Authors: Ms.K.Madhunitha, Bharath K, Deva Senathipathi M, Mukilan R

Abstract: Address Resolution Protocol (ARP) spoofing is a critical security threat in wireless networks where an attacker sends forged ARP messages to link their device with the IP address of a legitimate user. This attack allows malicious users to intercept, modify, or block data traffic between communicating devices, leading to serious issues such as data theft, session hijacking, and denial-of-service attacks. Traditional detection mechanisms mainly rely on software-based identifiers such as IP addresses and MAC addresses. However, these identifiers can be easily manipulated by attackers, making conventional solutions less effective in detecting sophisticated attacks. To overcome this limitation, this study proposes a defense mechanism against ARP spoofing in Wi-Fi networks using Radio Frequency (RF) fingerprinting. RF fingerprinting identifies wireless devices based on unique hardware-level characteristics of their transmitted signals. Features such as frequency offset, phase noise, and signal transient patterns are analyzed to generate distinct RF signatures for each device. The proposed system continuously monitors wireless transmissions and compares them with stored RF fingerprints to identify anomalies and detect unauthorized devices. By leveraging physical layer characteristics, the approach provides a reliable and difficult-to-forge method of authentication. Experimental results indicate that RF fingerprinting significantly improves the accuracy of ARP spoofing detection and strengthens overall wireless network security without requiring major modifications to existing infrastructure.

DOI: https://doi.org/10.5281/zenodo.19221425

 

Published by: vikaspatanker

Authors: Ashish Binay Pandey, Dr Sangeeta Gupta

Abstract: The decision of Indian youth to choose a career in science is one of the spheres of academic study because of its effects on the national development, innovation, and staff support. This paper will discuss the variables that affect science, technology, engineering, and mathematics (STEM) as a career option among Indian students with both theoretical approaches to career choice, including Social Cognitive Career Theory (SCCT), and practical results in both international and local settings. The study being examined is a quantitative descriptive study based on the data obtained in a survey to investigate how personal, social, and institutional factors influence career choices. Results indicate that parental effect, self-efficacy, academic success, socioeconomic status, and exposure to STEM education have a substantial influence on career aspirations. Perceived utility of science careers and social persuasion are mentioned as the leading predictors. Stereotypes and cultural norms of gender difference also shape the mode of decision making, which in most cases restricts the involvement of females in STEM. The researchers declare that the policy interventions, career guidance, and enhanced educational infrastructure should be put in place to boost STEM among young people in India. The results are valuable to the large discussions on the development of career among youths and offer practical implications on educators and policymakers.

Published by: vikaspatanker

Authors: Shekar Vollem

Abstract: Modern digital applications demand the ability to process massive numbers of transactions while maintaining reliability, scalability, and responsiveness across geographically distributed infrastructures. Traditional monolithic architectures often struggle to support the throughput requirements of large-scale distributed systems due to tight coupling between components, limited horizontal scalability, and the difficulty of isolating failures within a single codebase. As workloads grow and user bases expand globally, these limitations become increasingly evident in areas such as transaction latency, system availability, and deployment agility. Distributed microservices architectures offer a viable alternative by decomposing applications into smaller, independently deployable services that communicate through lightweight APIs or event-driven messaging systems. This architectural paradigm enables organizations to scale services horizontally, optimize resource utilization, and process transactions concurrently across distributed environments. In such systems, each microservice typically manages its own data store and business logic, allowing for flexible scaling and improved resilience. This paper examines the architectural principles, distributed transaction models, and performance optimization strategies that enable high-throughput transaction processing in microservices environments. The study reviews existing research on distributed transaction processing systems, including distributed OLTP platforms and main-memory databases that reduce I/O bottlenecks and improve transaction latency. It also analyzes microservice orchestration patterns and coordination mechanisms that enable reliable transaction management across multiple services. Particular attention is given to techniques such as data partitioning, asynchronous messaging, event-driven communication, and Saga-based transaction coordination, which collectively help maintain data consistency without sacrificing system performance. Through the analysis of existing systems, architectural patterns, and prior research studies, the paper highlights approaches that significantly improve transaction throughput while preserving fault tolerance, service autonomy, and data consistency in complex distributed computing environments.

