Authors: Chandra Perera

Abstract: Distributed systems have become a foundational technology in modern computing, enabling organizations to build scalable, reliable, and efficient applications across multiple interconnected nodes. These systems distribute computation, storage, and processing tasks across different machines, improving performance, fault tolerance, and resource utilization. This study explores the fundamental concepts of distributed systems, including communication models, consistency mechanisms, fault tolerance, and concurrency control. It also examines how distributed architectures are applied in various industries such as finance, healthcare, e-commerce, telecommunications, and cloud computing. The paper highlights key technologies supporting distributed systems, including microservices, containerization, distributed databases, and cloud platforms. Furthermore, it discusses major challenges such as network latency, data consistency, security risks, and system complexity. Emerging trends like edge computing, serverless architectures, and blockchain-based distributed systems are also analyzed. The findings emphasize that distributed systems are essential for supporting large-scale, high-performance applications in today’s interconnected digital world.

DOI: http://doi.org/10.5281/zenodo.20283802

 

Published by: vikaspatanker

Authors: Takeshi Nakamura

Abstract: Application Programming Interfaces (APIs) have become a fundamental component of modern software systems, enabling seamless communication and integration between applications, services, and platforms. With the rapid growth of cloud computing, microservices architectures, and mobile applications, API usage has increased significantly, making API management and security a critical concern. This study explores key aspects of API management, including API lifecycle management, rate limiting, authentication, monitoring, and version control. It also examines security challenges such as unauthorized access, data exposure, injection attacks, and misuse of API endpoints. The paper highlights essential security mechanisms such as OAuth, API gateways, encryption, token-based authentication, and access control policies. Furthermore, it discusses best practices for ensuring secure and efficient API deployment in distributed systems. Emerging trends such as API-first design, zero trust security models, and AI-driven API monitoring are also analyzed. The findings emphasize that effective API management and security are essential for maintaining system integrity, performance, and trust in modern digital ecosystems.

DOI: http://doi.org/10.5281/zenodo.20283978

 

Published by: vikaspatanker

Authors: Priya Narayanan

Abstract: Machine learning has emerged as a powerful approach for detecting anomalies in modern network environments, where traditional rule-based security systems often fail to identify evolving and sophisticated cyber threats. With the exponential growth of network traffic and the increasing complexity of distributed systems, ensuring real-time threat detection has become a critical requirement. This study explores the application of machine learning techniques for anomaly detection in network systems, focusing on supervised, unsupervised, and semi-supervised learning methods. These techniques enable the identification of unusual patterns in network traffic that may indicate intrusions, malware activity, or unauthorized access. The paper also examines the integration of machine learning models with network monitoring tools, intrusion detection systems, and cloud-based security platforms. Furthermore, it discusses key challenges such as high false-positive rates, data imbalance, concept drift, and scalability issues. Emerging solutions including deep learning models, autoencoders, and real-time streaming analytics are also highlighted. The findings indicate that machine learning significantly enhances the accuracy, adaptability, and efficiency of network anomaly detection systems, making them essential for modern cybersecurity frameworks.

DOI: http://doi.org/10.5281/zenodo.20284075

 

Published by: vikaspatanker

Authors: Hafiz Umar

Abstract: Scalable application design has become a fundamental requirement in modern software engineering due to the rapid growth of users, data, and distributed computing environments. Systems today must handle increasing workloads efficiently while maintaining performance, reliability, and availability. This review explores the principles and architectural patterns used in designing scalable applications, including horizontal and vertical scaling, microservices architecture, load balancing, caching strategies, and distributed databases. It also examines cloud-native approaches that enable elasticity and on-demand resource provisioning. The study highlights the importance of system design considerations such as fault tolerance, high availability, and performance optimization in building robust applications. Furthermore, it discusses challenges such as network latency, data consistency, system complexity, and cost management in large-scale systems. Emerging trends like serverless computing, edge computing, and container orchestration are also reviewed. The findings emphasize that effective system design is essential for ensuring scalability, efficiency, and reliability in modern distributed applications.

DOI: http://doi.org/10.5281/zenodo.20284144

 

Published by: vikaspatanker

Authors: Nguyen Thanh Binh

Abstract: Cloud computing has become an essential foundation for modern digital infrastructure, enabling organizations to store, process, and manage data efficiently over distributed environments. However, the widespread adoption of cloud services has also introduced significant security challenges, including data breaches, misconfigurations, unauthorized access, and compliance risks. This study explores cloud security best practices designed to mitigate these risks and strengthen the overall security posture of cloud-based systems. It examines key security mechanisms such as identity and access management (IAM), encryption techniques, multi-factor authentication, secure network architecture, and continuous monitoring. The paper also highlights the importance of shared responsibility models, where both cloud service providers and users play a role in ensuring security. In addition, emerging practices such as zero trust architecture, DevSecOps integration, and automated threat detection are discussed. The findings emphasize that adopting structured cloud security best practices significantly reduces vulnerabilities, enhances data protection, and ensures compliance with regulatory standards, making cloud environments more secure and reliable.

