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

Authors: Anshu Kumar Mishra, Sohail Verma

Abstract: This paper investigates the relationship between leadership styles and team performance in startup organisations, using survey-based data collected from 120 respondents comprising founders, co-founders, team leads and early-stage employees across multiple sectors. The study identifies transformational leadership as the dominant style in the sample and finds strong positive associations between vision-driven leadership, team trust, communication frequency and performance outcomes. Transactional leadership shows moderate relevance in goal-setting and accountability, while laissez-faire approaches correlate with lower performance consistency. Exploratory chi-square testing reveals significant concentration in leadership style distribution, a meaningful link between startup stage and performance rating, and a strong association between trust levels and team output. The paper concludes that startup performance is not driven by a single leadership template but by the leader's ability to adapt style to team maturity, organisational stage and the demands of rapid growth. A hybrid leadership model combining transformational inspiration with transactional clarity emerges as the most effective pattern for high-performing startup teams.

Published by: vikaspatanker

Authors: Shah Md. Tanzimul Kabir, Zahid Hassan Ome

Abstract: This paper provides a comprehensive analysis of scalable database systems, specifically designed to support big data analytics, and examines their evolution, challenges, and emerging technologies in the exascale data processing era. By examining recent research studies from 2021 to 2026, the current paper seeks to investigate how distributed database architectures, including NewSQL, cloud-native, and data lakehouse, address the fundamental scalability challenge known as the "scalability trilemma" consisting of consistency, availability, and partition tolerance. The current research introduces the Adaptive Scalability Evaluation Framework (ASEF), which integrates horizontal scaling, elastic resources, query optimization, and storage efficiency. The analysis shows that recent scalable database architectures are based on disaggregated storage and compute architectures, enabling near-linear scaling to thousands of nodes with query latencies under 100ms for petabyte-scale data sets. Cloud-native database architectures are shown to be highly elastic, with variations in query latency at the 95th percentile below 15% during scaling events. Newly emerging architectures for lakehouses, which bring the flexibility of data lakes and the performance of data warehouses, provide query performance that is 3 to 5 times better than traditional data lakes and reduce the total cost of ownership by 30 to 50 percent. Evaluation in five dimensions for analytical workloads, such as scaling behavior, consistency model, query performance, storage efficiency, and operational complexity, shows that systems with workload awareness and adaptivity perform much better than static configurations. Continuous optimization provides an improvement in throughput performance that is between 2 to 4 times.

DOI: https://doi.org/10.5281/zenodo.20270564

Published by: vikaspatanker

Authors: Sk Samsul Hoda, Dr. Vipin kumar

Abstract: Remote sensing techniques used for measurement of atmospheric cloud properties operate under the notion that light extinction caused by scattering and absorption is exponential due to Beer-Lambert law. This is expected to be valid for a uni-form medium with no spatial correlations between particle position. The aim of this research was to show that under turbulent conditions, cloud droplets cannot be inter-preted as non-correlated, and in turn will exhibit a lower than exponential light decay from scattering. The research took place at the MTU π-Chamber laboratory. A tem-perature difference between the floor and ceiling of the chamber was applied to create convection- driven turbulence. When turbulent cloud conditions were achieved, it’s optical depth properties was analyzed. This was done by deriving the optical depth by computational means through the acquisition of its droplet size distribution, and processing it through Mie scattering theory, while simultaneously acquiring direct measurement of optical depth using a Laser-Hygrometer. Results showed that there is a trend where larger temperature differences inside the chamber caused the direct extinction of light to deviate more strongly from the computed extinction. This less then exponential extinction parameter allows us to understand the significant effect that a turbulent cloud cover has on radar and satellite signals.

DOI: http://doi.org/10.5281/zenodo.20269814

Published by: vikaspatanker