Category Archives: Uncategorized

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

Authors: Jaswinder Dhillon

Abstract: The integration of Salesforce Service Cloud with Unix/Linux multi-cloud environments is critical for enterprises seeking to modernize CRM operations while maintaining legacy system reliability. This review examines strategies for connecting RESTful APIs and SOAP services with Unix/Linux backends, emphasizing secure data exchange, workflow orchestration, and operational resilience. Core topics include API integration best practices, middleware orchestration, high availability, predictive monitoring, and compliance with regulations such as GDPR, HIPAA, SOX, and PCI DSS. Industry case studies from financial services and healthcare illustrate practical applications and benefits, highlighting faster response times, enhanced customer engagement, and reduced operational risks. Emerging trends, including API-first strategies, microservices, containerized deployments, and AI-driven observability, are analyzed to provide a roadmap for future-ready hybrid architectures. The review concludes that combining cloud-based CRM automation with robust Unix/Linux integration enables enterprises to achieve efficient, secure, and scalable operations while preserving compliance and data integrity.

DOI: http://doi.org/10.5281/zenodo.17519608

Published by: vikaspatanker

Authors: Harjit Sekhon

Abstract: Enterprises seeking to modernize CRM operations face the challenge of integrating AI-driven tools with legacy Unix systems while maintaining compliance, security, and operational resilience. This review examines strategies for implementing Salesforce Einstein Copilot in hybrid environments comprising AIX, Solaris, and cloud platforms. Key topics include AI-assisted automation, predictive analytics, workflow orchestration, middleware and API integration, and monitoring for real-time synchronization. The study explores compliance and security requirements, highlighting access control, encryption, auditability, and regulatory adherence. Case studies from financial services, healthcare, and life sciences demonstrate practical applications, emphasizing best practices in system integration, high availability, and fault tolerance. Emerging trends such as cloud-native infrastructures, autonomous system management, and predictive analytics are discussed to provide a roadmap for future-ready CRM operations. The review concludes that combining AI-powered automation with resilient legacy infrastructure enables enterprises to achieve operational efficiency, secure and compliant workflows, and enhanced customer engagement.

DOI: http://doi.org/10.5281/zenodo.17519596

Published by: vikaspatanker

Authors: Kuldeep Mann

Abstract: Hybrid enterprise environments that combine legacy Unix systems with Salesforce CRM platforms face unique challenges in maintaining operational continuity, data consistency, and system resilience. This review examines strategies for implementing omni-channel automation in Salesforce while ensuring backend Unix systems remain reliable and scalable. Key topics include workflow orchestration, real-time data synchronization, AI-assisted monitoring, and predictive anomaly detection. Integration strategies using APIs and middleware are explored, along with security, compliance, and access control measures. Case studies from financial services and healthcare illustrate practical applications and highlight best practices for seamless automation and resilient hybrid cloud operations. Emerging trends, such as cloud-native resilience tools, AI-driven workflow optimization, and autonomous system management, are analyzed to provide future-ready guidance. The review concludes that combining omni-channel automation with robust hybrid Unix architectures enables enterprises to deliver efficient, secure, and uninterrupted CRM services, optimizing operational efficiency while enhancing customer experience and organizational agility.

DOI: http://doi.org/10.5281/zenodo.17519371

Published by: vikaspatanker

Authors: Amarjeet Cheema

Abstract: Hybrid enterprise environments combining legacy Unix systems and Salesforce CRM platforms require robust authentication frameworks to ensure operational continuity, data security, and regulatory compliance. This review explores secure integration strategies using LDAP/Active Directory (AD) with CentrifyDC to unify authentication across on-premises and cloud workloads. Directory synchronization, identity federation, and single sign-on (SSO) mechanisms enable seamless access to Unix systems and Salesforce CRM applications, while AI-assisted monitoring detects anomalies, predicts potential security risks, and automates remediation workflows. The review examines access control, role management, security policies, compliance considerations, and monitoring strategies, supported by case studies from financial services and healthcare sectors. Emerging trends, including zero trust frameworks, cloud-native authentication services, and AI-driven access management, highlight the evolving landscape of hybrid authentication. Challenges such as legacy system limitations, organizational readiness, and cost/resource planning are analyzed, providing actionable guidance for enterprises. By implementing these strategies, organizations can achieve secure, resilient, and compliant authentication, maintain seamless CRM operations, and enhance operational efficiency across hybrid Unix and Salesforce environments.

DOI: http://doi.org/10.5281/zenodo.17519290

Published by: vikaspatanker

Authors: Baldev Bajwa

Abstract: Hybrid Unix and Salesforce cloud environments require resilient disaster recovery (DR) strategies to ensure uninterrupted business operations and CRM continuity. This review explores best practices for implementing DR using Commvault, Copado, and AI-assisted automation. Commvault provides robust backup and recovery capabilities for Unix workloads, while Copado ensures automated version control, metadata backup, and rollback for Salesforce applications and AI-driven CRM workflows. AI orchestration enhances system resilience by predicting failures, dynamically allocating resources, and coordinating recovery processes across hybrid infrastructures. The review examines architecture design, backup strategies, automated recovery, monitoring, security, compliance, and industry-specific case studies from financial services and healthcare. Key challenges, including legacy system constraints, organizational readiness, and resource planning, are analyzed alongside emerging trends such as cloud-native DR, AI-augmented orchestration, and DevOps alignment. By synthesizing these insights, the review provides a comprehensive framework for enterprises to implement reliable, scalable, and automated disaster recovery strategies, minimizing downtime, preserving data integrity, and maintaining seamless CRM operations across complex hybrid environments.

