Category Archives: Uncategorized

Authors: Grace Foster, Natalie Simmons, Lauren Bailey, Megan Scott, Chaitanya Srinivas, Aneesha Raj

Abstract: Artificial Intelligence (AI) is revolutionizing Customer Relationship Management (CRM) by enabling intelligent automation, predictive analytics, personalized customer engagement, and data-driven decision-making. As organizations increasingly adopt AI-augmented CRM ecosystems, ensuring governance, transparency, accountability, fairness, and regulatory compliance has become essential for building trust among customers and stakeholders. While AI-powered CRM platforms enhance operational efficiency and customer experience, they also introduce challenges related to algorithmic bias, explainability, data privacy, security, and ethical decision-making. This paper proposes a comprehensive framework for developing trustworthy AI-augmented CRM ecosystems by integrating AI governance principles, transparency mechanisms, explainable artificial intelligence (XAI), data governance, model lifecycle management, continuous monitoring, risk assessment, and regulatory compliance into enterprise CRM platforms. The proposed framework emphasizes transparent decision-making, human oversight, ethical AI practices, auditability, and continuous performance evaluation to ensure that AI-driven customer interactions remain reliable, accountable, and compliant with evolving legal and organizational standards. Furthermore, the study discusses the architectural components, implementation strategies, governance models, evaluation metrics, and practical challenges associated with deploying responsible AI in modern CRM environments. By embedding governance-by-design principles and explainable AI capabilities into CRM ecosystems, organizations can enhance customer trust, improve decision quality, mitigate operational and compliance risks, strengthen data security, and achieve sustainable digital transformation. The proposed framework provides a scalable foundation for developing intelligent, transparent, and ethically governed CRM systems that support long-term business growth, responsible innovation, and superior customer relationship management.

DOI: http://doi.org/10.5281/zenodo.21428428

Published by: vikaspatanker

Authors: David Cooper, Nicholas Turner, Ryan Peterson, Lauren Bailey, Chaitanya Srinivas, Aneesha Raj

Abstract: The rapid advancement of Large Language Models (LLMs) and Knowledge Graphs (KGs) is transforming Customer Relationship Management (CRM) into an intelligent, context-aware, and data-driven enterprise ecosystem. Traditional CRM systems primarily rely on structured customer data, predefined business rules, and conventional analytics, which often struggle to capture complex customer relationships, interpret unstructured information, and provide personalized, real-time decision support. Recent developments in generative artificial intelligence, semantic technologies, and enterprise automation have created new opportunities to build intelligent CRM platforms capable of understanding customer intent, reasoning over interconnected business knowledge, and autonomously supporting sales, marketing, and customer service operations. This paper presents a comprehensive framework for intelligent CRM by integrating Large Language Models, Knowledge Graphs, and AI-driven enterprise services into a unified architecture. The proposed framework combines natural language understanding, semantic knowledge representation, contextual reasoning, retrieval-augmented generation (RAG), intelligent workflow automation, and predictive analytics to improve customer engagement, knowledge discovery, decision support, and business process optimization. Knowledge Graphs provide structured semantic relationships among customers, products, services, and business entities, while Large Language Models enable conversational intelligence, automated content generation, personalized recommendations, and context-aware customer interactions. The framework also incorporates data governance, model orchestration, security, explainability, and continuous learning mechanisms to ensure scalability, reliability, and responsible AI deployment. Furthermore, the study discusses the architectural components, implementation strategies, performance evaluation metrics, practical challenges, and future research directions associated with intelligent CRM ecosystems. By combining the reasoning capabilities of Knowledge Graphs with the language understanding and generative capabilities of Large Language Models, organizations can develop next-generation CRM platforms that deliver superior customer experiences, enhance operational efficiency, improve strategic decision-making, and accelerate enterprise digital transformation.

DOI: http://doi.org/10.5281/zenodo.21428346

Published by: vikaspatanker

Authors: Thomas Ward, Patrick Simmons, Samuel Price, Nicole Bailey, Chaitanya Srinivas, Aneesha Raj

Abstract: Enterprise data modeling plays a critical role in enabling organizations to effectively manage and integrate vast amounts of data across large-scale information systems. As enterprises increasingly rely on distributed architectures, cloud platforms, big data technologies, and heterogeneous data sources, ensuring data consistency, scalability, interoperability, and governance has become a significant challenge. This paper presents a comprehensive framework for enterprise data modeling that integrates conceptual, logical, and physical data models with metadata management, master data management, data governance, and data quality practices to establish a unified and scalable data architecture. The proposed framework supports modern enterprise technologies, including data warehouses, data lakes, distributed databases, microservices, and hybrid cloud environments, while facilitating seamless data integration and standardized information exchange across business domains. It further incorporates governance-driven policies, schema evolution mechanisms, security controls, privacy protection, and regulatory compliance to ensure sustainable data lifecycle management. Additionally, artificial intelligence-assisted metadata enrichment, automated schema discovery, and intelligent data validation techniques are integrated to enhance modeling accuracy, reduce manual effort, and improve operational efficiency. The framework enables organizations to build resilient and flexible enterprise data ecosystems capable of supporting high-volume transactional processing, real-time analytics, and data-driven decision-making. Overall, the proposed approach improves data quality, enterprise interoperability, business intelligence, and organizational agility while providing a practical foundation for digital transformation initiatives and next-generation enterprise information systems.

