International Journal of AI, BigData, Computational and Management Studies
type: channel
language: en
publication name: International Journal of AI, BigData, Computational and Management Studies
issn: 3050-9408
items: High-Throughput Sanctions Screening Using Event-Driven Micro-services in Financial Institutions
items: Programmatic Governance using Policy-as-Code and ML for Dynamic Compliance Enforcement
items: Data Mesh Meets AI: Federated Ownership and ML-Enabled Contracts for Cross-Domain Data Collaboration
items: AI-powered CRM Transformation: Enhancing Customer Experience through Intelligent Salesforce Solutions
items: Data Governance and Trust in Modern CRM Platforms: Strategies for Scalable Customer Data Management
items: Transforming Prior Authorization through Artificial Intelligence: A National Framework for Reducing Administrative Burden and Improving Patient Access
items: AI-Driven Governance and Privacy-Preserving Architecture for Healthcare CRM in Multi-Org Cloud Environments: A Human-Centered Big Data Framework for Trustworthy Decision-Making
items: Context-Aware Enterprise LLM Architecture for Trusted Customer Intelligence and Generative AI Applications in CRM Platforms
items: Runtime Causal Fingerprinting for Root-Cause-Aware Autoscaling and Adaptive Resource Governance in Kubernetes Service Meshes
items: A Cloud-Native Reference Architecture for Data Engineering, Generative AI, and Decision Intelligence Using AWS and Amazon Bedrock
items: Integrating Machine Learning, Data Governance, and Cybersecurity in Next-Generation Enterprise Data Ecosystems
items: Reducing Audit Failures through Proactive Infrastructure Compliance Monitoring
items: Machine Learning Framework for Electric Vehicle Customer Acquisition in the Automotive Industry
items: Examining the Interplay of Artificial Intelligence-Driven Sustainability and Consumer Trust in Luxury Fashion Purchasing Decisions
items: Reducing Overstock in Hospitality Lighting Inventory with Data Forecasting
items: Guard: A Governance-Anchored Framework for Runtime Monitoring and Incident Response in Enterprise Agentic AI Systems
items: A Reference AI Architecture for the Modern Data Organization: Mapping Seven Branches to a Ten-Layer Implementation Stack
items: Uncertainty-Aware Feature Selection Framework Based on Three-Way Decision Theory for Explainable Machine Learning
items: Large Language Model-Based Program Comprehension for Enterprise Software Repositories: A Context-Aware Framework for Code Summarization, Dependency Analysis, and Impact Prediction
items: Intelligent Test Generation Using Large Language Models in Microservices-Based Enterprise Applications
items: A Multi‑Layer Federated Architecture for Secure Cross‑Institution Fraud Detection
items: Model Context Protocol Servers as Orchestration Layers for Generative AI Agents in Enterprise Software Delivery
items: Enterprise Mobile Payment Gateway Architecture on IoT Handheld Devices: Security and Reliability Design
items: Azure DevOps CI/CD Pipeline Design for Regulated Financial Software Deployment Environments
items: Spring Batch Framework Patterns for Large-Scale Data Processing in Cloud-Native Microservices: A GCP Deployment Study
items: Quantum-Classical Hybrid Algorithms for Complex Query Planning: A Scalable Computational Framework for High-Dimensional Decision Optimization in Heterogeneous Data Ecosystems
items: Intelligent Risk Assessment and Classification in Project Management Using Data-Driven AI Models
items: A Conceptual Framework for Training Effectiveness through AI-enabled Digital Learning in Luxury Automotive Retail
items: Impact of IoT and Digital Technologies on Project Management Efficiency: A Survey Study
items: Examining the Role of Adaptive Policy Frameworks and Organisational Sustainability in the Indian Manufacturing Industry under Uncertain Business Conditions

High-Throughput Sanctions Screening Using Event-Driven Micro-services in Financial Institutions
type: item
description: In today's complex global financial environment, cross-border transactions, and constantly changing sanctions lists, compliance with sanctions screening has emerged as one of the most important compliance tasks for financial institutions. Third parties (including regulatory agencies like the OFAC, FATF and EU) have very strict requirements regarding screening of customers, payments and transactions against sanctioned entities, as well as PEP and restricted party lists. Traditional architectures for sanctions screening are generally monolithic and process batches of data, which can become cumbersome and inefficient as they scale up, have elevated latency, low resilience to failures, and inadequate ability to adjust to regulatory updates. These constraints are particularly significant in high-volume banking environments, both with regard to transaction throughput and efficiency of compliance. In this paper we propose an event-driven micro-services approach for high throughput financial institutions' sanctions screening. The suggested architecture splits up sanctions screening into independent scalable services that are connected by event-streaming infrastructure. Core services are transaction ingestion, sanctions list synchronization, fuzzy matching, risk scoring, decision orchestration and alert management. The architecture is designed to provide high throughput, low latency, and fault tolerance for real-time screening, thanks to the use of asynchronous messaging, distributed processing, and dynamic scaling. It covers the basic elements of architecture such as message brokers, stream processing pipelines, distributed databases and intelligent matching engines. The screening pipeline utilizes advanced matching techniques, including Levenshtein distance, phonetic, token-based and machine learning–assisted ranking, to enhance the accuracy of screening and minimize false-positive results. The event-driven model allows for seamless horizontal scaling, independent deployme…
subject: Articles
  topic: section
subject: Sanctions Screening
  topic: keywords
subject: Event-Driven Architecture
  topic: keywords
subject: Micro-services
  topic: keywords
subject: Financial Compliance
  topic: keywords
subject: AML
  topic: keywords
subject: Real-Time Processing
  topic: keywords
subject: Risk Scoring
  topic: keywords
subject: Transaction Monitoring
  topic: keywords
subject: Stream Processing
  topic: keywords
creator: Zaheer Syed
creator: Hani Patel
creator: Juwaria Naaz
rights: Copyright (c) 2026
date: 2026-07-05
publication date: 2026-07-05
starting page: 28
ending page: 36
doi: 10.63282/3050-9416.IJAIBDCMS-V7I3P104

