Authors: Narendra Reddy Burramukku

Abstract: Cloud-native networking has transformed modern enterprise and service provider infrastructures by enabling highly dynamic, scalable, and distributed environments based on microservices, containers, and multi-cloud deployments. While these architectures improve agility and resource efficiency, they also introduce significant challenges in maintaining visibility, performance assurance, and security. Traditional network monitoring approaches are inadequate for handling ephemeral workloads, high-velocity telemetry, and complex inter-service communications. This paper presents a comprehensive review of cloud-native network monitoring, focusing on monitoring tools, architectural frameworks, and operational best practices suitable for modern cloud-native ecosystems. It systematically analyzes open-source and commercial monitoring solutions, including Prometheus, Grafana, OpenTelemetry, ELK Stack, and cloud-provider-native platforms, highlighting their roles in metrics collection, logging, and distributed tracing. The study further examines key architectural models such as centralized, distributed, and hybrid monitoring frameworks, as well as agent-based and agentless approaches, emphasizing scalability, fault tolerance, and integration with orchestration platforms like Kubernetes. Best practices for observability design, metric selection, alerting, and automated incident management are discussed in the context of DevOps and Site Reliability Engineering (SRE). Additionally, the paper identifies critical challenges related to scalability, hybrid and multi-cloud observability, security, and privacy, while outlining emerging research directions including AI/ML-driven monitoring, autonomous remediation, and edge observability. By consolidating tools, architectures, and operational strategies, this paper provides a structured reference for researchers and practitioners seeking to design, deploy, and optimize effective cloud-native network monitoring systems.

 

 

Published by: vikaspatanker

Authors: Dr. Suman Dhawan

Abstract: Professional development programs for higher education faculty are increasingly being recognised as vital to enhancing teaching effectiveness, research productivity, and institutional quality. However, faculty at different career stages have varying professional development needs. The current study examines the experience-based differences in perceived professional development priorities among faculty members in higher education. Survey responses from 302 faculty members across universities and colleges were used, and a one-way ANOVA was conducted on four experience groups (0–5 years, 6–10 years, 11–15 years, and 16 years and above) to examine the differences in professional development needs. Statistically significant results are obtained across several dimensions, such as knowledge about education-related subjects, educational support activities, acquisition of knowledge for professional development, competency building, and orientation towards national development. Findings have implications for framing differentiated faculty development programs according to the career stage.

DOI: http://doi.org/10.5281/zenodo.18464963

Published by: vikaspatanker

Authors: Naveen Reddy Burramukku

Abstract: Modern enterprise networks are increasingly dynamic, driven by virtualization, cloud adoption, and the proliferation of distributed workloads. Traditional network segmentation approaches, which rely primarily on IP addresses, VLANs, and perimeter-based firewalls, are no longer sufficient to protect against sophisticated cyber threats, particularly those involving lateral movement within the data center. As attackers increasingly exploit compromised credentials and trusted internal access, there is a growing need for security models that are both granular and identity-centric.This research explores the concept of identity-aware network segmentation by integrating VMware NSX microsegmentation with Next-Generation Firewalls (NGFWs) to enforce security policies based on user, application, and workload identities rather than static network parameters. The proposed approach aligns with Zero Trust principles by assuming no implicit trust within the network and enforcing continuous verification of identity and context for every communication flow.The study presents an architectural framework that combines NSX’s distributed firewall capabilities with advanced NGFW features such as deep packet inspection, application identification, and user-based policy enforcement. A controlled virtual testbed is used to evaluate the effectiveness of the proposed model in mitigating east-west traffic threats, reducing attack surfaces, and limiting lateral movement within a virtualized data center environment. Performance impacts, scalability considerations, and operational complexity are also assessed to determine the feasibility of large-scale deployment.Results indicate that identity-aware segmentation significantly enhances internal network security by enabling fine-grained, context-aware policy enforcement without introducing substantial performance degradation. The integration of NSX and NGFW technologies provides improved visibility, simplified policy management, and stronger alignment with modern Zero Trust architectures. This research contributes to the growing body of work on software-defined security by demonstrating how identity-driven controls can be practically implemented to strengthen enterprise network defenses in hybrid and cloud-based environments.

