????? ?????????, ?????? ?????? ?? ???????????? ????-????? ?? ??? ??? ?????-???? ?????? ?? ????? ?????? ???? ???? ??? ?????? ?? ???? ???? ?? ?????? ???? ?? ?????? ???? ?? ??? ???, ????? ???????????? ??????????? ??? ????, ???? ?? ?????? ?????? ?? ??? ??? ???????? ?????? ?????-???? ?????? ?? ???????????? ????????? ?? ???????? ?? ????? ???? ??? ?? ??? ?? ?????????? ???? ????????????? ?? ?????????? ;????????? ??????? ?? ????.?????????????? ??????? ;???????? ?? ?????? ??, ?? ?? ?????? ???? ?? ?? ?????-???? ??????? ???????? (???, ?????, ??????? ?????) ?? ???? ??? ???????????? ?????????? (?????????? ?? ??????? ?????) ?? ?????? ??? ?????? ?? ???? ???? ??? ???????? ?????? ?? ??????? ??????????? (2000-2024) ?? ???????? ?? ??? ???? ?? ?? ???? ??? ??????? ???? ?? ??????? ??????? ?? ???????? ???? 50-52 ???? ????????? ????? ???? ???, ?? ??????? ??? (40 ????) ?? ???? ???? ??? ???? ?????? ?? - ????, ???????, ???????? ???, ???? ?? ????? ??????? ?????? ?????? ??? ?????? ???????? ?? ????????? ?? ?? ??? ???? ??? ??????? ??????? ?????? ????????? ?? ???-??? ????? ????????????? ?? ?? ??? ??? ???? ???? ?????? ?? ???????? ???????? ???? ?? ?? ?????????? ????? (???? - ????? ????? ???????, ????????? ?????? ???? ???????) ?? ????????? ???????? ?? ?? ???? ?? ?????? ????????? ?? ????? ???? ??? ?????????? ?????? ?????? ????

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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This study presents a cross-country analysis of higher education and its components leveraging insights from the Global Knowledge Index (GKI). The GKI provides a comprehensive framework for assessing knowledge related performance with a focus on seven composite sub-indices, including higher education as one of its sub-indices. The present study compares the performance of six South Asian countries, highlighting their strengths, weaknesses and the areas for improvement. The core focus of the study is to identify the areas where India lags behind other South Asian countries and investigate the root cause of these disparities. Our analysis reveals that India underperforms in the 'Employment' subcomponent which is a crucial aspect of higher education. Further examination suggests that India's socio-economic set up is the major contributor to this underperformance. Thus, the study provides valuable insights into the dynamics of Higher Education in South Asia, emphasizing the need to address the socio-economic challenges to improve the employability of educated youth.

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Keywords: Higher Education, Government Expenditure, Enrolment, Education Attainment Rate, Employment, Gender Equity

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Creative Commons Attribution 4.0 and The Open Definition

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Lateral movement continues to pose a significant threat within advanced persistent threat (APT) campaigns, allowing attackers to escalate privileges, navigate across systems, and stealthily access critical enterprise resources. Conventional privilege administration and identification strategies, which rely mostly on static guidelines, sign-based archives and hand-on controls, are unable to respond rapidly to such in motion intrusions. In this paper, we present the idea of an Autonomous Privilege Management (APM) system with the assistance of artificial intelligence that allows, in real-time, to predict and prevent the use of privileges to successfully prevent lateral movement. The architecture integrates endpoints, network and identity telemetry with machine-learning algorithms to detect anomalies in privilege usage and to calculate adaptive threat scores. By using a reinforced learning powered decision module, the APM automatically initiates privilege downgrades or revocations with little operational effect. APM was also tested in a hybrid enterprise setting involving both synthetic and real-world attack models and has significantly outperformed traditional Privileged Access Management (PAM) solutions due to a 43 % increase in the mean time to contain (MTTC) and a 31 % decrease in false positives. These results highlight the capability of the system to offer, in a context-aware fashion, rapid mitigation and update to new attack vectors without requiring manual system updates. In operationalizing the zero-trust principles by autonomous, intelligent control, the work presents an effective solution that is a scalable and proactive approach to the lateral movement defense.

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 Keywords

AI Security, Lateral Movement Detection, Privilege Restriction, Autonomous Access Control, Real-Time Threat Mitigation, Zero Trust.

