Authors: Allagadapa Bharath, Deshaboina Sumanth

 

Abstract: This paper presents a comprehensive strategy for the efficient management of electric vehicle charging stations (EVCSs) integrated with a grid-side Modular Multilevel Converter (MMC) interface. The MMC topology is selected for its ability to directly connect to medium voltage grids without transformers, offering benefits such as reduced space and material usage. A key challenge addressed is the unbalanced load distribution caused by varying charging demands and random EV arrival-departure patterns, which can lead to significant disparities among MMC arms and internal modules. To ensure balanced and sinusoidal grid currents as well as stable module voltages, the proposed method integrates a Load Management (LM) algorithm with a Power Flow Management (PFM) algorithm. The LM algorithm schedules and allocates EV charging sessions to reduce phase and arm-level load variations, while the PFM regulates circulating currents to mitigate any remaining unbalances. Simulation results on a realistic scenario—such as a shopping mall EVCS—demonstrate that the proposed LM-PFM approach significantly enhances energy delivery and system stability, outperforming conventional rule-based methods. Real-time simulations further validate the feasibility of the proposed solution for practical deployment.

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Published by: vikaspatanker

Authors: Aviichal Sharma

Abstract: This study explores how machine learning algorithms can help to increase fuel efficiency. The implemented model is trained based on a dataset that consists of many features and attributes affecting a vehicle’s fuel efficiency such as MPG, Number of cylinders, Horsepower, Vehicle weight, and many more. For training the model, many machine learning models that fit the dataset variables were studied and implemented. It was found that the Random Forest Regression technique performed better than other algorithms in predicting fuel economy after extensive testing and analysis. It was the most appropriate algorithm for my research goal because of its capacity to manage intricate interactions between the input variables and accurately anticipate fuel usage. Random Forest Regression was demonstrated to be a potent approach to improving fuel economy prediction accuracy by utilizing the ensemble of decision trees and feature unpredictability.This study's conclusion emphasizes the enormous potential of machine learning for enhancing fuel efficiency in the automotive sector. It was determined that Random Forest Regression is the best technique for forecasting fuel efficiency after investigation. It paved the path for improvements in resource optimization and environmental sustainability by taking into account several important criteria and investigating alternative algorithms. The objective is to encourage industry leaders to use machine learning as a catalyst for change, advancing the automobile industry toward a greener and more effective future.

DOI: https://doi.org/10.5281/zenodo.16885367

Published by: vikaspatanker

Authors: Pratik Nikam, Dr. Priyanka Singh

Abstract: Artificial intelligence (AI) is transforming higher education in India by enabling personalized learning, enhancing student engagement, and providing educators with data-driven tools to optimize teaching. This paper explores AI’s potential to create adaptive learning environments, improve accessibility, and foster holistic student development. Through AI-powered platforms, virtual tutors, and analytics, education is becoming more inclusive and efficient. However, challenges like ethical concerns, data privacy, and equitable access must be addressed to ensure responsible adoption. This study advocates for a future where AI enhances learning outcomes while maintaining fairness and inclusivity, preparing students for a dynamic world.

Published by: vikaspatanker

Authors: Assistant professor R .Saranya , Naviya Ka, Brindhavini P, Nisha L

Abstract: – she-shield Designed to enhance personal safety for women, the wearable device offers a dual solution by combining a device integrates several critical sensors to enhance personal security and wearable IoT device developed on a Raspberry Pi platform, integrating a range of sensors ,The temperature and heartbeat sensors monitor physiological changes that may indicate distress or health issues .In active mode, the touch sensor allows the user to manually activate the device during an emergency,initiating a loud buzzer and alarm to deter attackers. The device is powered by a rechargeable lithium battery, ensuring long-lasting performance and reliability , The voice detection sensor uses a Support Vector Machine (SVM) algorithm to identify both general vocal patterns and distress screams, ensuring that the device can accurately recognize a woman’s voice in various contexts.the built-in GPS provides real-time location tracking,Combined with real-time streaming, video storage, and alert messaging, the device employs a dualalarm system: one alarm is emitted from the device itself second alert is sent via i

Published by: vikaspatanker

Authors: Joseph Kuriakose

Abstract: Legacy Unix-based CRM systems, once known for their stability and control, are rapidly becoming obsolete in the face of modern customer engagement demands. This review explores the pivotal role of Linux in transforming customer relationship management infrastructure for the cloud-native era. By embracing Linux-based microservices, containerization, and open-source CRM platforms such as SuiteCRM, EspoCRM, and OroCRM, organizations can reduce licensing costs, increase scalability, and automate CRM operations across hybrid infrastructures. The article examines architectural transitions from monolithic Unix stacks to modular, container-driven Linux deployments. It further details how Linux tools—from shell scripting to Kubernetes—enable automation, performance optimization, secure data handling, and integration with third-party business tools. Case studies across telecom, healthcare, retail, and government highlight the practical benefits and challenges of Linux-based CRM modernization. The review concludes with strategic recommendations for IT leaders seeking to align customer experience platforms with modern DevOps and digital transformation goals.