DOI: http://doi.org/10.5281/zenodo.21428282

Published by: vikaspatanker

Authors: Rajesh Chauhan, Akshay Bhardwaj, Vinay Kumar

Abstract: Writing correct, idiomatic Django Object-Relational Mapper (ORM) code remains a lasting productivity bottleneck for developers, especially developers who are new to the framework or working against unfamiliar schemas. In this paper, we present ORM Forge, a Django web application that utilizes the Anthropic Claude large language model (LLM) to translate natural language descriptions into production-ready Django ORM queries, and describe an empirical evaluation of the underlying natural language to query translation task, based on a publicly available benchmark, distributed as a structured tabular dataset. Instead of reiterating the system overview, we reframe ORM Forge as a research problem in natural-language-to-structured-query (NL2Query) translation, contextualize it within the wider text-to-SQL literature, and perform a quantitative analysis of translation difficulty based on a stratified sample of 120 question-query pairs from a cross-domain text-to-SQL benchmark.Queries were categorized by structural complexity (single-table, join, aggregation, nested/subquery) and manually mapped to their idiomatic Django ORM equivalents to evaluate how readily each SQL construct translates into ORM syntax such as Q objects, F expressions, annotate(), select_related(), and prefetch_related(). The results show that single-table filter and simple join queries translate almost directly (over 90% direct mapping) while nested subqueries and multi-level aggregations require non-trivial restructuring. This confirms that the query complexity is the dominant factor of translation difficulty. These observations motivate the design choices made by ORM Forge: structured JSON output, schema-grounded prompting, and bounded multi-turn refinement, and set the stage for discussing the limitations and future extensions of LLM-assisted ORM tooling.

DOI: http://doi.org/10.5281/zenodo.21427283

Published by: vikaspatanker

Authors: Mariyam Malik, Professor Dr. B Sasi Kumar

Abstract: Artificial Intelligence (AI) is now widely used in decision-making systems developed by technology giants to drive decisions, triggering concerns related to fairness, transparency, and accountability. In response, organizations such as IBM, Microsoft, and Google have published internal AI ethics policies aligned with international standards. Crucially, these policies prove descriptive in nature and lack measurable methods for evaluation. The work envisioned in this paper, purposefully, a quantitative framework to assess the alignment between corporate AI ethics policies and deployed machine learning systems. A supervised learning model is implemented as a case study using a public income prediction dataset containing sensitive demographic attributes. Fairness is evaluated using demographic parity, while model transparency is examined through SHAP-based feature attribution techniques. We apply additional plausible constraints to address privacy and accountability concerns by limiting sensitive identifiers and enforcing a modular, reproducible pipeline. An aggregated Ethics Compliance Score combines multiple ethical dimensions into a single evaluation measure, showing that ethical risks may persist even in accurate models. Unlike prior work that focuses 27 separately on principles or tools, the proposed framework links corporate ethics commitments directly to quantitative system-level indicators, providing a practical basis for internal AI governance.

Published by: vikaspatanker

Authors: Nehneen Ali, Neenansha Jain Assistant Professor , Dr.Divya Jain Associate Professor

Abstract: Ensuring reliable spectrum efficiency in modern wireless communication networks requires robust and automated signal interference management. However, the dynamic and non-uniform nature of wireless environments introduces complex overlapping signals, complicating traditional energy-detection methods. This study evaluates the performance of advanced machine learning and deep learning models for detecting and classifying co-channel and adjacent-channel wireless interference. Through comprehensive experimental testing and simulation, an optimized neural network architecture is identified. Subsequently, the capability of the detection system is assessed under varying signal-to-noise ratios (SNR). The results indicate that while traditional threshold-based methods fail under fluctuating noise floor conditions, the proposed model maintains a detection accuracy above 98% even at low SNR levels down. As a typical example of intelligent spectrum management, this study provides a crucial reference for the optimization of next-generation cognitive radio and 5G/6G wireless network.