Published by: vikaspatanker

Authors: Rodolfo L. Rabia, Ashley Nicole L. Tizon, Arvey Faith B. Paquibot, Ritchen G. Ibañez, Samuel P. Tabuena, Regine R. Ruallo

Abstract: The objectives of this project were to design and develop SunVolt, a solar-powered battery charging system using an Arduino Uno R3, to address energy shortages and high electricity costs in rural off-grid areas. SunVolt enables efficient solar-powered battery charging to support household and agricultural activities in locations with limited or unstable electricity access. The system integrated a solar panel, lead-acid battery, MPPT charge controller, and sensors that monitor light, temperature, and voltage, managed by the microcontroller. SunVolt independently finds an optimal energy conversion rate, prevents overcharging, and displays visual notifications with performance in real time to the user. Performance testing evaluated daily energy output, charging efficiency, and long-term reliability under real-world conditions. The results demonstrate that SunVolt effectively stores solar energy, meets residential and agricultural energy needs, and remains durable under varying environmental conditions. Overall, SunVolt offers a practical solution for improving energy self-sufficiency, reducing reliance on fossil fuels, and promoting sustainable development in undeserved communities.

DOI: https://doi.org/10.5281/zenodo.18454945

Published by: vikaspatanker

Authors: Nwagwu, Glory C, Obasi, Chinedu K, Nduka, Modestus U

Abstract: The HIV virus is a cankerworm that is bedeviling human-kind, with sequel advancement to Acquired Immuno-deficiency Syndrome (AIDS), if not properly managed can have effect on the socio-demographic factors. The study aimed at determining the impact of socio-demographic factors that affects HIV status of pregnant women in Imo state using a Proportional Odds Model. It was discovered that single women within the Age (15-19) years and resident in the rural area were the factors that contributed to the reason why these pregnant women are prone to contacting HIV/AIDS infection in Imo State.

DOI: https://doi.org/10.5281/zenodo.18454133

Published by: vikaspatanker

Authors: Naveen Reddy Burramukku

Abstract: Identity and Access Management has become a foundational pillar of modern information security, governing how users, devices, and applications authenticate and gain access to organizational resources. As enterprises increasingly operate across hybrid environments that combine on-prem infrastructure with cloud platforms, the complexity of managing identities and enforcing access controls has grown substantially. Traditional identity models designed for centralized, perimeter-based systems are often inadequate in distributed environments where users access resources from diverse locations and devices. In this context, IAM serves as a critical mechanism for enforcing security policies, maintaining accountability, and reducing the risk of unauthorized access. Cloud computing has introduced new identity paradigms that emphasize federated authentication, dynamic authorization, and service-based identities. These paradigms differ significantly from on-prem identity systems, which typically rely on directory services, static roles, and network-based trust assumptions. Integrating these two models presents both opportunities and challenges, requiring careful alignment of identity lifecycles, access policies, and governance frameworks. Misconfigurations or inconsistencies across environments can lead to privilege escalation, data exposure, and compliance failures. This review examines Identity and Access Management in cloud and on-prem infrastructure environments, focusing on architectural models, authentication mechanisms, authorization strategies, and operational considerations. It explores how IAM technologies have evolved to support hybrid deployments and analyzes common risks associated with identity sprawl, excessive privileges, and fragmented policy enforcement. The article also highlights the role of IAM in enabling modern security approaches such as Zero Trust and least privilege access. By synthesizing established research and industry practices, this review provides a comprehensive understanding of IAM’s role in securing hybrid infrastructures. The discussion aims to assist practitioners, researchers, and decision-makers in designing IAM strategies that balance security, usability, and scalability across diverse deployment models.