DOI: https://doi.org/10.5281/zenodo.19219630

Published by: vikaspatanker

Authors: Shekar Vollem

Abstract: Modern digital platforms require infrastructure that can scale dynamically, recover quickly from failures, and operate with minimal operational overhead while supporting rapidly changing workloads. Traditional infrastructure models often require significant manual configuration and capacity planning, which can limit scalability and increase operational complexity. Serverless computing has emerged as a promising cloud computing paradigm that abstracts infrastructure management from developers, allowing applications to run in environments where the cloud provider automatically handles resource provisioning, scaling, monitoring, and fault tolerance. In serverless architectures, developers deploy small, stateless functions or services that are executed in response to events such as API requests, database updates, or messaging events. This event-driven execution model enables systems to scale automatically according to workload demand, ensuring that resources are allocated efficiently without manual intervention. Cloud platforms such as AWS Lambda, Azure Functions, and Google Cloud Functions provide built-in mechanisms for automatic scaling, load balancing, and fault recovery, which contribute to high system availability. This article examines deployment strategies for building high-availability platforms using serverless architectures, focusing on how distributed cloud services can support reliable and scalable application infrastructures. The study analyzes architectural models that combine event-driven processing patterns, stateless computing components, and distributed service orchestration to achieve resilient system designs. It also explores how serverless frameworks integrate capabilities such as auto-scaling, multi-region redundancy, and managed infrastructure services to ensure continuous system availability even under fluctuating workloads or infrastructure failures.

DOI: https://doi.org/10.5281/zenodo.19219568

Published by: vikaspatanker

Authors: Chaitanya Khandbahale, Mohammad Junaid Shaikh, Arnav Raut, Darshan Sonar, Professor Kalyani Pawar

Abstract: Smart agriculture has emerged as a key solution to address critical challenges in traditional farming, including inefficient irrigation, excessive resource usage, delayed disease detection, and limited accessibility to modern technologies, especially in rural areas. The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) has enabled data- driven decision-making, real-time monitoring, and automation in agricultural practices. This survey presents a comprehensive review of IoT- and AI-based smart agriculture systems reported in recent literature. Various system architectures, sensing technologies, communication methods, and AI techniques used for irrigation control, crop health monitoring, disease detection, and yield prediction are analyzed and compared. The survey also examines connectivity models, including internet- dependent and offline solutions, power management approaches such as solar-based systems, and user-access mechanisms like mobile applications, SMS alerts, and voice interfaces. Key challenges related to cost, scalability, data reliability, and rural deployment are discussed. Finally, the paper identifies existing research gaps and outlines future directions for developing affordable, scalable, and intelligent smart farming solutions, providing design insights for next- generation agricultural monitoring systems.

Published by: vikaspatanker

Authors: Mrs. P. Valarmathi, S.Abilesh, K.Karthick, T.Manoj

Abstract: In the current digital health ecosystem, users often rely on multiple fragmented applications for food tracking, nutrition analysis, and fitness planning, leading to poor user experience, limited personalization, and reduced adherence. This project proposes an AI-Powered Smart Diet and Workout Assistant, a unified web-based platform that integrates diet planning, calorie tracking, recipe generation, and workout recommendations into a single, personalized system. Users securely register, set health goals, and receive tailored plans based on their profiles, with AI-driven food recognition from images or text inputs, nutritional estimation, and deep learning models for diverse cuisines. Built with HTML/CSS/JS frontend, Node.js backend, MongoDB, and TensorFlow, it features progress dashboards, quizzes, and motivational tools. The system enhances engagement, consistency, and long-term health outcomes by minimizing app fragmentation and delivering intelligent, interactive fitness support.

DOI: https://doi.org/10.5281/zenodo.19216771

Published by: vikaspatanker

Authors: Thenmozhi P, Abarna M, Mahalakshmi D, Malini S

Abstract: Hybrid and nearshore outsourcing models are widely used to balance cost efficiency, talent availability, and operational flexibility, but they face challenges such as time-zone misalignment, uneven workload distribution, and limited performance monitoring. Traditional project management tools rely on static coordination and lack intelligent decision-making. This work proposes a smart platform based on an agentic AI-driven multi-agent architecture to manage distributed teams. The system decomposes project goals into tasks and assigns them using expertise, time-zone compatibility, and historical data. Specialized AI agents handle scheduling, performance prediction, and risk assessment. Built on an event-driven architecture, the platform enables real-time synchronization and continuous learning. Results show improved task allocation, early risk detection, and enhanced productivity compared to traditional approaches.