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Creative Commons Attribution 4.0 and The Open Definition

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The proliferation of latency-sensitive, mission-critical applications has exposed the limitations of conventional BGP routing in meeting stringent service-level agreements (SLAs) within modern Software-Defined Wide Area Networks (SD-WANs). This review synthesizes a decade of research on artificial intelligence (AI) techniques applied to BGP route optimization in SLA-driven SD-WAN architectures. We examine supervised learning, deep reinforcement learning, multi-agent systems, and graph neural networks, alongside predictive analytics for proactive SLA maintenance. Experimental evidence from both simulation and real-world testbeds indicates that AI-augmented approaches improve SLA compliance by 10-22 percentage points over default BGP and 5-16 points over heuristic traffic engineering, while reducing route churn and operational costs. Key challenges include ensuring trustworthy AI, handling cross-domain optimization, safeguarding against adversarial threats, and standardizing interoperability. Future research directions emphasize explainable AI, federated optimization, carbon-aware routing, and integration with intent-based networking. This review provides both a comprehensive state-of-the-art summary and a forward-looking agenda for researchers and practitioners in AI-driven network control.

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Creative Commons Attribution 4.0 and The Open Definition

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AI-augmented BGP; SLA-driven SD-WAN; reinforcement learning; graph neural networks; intent-based networking; network optimization; route stability; explainable AI; multi-domain cooperation; sustainable networking.

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Creative Commons Attribution 4.0 and The Open Definition

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Accurate impressions are crucial in the success of fixed dental prostheses, where marginal adaptation and soft tissue management directly influence the longevity of restorations. The windows impression technique is a modified impression method designed to enhance subgingival detail reproduction, especially in cases where tissue management is complex. This study aims to evaluate the awareness, adoption, challenges, and clinical perceptions of dental professionals regarding the windows impression technique using a structured questionnaire. The findings are expected to provide insight into current practices and inform potential improvements in education and clinical training.

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Creative Commons Attribution 4.0 and The Open Definition

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Impression tech,fpd,prosthetic rehabilitation.

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Creative Commons Attribution 4.0 and The Open Definition

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Now a day, With the growth of digital tools and social media, traditional recruitment methods have undergone a complete transformation. In this study, the focus is on understanding how social media learning and its proper use influence modern hiring methods. It outlines the process in which the likes of LinkedIn, YouTube, Twitter, Facebook and Instagram have become important weapons in the arsenal of recruiters to attract, engage, and retain talent. This research adopts descriptive and qualitative methodologies and collects data entirely from secondary sources such as academic journals, HR reports, surveys, and other reliable online sources. The evidence reveals that acquiring knowledge of use social media improves a recruiter's efficiency and overall performance, builds employer branding, lowers recruitment costs, and can make talent acquisition more active. The issues that the authors of the paper consider include privacy concerns, ethical issues, and the necessity of continuous skill development because of the rapid change of platform. They give some suggestions to HR professionals on how they can implement social media training that is well-structured, keep up with the ethics, and use a hybrid method of recruitment that involves both digital and traditional methods. To sum up, the human resources skills related to social media have been so significantly enhanced that they can now be seen as a way of connecting employers and job-seekers and as a factor that makes the recruitment process more efficient, transparent, and dynamic, all of which are congruent with the digital era we live in.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Digital Recruitment, Employer Branding, Human Resource Management (HRM), LinkedIn, Modern Hiring, Qualitative Research, Recruitment Strategy, Secondary Data, Skill Development, Social Media, Social Media Learning, Talent Acquisition, Technology in HR, Training and Development.

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Creative Commons Attribution 4.0 and The Open Definition

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Murraya koenigii (curry leaf), a member of the Rutaceae family, is widely used in traditional medicine due to its rich phytochemical composition and therapeutic properties. This review summarizes existing research on the phytochemical constituents and antimicrobial potential of M. koenigii leaves. Various studies have identified alkaloids, flavonoids, terpenoids, and phenolic compounds in its extracts, which contribute to its antibacterial, antifungal, and antioxidant effects. The antimicrobial activity of M. koeni gii has been tested against pathogens such as Staphylococcus aureus, Escherichia coli, and Candida albi cans, demonstrating significant inhibitory effects. This paper consolidates findings from multiple studies to highlight the plant's medicinal value and potential applications in pharmaceutical and food preserva tion industries. A comparative antimicrobial activity of dried leaf extracts of M. koenigii were evaluated against two gram negative bacterial strains namely Escherichia coli and Pseudomonas aeroginosa and two clinical fungal pathogens namely Candida albicans and Aspergillus niger by agar cup method. The leaf extracts of M. koenigii was found to have high antibacterial activity than anti fungal activity. The results suggest that the leaves are a rich source of valuable primary and secondary metabolites exhibiting the antimicrobial activity.

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Creative Commons Attribution 4.0 and The Open Definition

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Murraya koenigii, Phytochemicals, Antimicrobial Activity, Curry Leaf, Rutaceae

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

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drug development, precision medicine, digital twin, computational modeling, and in silico pharmacology.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition