Category Archives: Uncategorized

Authors: Kura Sairam, Kurva Saisharath, Dr. P. Kowstubha, A. Sai Aditya

Abstract: Renewable energy sources such as solar power are highly dependent on environmental conditions, which often leads to fluctuations in output voltage and current. These variations create challenges for conventional inverter systems like Voltage Source Inverters (VSI), Current Source Inverters (CSI), and even traditional Z-Source Inverters (ZSI), affecting their efficiency and reliability. To address these issues, this paper focuses on the design and simulation of a Quasi Z-Source Inverter (QZSI) for photovoltaic (PV) energy conversion. The QZSI is an improved version of the ZSI, achieved by modifying the impedance network. This topology offers several advantages, including the ability to perform both buck and boost operations in a single stage, reduced component stress, and a continuous input current, which is particularly beneficial for PV systems. Additionally, the QZSI allows the use of shoot- through states without damaging the inverter, enabling effective voltage boosting under varying input conditions. In this work, the operating principle, voltage boost capability, and control strategy of the QZSI are studied. A simulation model is developed using MATLAB/Simulink to evaluate system performance under different operating scenarios. The results demonstrate that the QZSI provides improved voltage stability and overall efficiency, making it a suitable choice for renewable energy applications.

DOI: https://doi.org/10.5281/zenodo.19766037

Published by: vikaspatanker

Authors: Dr. Jonathan Miller, Dr. Emily Carter, Michael Anderson, Dr. Sophia Reynolds, Daniel Thompson, Chaitanya Srinivas

Abstract: The rapid evolution of cloud computing has significantly transformed the role of database leadership, necessitating a shift from traditional management approaches to dynamic, automation-driven, and resilience-oriented strategies. This paper explores the redefinition of database leadership within cloud-native environments, where scalability, distributed architectures, and continuous integration and deployment pipelines are essential. It highlights the importance of leveraging automation, intelligent monitoring, and self-healing systems to ensure high availability and operational resilience. The study addresses key challenges such as maintaining data consistency across distributed systems, ensuring security in multi-tenant cloud environments, and optimizing performance under variable workloads. Furthermore, it examines how modern leadership practices incorporate cloud-native principles, including microservices architecture, containerization, and Infrastructure as Code (IaC), to enhance efficiency and system reliability. Based on conceptual analysis and practical insights, the paper proposes a strategic framework that emphasizes proactive decision-making, automation adoption, and resilience engineering to achieve scalable, fault-tolerant, and robust database systems while minimizing operational risks and downtime, ultimately underscoring the critical role of adaptive leadership in meeting the demands of modern cloud-native ecosystems.

DOI: https://doi.org/10.5281/zenodo.19765416

Published by: vikaspatanker

Authors: Dr. Jonathan Reed, Dr. Emily Carter, Michael Thompson, Dr. Sarah Williams, David Anderson, Chaitanya Srinivas

Abstract: Modern enterprise platforms increasingly depend on data from multiple heterogeneous sources such as legacy systems, cloud applications, and real-time streams, making scalable and efficient data integration a critical challenge. This paper presents a comprehensive study of data integration architectures for multi-source enterprise environments, with a particular focus on Extract, Transform, Load (ETL) processes and Oracle Data Integrator (ODI) implementations. It evaluates centralized, distributed, and hybrid architectural models to determine their effectiveness in handling large-scale and high-velocity data workloads. An empirical analysis based on real-world enterprise scenarios is conducted to assess key performance factors including scalability, data consistency, fault tolerance, and maintainability. The study further investigates the role of ETL pipelines in enabling structured data transformation and highlights how ODI’s declarative approach and pushdown optimization techniques improve processing efficiency. Additionally, best practices such as parallel processing, metadata-driven integration, and incremental data loading are explored to enhance system performance. The results demonstrate that the integration of robust ETL strategies with ODI-based optimizations significantly improves throughput and reduces latency in complex enterprise systems, providing valuable insights for designing scalable and reliable data integration solutions.