Programmatic Governance using Policy-as-Code and ML for Dynamic Compliance Enforcement
type: item
description: In the intricate world of laws and regulations we have today, companies require more than just non-flexible guidelines to be on the right side of the law. They require systems that are not only adaptable but that also change with them in real time. This document investigates a futuristic programmatic governance way of handling compliance issues by combining Policy-as-Code (PaC) and Machine Learning (ML). The core of the idea is in the impairments of manual policy enforcement and rigid compliance checks that are always behind in changes of business operations or regulations. Organizations turn policies into executable code so as they can automate enforcement in distributed environments, thus achieving both uniformity and accountability as a result. Moreover, when combined with ML, these machines become capable of learning from previous compliance behavior, anticipating future violations, and even suggesting the necessary preventive measures. The approach proposed in this study consists of the following steps: writing the policy with a declarative language such as Open Policy Agent (OPA), linking the policy to an event-driven architecture, and allowing ML models to receive and analyze the data in real-time, be they anomaly or risk cases. The main feature of this work is the presentation of an architecture where cloud-native is integrated with dynamic policy engines and ML classifiers, enabling organizations to respond to compliance drifts as they happennot after. The architecture demonstrated in a multi-cloud case shows how it identified access control violations and went ahead to change settings automatically without requiring the involvement of a human. The findings, thus, indicate faster response time, shrinkage of compliance gaps, and notable cost savings as compared to the traditional governance models. This is, in fact, a very strong argument for using a live, learning compliance infrastructure that can not only adjust itself to changes but can actually benefit …
subject: Articles
  topic: section
subject: Programmatic Governance Integrates Policy-as-Code (Pac)
  topic: keywords
subject: Machine Learning (ML)
  topic: keywords
subject: Compliance Automation to Enable Dynamic Policy Enforcement
  topic: keywords
subject: Leveraging Infrastructure as Code (IAC)
  topic: keywords
subject: Governance-as-Code for Robust Regulatory Compliance
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: Devsecops Alignment
  topic: keywords
creator: Sivadeep Katangoori
creator: Diganto Ghosh
rights: Copyright (c) 2025
date: 2025-08-14
publication date: 2025-08-14
starting page: 158
ending page: 167
doi: 10.63282/3050-9416.IJAIBDCMS-V6I3P118

Data Mesh Meets AI: Federated Ownership and ML-Enabled Contracts for Cross-Domain Data Collaboration
type: item
description: In modern data-driven companies, the problem of fragmented data continues to stifle innovation, make it harder to make these decisions, and make it harder for people from different departments to work together. As companies grow, traditional centralized data architectures have trouble growing quickly & meeting the needs of various fields. This has led to the creation of the Data Mesh paradigm, which supports a decentralized approach to data management in which domain teams own, share, and utilize data as a product. However, enabling substantial cross-domain data collaboration within a distributed framework remains a considerable challenge, particularly regarding the establishment of trust, governance & interoperability across many other domains. This essay looks at how federated ownership and AI-driven smart contracts might change the way information is shared and managed in many other different fields. By adding ML to contract logic, businesses can automate access control, usage limitations, compliance checks & predictive alerts. This reduces friction and builds trust between teams. Through case studies and architectural analyses, we show how machine learning-enabled contracts allow for secure, scalable, and auditable data exchange while keeping autonomy and avoiding bottlenecks. Our study shows that adding AI to federated data governance makes data easier to get to and better quality, and it also gives domain teams more flexibility and responsibility to come up with new ideas. The paper offers a paradigm for businesses seeking to implement Data Mesh via intelligent automation, including practical insights into the design, implementation & measurable impacts of this technique in real-world contexts.
subject: Articles
  topic: section
subject: Data Mesh
  topic: keywords
subject: Federated Ownership
  topic: keywords
subject: Smart Contracts
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Data Collaboration
  topic: keywords
subject: AI Governance
  topic: keywords
subject: Cross-Domain Data Sharing
  topic: keywords
subject: Data as a Product
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Trust Automation
  topic: keywords
subject: Data Contract Enforcement
  topic: keywords
subject: Metadata Intelligence
  topic: keywords
subject: Decentralized Data Infrastructure
  topic: keywords
subject: Real-Time Compliance
  topic: keywords
subject: AI-Driven Policy Enforcement
  topic: keywords
creator: Sivadeep Katangoori
creator: Diganto Ghosh
rights: Copyright (c) 2025
date: 2025-06-02
publication date: 2025-06-02
starting page: 148
ending page: 157
doi: 10.63282/3050-9416.IJAIBDCMS-V6I2P117

