Category Archives: Uncategorized

Authors: NahulRaj.K

Abstract: The study titled “A Study on Quality Management Practices in Small-Scale Manufacturing Units in Karur, Tamil Nadu” aims to examine the extent of adoption, challenges, and impact of quality management practices (QMPs) in the region’s manufacturing sector. Small-scale industries (SSIs) play a vital role in Karur’s industrial landscape, particularly in textile and allied manufacturing, contributing significantly to local employment and exports. However, these units often face barriers in implementing structured quality systems due to constraints such as limited financial resources, lack of technical expertise, and inadequate awareness.This research investigates the various quality management tools and practices adopted by SSIs, including Total Quality Management (TQM), ISO certification, and continuous improvement initiatives. It also analyses the challenges faced in implementing these systems and evaluates their influence on business performance indicators such as customer satisfaction, productivity, competitiveness, and profitability. The findings reveal that while awareness of quality management practices is increasing, the degree of implementation remains moderate due to cost and resource limitations. Units that have adopted structured QMPs report noticeable improvements in product quality and customer satisfaction. The study concludes that fostering government support, providing technical training, and promoting awareness can significantly enhance the quality performance of small-scale manufacturing units in Karur

Published by: vikaspatanker

Authors: Koushal.M

Abstract: The outbreak of the COVID-19 pandemic brought severe disruptions to global and local supply chains, impacting production, labour, transportation, and market dynamics across various sectors. The tea industry, a vital component of Tamil Nadu’s agricultural economy, was particularly affected due to its heavy reliance on manual labor and complex distribution networks. This study investigates the post-pandemic supply chain challenges faced by tea estates in Tamil Nadu, focusing on major tea-producing regions such as the Nilgiris, Coimbatore, and Anamalai Hills. The research aims to identify key factors influencing supply chain resilience and sustainability after the pandemic. Primary data were collected through structured questionnaires from 160 respondents, including estate managers, supervisors, and supply chain personnel. The study examines the role of technology adoption, supplier relationships, workforce flexibility, risk management practices, and market access in strengthening supply chain resilience. The collected data were analysed using SmartPLS (Partial Least Squares Structural Equation Modelling) to validate the conceptual model and assess the significance of hypothesised relationships. The model demonstrated a good fit (SRMR = 0.047, NFI = 0.868), confirming the reliability of the proposed framework. The findings revealed that all five independent variables have a positive and significant impact on supply chain resilience, indicating that digital transformation, strong supplier networks, flexible labor practices, and proactive risk management collectively enhance post-pandemic recovery. This study contributes to the limited literature on supply chain resilience in the Indian plantation sector and provides practical insights for tea estate managers and policymakers. It emphasises the need for modernising the supply chain through technology integration, digital forecasting tools, and workforce development to ensure long-term competitiveness and sustainability.

Published by: vikaspatanker

Authors: Vijayalakshmi. G, Ms. P. Kalaiselvi, B. Lalitha

Abstract: Crack detection in building structures is critical for ensuring safety and preventing costly repairs. Traditional crack detection methods often face challenges in accurately identifying cracks due to the complexity of the structure and the subtlety of the damage. This work proposes a hybrid deep learning framework that integrates CaTNet (ConvNeXt + Transformer Block) and Vision Transformer (ViT) for effective feature extraction, followed by XGBoost for classification. CaTNet combines ConvNeXt-style convolutional blocks and Transformer encoders to capture both fine-grained spatial details and global contextual relationships within the building images, while ViT processes the images as patch sequences to further enhance the capture of global structural patterns. The extracted features from both models are fused using dense layers with dropout for refinement. XGBoost is then employed for classification, optimized using multi-log loss (mlogloss) and evaluated with classification reports, confusion matrices, and training loss curves. Experimental results show that the proposed model significantly outperforms conventional crack detection methods in terms of accuracy, robustness, and real-time applicability, positioning it as a promising approach for crack detection in building infrastructure

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Authors: Tejinder Sandhu

Abstract: The integration of Salesforce AI agents across hybrid multi-cloud environments is redefining the enterprise Customer Relationship Management (CRM) landscape. Middleware solutions, particularly Apache Tomcat and JBoss, play a critical role in enabling seamless interoperability between Salesforce’s AI-driven services and diverse enterprise systems hosted on Unix/Linux and cloud infrastructures. This review explores how Tomcat’s lightweight architecture and JBoss’s enterprise-grade features collectively support API management, workflow orchestration, transaction integrity, and scalability. It also examines performance optimization strategies, industry-specific applications, and comparative insights with alternative middleware platforms such as MuleSoft, WebSphere, and Apache Kafka. Furthermore, the study highlights future directions, including AI-driven orchestration, edge computing integration, generative AI for middleware automation, and security-first architectural models. By providing a comprehensive analysis, this review underscores how middleware technologies are foundational for deploying Salesforce AI agents in complex enterprise ecosystems, ultimately enabling organizations to achieve resilience, compliance, and customer-centric innovation in the digital age.

