Daily Archives: November 4, 2025

Authors: Maj Nilam Gorwade, Dr. John A

Abstract: Last-mile delivery in a military context can often be dangerous, putting personnel and the supplies they carry at risk. The emergence of aerial ‘delivery drones’ from the commercial delivery sector highlights the possibilities of uncrewed vehicles being used in last-mile delivery. However, demonstrations of such technology have been limited to single vehicle deliveries, where only small portions of supplies can be delivered at once. This paper explores the concept of low-cost, uncrewed vehicle swarming for tactical last-mile delivery in a deployed setting. The benefits of uncrewed swarming systems over conventional methods of resupply are discussed, as well as the vulnerabilities and challenges faced by such systems.

DOI: https://doi.org/10.5281/zenodo.17522385

 

Published by: vikaspatanker

Authors: Kamlesh Jangra

Abstract: The convergence of legacy Unix systems with AI-powered Customer Relationship Management (CRM) platforms, such as Salesforce Einstein Copilot, represents a critical strategy for modern enterprises seeking operational continuity and enhanced customer engagement. Legacy Unix servers, including AIX, Solaris, and older Linux distributions, continue to support mission-critical CRM workloads, storing historical data and managing transactional processes with high reliability. At the same time, AI-driven CRM introduces predictive analytics, automated workflows, and intelligent insights that enable personalized customer interactions and strategic decision-making. This review examines methodologies, architectures, and best practices for bridging legacy Unix infrastructure with AI-enhanced CRM, highlighting middleware solutions, API frameworks, hybrid deployment models, and automated data pipelines. It explores challenges related to data compatibility, infrastructure limitations, security, compliance, and organizational change management. Case studies from financial and healthcare sectors illustrate practical implementations and lessons learned, emphasizing phased migration, continuous monitoring, and performance optimization. By synthesizing technical strategies and industry examples, this review provides actionable guidance for IT architects, administrators, and decision-makers to modernize CRM operations, maintain data integrity, and ensure seamless integration of AI capabilities while leveraging the reliability of existing Unix environments. The discussion concludes with insights on emerging trends, including predictive analytics enhancements, cloud-first strategies, edge computing, and automation-driven Unix modernization, framing a future-ready approach to enterprise AI CRM adoption.

DOI: https://doi.org/10.5281/zenodo.17520771

 

Published by: vikaspatanker

Authors: Deepika Sirohi

Abstract: In modern enterprise ecosystems, maintaining continuous operations of Customer Relationship Management (CRM) platforms like Salesforce Experience Cloud is critical for revenue, customer engagement, and regulatory compliance. Disaster recovery (DR) strategies are essential to ensure uninterrupted CRM services across hybrid IT environments, encompassing UNIX, Linux, Windows, and cloud platforms. This review examines the implementation of DR frameworks using Commvault and IBM TSM (Spectrum Protect), highlighting their capabilities, integration approaches, and complementary strengths. Commvault offers hybrid cloud replication, orchestration, and automated failover, while TSM provides efficient incremental backups, long-term retention, and reliable on-premises support. By combining these solutions in a hybrid DR model, enterprises can achieve defined Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO) for Salesforce workloads. The review further explores risk assessment, backup strategies, multi-platform integration, DR testing, monitoring, and continuous improvement processes, emphasizing practical approaches for preserving CRM continuity. Additionally, emerging trends such as AI-driven automation and cloud-native DR strategies are discussed, illustrating how predictive and adaptive technologies can enhance operational resilience. This comprehensive analysis provides IT architects, administrators, and decision-makers with actionable insights to design, implement, and optimize disaster recovery frameworks that safeguard Salesforce Experience Cloud operations while maintaining business continuity, data integrity, and compliance.