AI-powered CRM Transformation: Enhancing Customer Experience through Intelligent Salesforce Solutions
type: item
description: Customer relationship management is frequently abbreviated to CRM. It has evolved well beyond a customer information storage system and is now considered a vital business tool through which a company can deliver personalized customer engagement, increase operational efficiency, and even enable promotion. Nowadays, when organizations handle not only elevated customer expectations and much larger amounts of data but the issue of extremely quick decision-making as well, most of the conventional CRM approaches will be incapable of offering the kind of instant response and personalization that the competition requires. The entry of Artificial Intelligence (AI) in the CRM industry has brought a revolutionary change by enabling enterprises to not only extract value from customer data but also to anticipate their actions and automate their daily tasks. Since Salesforce is a top CRM system, it too has experienced tremendous growth through deployment of very complex AI features such as Einstein AI, predictive analytics, intelligent automation, natural language processing, and machine learning-based ​‍​‌‍​‍‌recommendations. The paper discusses how the Salesforce solutions empowered with AI are revolutionizing customer experience management by providing deeper customer engagement, enhancing sales productivity, optimizing service operations, and supporting data-based decisions. The study investigates the embedding of AI capabilities in Salesforce programs and measures the effect of such technologies with the help of case studies from the industry, a review of published work, as well as direct experience with use. Results show that businesses using Salesforce solutions supported by AI have a better relationship with customers, more sales made, faster fixing of issues, more accurate forecasting, and overall better working of the company. Besides this, through automation, AI helps reduce the time spent by people on the work that can be done by machines, which gives the staff a chan…
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Customer Relationship Management (CRM)
  topic: keywords
subject: Salesforce
  topic: keywords
subject: Salesforce Einstein
  topic: keywords
subject: Intelligent Automation
  topic: keywords
subject: Customer Experience (CX)
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Customer Engagement
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Customer 360
  topic: keywords
subject: Sales Automation
  topic: keywords
subject: Service Cloud
  topic: keywords
subject: Marketing Cloud
  topic: keywords
subject: Lead Scoring
  topic: keywords
subject: Personalized Customer Interactions
  topic: keywords
subject: Real-Time Analytics
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Customer Retention
  topic: keywords
subject: Data-Driven Decision Making
  topic: keywords
creator: Karthik Allam
rights: Copyright (c) 2025
date: 2025-05-31
publication date: 2025-05-31
starting page: 138
ending page: 147
doi: 10.63282/3050-9416.IJAIBDCMS-V6I2P116

Data Governance and Trust in Modern CRM Platforms: Strategies for Scalable Customer Data Management
type: item
description: Customer​‍​‌‍​‍‌ Relationship Management (CRM) systems have become the mainstay of customer-centric organizations, enabling them to collect, manage, and analyze customer information from multiple channels and touchpoints. As more companies are turning to data for their decision-making, high-quality, consistent, safe, and reliable customer data have not only become key factors that influence business outcomes and customer satisfaction, but also play a role in complying with regulations. In this regard, data governance and trust are crucial in ensuring that customer data remain correct, accessible, safe, and ethically handled throughout their lifecycle. This article explores how data governance structures and trust-enhancing practices are becoming part and parcel of CRM tools nowadays, most importantly concentrating on those methods that allow scalable handling of customer data even in rapidly changing business circumstances. The paper aims to identify primary governance measures, quantify their overall impact on data quality and organizational trust, and determine how today's businesses can maintain a balance between data openness and ​‍​‌‍​‍‌protection/privacy. A​‍​‌‍​‍‌ qualitative methodology supported by a comprehensive case study of a CRM system implementation has been the method of this work exploring governance policies, data stewardship frameworks, metadata handling procedures, access control mechanisms, and compliance tactics which are the main factors for successful customer data management. The findings indicate that organizations that articulate their governance frameworks clearly, standardize their data management processes, and publicly set up accountability lines, experience a substantial increase in the accuracy of data, operational efficiency, customer trust, and regulatory preparedness. Also, the study highlights the role of technologically advanced tools like AI, automation, and live monitoring in enhancing governance accuracy and customer data res…
subject: Articles
  topic: section
subject: Data Governance
  topic: keywords
subject: Customer Relationship Management (CRM)
  topic: keywords
subject: Customer Data Management
  topic: keywords
subject: Data Quality
  topic: keywords
subject: Data Trust
  topic: keywords
subject: Data Privacy
  topic: keywords
subject: Data Security
  topic: keywords
subject: Master Data Management (MDM)
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
subject: Cloud CRM Platforms
  topic: keywords
subject: Data Stewardship
  topic: keywords
subject: Customer Trust Frameworks
  topic: keywords
subject: Scalable Data Architecture
  topic: keywords
subject: Data Lifecycle Management
  topic: keywords
subject: Governance Frameworks
  topic: keywords
subject: Metadata Management
  topic: keywords
subject: Customer Experience
  topic: keywords
subject: Data Integrity
  topic: keywords
subject: Risk Management
  topic: keywords
subject: Digital Transformation
  topic: keywords
creator: Srichandra Boosa
rights: Copyright (c) 2025
date: 2025-08-12
publication date: 2025-08-12
starting page: 148
ending page: 157
doi: 10.63282/3050-9416.IJAIBDCMS-V6I3P117

Transforming Prior Authorization through Artificial Intelligence: A National Framework for Reducing Administrative Burden and Improving Patient Access
type: item
description: Prior authorization is widely used to manage healthcare utilization and control costs, yet its current implementation often creates substantial administrative burden, delays in treatment, inconsistent insurer requirements, and barriers to patient access. Healthcare professionals frequently spend considerable time preparing documentation, responding to payer queries, and managing appeals, while patients may experience uncertainty, interrupted care, and delayed access to clinically necessary services. This paper examines how artificial intelligence can transform prior authorization through a national framework designed to reduce administrative workload while improving timeliness, transparency, equity, and clinical accountability. The proposed framework integrates interoperable health-data exchange, AI-supported documentation extraction, eligibility checking, risk-based case triage, clinician-led review, patient communication tools, and continuous performance monitoring. AI is positioned as a decision-support mechanism rather than an autonomous decision-maker, ensuring that complex, high-risk, and denied cases remain subject to qualified human oversight. The framework also incorporates safeguards for privacy, explain ability, bias detection, patient appeal rights, and independent governance. By standardizing workflows and reducing repetitive administrative tasks, an AI-enabled national prior-authorization system may improve provider efficiency, accelerate appropriate treatment access, and support a more patient-centered healthcare system. Future empirical research should evaluate the framework using real-world payer, provider, and patient outcome data.
subject: Articles
  topic: section
subject: Prior Authorization
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Administrative Burden
  topic: keywords
subject: Patient Access
  topic: keywords
subject: Interoperability
  topic: keywords
subject: Clinical Decision Support
  topic: keywords
subject: Healthcare Governance
  topic: keywords
creator: Tan Tho Nguyen
rights: Copyright (c) 2026
date: 2026-07-03
publication date: 2026-07-03
starting page: 17
ending page: 27
doi: 10.63282/3050-9416.IJAIBDCMS-V7I3P103

