Daily Archives: May 19, 2026

Authors: Fatima Malik

Abstract: AI-driven automation in software engineering is transforming the way software systems are designed, developed, tested, and maintained. By integrating artificial intelligence techniques such as machine learning, natural language processing, and deep learning into development workflows, organizations can significantly enhance productivity, accuracy, and efficiency. This study explores the role of AI in automating key phases of the software development lifecycle, including requirement analysis, code generation, testing, debugging, and deployment. AI-powered tools enable intelligent code suggestions, automated bug detection, and predictive maintenance, reducing manual effort and minimizing errors. The paper also examines the integration of AI with DevOps practices, where automation pipelines are enhanced with intelligent decision-making capabilities to improve continuous integration and continuous deployment processes. Various real-world applications, including agile development environments, cloud-based systems, and large-scale enterprise applications, are discussed to demonstrate the practical impact of AI-driven automation. Despite its advantages, challenges such as data quality, model bias, security concerns, and lack of transparency in AI decisions remain significant. The study highlights potential solutions, including explainable AI, robust data governance, and continuous model evaluation. The findings emphasize that AI-driven automation is a key enabler for building efficient, scalable, and high-quality software systems in modern engineering practices.

DOI: https://doi.org/10.5281/zenodo.20281995

Published by: vikaspatanker

Authors: Professor Mayur Patil, Sayyad Ayaj Riyaj, Sayyad Sameer Shahabuddin, Wadgaonkar Hrushikesh Kanifnath

Abstract: Dairy processing, including pasteurization, storage, and packaging, demands precise control to ensure product safety, quality, and efficiency. This report presents the design of an automated dairy processing system using PLC (Programmable Logic Controllers) and SCADA (Supervisory Control and Data Acquisition) technology. The proposed system integrates sensors (temperature, level, flow, and pH) and actuators (valves, pumps, motors) with PLCs to execute control logic, and a SCADA HMI for real-time monitoring, data logging, and operator interaction. Automation is essential in large- scale dairy plants to reduce manpower, prevent contamination, and optimize processes. The system aims to automate milk pasteurization, Clean-In-Place (CIP) cleaning cycles, and packaging lines, resulting in consistent product quality, improved throughput, and traceability. Technical specifications, software details, and implementation methodology are discussed, and advantages and limitations of the PLC/ SCADA solution are highlighted.

Published by: vikaspatanker

Authors: Aarav Nambiar

Abstract: Enterprise system scalability is a critical factor in ensuring that modern organizations can effectively handle increasing workloads, user demands, and data volumes without compromising performance or reliability. This study examines the key principles, architectures, and technologies that support scalability in enterprise systems, including vertical and horizontal scaling approaches, distributed computing, and cloud-based infrastructures. It explores how scalable system design enables efficient resource utilization, high availability, and seamless performance under varying load conditions. The paper highlights the role of microservices architecture, containerization, and load balancing techniques in achieving dynamic scalability. Additionally, it discusses the importance of performance monitoring, capacity planning, and automated scaling mechanisms in maintaining system efficiency. Real-world applications across industries such as finance, healthcare, e-commerce, and telecommunications are analyzed to demonstrate the practical significance of scalability. The study also addresses challenges such as system complexity, data consistency, cost management, and security concerns, proposing solutions such as adaptive resource allocation, robust architectural design, and intelligent monitoring systems. The findings emphasize that achieving enterprise system scalability requires a comprehensive and strategic approach that integrates advanced technologies and best practices to support sustainable growth and operational excellence.