DOI: http://doi.org/10.5281/zenodo.20284178

Published by: vikaspatanker

Authors: Liyana Abdullah

Abstract: Artificial intelligence (AI) has become a transformative force in modern enterprises by enabling data-driven decision-making through advanced analytics and predictive modeling. AI-driven insights allow organizations to process vast volumes of structured and unstructured data, uncover hidden patterns, and generate actionable intelligence for strategic and operational decisions. This study explores the role of AI in enhancing enterprise decision-making processes, focusing on techniques such as machine learning, deep learning, natural language processing, and data mining. It examines how AI systems integrate with enterprise platforms such as cloud computing, business intelligence tools, and data warehouses to support real-time and informed decision-making. The paper also highlights applications across domains including finance, healthcare, supply chain management, marketing, and human resource management. Furthermore, it discusses key challenges such as data quality issues, algorithmic bias, lack of transparency, security concerns, and integration complexities. Emerging solutions such as explainable AI, federated learning, and AI governance frameworks are also analyzed. The findings emphasize that AI-driven insights significantly enhance decision accuracy, operational efficiency, and strategic planning, making AI a critical component of modern enterprise decision-making systems.

DOI: http://doi.org/10.5281/zenodo.20284214

 

Published by: vikaspatanker

Authors: Sunita Rao

Abstract: DevOps has emerged as a transformative approach in modern software engineering, integrating development and operations to enhance collaboration, automation, and continuous delivery. In cloud environments, DevOps practices play a crucial role in improving scalability, reliability, and speed of software deployment. This study provides an analysis of DevOps practices within cloud computing environments, focusing on key components such as continuous integration and continuous deployment (CI/CD), infrastructure as code (IaC), automation, containerization, and monitoring. It examines how cloud platforms enable seamless implementation of DevOps pipelines and support rapid application development and deployment. The paper also explores the impact of DevOps on software quality, deployment frequency, system stability, and operational efficiency. Furthermore, it discusses challenges such as tool integration complexity, security concerns, cultural resistance, and skill gaps in DevOps adoption. Emerging trends such as DevSecOps, GitOps, and AI-driven automation are also analyzed. The findings highlight that DevOps practices in cloud environments significantly enhance agility, reduce time-to-market, and improve system reliability, making them essential for modern digital transformation initiatives.

DOI: http://doi.org/10.5281/zenodo.20284248

 

Published by: vikaspatanker

Authors: Associate Professor Dr. S.Sureshbabu, Research Scholar Mr. A.kannan

Abstract: This study examines farmers' perceptions of marketing functions and services rendered by Cooperative Marketing Societies (CMS) through comprehensive exploratory factor analysis of data from 620 farm-members. Principal Components Analysis with Varimax rotation identifies five distinct dimensions of CMS marketing functions: Market Operations and Transaction Efficiency, Pricing and Bargaining Effectiveness, Market Access and Infrastructure Support, Post-Harvest and Quality Support Services, and Information and Financial Support Services. The findings reveal that farmers rate infrastructure support (mean = 4.30) and storage facilities (mean = 4.23) most favorably, while expressing moderate satisfaction with pricing transparency (mean = 2.69) and income impact (mean = 3.28). Cluster analysis segments farmers into three groups: 62.4% highly satisfied, 23.2% moderately satisfied, and 14.4% less satisfied with CMS functions. The five-factor model explains 64.183% of cumulative variance, establishing a robust framework for understanding CMS service quality and performance. The study provides evidence-based insights for strengthening cooperative marketing functions and designing targeted interventions to enhance farmer satisfaction across service dimensions.

DOI: https://doi.org/10.5281/zenodo.20242694

Published by: vikaspatanker

Authors: Dr Vijayalakshmi V, Ms.Sneha R. V. Kumbhar

Abstract: However, the rise in cloud computing usage has resulted in increased complexity and vulnerability of organizations' IT infrastructure. In addition, cloud services have created new vulnerabilities that can easily be targeted by sophisticated attacks since traditional intrusion detection methods lack the ability to cope with the dynamically changing nature of cloud environments. This paper offers a novel, AI-powered hybrid deep learning framework for intrusion detection in cloud environments. The hybrid IDS is based on a combination of Triplet Attention-based Residual CNN for spatial feature extraction of network traffic, Bi-LSTM with attention mechanism for temporal dependency modeling, and Particle Swarm Optimization for hyperparameter optimization. Based on the evaluation results performed on the CSE-CIC-IDS2018 and UNSW-NB15 dataset, the suggested hybrid architecture attains an impressive accuracy of 99.12%, precision of 98.9%, and recall of 99.0%, outperforming the performance of individual CNN (96.4%) and Bi-LSTM (95.8%). In terms of efficiency, the PSO-based architecture has a latency less than 50 ms with minimal false positive rate of only 1.2%.

DOI: https://doi.org/10.5281/zenodo.20233688

Published by: vikaspatanker

Authors: Harishankar T, Dr.T.R.Ganesh Babu

Abstract: In the context of their growing use in critical fields of application, like aviation electronics, automotive control systems, and industrial automation, FPGA circuits' operation must be guaranteed against both soft errors and any other defects that may arise during run-time. This paper analyzes in depth an approach for implementing Totally Self-Checking (TSC) comparators for online diagnostics in FPGAs in a way which maximizes its effectiveness in terms of test pattern complexity and hardware overhead. In particular, the presented technique utilizes the circuitry features of Look-Up Tables (LUTs) to provide comprehensive online testing with a number of test vectors proportional to O(n), while guaranteeing complete fault coverage and regardless of the specific LUTs configuration. The results of a comparison among recent techniques for implementing TSC, both BIST-based and Dual Modular Redundancy (DMR), show that the described solution offers an outstandingly effective performance with regard to SER (0.055 FIT).

DOI: https://doi.org/10.5281/zenodo.20233433

Published by: vikaspatanker