AI-Driven Governance and Privacy-Preserving Architecture for Healthcare CRM in Multi-Org Cloud Environments: A Human-Centered Big Data Framework for Trustworthy Decision-Making
type: item
description: Healthcare and insurance organizations are increasingly expected to modernize digital service operations while protecting privacy, preserving trust, and improving the quality of decisions made across large and complex ecosystems. In multi-organization cloud environments, healthcare customer relationship management platforms support interactions among members, agents, care coordinators, operational teams, and enterprise partners. Artificial intelligence can improve these interactions through guided recommendations, summarization, risk insight, and service orchestration, but it also introduces new concerns about data exposure, accountability, and governance. This paper proposes an AI-driven governance and privacy-preserving architecture for healthcare CRM in multi-org cloud environments. The framework combines human-centered design, big-data intelligence, privacy-preserving controls, and management oversight into a unified operating model for trustworthy decision-making. The paper argues that in healthcare service systems, AI cannot be treated as an isolated automation layer. It must be shaped by privacy boundaries, reviewable controls, explainability, and organizational discipline. The contribution of this study lies in presenting a practical architecture that links advanced computational capabilities with management-level trust, operational resilience, and more humane digital experiences.
subject: Articles
  topic: section
subject: AI Governance
  topic: keywords
subject: Privacy-Preserving Architecture
  topic: keywords
subject: Healthcare CRM
  topic: keywords
subject: Multi-Org Cloud
  topic: keywords
subject: Big Data
  topic: keywords
subject: Trustworthy Decision-Making
  topic: keywords
creator: Susil Kumar Sahu
rights: Copyright (c) 2024
date: 2024-12-30
publication date: 2024-12-30
starting page: 273
ending page: 276
doi: 10.63282/3050-9416.IJAIBDCMS-V5I4P128

Context-Aware Enterprise LLM Architecture for Trusted Customer Intelligence and Generative AI Applications in CRM Platforms
type: item
description: Customer relationship management (CRM) platforms increasingly depend on artificial intelligence to convert fragmented customer interactions into actionable intelligence. However, conventional CRM analytics pipelines typically separate predictive modeling, knowledge retrieval, governance, and human decision support into loosely connected components. This separation becomes problematic when large language models (LLMs) are introduced into enterprise environments, because generative systems can produce fluent but unsupported content, expose sensitive information, amplify biased patterns, and misalign recommendations with business context. This paper proposes a context-aware enterprise LLM architecture for trusted customer intelligence and generative AI applications in CRM platforms. The proposed framework integrates contextual signal modeling, retrieval-augmented generation, enterprise knowledge grounding, privacy-preserving data governance, explainability, human-in-the-loop control, and continuous monitoring. The architecture is designed to support customer service automation, sales enablement, churn-risk interpretation, next-best-action recommendation, knowledge-assisted agent support, and executive customer intelligence. Methodologically, the paper develops a layered conceptual model and an evaluation framework that jointly assesses factuality, contextual relevance, privacy risk, fairness, latency, business utility, and governance compliance. The analytical discussion shows that enterprise LLM value in CRM depends less on model scale alone and more on the alignment among customer context, authoritative enterprise knowledge, operational workflows, and trustworthy control mechanisms. The paper contributes a structured reference architecture and a multidimensional performance framework for deploying LLM-enabled CRM capabilities in high-accountability organizations.
subject: Articles
  topic: section
subject: Context-Aware Computing
  topic: keywords
subject: Enterprise LLM
  topic: keywords
subject: Customer Intelligence
  topic: keywords
subject: CRM Platforms
  topic: keywords
subject: Retrieval-Augmented Generation
  topic: keywords
subject: Trustworthy AI
  topic: keywords
subject: Generative AI
  topic: keywords
subject: Privacy-Preserving Analytics
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Enterprise Architecture
  topic: keywords
creator: Achuta Krishna Kishore Varma Alluri
rights: Copyright (c) 2021
date: 2021-06-30
publication date: 2021-06-30
starting page: 146
ending page: 155
doi: 10.63282/3050-9416.IJAIBDCMS-V2I2P116

Runtime Causal Fingerprinting for Root-Cause-Aware Autoscaling and Adaptive Resource Governance in Kubernetes Service Meshes
type: item
description: Kubernetes-orchestrated microservice systems increasingly operate under volatile workload patterns, strict latency service-level objectives, and complex inter-service dependencies introduced by service mesh communication layers. Although Kubernetes provides established autoscaling mechanisms, conventional reactive autoscaling policies frequently interpret symptoms such as CPU saturation, request latency, or queue growth as direct scaling triggers without distinguishing whether the observed degradation is caused by workload demand, downstream dependency propagation, resource contention, noisy-neighbor effects, configuration drift, or release-induced regression. This limitation often leads to unnecessary scaling, delayed remediation, over-provisioning, and repeated service-level objective violations. This paper proposes a runtime causal fingerprinting framework for root-cause-aware autoscaling and adaptive resource governance in Kubernetes service meshes. The framework constructs continuously updated causal fingerprints from distributed traces, service mesh telemetry, container metrics, event streams, and workload signals. These fingerprints encode not only anomaly signatures but also causal propagation patterns across services, pods, nodes, and resource dimensions. The proposed approach integrates causal graph inference, temporal fingerprint matching, SLO-risk estimation, and policy-constrained scaling actions within a closed-loop governance architecture. Unlike conventional autoscalers that scale based primarily on local resource utilization, the proposed framework differentiates demand-driven saturation from causally propagated degradation and selects remediation strategies accordingly. The paper develops the conceptual model, methodological pipeline, evaluation criteria, and analytical discussion necessary for implementing root-cause-aware autoscaling in production-grade Kubernetes environments. The study contributes a technically grounded framework for improving …
subject: Articles
  topic: section
subject: Kubernetes
  topic: keywords
subject: Service Mesh
  topic: keywords
subject: Autoscaling
  topic: keywords
subject: Causal Inference
  topic: keywords
subject: Root-Cause Analysis
  topic: keywords
subject: Distributed Tracing
  topic: keywords
subject: Microservices
  topic: keywords
subject: Adaptive Resource Governance
  topic: keywords
subject: SLO-Aware Resource Management
  topic: keywords
subject: Cloud-Native Systems
  topic: keywords
creator: Nilesh Mutyam
rights: Copyright (c) 2025
date: 2025-08-10
publication date: 2025-08-10
starting page: 137
ending page: 147
doi: 10.63282/3050-9416.IJAIBDCMS-V6I3P116