DOI: https://doi.org/10.5281/zenodo.17520456

 

Published by: vikaspatanker

Authors: Rajat Bhardwaj

Abstract: – Enterprises are increasingly adopting hybrid IT architectures that combine legacy UNIX/Linux systems with cloud-based CRM platforms and AI-driven workflows. AIX, Solaris, and modern Linux distributions provide reliability, scalability, and security for mission-critical applications, while Salesforce Lightning Web Components (LWC) and AI-enhanced services such as Salesforce Einstein enable intelligent customer engagement, predictive analytics, and workflow automation. This review explores strategies for integrating these technologies, focusing on architectural models, performance optimization, security, compliance, and automation frameworks. Case studies across finance, healthcare, retail, and manufacturing illustrate practical applications, highlighting both operational benefits and technical challenges. Emerging trends, including edge computing, self-healing systems, AI-driven infrastructure optimization, and quantum-safe security, are examined to provide future-ready guidance. The review emphasizes how enterprises can leverage hybrid integration to achieve scalable, secure, and intelligent CRM environments, fostering innovation, operational resilience, and enhanced customer experiences.

Published by: vikaspatanker

Authors: Anjali Kathuria

Abstract: The convergence of Red Hat Satellite and Salesforce Einstein Copilot offers enterprises a transformative approach to hybrid cloud CRM environments, combining robust infrastructure management with AI-driven customer engagement. Red Hat Satellite provides centralized provisioning, configuration, patching, and lifecycle management for Linux-based servers, ensuring security, compliance, and operational resilience across on-premises and cloud platforms. Salesforce Einstein Copilot delivers predictive analytics, workflow automation, and personalized CRM insights, enabling proactive and intelligent customer engagement. This review explores architectural synergies, automation frameworks, security considerations, and performance optimization strategies necessary for integrating these technologies within hybrid cloud ecosystems. Real-world applications across finance, healthcare, retail, and manufacturing illustrate measurable improvements in operational efficiency, regulatory compliance, and customer satisfaction. Challenges such as legacy system integration, data synchronization, multi-cloud security risks, and AI workload management are analyzed alongside strategic frameworks for seamless integration, governance, and orchestration. The findings highlight that hybrid CRM environments leveraging Red Hat Satellite and Salesforce Copilot can achieve scalable, secure, and automated operations while maintaining high availability and cost-efficiency. Emerging trends in AI, edge computing, and self-healing infrastructure are expected to further enhance these ecosystems, providing enterprises with a blueprint for sustainable digital transformation, innovation, and growth.

DOI: https://doi.org/10.5281/zenodo.17520055

 

Published by: vikaspatanker

Authors: Deepika Upadhyay, Shubham Ubale, Mandar Gholap, Aditya Dhavale, Aditya Habbu, Akash Gaikwad

Abstract: The AI Fitness Assistant is a comprehensive web application built using Next.js, React, and TailwindCSS that revolutionizes personalized fitness and nutrition planning. The platform integrates voice AI capabilities through Vapi and leverages Gemini AI for intelligent program generation, enabling users to engage in natural conversations about their fitness goals, physical limitations, and dietary preferences. The system generates customized workout routines and meal plans tailored to individual needs, including accommodations for injuries and allergies. Key features include secure authentication via Clerk, real-time database management through Convex, and responsive design for cross-device accessibility. The application supports multiple program creation while maintaining focus on the most recent active plan, ensuring streamlined user experience and effective fitness journey management.

Published by: vikaspatanker

Authors: Gopal Sehrawat

Abstract: The rapid evolution of enterprise IT demands solutions that combine innovation with stability, intelligence with security, and customer engagement with operational efficiency. This review explores the convergence of AI-powered Salesforce Customer Relationship Management (CRM) platforms with traditional Unix/Linux hybrid infrastructures, highlighting how enterprises can unlock synergies to drive sustainable growth. Salesforce CRM, augmented by artificial intelligence, provides predictive analytics, intelligent automation, and personalized customer experiences. Unix/Linux, long valued for its reliability, scalability, and compliance-ready frameworks, continues to power mission-critical systems across industries such as finance, healthcare, retail, and manufacturing. The integration of these two domains creates a hybrid ecosystem where Salesforce delivers intelligent front-end capabilities while Unix/Linux ensures robust back-end processing and governance. The article examines technical challenges including legacy compatibility, data synchronization, and regulatory compliance, before presenting strategic frameworks such as architectural blueprints, governance models, automation-driven orchestration, and cloud–on-premises balance. Case studies illustrate how different industries leverage this synergy for measurable business value. Future trends—edge computing, quantum-safe cryptography, AI-driven automation, and containerized microservices—are identified as critical enablers for next-generation hybrid ecosystems. By aligning AI-powered Salesforce CRM with Unix/Linux infrastructures, enterprises can enhance customer engagement, optimize operations, and maintain compliance while future-proofing their digital strategies.