DOI: https://doi.org/10.5281/zenodo.20281208

Published by: vikaspatanker

Authors: Mazlan Othman

Abstract: Machine learning (ML) has emerged as a powerful approach for enhancing network security by enabling intelligent detection, prevention, and response to cyber threats. With the increasing complexity and scale of modern networks, traditional rule-based security systems are often insufficient to identify sophisticated attacks such as zero-day exploits, phishing, and advanced persistent threats (APTs). This paper explores the application of machine learning techniques in network security, focusing on how supervised, unsupervised, and reinforcement learning models can analyze network traffic patterns to detect anomalies and malicious activities. It also examines the role of ML in intrusion detection systems (IDS), intrusion prevention systems (IPS), malware detection, and behavioral analysis. Cloud-based and real-time security monitoring systems are discussed as key enablers for scalable ML deployment in distributed network environments. Additionally, the study highlights challenges such as adversarial attacks, data imbalance, privacy concerns, and model interpretability. Emerging solutions including federated learning, explainable AI, and edge-based security analytics are also reviewed. The findings emphasize that machine learning significantly strengthens network security frameworks by enabling proactive, adaptive, and intelligent threat detection mechanisms.

DOI: https://doi.org/10.5281/zenodo.20281072

Published by: vikaspatanker

Authors: Mazlan Othman

Abstract: Machine learning (ML) has emerged as a powerful approach for enhancing network security by enabling intelligent detection, prevention, and response to cyber threats. With the increasing complexity and scale of modern networks, traditional rule-based security systems are often insufficient to identify sophisticated attacks such as zero-day exploits, phishing, and advanced persistent threats (APTs). This paper explores the application of machine learning techniques in network security, focusing on how supervised, unsupervised, and reinforcement learning models can analyze network traffic patterns to detect anomalies and malicious activities. It also examines the role of ML in intrusion detection systems (IDS), intrusion prevention systems (IPS), malware detection, and behavioral analysis. Cloud-based and real-time security monitoring systems are discussed as key enablers for scalable ML deployment in distributed network environments. Additionally, the study highlights challenges such as adversarial attacks, data imbalance, privacy concerns, and model interpretability. Emerging solutions including federated learning, explainable AI, and edge-based security analytics are also reviewed. The findings emphasize that machine learning significantly strengthens network security frameworks by enabling proactive, adaptive, and intelligent threat detection mechanisms.

DOI: https://doi.org/10.5281/zenodo.20281072

Published by: vikaspatanker

Authors: Myana Ramesh, Kanchapogu Prasanth, Mr. T. Srinivas

Abstract: Every PKL match across multiple seasons outcomes, dates, venues, scores, teams in one place. That's what this dataset is. What you can actually do with it is more interesting than the description suggests. Win/loss trends show which teams hold up across a full season and which ones are inconsistent. Scoring patterns reveal whether a team plays the same way regardless of opponent or adjusts. Venue data is underrated — some teams genuinely perform differently away from home, and the numbers show it. Zoom out across seasons and the league's own growth becomes visible too. More cities, more matches, more structure. PKL didn't stay the same sport it was in its first season, and this data captures that shift better than any summary could.

Published by: vikaspatanker

Authors: Prof .P.V. Nimbalkar, S. D. Magar, P. S. Nimbalkar, N. D. Chormal

Abstract: This paper presents the design and implementation of a Smart Vending Machine System using Internet of Things (IoT) technology for automated dispensing of ready-made food items. The main objective of the proposed system is to provide a contactless, efficient, and user-friendly vending solution that reduces human intervention and waiting time. The system is built using an Arduino UNO microcontroller integrated with a Wi-Fi module to enable real-time monitoring and control. A QR code–based cashless payment mechanism is incorporated to enhance convenience and security. Once the payment is successfully verified, the controller activates the dispensing mechanism through a motor driver to deliver the selected food item automatically. The developed prototype was tested under different operating conditions and demonstrated reliable performance with accurate item delivery and quick response time. The proposed IoT-based vending machine system is cost-effective, scalable, and suitable for deployment in public places such as colleges, offices, and railway stations. Future enhancements can include mobile application integration and advanced inventory management for improved automation.

DOI: http://doi.org/10.5281/zenodo.20279899

Published by: vikaspatanker