A Cloud-Native Reference Architecture for Data Engineering, Generative AI, and Decision Intelligence Using AWS and Amazon Bedrock
type: item
description: As data in enterprises is growing at an accelerated pace and with the advancement of artificial intelligence, how the organizations are obtaining business intelligence and operational insights has undergone a significant change. Today's companies are increasingly relying on scalable, secure, intelligent, cloud-native architectures that can support high-volume data engineering, generative artificial intelligence (GenAI), and real-time decision intelligence. However, traditional data platforms are not designed to handle data silos, low latency, complex infrastructure, or integration with AI, and these are some of the challenges they face in today's business context. Solutions that leverage elastic infrastructure and elastic services are a good way to overcome these challenges. This paper suggests a cloud-native reference architecture for a data engineering pipeline, AI systems, and decision intelligence integration system with Amazon Web Services (AWS) and Amazon Bedrock. The architecture utilizes AWS services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon EMR, Amazon Kinesis, AWS Lambda, Amazon SageMaker, and Amazon Bedrock to create a scalable intelligence ecosystem for enterprise. The proposed architecture provides the possibility of collecting, processing, transforming and analyzing structured and unstructured data, as well as integrating foundation models for intelligent automation and augmentation of decisions. The architecture is built around four key layers: data ingestion, data processing and storage, AI intelligence and delivery of decision intelligence. The data ingestion layer can ingest data using AWS-native services, such as batch and streaming workloads. The processing layer provides scalable ETL, ELT and data transformation pipelines. The AI intelligence layer includes AI and generative AI features such as natural language analytics, intelligent summarization, anomaly detection, predictive analytics, and recommendation systems. The decision intel…
subject: Articles
  topic: section
subject: Cloud-Native Architecture
  topic: keywords
subject: Data Engineering
  topic: keywords
subject: Generative AI
  topic: keywords
subject: Decision Intelligence
  topic: keywords
subject: Aws
  topic: keywords
subject: Amazon Bedrock
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Data Lake
  topic: keywords
subject: Enterprise Analytics
  topic: keywords
creator: Ramakrishna Taluri
rights: Copyright (c) 2024
date: 2024-03-30
publication date: 2024-03-30
starting page: 218
ending page: 227
doi: 10.63282/3050-9416.IJAIBDCMS-V5I1P122

Integrating Machine Learning, Data Governance, and Cybersecurity in Next-Generation Enterprise Data Ecosystems
type: item
description: The rapid growth of digital transformation initiatives has significantly increased the volume, velocity, and variety of enterprise data. Organizations increasingly rely on advanced analytics, artificial intelligence (AI), machine learning (ML), cloud computing, and distributed data architectures to derive strategic insights and maintain competitive advantage. However, the expansion of enterprise data ecosystems introduces substantial challenges related to data governance, security, privacy, compliance, and operational resilience. Traditional approaches that treat machine learning, data governance, and cybersecurity as independent disciplines are no longer sufficient to address the complexities of modern data-driven enterprises. This study explores the integration of machine learning, data governance, and cybersecurity within next-generation enterprise data ecosystems and proposes a comprehensive framework that aligns intelligent analytics, governance policies, and security controls. The research investigates the interdependencies among these domains and evaluates their collective impact on organizational performance, risk management, regulatory compliance, and data quality. Through a conceptual research methodology supported by comparative analysis of existing frameworks and enterprise practices, the study identifies critical success factors and emerging challenges associated with integrated enterprise data management. The findings indicate that organizations adopting unified governance-security-analytics frameworks achieve higher levels of trustworthiness, operational efficiency, and cyber resilience. Furthermore, machine learning technologies contribute significantly to proactive threat detection, automated governance enforcement, and intelligent data lifecycle management. The study concludes by outlining future research directions focusing on explainable AI, autonomous governance systems, privacy-preserving machine learning, and zero-trust enterprise architecture…
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Enterprise Data Ecosystems
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Data Security
  topic: keywords
subject: Data Analytics
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Data Quality
  topic: keywords
subject: Risk Management
  topic: keywords
creator: M. Riyaz Mohammed
rights: Copyright (c) 2026
date: 2026-06-12
publication date: 2026-06-12
starting page: 391
ending page: 397
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P150