DOI: https://doi.org/10.5281/zenodo.17519957

 

Published by: vikaspatanker

Authors: Gabriel Pinto

Abstract: Managing LAMP (Linux, Apache, MySQL, PHP) stacks in hybrid Unix environments presents unique operational and integration challenges for modern enterprises. Simultaneously, AI-driven customer relationship management platforms, such as Salesforce Lightning and Einstein Copilot, require reliable, real-time access to operational data for predictive analytics, automation, and enhanced customer engagement. This review explores strategies for deploying and managing LAMP stacks across heterogeneous Unix systems while integrating with Salesforce AI workflows. Key topics include automated deployment using Kickstart and Jumpstart, configuration management, orchestration of end-to-end workflows, security and compliance considerations, and practical case studies demonstrating measurable benefits. Additionally, emerging trends such as cloud-native deployments, containerization, and AI-driven orchestration are examined to provide insights into the future of hybrid Unix infrastructure supporting intelligent CRM systems. By aligning infrastructure automation with AI-enhanced CRM capabilities, enterprises can achieve operational efficiency, improved system reliability, and optimized customer engagement in a secure and compliant manner.

DOI: http://doi.org/10.5281/zenodo.17519763

Published by: vikaspatanker

Authors: Prof. Meghraj Patil, Ms. Priyanka Jondhale, Ms. Sakshi J. Pawale, Ms. Neha Marathe, Ms. Sakshi S. Pawale

Abstract: Agriculture plays a vital role in sustaining the global economy, and plant health directly influences crop productivity and food security. However, plant diseases often go undetected at early stages due to the limitations of manual inspection, which requires expert knowledge and is prone to human error. To address this challenge, the proposed project introduces an intelligent plant disease detection system based on machine learning and image processing techniques. The system operates by acquiring leaf images, which are then preprocessed through resizing, normalization, and noise reduction to enhance visual quality. Advanced deep learning models such as Convolutional Neural Networks (CNN) are employed to automatically extract relevant patterns and classify leaf images into healthy or diseased categories. A labeled dataset containing multiple plant species and disease variants is used to train and evaluate the model for high accuracy and generalization. Once trained, the model is integrated into a user-friendly interface, allowing farmers or agricultural professionals to upload or capture images using a mobile or web application and receive instant diagnostic results along with suggested remedies. This automated solution significantly reduces dependency on expert consultation, minimizes economic loss due to late detection, and promotes precision agriculture. Moreover, the system can be continuously improved by expanding the dataset to support additional crops and diseases, making it scalable and sustainable for real-world deployment. Overall, this project demonstrates an efficient, low-cost, and technology-driven approach to plant disease management, enabling smarter decision-making and contributing to global agricultural resilience.

Published by: vikaspatanker

Authors: Tejinder Brar

Abstract: Enterprises today face the dual challenge of rapidly deploying Unix infrastructure while integrating AI-driven applications such as Salesforce CRM. Manual server provisioning and configuration often lead to delays, errors, and inconsistent operational environments, impacting the efficiency of AI-powered workflows. Automation tools such as Kickstart for Linux and Jumpstart for Solaris enable standardized, rapid, and reliable deployment of servers across heterogeneous Unix landscapes. When combined with Salesforce AI capabilities, including predictive analytics, lead scoring, and intelligent customer engagement, these automated deployments facilitate seamless integration, improved operational efficiency, and enhanced customer outcomes. This review explores best practices for orchestrating Unix deployments alongside AI-driven CRM workflows, highlights integration challenges, and presents practical case studies demonstrating measurable benefits in speed, reliability, and compliance. Additionally, it examines emerging trends, including AI-based orchestration, cloud-native deployment, and containerization, which promise to further optimize hybrid enterprise environments. By bridging infrastructure automation with intelligent CRM systems, organizations can achieve faster deployment cycles, maintain consistent system configurations, and unlock the strategic potential of AI-enhanced business processes

DOI: http://doi.org/10.5281/zenodo.17519520

Published by: vikaspatanker