DOI: https://doi.org/10.5281/zenodo.17368656

 

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Authors: Balvinder Deol

Abstract: In today’s enterprise landscape, seamless and intelligent communication flows are critical for delivering superior customer experiences. This review examines the integration of AI-powered Computer Telephony Integration (CTI) and Salesforce Omni-Channel with hybrid Unix/Linux infrastructures to achieve secure, scalable, and context-aware customer engagement. It highlights how CTI has evolved from basic telephony management to AI-driven workflows incorporating speech recognition, sentiment analysis, and predictive routing. Salesforce Omni-Channel is explored as a unified engagement hub that orchestrates voice and digital interactions across multiple channels, ensuring consistency and efficiency. The role of Unix/Linux systems as reliable, secure backends supporting telephony services and middleware integration is emphasized, particularly in hybrid architectures.The article discusses middleware and API-driven frameworks as enablers for interoperability, while addressing performance optimization strategies such as load balancing, elastic scaling, and AI-driven orchestration. Industry applications in finance, healthcare, retail, and telecommunications are examined, illustrating real-world benefits of these integrations. Comparative analysis with other CRM platforms—Microsoft Dynamics 365, Oracle CX Cloud, and SAP Customer Experience—underscores Salesforce’s strengths in flexibility, AI capabilities, and hybrid adaptability. Future research directions include the adoption of generative AI, autonomous self-healing communication systems, edge computing for real-time optimization, and security-first communication models. By combining Salesforce’s cloud-native intelligence with Unix/Linux reliability, enterprises can deliver customer-centric communication flows that are resilient, secure, and adaptive to evolving business needs.

DOI: https://doi.org/10.5281/zenodo.17368515

 

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Authors: Gurpal Mann

Abstract: Hybrid Customer Relationship Management (CRM) infrastructures are increasingly critical in enterprises that balance cloud agility with on-premise reliability. This review examines the role of Salesforce Flows, Omni-Channel automation, and AI-driven service intelligence in optimizing CRM operations within hybrid Unix/Linux environments. It highlights how Salesforce Flows streamline cross-platform workflows, how Omni-Channel automation enables unified and consistent customer engagement, and how AI enhances decision-making through predictive analytics and autonomous orchestration. Integration frameworks, performance optimization strategies, and real-world industry applications in finance, healthcare, retail, and telecommunications are explored in depth. A comparative analysis of Salesforce against other CRM platforms such as Microsoft Dynamics 365, Oracle CX Cloud, and SAP Customer Experience underscores Salesforce’s flexibility and forward-looking AI capabilities. The review also discusses future trends, including self-healing systems, zero-trust security, and generative AI, which will further shape the evolution of hybrid CRM environments. Ultimately, the study demonstrates that enterprises leveraging Salesforce’s automation and AI capabilities alongside Unix/Linux reliability can achieve secure, scalable, and customer-centric CRM infrastructures.

DOI: https://doi.org/10.5281/zenodo.17368364

 

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Authors: Shweta Arakeri, Dayanand G Savakar, Anjali Deshapande

Abstract: In today’s digital era, information spreads rapidly through social media and online platforms. However, this convenience has led to the rise of misinformation, commonly referred to as fake news. This paper presents a machine learning-based approach to detect fake news articles by analyzing text content using Natural Language Processing (NLP) techniques. The system preprocesses data, extracts features through TF-IDF vectorization, and classifies news using multiple algorithms such as Logistic Regression, Decision Tree, Gradient Boosting, and Random Forest. The project is implemented using a Flask web application to make the tool user-friendly and accessible. The results demonstrate that the ensemble models provide high accuracy and reliability in identifying misinformation

