Category Archives: Uncategorized

Authors: Niloofar Farrukhzoda Rajabova, Daler Bahromovich Toshmatov, Sherzod Mahmudzoda Nasimov, Aziza Akbarzoda Komilova

Abstract: As enterprises increasingly migrate to cloud-native platforms like Salesforce, the security of multi-tenant environments becomes paramount, particularly in regulated industries. Salesforce’s multi-tenancy architecture provides scalability and cost-efficiency, but also raises concerns around data isolation, regulatory compliance, and shared infrastructure risks. This article offers a compliance-oriented examination of Salesforce security in multi-tenant clouds, exploring the architecture, built-in controls, shared responsibility models, and strategies for adhering to regulations such as GDPR, HIPAA, and SOC 2. By aligning platform capabilities with compliance mandates, organizations can ensure secure operations without sacrificing agility and innovation.

DOI: https://doi.org/10.5281/zenodo.16312240

Published by: vikaspatanker

Authors: Nargiz Eldar qizi Aliyeva, Kamran Vidadi oglu Mustafayev, Lala Elshan qizi Mammadova, Emil Rovshan oglu Gurbanov

Abstract: – In the era of hyper-personalized customer engagement, businesses are increasingly turning to cloud-based Business Intelligence (BI) systems integrated with Cognitive Customer Relationship Management (CRM) models to gain competitive advantage. Cognitive CRM extends traditional CRM by embedding AI capabilities such as natural language processing, machine learning, and sentiment analysis to generate deeper insights from structured and unstructured data. This article explores the practical application of Cognitive CRM within cloud-based BI ecosystems, focusing on architecture, integration strategies, real-time analytics, and decision automation. It highlights case studies where companies have successfully leveraged these models to optimize customer retention, improve service personalization, and boost operational efficiency, while also addressing challenges like data privacy, system complexity, and model governance.

DOI: https://doi.org/10.5281/zenodo.16311390

 

Published by: vikaspatanker

Authors: Professor Nikita Bante, Professor Uday Mahure, Professor Prajakta Helonde, Professor Radha Yete, Professor Aachal Aakre

Abstract: The advent of Agentic AI systems—AI entities that possess autonomy, contextual awareness, and adaptive learning capabilities—has revolutionized the landscape of software development. Unlike traditional rule-based automation tools, agentic AI can perform high-level cognitive functions, including code generation, optimization, debugging, and collaborative task execution without constant human oversight. This paper explores the role of agentic AI in automating various phases of the software development lifecycle (SDLC), from requirements gathering to deployment and maintenance. The research highlights the growing integration of Large Language Models (LLMs), multi-agent systems, and self-improving codebases. It discusses how these intelligent agents enhance developer productivity, reduce time-to-market, and minimize manual coding errors. Through a blend of empirical evidence, recent technological advancements, and case studies, the study showcases the operational and strategic implications of adopting agentic AI. It further identifies potential challenges, such as security risks, interpretability, over-reliance, and ethical dilemmas. The goal is to contribute to a better understanding of how agentic systems are reshaping software engineering practices and to offer practical recommendations for integrating these tools in development workflows responsibly and efficiently.

DOI: https://doi.org/10.5281/zenodo.16316781

Published by: vikaspatanker

Authors: Ms. Roshni Shailesh Gupta

Abstract: Machine learning (ML) models are being widely adopted in high-stakes sectors such as healthcare and finance due to their ability to uncover patterns in data and produce predictive insights. However, many of these models function as opaque "black boxes," making it difficult for end-users and stakeholders to understand how specific decisions are derived. This lack of interpretability can erode trust, hinder adoption, and raise ethical and regulatory concerns, particularly when decisions affect individuals' health or financial well-being. Explainable Machine Learning (XML) aims to mitigate these issues by introducing methods that make ML models more transparent and understandable. This paper presents a comprehensive examination of XML techniques, evaluates their implementation across healthcare and finance, and proposes a methodological framework to enhance both accuracy and interpretability in ML systems. The findings highlight that XML is not merely a technical enhancement but a critical enabler of trustworthy, fair, and responsible artificial intelligence (AI).

Published by: vikaspatanker

Authors: Hemant Iklodiya, Madhvi singh Bhanwar

Abstract: Due to massive connectivity and increasing demands of various services and data hungry applications, a full-scale implementation of the fifth generation (5G) wireless systems requires more effective radio access techniques. In this regard, non-orthogonal multiple access (NOMA) has recently gained ever-growing attention from both academia and industry. Compared to orthogonal multiple access (OMA) techniques, NOMA is superior in terms of spectral efficiency and is thus appropriate for 5G and beyond. In this article, we provide an overview of NOMA principles and applications.