Reducing Audit Failures through Proactive Infrastructure Compliance Monitoring
type: item
description: Cloud computing, Infrastructure as Code (IaC), and continuous delivery have made it harder to maintain regulatory compliance. Traditional compliance models that rely on periodic checks and manual evidence are no longer effective. This paper looks at how proactive infrastructure compliance monitoring can help reduce audit failures in today’s IT systems. It covers the technical, operational, and cultural changes needed for continuous compliance, including policy-as-code, agentless scanning, automation, and integration with CI/CD pipelines. The results show that proactive monitoring improves system reliability, reduces regulatory risk, and simplifies audits in complex environments.
subject: Articles
  topic: section
subject: Infrastructure Compliance
  topic: keywords
subject: Continuous Monitoring
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Audit Failure
  topic: keywords
subject: Proactive Governance
  topic: keywords
subject: Devsecops
  topic: keywords
subject: Policy-As-Code
  topic: keywords
creator: Nadeem Siddiqui
rights: Copyright (c) 2026
date: 2026-07-01
publication date: 2026-07-01
starting page: 7
ending page: 16
doi: 10.63282/3050-9416.IJAIBDCMS-V7I3P102

Machine Learning Framework for Electric Vehicle Customer Acquisition in the Automotive Industry
type: item
description: The rapid growth of the EV market presents a major opportunity for automotive companies to expand their customer base and drive sustainable revenue. This paper outlines the development of a data-driven customer acquisition strategy for an automotive client—their first such initiative. The strategy leveraged historical EV sales trends to identify two key segments: existing brand customers and conquest customers. Initially focusing on existing customers, we built a scalable framework for future expansion. A machine-learning-based propensity model was developed using demographic and lifestyle attributes, sales and service history, charging infrastructure, state incentives, and fuel price trends. Key predictors included purchase recency, financial health, infrastructure availability, and technological affinity. Implemented end-to-end, from data preparation to model deployment. This paper explores the methodology, insights, challenges, and the broader impact of data science in advancing EV adoption
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Data-Driven Marketing
  topic: keywords
subject: Luxury Automotive
  topic: keywords
subject: Customer Acquisition
  topic: keywords
subject: Electric Vehicles (EV)
  topic: keywords
creator: Vaibhav Tummalapalli
rights: Copyright (c) 2026
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 6
doi: 10.63282/3050-9416.IJAIBDCMS-V7I3P101

Examining the Interplay of Artificial Intelligence-Driven Sustainability and Consumer Trust in Luxury Fashion Purchasing Decisions
type: item
description: This Luxury Fashion Industry (LFI) is going through a radical change because of the adoption of AI technologies and the role played by sustainability. These Luxury Fashion Brands (LFBs) are embracing innovative technologies like machine learning, predictive analytics, blockchain technology, virtual assistants, and intelligent recommendation tools for their business efficiencies, enhanced customer satisfaction, and sustainable practices. Meanwhile, people have become more aware about environmental sustainability, ethical sourcing, transparency, and sustainable consumption, which put pressure on LFBs for being more sustainable. This review paper identified the relationship between sustainability enabled by AI and consumer trust in purchasing Luxury Fashion Products (LFPs). It focuses on the ways through which these advanced technologies contribute to sustainable LF practices via waste elimination, smart inventory management, ethical supply chain activities, personalized customer engagement, and circular economy approach. In addition, this research focuses on the significance of factors that contribute to consumer trust such as transparency, authenticity, privacy assurance, use of AI technology ethically, and brand reputation to shape consumers’ buying decisions. Moreover, some of the problems and ethical issues linked with the implementation of AI technology in organizations have been identified such as data privacy, lack of transparency in AI technology, greenwashing, job displacement, and dependence on technology. Furthermore, the study considers various possibilities in the future and suggestions to ensure that both human oriented LF and technological advancement can coexist successfully. This literature review suggests that the integration of AI technologies can support sustainability initiatives, enhance consumer trust, and improve operational efficiency within the LFI.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Sustainability
  topic: keywords
subject: Consumer Trust
  topic: keywords
subject: Luxury Fashion
  topic: keywords
subject: Purchasing Decisions
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Ethical Fashion
  topic: keywords
subject: Smart Technologies
  topic: keywords
subject: Brand Loyalty
  topic: keywords
creator: Jagadishwar Reddy Reddygari
rights: Copyright (c) 2026
date: 2026-06-10
publication date: 2026-06-10
starting page: 383
ending page: 390
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P149

Reducing Overstock in Hospitality Lighting Inventory with Data Forecasting
type: item
description: In the hospitality lighting sector, man-aging inventory efficiently remains a persistent challenge. Overstocking not only ties up capital but also consumes valuable warehouse space, es-pecially in an industry characterized by seasonal demand and complex project timelines. This paper presents a data-driven forecasting methodology to reduce overstock in hospitality lighting. By lever-aging historical sales data and applying time series analysis, suppliers can more accurately predict de-mand patterns, streamline procurement, and re-duce waste. The proposed approach improves in-ventory turnover and helps lighting vendors align with the needs of the dynamic hospitality sector. The analysis demonstrates that proper forecasting methods not only mitigate risks associated with surplus inventory but also enhance the respon-siveness of the supply chain. This ultimately re-sults in cost savings, improved margins, and ele-vated customer satisfaction.
subject: Articles
  topic: section
subject: Inventory Management
  topic: keywords
subject: Demand Forecasting
  topic: keywords
subject: Hospitality Lighting
  topic: keywords
subject: Overstock Reduction
  topic: keywords
subject: Data Analytics
  topic: keywords
subject: Time Series Analysis
  topic: keywords
creator: Kishan Patel Alabama
rights: Copyright (c) 2026
date: 2026-06-08
publication date: 2026-06-08
starting page: 380
ending page: 382
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P148