DOI: http://doi.org/10.5281/zenodo.17519271

Published by: vikaspatanker

Authors: Sai Sandra, Niranjini Shibu

Abstract: The sustainable development goal-11 symbolizes the need for sustainable cities and communities in the forthcoming generation. It is necessary to built safe, resilient and greener environments in and around the habitats. The growing emphasis on sustainability in the construction industry has led to the exploration of biobased and eco-friendly materials. Among these, cork has gained significant attention due to its renewable nature, versatility, and exceptional physical properties. Derived from the bark of the cork oak tree, cork offers remarkable environmental, mechanical, and thermal advantages, making it a promising material for sustainable construction. This paper explores the origin, environmental significance, material properties, applications in the construction Industry and future potential of cork as a sustainable building material. By integrating cork into modern construction practices, the industry can substantially reduce the carbon emissions, safer communities and environmental footprint while enhancing the durability and efficiency of built environments.

DOI: https://doi.org/10.5281/zenodo.17519248

 

Published by: vikaspatanker

Authors: T. Sekar, A. Sangeetha

Abstract: The increased number of road accidents associated with violating two-wheeler helmet usage is very alarming and this situation demands the introduction of smart surveillance systems to maintain safety of the people. In this paper, we present the idea of a new system of helmet detection using deep learning algorithms and image processing to detect whether a person is not wearing a helmet automatically or not. The publicly available Kaggle Helmet Detection dataset that includes 7,500 images having annotations of bounding-boxes of helmet head, and person is used by the system. We transform the annotations to a binary classification task – Helmet and No Helmet and use the YOLOv5 object detection model because of its speed and accuracy of the inference. This was done by training the model using transfer learning and optimizing the model with data augmentation techniques to achieve cross generalization under different kinds of light and environmental conditions. Our system is tenable based on the results of experiments because it took into consideration a real-world scenario. The model based on the YOLOv5 had a generally high accuracy of 95.64%, precision of 94.32%, recall of 91.23% and an F1-score of 92.75. Real-time inference can also be done with the system as it can perform 24.56 ms/frame. This will fit it to be used in surveillance systems in a city environment. Also, Deep SORT tracking has been integrated to provide effective tracking without redundancy. This project will be useful in the development of intelligent traffic systems to automate the process of identifying non-helmets with high precision making it useful to law enforcement and citizen and driver safety on the road. It may be extended in the future to have modules of number plate recognition and fine imposing modules to be able to implement all the traffic rules.

Published by: vikaspatanker

Authors: Pushkar Chaudhari, Vaibhav Thakre, Tushar Chaudhari, Tanaya Bhaute, Dr.Rais Khan

Abstract: Port scanning is an essential technique in the arsenal of network security professionals, enabling the identification of active services and potential vulnerabilities on target systems. Despite advances in the field, traditional tools like Nmap and Masscan face limitations in usability, scan speed, resource efficiency, and integration capabilities. This paper presents the development and robust evaluation of an advanced port scanning tool built with Python, applying multi-threading and asynchronous techniques. Through comparative assessments, the proposed tool demonstrates compelling advantages in speed, resource efficiency, and cross-platform support, contributing to both practical and academic applications in cybersecurity.

Published by: vikaspatanker

Authors: M. Anbarasan, Dr. P. Guhan

Abstract: Fruit quality inspection plays a critical role in reducing post-harvest losses and ensuring consumer safety in the agricultural supply chain. Conventional manual inspection techniques are time-consuming, manual and ineffective on larger scales. To address these constraints, the paper introduces a model of identifying fruit quality using deep learning techniques that employ methods of digital image processing. The model exploits two-stage and evaluation procedure including classification and detection operation. We used pre-trained DenseNet networks with transfer learning to divide the fruits into three quality levels of Fresh, Overripe, and Spoiled quality. The method of image preprocessing normalization, filtering, and augmentation were used to increase the model robustness. The DenseNet model had an evaluation accuracy of 97.82%, which was higher as compared to SVM (89.53%) and Random Forest (90.21%) which are the conventional classifiers. Parallel to it, we also tested object detection models such as YOLOv8 to recognize and bound fruits with bounding boxes and label quality. YOLOv8 was revealed to be very fast with an average precision (mAP) of 96.1% and intersection over union (IoU) of 87.3%. It was also calculated that precision, recall, F1-score, and the time of inference were taken across 10 models. Findings confirm the efficiency of deep learning in automating the process of fruit quality determination to consequently deploy real-time applications in separating systems. The presented model is very flexible to other types of agricultural products and compatible with smart farming and automation processes that include retailing. Generally, this work fills the nexus between manual inspection and smart visual systems by making the fruit quality monitoring scalable, consistent, and efficient.

Published by: vikaspatanker