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Published by: vikaspatanker

Authors: Seyyed Moslem Mousavi Khademi, Ali Shafiee, Abbas Najafizadeh

Abstract: In this paper, the dissimilar welding microstructure of the duplex stainless steel SFA 2205 with the high strength low alloy A378 Gr.11 was studied.The microstructure investigations indicated that the weld obtained has a two-phase structure, including dendritic and interdendritic areas. A high hardness transition area was detected in the interface of the A378 low alloy steel and ER 309L metal filler. An unmixed area was observable at the melting boundary of SFA2205 duplex steel and both austenitic and duplex filler metals. The results showed that for joining the two-phase stainless steel SFA2205 with the high strength low alloy A378 Gr.11, using the metal filler ER2209 is more appropriate as a result of forming a more suitable properties microstructure.

DOI: https://doi.org/10.5281/zenodo.16266378

Published by: vikaspatanker

Authors: Dr. Neha Bhat, Mr. Amit Punia

Abstract: The convergence of Customer Relationship Management (CRM) systems with digital marketing techniques has significantly transformed how organizations interact with their customers. In today’s digital economy, data-driven decision-making is essential. By integrating CRM with technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Cloud Computing, businesses can enhance personalization, accurately segment customers, and foster greater loyalty. This paper adopts a computer science-centric approach to examine the architecture, intelligent algorithms, and system integration techniques that enable CRM to serve as an effective digital marketing tool. Real-world case studies from Amazon, Salesforce, and Zoho demonstrate how CRM systems contribute to operational efficiency, improved conversion rates, and long-term customer engagement. A technical framework for AI-enhanced CRM in omnichannel environments is also proposed.

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Published by: vikaspatanker

Authors: Mr. Ankit Sethi, Abhishek

Abstract: The construction industry contributes approximately 39% of global CO₂ emissions, with embodied carbon—emissions from material extraction, manufacturing, and transportation—accounting for 11%. Traditional life cycle assessment (LCA) tools for estimating embodied carbon are often disconnected from Building Information Modeling (BIM) environments and require manual input, limiting their usability during early design stages. This study presents an AI-integrated BIM framework that enables real-time embodied carbon estimation directly within Autodesk Revit. Using Python-based machine learning models—Random Forest, Gradient Boosting, and Support Vector Regression—trained on structural data extracted via Dynamo, the system predicts carbon values and visualizes results through heatmaps in the Revit model. The Random Forest model achieved the highest accuracy (MAE: 5.4 kg CO₂, R²: 0.93) and outperformed traditional tools like One Click LCA in both speed and precision. The framework enhances decision-making during the design phase and demonstrates strong potential for scalable, automated, and sustainable design practices in the built environment

Published by: vikaspatanker

Authors: Vivek Choudhary, Rahul Kumar Satbhaiya

Abstract: The safety of people inside a building depends on its ability to withstand seismic waves and survive an earthquake with minimal damage and repairs, and without collapsing easily. Various systems are used to absorb seismic energy, including dampers, seismic isolation devices, earthquake-resistant walls, and underground water tanks. The effectiveness of these systems depends on their type and location. In this study, the seismic analysis of a 16-story G+ residential building is carried out based on the analysis of the dynamics of floor shear stress and overturning moment. The ground motion dynamics data are taken from the PEER database. Based on the maximum floor shear stress and maximum overturning moment, the performance of transverse bracing and seismic isolation structures is compared with that of conventional moment structures. By placing these elements at the corners of the building in different models, an efficient and adequate model is obtained.

DOI: https://doi.org/10.5281/zenodo.16410753

Published by: vikaspatanker

Authors: Vivek Choudhary, Rahul Kumar Satbhaiya

Abstract: The growing demand for resilient high-rise structures in seismic-prone regions has led to the widespread adoption of seismic isolation and bracing systems. This review paper presents a comprehensive evaluation of the structural performance of tall buildings equipped with bracing and base isolation techniques, focusing on their seismic response. Emphasis is placed on research conducted using ETABS software, which provides advanced modeling and analysis tools for evaluating high-rise buildings under seismic loads. Key parameters such as story drift, base shear, lateral displacement, and floor acceleration are examined in the context of various isolation and bracing configurations. The review integrates findings from multiple studies, highlighting the effectiveness of lead-rubber bearings, friction pendulum systems, and bracing systems (X, V, and Z-bracing) in enhancing the lateral stability and energy dissipation capacity of structures. Comparative analyses demonstrate that the combination of base isolation and bracing significantly improves performance compared to conventional fixed-base models. Moreover, the role of soil-structure interaction and building geometry is also discussed to understand their influence on the overall response. This paper concludes that selecting an appropriate seismic control system based on building height, seismic zone, and soil type is critical for optimizing performance. The findings support the continued use of ETABS as a powerful tool for analyzing and designing seismically resistant tall buildings. This review aims to guide engineers, researchers, and designers in selecting efficient seismic mitigation strategies for modern structural systems.

Published by: vikaspatanker