DOI: https://doi.org/10.5281/zenodo.19765226

Published by: vikaspatanker

Authors: Dr. James Anderson, Emily Carter, Dr. Michael Thompson, Daniel Roberts, Dr. Sophia Williams, Chaitanya Srinivas

Abstract: The rapid growth of data-intensive applications has necessitated the adoption of diverse data storage technologies to meet evolving performance, scalability, and reliability requirements. Traditional single-database approaches often fail to address the heterogeneous data needs of modern systems, leading to inefficiencies in data management and processing. This research proposes a unified hybrid persistence framework that integrates in-memory, NoSQL, and relational databases—specifically Redis, MongoDB, and PostgreSQL—to optimize data storage and retrieval strategies in high-performance environments. The framework leverages Redis for low-latency caching and real-time data access, MongoDB for flexible schema design and efficient handling of semi-structured data, and PostgreSQL for strong transactional integrity and advanced querying capabilities. By combining these systems within a cohesive architecture, the proposed approach enables intelligent data tiering, workload distribution, and consistency management. Furthermore, the study introduces adaptive data routing and synchronization mechanisms to ensure seamless interoperability across multiple persistence layers. Experimental evaluation indicates that the proposed framework significantly improves system throughput, reduces query response time, and enhances scalability compared to traditional monolithic database solutions. Additionally, it strengthens fault tolerance and supports dynamic scaling in distributed environments, making it highly suitable for modern cloud-native and enterprise-scale applications.

DOI: https://doi.org/10.5281/zenodo.19763840

Published by: vikaspatanker

Authors: Dr. Andrew Collins, Dr. Rebecca Turner, James Walker, Dr. Olivia Bennett, Matthew Harris, Chaitanya Srinivas

Abstract: The increasing complexity of regulated financial systems demands robust Continuous Integration and Continuous Deployment (CI/CD) pipelines that ensure high standards of quality, security, and regulatory compliance. This paper explores the design and implementation of resilient CI/CD pipelines tailored for financial applications operating under strict regulatory frameworks. It emphasizes the integration of automated testing, continuous monitoring, and compliance validation throughout the software delivery lifecycle to minimize risks and ensure consistent quality assurance. The study examines key challenges such as managing sensitive data, adhering to regulatory requirements, maintaining auditability, and ensuring system reliability in dynamic deployment environments. Furthermore, it highlights the role of DevOps practices, security integration (DevSecOps), and Infrastructure as Code (IaC) in enabling scalable and repeatable pipeline architectures. A structured framework is proposed to guide organizations in building robust CI/CD pipelines that incorporate automated quality gates, security checks, and compliance controls, thereby enhancing operational efficiency and reducing deployment failures. The findings underscore the importance of aligning technical practices with regulatory expectations to achieve secure, reliable, and high-quality software delivery in modern financial systems.

DOI: https://doi.org/10.5281/zenodo.19763655

Published by: vikaspatanker

Authors: Subasree, Harshavardhini N, Dhaarani S, Avinash R K, Karthik Prakash M

Abstract: The rapid expansion of cloud computing has led to the continuous generation of massive system log data, making manual analysis difficult, time-consuming, and prone to errors [1][2][6][10]. This work proposes an LLM-based cloud log analyzer that automates the interpretation of logs and assists in identifying root causes using Artificial Intelligence. The system gathers logs from cloud platforms such as AWS CloudWatch and CloudTrail, processes them to extract meaningful attributes, and applies Large Language Models (LLMs) for efficient log analysis [1][2][3][4]. The proposed approach detects anomalies, recognizes patterns, and identifies root causes including permission-related issues, resource limitations, network configuration errors, and application-level failures [5][8][12][13]. In addition, it produces clear human-readable explanations and suggests automated corrective actions, thereby reducing reliance on domain experts and lowering system downtime [12][14]. A web-based dashboard is also implemented to present error summaries, root cause insights, and recommended solutions in an understandable format. By combining cloud computing with Generative AI, the system improves operational efficiency, strengthens cloud reliability, and supports the evolution of AIOps in modern IT environments [3][5][8][12].

DOI: https://doi.org/10.5281/zenodo.19763530

Published by: vikaspatanker

Authors: Dr. Alexander Hughes, Olivia Bennett, Dr. Christopher Nolan, Ethan Walker, Dr. Amelia Clarke, Chaitanya Srinivas

Abstract: The increasing complexity of enterprise applications and the demand for scalability, reliability, and maintainability have driven the need for advanced architectural approaches in software development. This research presents a next-generation modular Java architecture designed to support scalable and highly reliable enterprise systems. The proposed approach emphasizes the decomposition of applications into loosely coupled, independently deployable modules, enabling improved flexibility, fault isolation, and ease of maintenance. By leveraging modern Java frameworks and design principles such as dependency injection, microservices alignment, and service-oriented architecture, the framework enhances system resilience and adaptability to changing business requirements. The architecture incorporates robust error-handling mechanisms, efficient resource management, and scalable deployment strategies to ensure consistent performance under varying workloads. Additionally, it supports seamless integration with cloud-native environments, enabling dynamic scaling and high availability. Experimental evaluation demonstrates that the proposed modular architecture significantly improves system reliability, reduces downtime, and enhances development productivity compared to traditional monolithic systems. The findings highlight the effectiveness of modular design principles in building next-generation enterprise applications that are both scalable and resilient in distributed computing environments.