 

 

Published by: vikaspatanker

Authors: Hansikaa M

Abstract: The rapid growth of digital technologies and interconnected systems has greatly increased the complexity and scale of cyber threats. Traditional cybersecurity methods, which depend on predefined rules and signature-based detection, often have difficulty identifying advanced, dynamic, and new attacks. In this situation, artificial intelligence has emerged as a powerful tool for improving cybersecurity by enabling smart, flexible, and automated defence systems. This research offers a thorough look at how artificial intelligence is used in cybersecurity, focusing on machine learning, deep learning, and anomaly detection techniques for identifying and responding to threats. The study reviews current security methods, examines their weaknesses, and discusses how AI-driven approaches enhance detection accuracy, lower false positives, and support proactive security management. An AI-based cybersecurity framework is also presented to show how smart models can work with security monitoring, data processing, and automated response features. The effectiveness of AI-based cybersecurity solutions is assessed through performance analysis and discussions of experimental results, highlighting improvements in real-time threat detection and system efficiency. Additionally, the research explores important application areas, challenges, and future directions, including explainable artificial intelligence, privacy-preserving learning, and autonomous security operations. Overall, this study emphasizes the important role of artificial intelligence in strengthening modern cybersecurity systems and underscores its potential to tackle evolving cyber threats through ongoing learning and smart decision-making.

Published by: vikaspatanker

Authors: Ms. Nirzhara Suryawanshi, Ms. Manasi Patange, Ms. Pallavi Thakare, Ms. Harshada Sonar, Mrs Samiksha Gawali

Abstract: The AI-Powered Interview System Based on Resume Analysis is an intelligent and interactive platform designed to enhance students’ interview preparation through automation and personalized evaluation. The system enables users to upload their resumes, which are then processed using Natural Language Processing (NLP) techniques to extract key skills, academic achievements, and relevant experience. Based on this analysis, the system dynamically generates field-specific interview questions tailored to the candidate’s profile. To simulate a realistic interview environment, the platform incorporates voice-based interaction using speech-to-text and text-to-speech technologies. Users respond to questions through audio, and the system evaluates their answers in real time using NLP- based answer analysis. The system further provides performance ratings, personalized feedback, and improvement suggestions, helping candidates identify their strengths and areas of development.

DOI: https://doi.org/10.5281/zenodo.18448371

Published by: vikaspatanker

Authors: P. Vijetha, Sk Maqbool basha

Abstract: The risk–return relationship is a fundamental concept in finance, guiding investment decisions and portfolio management. This study empirically examines the relationship between risk and return among 10 actively traded equity stocks over a five-year period (2019–2024). Both systematic risk (beta) and total risk measures (standard deviation and variance) are analyzed to determine their influence on equity returns. Secondary data from NSE, BSE, and financial databases were used, and statistical techniques including descriptive statistics, correlation analysis, regression analysis, and t-tests were employed. The findings reveal a positive and statistically significant relationship between risk and return, with beta emerging as the strongest predictor. Regression results indicate that risk measures collectively explain over 50% of the variance in returns. The study validates the traditional risk–return tradeoff and highlights the importance of incorporating multiple risk metrics for informed investment decisions. Implications for investors, portfolio managers, and policymakers are discussed, emphasizing strategies for optimizing returns while managing risk in dynamic equity markets.

Published by: vikaspatanker

Authors: Vishal Singh, Hemant Singh, Ajay Rawat, Shivam Kumar Jha

Abstract: Nutrition-analysis applications traditionally provide qualitative, binary guidance such as “healthy,” “unhealthy,” or “avoid.” However, recent advances in generative artificial intelligence (AI) enable hyper-personalized, quantitative moderation advice that recommends specific serving sizes, risk thresholds, and actionable alternatives. This paper investigates whether quantitative, personalized recommendations enhance user comprehension, confidence, and dietary intent compared to generic, qualitative warnings. We conduct a randomized controlled A/B user study with 100 participants and compare a qualitative control interface against a quantitative, generative-AI- powered interface offering explicit serving guidance and alternatives. Results show that quantitative moderation advice significantly improves comprehension accuracy, user confidence, trust, and positive dietary intent. These findings provide strong HCI evidence supporting the integration of precise, personalized guidance in digital nutrition applications.

Published by: vikaspatanker