DOI: https://doi.org/10.5281/zenodo.19216630

 

Published by: vikaspatanker

Authors: Haseja Monika, Rathod Nidhi, Prof. Harkishan Gohil

Abstract: Software maintenance is one of the most cost-intensive phases in the software development lifecycle. A prevalent and paradoxical phenomenon — wherein the act of fixing a defect inadvertently introduces one or more new defects — significantly undermines software quality and reliability. These newly introduced defects, commonly termed regression bugs or fix-inducing changes, account for a substantial portion of post-release failures. This paper presents a comprehensive review of the causes, patterns, and mitigation strategies associated with bug-fix- induced regressions. We examine the theoretical foundations of software coupling and co-change dependencies, analyze empirical studies across open-source and industrial codebases, and survey state-of-the-art techniques including regression test selection, change impact analysis, automated patch validation, and AI-assisted code review. Our review identifies that insufficient test coverage, poor change impact analysis, high code coupling, and developer cognitive overload are the primary contributors to regression introduction. We further discuss the role of technical debt and architectural erosion in amplifying this phenomenon.

DOI: https://doi.org/10.5281/zenodo.19215822

 

Published by: vikaspatanker

Authors: Shekar Vollem

Abstract: Modern distributed computing environments support critical digital services but frequently encounter operational instability caused by complex interdependencies, infrastructure failures, and delayed incident response. These challenges highlight the need for intelligent infrastructure systems capable of identifying anomalies early and initiating automated corrective actions without human intervention. This study investigates the development of an autonomous self healing infrastructure framework that integrates predictive monitoring with intelligent automation to strengthen reliability, resilience, and operational continuity across distributed computing platforms. The research addresses the problem of reactive infrastructure management by proposing a proactive model that continuously analyzes operational telemetry, predicts potential system failures, and triggers automated remediation workflows. A mixed methodological approach is adopted, combining quantitative analysis of system performance metrics with qualitative evaluation of automation effectiveness in simulated distributed infrastructure environments. Predictive models analyze infrastructure signals such as resource utilization patterns, system logs, and service latency to detect early indicators of degradation, while automation components coordinate corrective responses including resource reconfiguration, service restart, and workload redistribution. Experimental observations indicate that the proposed framework significantly reduces incident response time, improves system availability, and enhances infrastructure stability during abnormal operating conditions. The findings demonstrate the strategic value of predictive automation in enabling autonomous infrastructure operations and minimizing manual intervention. This research contributes to the advancement of resilient infrastructure engineering by providing a scalable framework that supports proactive infrastructure management and strengthens reliability across complex distributed computing ecosystems.

DOI: https://doi.org/10.5281/zenodo.19208689

Published by: vikaspatanker

Authors: Shekar Vollem

Abstract: Enterprise cloud applications are increasingly required to support rapid software delivery, continuous updates, and highly reliable deployment cycles in order to meet the growing demands of digital transformation, global scalability, and user expectations for uninterrupted services. Continuous Integration and Continuous Delivery (CI/CD) pipelines have emerged as critical infrastructure components that enable automated building, testing, and deployment of applications in modern DevOps environments. These pipelines integrate development, testing, and operational workflows, allowing software changes to be validated and deployed in a consistent and repeatable manner. However, large-scale enterprise systems face significant challenges in optimizing CI/CD pipelines due to complex application architectures, distributed development teams, microservice dependencies, heterogeneous cloud infrastructures, and stringent compliance or security requirements. Inefficient pipelines can introduce bottlenecks in build processes, increase testing overhead, and slow down deployment cycles, thereby affecting overall software delivery performance. This paper explores strategies for optimizing CI/CD pipelines in enterprise cloud environments, focusing on automation frameworks, pipeline orchestration mechanisms, intelligent test management, infrastructure-as-code practices, and scalable deployment models that support cloud-native architectures. By analyzing existing research studies, DevOps methodologies, and industry practices, the study highlights architectural patterns, deployment pipeline designs, and continuous engineering principles that enhance the efficiency, scalability, and reliability of software delivery systems. The findings demonstrate that optimized CI/CD pipelines significantly improve release velocity, enable faster feedback loops for developers, reduce operational risks associated with manual deployments, and support scalable cloud-native application development while maintaining high standards of software quality and system stability.

DOI: https://doi.org/10.5281/zenodo.19208630

Published by: vikaspatanker