DOI: http://doi.org/10.5281/zenodo.17368296

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Authors: Harpreet Mahal

Abstract: The integration of Salesforce Experience Cloud with hybrid Unix and multi-cloud infrastructures demands a strategic approach to security, compliance, and operational resilience. This review examines the combined use of Salesforce Einstein Copilot and IBM Tivoli to enhance threat detection, automate incident response, and maintain regulatory compliance across AIX, Solaris, and Linux systems. Middleware orchestration using Apache and JBoss, coupled with AI-driven predictive analytics, ensures seamless communication between cloud and on-premises components while optimizing system performance. High availability and disaster recovery strategies, including clustering, replication, and automated failover, are analyzed to sustain uninterrupted CRM operations. Real-world case studies from finance, healthcare, and government sectors illustrate practical implementations, highlighting operational efficiency, risk mitigation, and compliance enforcement. The review further explores challenges such as legacy system integration, resource management, and skill gaps, and outlines emerging trends including AI-driven self-healing, cloud-native microservices, serverless architectures, and blockchain-based auditability. By synthesizing hybrid Unix reliability, middleware orchestration, AI-enhanced monitoring, and Tivoli-driven compliance, this article provides a comprehensive roadmap for enterprises seeking secure, scalable, and resilient Salesforce Experience Cloud deployments in complex multi-cloud environments.

DOI: https://doi.org/10.5281/zenodo.17367425

 

Published by: vikaspatanker

Authors: Jatinder Dhaliwal

Abstract: Scaling Salesforce Experience Cloud across hybrid Unix infrastructures presents unique challenges and opportunities for enterprises aiming to deliver high-performance, resilient, and compliant CRM operations. This review examines strategies for integrating Experience Cloud with AIX, Solaris, and Linux systems, leveraging middleware platforms such as Apache and JBoss, and incorporating AI-enhanced automation tools for dynamic orchestration, predictive scaling, and anomaly detection. High availability and disaster recovery mechanisms, including clustering, replication, and automated failover, are evaluated to ensure uninterrupted CRM services. Security and compliance hardening across Unix systems, middleware layers, and Salesforce-specific frameworks, such as Salesforce Shield and Field Audit Trail, are explored to meet regulatory requirements including GDPR, HIPAA, SOX, and PCI-DSS. Real-world case studies from finance, healthcare, and government sectors illustrate practical implementations, highlighting the integration of AI-driven monitoring, hybrid cloud orchestration, and middleware optimization. The review also addresses operational challenges, legacy system integration, resource management, and skill gaps, offering insights into best practices and emerging trends such as self-healing infrastructure, serverless architectures, and blockchain-based auditability. By synthesizing hybrid Unix reliability, middleware scalability, AI-driven orchestration, and compliance frameworks, this article provides a comprehensive roadmap for enterprises seeking to scale Salesforce Experience Cloud efficiently while ensuring resilience, security, and regulatory alignment.

DOI: https://doi.org/10.5281/zenodo.17367260

 

Published by: vikaspatanker

Authors: Harsimran Aulakh

Abstract: Ensuring resilience and compliance in Salesforce CRM operations is critical for enterprises managing sensitive customer and transactional data. This review examines strategies for integrating Salesforce CRM with AIX and Solaris Unix platforms, emphasizing disaster recovery architectures, high-availability mechanisms, and compliance hardening tools. AIX and Solaris provide robust back-end environments capable of sustaining mission-critical CRM workloads, while advanced monitoring, clustering, and fault-tolerance strategies ensure minimal disruption during hardware failures or cyber incidents. The article evaluates backup solutions, replication methods, and hybrid cloud integration, highlighting how these measures protect CRM data and maintain operational continuity. Compliance hardening, including the use of Unix security baselines, auditing tools, and Salesforce Shield, ensures alignment with regulations such as HIPAA, GDPR, SOX, and PCI-DSS. Challenges in legacy application compatibility, hybrid DR complexity, performance impacts, and skill gaps are also discussed, alongside emerging trends in AI-driven auditing, blockchain-enabled audit trails, and cloud-native disaster recovery. By synthesizing resilience and compliance frameworks, this review provides a roadmap for enterprises seeking to optimize Salesforce CRM operations within hybrid Unix ecosystems, ensuring continuous availability, regulatory adherence, and enhanced operational efficiency.

DOI: https://doi.org/10.5281/zenodo.17366980

 

Published by: vikaspatanker