DOI: https://doi.org/10.5281/zenodo.21426161

Published by: vikaspatanker

Authors: Aravind Chagantipati

Abstract: A persistent obstacle in deploying artificial intelligence within enterprise network defense systems is the tension between operational accuracy and human transparency. Conventional, rule-guided systems such as decision trees lack the scale and sophistication required to detect multi-stage, contemporary cyber threats. Conversely, complex neural networks (including LSTM and CNN designs) provide superior classification rates but act as opaque models, making validation difficult within strict enterprise compliance structures. This study presents a tiered, dual-layered architecture that integrates high-capacity deep learning classifiers with post-hoc explainability engines, effectively bridging the gap between classification performance and regulatory auditing requirements.

DOI: http://doi.org/10.5281/zenodo.21412923

Published by: vikaspatanker

Authors: Sheid, Avidime Momohjimoh, Sheidu Omeiza Momoh, Oyem Onyekachi Anslem, Shuaibu, Muhib Amoto

Abstract: This study presents a comprehensive numerical investigation of magnetohydrody-namic (MHD) blood-based nanofluid flow with coupled heat and mass transfer for cardiovascular prosthetic applications. The research addresses critical gaps in thermal regulation and targeted drug delivery modeling by developing a physiolog-ically realistic mathematical framework that incorporates electromagnetic effects, nanoparticle dynamics, and the non-Newtonian rheology of blood. The govern-ing conservation equations for mass, momentum, energy, and species concentration are formulated within a boundary layer framework, incorporating Lorentz forces, variable thermal conductivity, viscous dissipation, Joule heating, Brownian motion, thermophoresis, and chemical reactions. Through similarity transformations, the nonlinear partial differential equations are reduced to a system of coupled ordinary differential equations, which are solved numerically using a collocation method im-plemented in Python (scipy.integrate.solve_bvp). A comprehensive parametric analysis reveals that thermal and solutal buoyancy significantly enhance momentum transport, while the Prandtl number governs thermal boundary layer characteristics. Nanoparticle transport is predominantly controlled by thermophoresis and Brown-ian motion, with thermophoresis promoting nanoparticle accumulation and Brown-ian motion enhancing diffusion. The Schmidt number suppresses species diffusion, while the Casson parameter exhibits minimal influence on velocity but significantly affects thermal distribution. The magnetic parameter introduces resistive Lorentz forces that modify both momentum and thermal fields. These findings provide valu-able insights for the design of cardiovascular prosthetics, thermal therapy systems, and targeted drug delivery platforms.

DOI: http://doi.org/10.5281/zenodo.21407708

Published by: vikaspatanker

Authors: Jereen Susan John

Abstract: Design thinking has emerged as an innovative, human-centered approach to addressing complex challenges in education by fostering creativity, collaboration, critical thinking, and problem-solving. This paper explores the versatility of design thinking and its application across diverse educational contexts, including school, higher, and professional education. It examines the core stages of the design thinking process—empathize, define, ideate, prototype, and test—and their role in promoting learner-centered, experiential, and inquiry-based learning. The study highlights how design thinking enables educators to develop inclusive teaching strategies, redesign curricula, enhance student engagement, and encourage interdisciplinary collaboration. Furthermore, it discusses the integration of digital technologies and real-world problem-solving activities that prepare learners with essential 21st-century skills. The paper argues that adopting design thinking as both a pedagogical framework and an institutional innovation strategy can transform teaching and learning environments by encouraging adaptability, empathy, and continuous improvement. It concludes that the widespread implementation of design thinking can contribute significantly to educational quality, innovation, and lifelong learning.

Published by: vikaspatanker

Authors: Gohit Arun Pedhekar

Abstract: Transformational leadership has become increasingly important in manufacturing organisations because employees are required to respond to technological change, productivity pressures, quality requirements and changing production practices. This study examines the relationship between transformational leadership and employee performance in manufacturing organisations in Pune, Maharashtra. The study adopts a quantitative, cross-sectional research design and focuses on four dimensions of transformational leadership: idealised influence, inspirational motivation, intellectual stimulation and individualised consideration. Employee performance is examined through task performance, contextual performance, quality of work and productivity. Primary data were assumed to be collected from 300 employees working in manufacturing organisations in Pune using a structured five-point Likert-scale questionnaire. Descriptive statistics, Cronbach’s alpha, Pearson correlation and multiple regression were used for analysis. The results indicate positive and statistically significant relationships between transformational leadership and employee performance. The findings suggest that inspirational motivation and intellectual stimulation are particularly important in encouraging employees to improve their work performance, while individualised consideration contributes to employee development and contextual performance. The study concludes that transformational leadership can provide an important managerial mechanism for improving employee performance in manufacturing organisations. The findings have implications for manufacturing managers, human-resource professionals and organisational-development practitioners in Pune.

DOI: http://doi.org/10.5281/zenodo.22043752

Published by: vikaspatanker