Guard: A Governance-Anchored Framework for Runtime Monitoring and Incident Response in Enterprise Agentic AI Systems
type: item
description: The rapid enterprise deployment of agentic artificial intelligence (AI) systems introduces operational risks that existing monitoring and incident response (IR) frameworks cannot address. Agentic systems exhibit non-deterministic behavior, autonomous tool invocation, dynamic reasoning chains, and emergent capabilities arising from multi-agent composition — properties that invalidate the static monitoring assumptions of DevOps, MLOps, and LLMOps paradigms. The National Institute of Standards and Technology (NIST AI 800-4, 2026) documents these gaps comprehensively, yet no validated runtime enforcement or IR framework exists for enterprise agentic AI. This paper presents GUARD — Governance-Unified Agentic Runtime Detection and Response — extending the prior enterprise agentic AI lifecycle governance framework of Anuguthala (2026), which established mandatory system-of-record registration, risk tiering, and governance checkpoints but did not specify runtime enforcement mechanisms or structured IR procedures. GUARD closes this gap through three primary contributions: (1) the Agentic System of Record (SoR), extended with a three-entity registration model covering individual agents, workflows, and inter-agent composition boundaries, serving as the authoritative runtime enforcement reference for all agent resource decisions; (2) Registry-Bound Execution Control (RBEC), a three-state runtime mechanism — allow, human-in-the-loop (HITL) pause, or kill-switch — validating every agent resource access against the SoR before execution; and (3) the Agentic Incident Response (AIR) lifecycle, a six-phase risk-tiered IR process anchored to the SoR. Two supporting contributions accompany these: a formal Lethal Trifecta boundary condition — adapted from the risk intersection concept articulated by Willison (2025) — operationalizing risk-tier enforcement within RBEC; and an empirical reference implementation on LangGraph evaluated across 100 trials per scenario. Empirical evaluation acr…
subject: Articles
  topic: section
subject: Agentic AI
  topic: keywords
subject: Enterprise Governance
  topic: keywords
subject: Runtime Monitoring
  topic: keywords
subject: Incident Response
  topic: keywords
subject: System of Record
  topic: keywords
subject: Registry-Bound Execution
  topic: keywords
subject: Kill-Switch
  topic: keywords
subject: Lethal Trifecta
  topic: keywords
subject: RBEC
  topic: keywords
subject: GUARD
  topic: keywords
creator: Sandeep Kumar Anuguthala
rights: Copyright (c) 2026
date: 2026-06-08
publication date: 2026-06-08
starting page: 370
ending page: 379
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P147

A Reference AI Architecture for the Modern Data Organization: Mapping Seven Branches to a Ten-Layer Implementation Stack
type: item
description: Enterprise data organizations are pursuing AI augmentation, but practitioner literature offers either single-branch agent systems (a data-science agent, a stewardship agent, a project-management agent) or unbounded surveys of agentic capabilities. Neither is implementable on day one. This paper proposes a complete task-to-architecture-to-tooling mapping for a Director of Data Engineering, or technology executive in charge of an enterprise data function. It identifies the seven canonical branches of a mature data organization (BI Engineering, AI/ML Engineering, Analytics, Data Science, Data Stewardship, Project Management Office, and Product Ownership), enumerates the three highest-volume operational tasks per branch (twenty-one tasks total), and proposes a ten-layer reference AI architecture that binds each layer to representative open-source and commercial tools (Cube, AtScale, Snowflake, Databricks, Collibra, Atlan, LangGraph, Pinecone, Monte Carlo, and others). A worked trace shows how a real cross-branch request moves through the architecture, and a phased adoption roadmap converts the framework into a 0-180 day implementation plan that the reader can take to a steering committee on Monday morning. The framework is LLM-agnostic, vendor-substitutable at the capability class, and protocol-aligned with Model Context Protocol and Agent2Agent. Reproducibility and ethical guardrails appear as standalone sections.
subject: Articles
  topic: section
subject: AI-Augmented Data Organization
  topic: keywords
subject: Multi-Agent Systems
  topic: keywords
subject: Semantic Layer
  topic: keywords
subject: Reference Architecture
  topic: keywords
subject: MCP
  topic: keywords
subject: A2A
  topic: keywords
subject: Langgraph
  topic: keywords
subject: Cube
  topic: keywords
subject: Snowflake
  topic: keywords
subject: Enterprise Data Engineering
  topic: keywords
subject: Mlops
  topic: keywords
subject: Phased Adoption
  topic: keywords
creator: Smeet H Patel
rights: Copyright (c) 2026
date: 2026-06-06
publication date: 2026-06-06
starting page: 364
ending page: 369
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P146

Uncertainty-Aware Feature Selection Framework Based on Three-Way Decision Theory for Explainable Machine Learning
type: item
description: Feature selection is a critical stage in machine learning because it improves predictive performance, reduces computational complexity, and enhances model interpretability. However, many existing feature selection methods rely on binary selection strategies that either retain or discard features based on fixed relevance thresholds. This approach can be inadequate when features contain uncertainty, instability, redundancy, or borderline predictive value. To address this limitation, this article proposes an uncertainty-aware feature selection framework based on three-way decision theory for explainable machine learning. The proposed framework classifies features into three decision regions: accepted features, rejected features, and deferred features. Accepted features are considered highly relevant and reliable, rejected features are removed due to low relevance or high redundancy, while deferred features are subjected to further evaluation because of uncertain or inconsistent importance. By integrating uncertainty measurement with feature relevance scoring, the framework provides a more flexible and transparent alternative to conventional two-way feature selection. The framework also supports explainability by providing clear justification for why each feature is selected, rejected, or deferred. This makes it particularly useful for high-dimensional and high-stakes machine learning applications where model transparency, reliability, and decision accountability are important. The proposed approach is expected to improve feature selection stability, reduce uncertainty in selected feature subsets, and enhance the interpretability of machine learning models.
subject: Articles
  topic: section
subject: Three-Way Decision Theory
  topic: keywords
subject: Uncertainty-Aware Feature Selection
  topic: keywords
subject: Explainable Machine Learning
  topic: keywords
subject: Feature Relevance
  topic: keywords
subject: Feature Uncertainty
  topic: keywords
subject: Rough Set Theory
  topic: keywords
subject: Model Interpretability
  topic: keywords
subject: Decision-Theoretic Learning
  topic: keywords
creator: Hassaan Mehmood
rights: Copyright (c) 2026
date: 2026-06-04
publication date: 2026-06-04
starting page: 346
ending page: 363
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P145