DOI: https://doi.org/10.5281/zenodo.19763460

Published by: vikaspatanker

Authors: Nikhil Kumar, Shekhar Kumar Purbe, Dr. Jyoti Gautam

Abstract: Phishing attacks are now considered to be the greatest cyber security threats in online banking, mobile wallet, and other online financial systems. Attackers launch such attacks not only exploit the vulnerabilities in systems but also exploit human factors to steal sensitive financial information and cause huge monetary losses and destroy users’ credibility. Current defense strategies are blacklist based URL filtering and static rules based detection, which are unable to cope with modern phishing attacks. Modern phishing attacks are carried out by employing advanced techniques such as domain spoofing, adversary-in-the-middle (AiTM) attacks, and dynamic web contents . This paper proposes a multi-layered intelligent phishing detection architecture to defend online banking platforms. The proposed system uses URL analysis, content inspection, and transaction behavior analysis to protect online banking systems from different angles. The system uses machine learning algorithms such as Random Forest, Support Vector Machine, and Logistic Regression to classify phishing attacks based on the features extracted from URL, web pages, and user transactions. Unlike previous approaches which only use single layer detection, this paper proposes a hybrid system architecture with real-time detection and behavioral analysis to detect phishing attacks. The system is trained with datasets collected from multiple repositories which are publicly available phishing repositories. The experimental results show that the model trained by the proposed method achieves an accuracy of 96.5% with high precision and recall and low latency to be applied in real-time systems. The system also provides an alert and response mechanism to notify users and stop fraudulent transactions as soon as possible.

DOI: https://doi.org/10.5281/zenodo.19762735

Published by: vikaspatanker

Authors: Sarwesh kumar, Keshav Kumar

Abstract: With the increase in the adoption of digital education and online certifications, there is a remarkable need for certificate verification systems that are secure and reliable. Certificate issuance and validation are traditionally conducted through a manual process relying on centralized databases that is easily forged, manipulated, and fails in the occurrence of an accidental data loss. A big cause of concern for educational institutions, employers and government agencies is fake certificates. To alleviate such problems, a tamper-proof and distributed certificate verification mechanism is proposed on the basis of block chain technology. The blockchain technology offers a decentralized and unchangeable ledger, like cannot change the data once stored in that ledger. The new system stores the verified certificates into a blockchain network securely after being issued by institutions. It assigns a unique cryptographic hash value for each certificate to ensure its authenticity and integrity.

DOI: https://doi.org/10.5281/zenodo.19761949

Published by: vikaspatanker

Authors: Bejini Chidananda Krishna, Naveen KR, D.Ravi

Abstract: This study examines how a diesel engine behaves when ethanol and butanol are mixed with regular diesel fuel in different proportions. The aim is to determine whether these cleaner, renewable fuels can partially replace diesel without compromising engine performance while reducing harmful emissions. To do this, several fuel blends were prepared and tested in a diesel engine under different load conditions and ratios (D85E7.5B7.5, D80E10B10, D75E12.5B12.5). During the experiments, important performance factors such as fuel efficiency, fuel consumption, and exhaust temperature were recorded. At the same time, emissions such as carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), and smoke levels were measured to assess the environmental friendliness of each blend. The results show that adding ethanol and butanol improves the combustion process because these fuels contain oxygen, which helps the fuel burn more completely. This leads to lower emissions of pollutants like CO, HC, and smoke, making the engine cleaner compared to using pure diesel. However, when the proportion of ethanol and butanol is increased too much, the engine may consume more fuel and show a slight drop in efficiency, mainly because these fuels have lower energy content than diesel. In some cases, higher blends can also affect how smoothly the engine runs. Overall, the study suggests that using an optimal mix of ethanol and butanol with diesel can provide a good balance between performance and emission reduction, making it a practical and eco-friendly alternative for diesel engines.

DOI: https://doi.org/10.5281/zenodo.19761417

Published by: vikaspatanker