DOI: http://doi.org/10.5281/zenodo.16881228

Published by: vikaspatanker

Authors: Naveen Kannan

Abstract: As customer engagement demands evolve, traditional CRM systems often monolithic and proprietary struggle to keep pace with the need for agility, scalability, and cost-efficiency. This review explores how Linux serves as the foundational enabler for next-generation CRM platforms that are cloud-native, open source, and DevOps-driven. We examine the technical and architectural limitations of legacy CRMs and illustrate how Linux-based infrastructures overcome these challenges through modularity, containerization, and automation. By leveraging lightweight distributions such as Alpine and Debian, businesses can deploy CRMs with reduced resource overhead and enhanced security. Open source CRM solutions like SuiteCRM, EspoCRM, and OroCRM, when deployed on Linux, offer extensibility, transparency, and freedom from vendor lock-in. The review further analyzes how Linux integrates with Kubernetes, CI/CD pipelines, and observability tools to manage CRM lifecycles efficiently. Real-world case studies in telecommunications, finance, healthcare, and retail demonstrate measurable gains in performance, security, and cost reduction. Finally, the paper presents a forward-looking perspective on integrating AI, edge computing, and DevSecOps into Linux-based CRM architectures, emphasizing the strategic role of Linux in driving CRM innovation.

DOI: http://doi.org/10.5281/zenodo.16881203

Published by: vikaspatanker

Authors: Santhosh Reddy

Abstract: Modern customer data ecosystems are increasingly complex, often hindered by bloated CRM platforms, vendor lock-in, and opaque ETL pipelines. This review explores how applying core Unix principles modularity, portability, minimalism, and composability can simplify and modernize customer data management. By leveraging traditional Unix tools such as awk, sed, jq, and rsync, organizations can build lean, transparent workflows for ETL, analytics, reporting, and compliance. We examine how shell scripting and CLI utilities empower developers and IT teams to create automation pipelines that are easy to deploy, maintain, and scale across hybrid infrastructures. Real-world case studies highlight how businesses in retail, healthcare, NGOs, and finance have used Unix-based toolchains to improve customer insights, enhance security, and reduce operational overhead. The review also discusses challenges such as script maintainability, scaling constraints, and cross-platform compatibility, while presenting a forward-looking view of AI-enhanced and cloud-integrated Unix pipelines. Ultimately, the Unix philosophy offers a strategic framework for designing efficient, portable, and resilient customer data operations.

DOI: http://doi.org/10.5281/zenodo.16881168

Published by: vikaspatanker

Authors: Sorab Wadia

Abstract: Customer Relationship Management (CRM) platforms are critical to modern business success, but many organizations particularly those operating in hybrid or legacy IT environments struggle with workflow inefficiencies and integration limitations. This review explores the use of Unix shell scripting as a lightweight, versatile solution for CRM automation. From data extraction and transformation using command-line tools like jq and awk, to workflow orchestration via cron, inotify, and Bash pipelines, shell scripts provide reliable alternatives to proprietary automation platforms. The article presents technical patterns for API integration, system maintenance, real-time event handling, and business intelligence generation. It also highlights security best practices and challenges such as debugging complexity and cross-platform compatibility. Through real-world case studies, the review demonstrates how organizations across sectors can automate CRM operations, streamline processes, and extract actionable insights without major platform investments. Shell scripting emerges as a powerful bridge between traditional CRM systems and modern DevOps-ready architectures.

DOI: http://doi.org/10.5281/zenodo.16881136

Published by: vikaspatanker

Authors: Tenzing Bhutia

Abstract: Traditional monolithic CRM systems are increasingly challenged by demands for flexibility, scalability, and rapid digital transformation. This review explores how Linux containers and microservices enable the development of modular, scalable CRM platforms tailored to dynamic business needs. It begins with an analysis of architectural evolution from monoliths to microservices—and outlines the role of container technologies like Docker, Podman, and Kubernetes in modern CRM infrastructure. Core design principles such as domain-driven design, API gateways, service discovery, and CI/CD automation are examined to showcase how microservices enhance agility and maintainability. The paper also addresses persistent data handling, security best practices, and observability using tools like Prometheus, ELK, and Jaeger. Real-world case studies from finance, retail, healthcare, and telecom illustrate successful microservice-based CRM deployments. Challenges around complexity, skill gaps, and migration are acknowledged, along with mitigation strategies. Ultimately, the article demonstrates that containerized, microservices-driven CRMs offer a robust foundation for future-ready, adaptive customer engagement systems.

DOI: http://doi.org/10.5281/zenodo.16881109

Published by: vikaspatanker

Authors: Rohan D’Souza

Abstract: Legacy CRM systems, typically built on proprietary architectures, have long constrained enterprises with licensing costs, scalability limitations, and rigid integration pathways. As digital transformation accelerates, organizations are increasingly transitioning toward open source CRM platforms hosted on lightweight, modular Linux infrastructures. This review presents a comprehensive analysis of the technical, operational, and strategic advantages of Linux-based CRM architectures, emphasizing their cost-effectiveness, performance optimizations, and automation compatibility. The paper explores key components such as containerization, DevOps integration, security hardening, and compliance adherence, drawing comparisons between traditional CRM stacks and modern open source alternatives like SuiteCRM, EspoCRM, and OroCRM. Detailed performance benchmarks and migration strategies are examined, along with real-world case studies from financial services, healthcare, telecom, and retail sectors. Additionally, the review addresses common challenges including skill gaps, performance tuning, and support structures and proposes mitigation strategies rooted in automation, training, and architectural best practices. Future-oriented insights are provided on AI/ML integration, edge deployment scenarios, and evolving DevSecOps methodologies. Ultimately, this paper positions Linux-based open source CRM platforms as sustainable, agile, and scalable foundations for next-generation customer engagement.

DOI: http://doi.org/10.5281/zenodo.16881066

Published by: vikaspatanker