Large Language Model-Based Program Comprehension for Enterprise Software Repositories: A Context-Aware Framework for Code Summarization, Dependency Analysis, and Impact Prediction
type: item
description: Enterprise software repositories contain complex, long-lived, and continuously evolving code assets whose behavior is distributed across services, frameworks, configuration files, database schemas, event streams, and deployment scripts. Conventional program comprehension tools provide useful static analysis, search, and dependency visualization capabilities, but they often fail to explain why a code element exists, how a change propagates across enterprise boundaries, and which downstream artifacts are likely to be affected by a modification. Large language models have recently demonstrated strong ability in source code understanding, documentation generation, and natural-language reasoning over program artifacts. However, direct application of generic LLMs to enterprise repositories is insufficient because enterprise comprehension requires contextual grounding, dependency-aware retrieval, architectural constraints, security awareness, and traceable impact prediction. This paper proposes RepoComprehend-LLM, a context-aware framework for LLM-based program comprehension in enterprise software repositories. The framework integrates repository mining, static dependency extraction, semantic code representation, retrieval-augmented generation, graph-based impact propagation, and confidence-calibrated explanation generation. Unlike isolated method-level summarization, the proposed framework generates hierarchical summaries at method, class, service, API, and business-capability levels; constructs multi-layer dependency graphs across code, configuration, database, event, and deployment artifacts; and predicts change impact using structural, semantic, historical, and developer-activity signals. The paper presents the architecture, methodology, scoring functions, evaluation protocol, implementation considerations, and enterprise governance controls for deploying such a framework in regulated software engineering environments. The central contribution is a research-oriented, i…
subject: Articles
  topic: section
subject: Large Language Models
  topic: keywords
subject: Program Comprehension
  topic: keywords
subject: Software Repository Mining
  topic: keywords
subject: Code Summarization
  topic: keywords
subject: Dependency Analysis
  topic: keywords
subject: Change Impact Prediction
  topic: keywords
subject: Retrieval-Augmented Generation
  topic: keywords
subject: Enterprise Software Engineering
  topic: keywords
subject: Software Maintenance
  topic: keywords
subject: Technical Debt
  topic: keywords
creator: S. Karthik
creator: Adithya Balasubramanyam
creator: Roopa Ravish
creator: Surabhi Narayan
rights: Copyright (c) 2024
date: 2024-12-30
publication date: 2024-12-30
starting page: 261
ending page: 272
doi: 10.63282/3050-9416.IJAIBDCMS-V5I4P127

Intelligent Test Generation Using Large Language Models in Microservices-Based Enterprise Applications
type: item
description: Automated software testing has become a fundamental requirement for maintaining quality, reliability, and scalability in modern enterprise applications. The rapid adoption of microservices architecture has significantly improved system modularity and deployment flexibility; however, it has introduced complex testing challenges due to distributed service interactions, dynamic API contracts, independent deployment cycles, and heterogeneous technology stacks. Traditional automated testing approaches often fail to generate comprehensive test cases for microservices because they lack contextual understanding of service behavior, business logic, and inter-service dependencies. This research presents an intelligent test generation framework using Large Language Models (LLMs) to automate the creation of unit tests, integration tests, and contract tests for microservices-based enterprise applications. The proposed approach leverages LLM-based code generation capabilities combined with service metadata, API specifications, dependency graphs, historical defect information, and application source code to produce context-aware test suites. The framework focuses on Spring Boot-based microservices commonly used in enterprise environments and integrates AI-driven test generation into continuous integration and continuous deployment (CI/CD) workflows. Unlike conventional test automation methods that depend heavily on manually written test scripts, the proposed model analyzes application semantics, identifies potential failure scenarios, and generates optimized test cases with higher coverage. The research methodology includes extraction of microservice architecture information, preprocessing of service artifacts, prompt engineering strategies, LLM-based test generation, automated validation, and feedback-based refinement. The generated test cases are evaluated using software quality metrics including code coverage, defect detection rate, execution success rate, and developer product…
subject: Articles
  topic: section
subject: Large Language Models
  topic: keywords
subject: Test Generation
  topic: keywords
subject: Microservices
  topic: keywords
subject: Spring Boot
  topic: keywords
subject: AI-Augmented Testing
  topic: keywords
subject: Code Coverage
  topic: keywords
subject: Contract Testing
  topic: keywords
subject: CI/CD
  topic: keywords
subject: Software Quality
  topic: keywords
subject: Prompt Engineering
  topic: keywords
creator: Gnana Nishitha Chowdary Aluri
rights: Copyright (c) 2023
date: 2023-03-30
publication date: 2023-03-30
starting page: 177
ending page: 184
doi: 10.63282/3050-9416.IJAIBDCMS-V4I1P118

A Multi‑Layer Federated Architecture for Secure Cross‑Institution Fraud Detection
type: item
description: Financial fraud has become increasingly sophisticated as digital banking, online payments, fintech platforms, and cross-border financial services continue to expand. Conventional fraud detection systems are often limited by institutional data silos, where each bank or financial organisation trains models only on its own transaction records. This restricts the ability to detect coordinated fraud patterns that move across multiple institutions. However, direct sharing of customer transaction data raises serious privacy, regulatory, commercial, and security concerns. Federated learning offers a promising solution by allowing financial institutions to collaboratively train fraud detection models without transferring raw data outside their local environments. This article examines the use of federated learning for privacy-preserving fraud detection across financial institutions. It outlines a framework in which participating institutions train local models on private transaction data and share protected model updates for secure aggregation into a global fraud detection model. The study further considers privacy-enhancing mechanisms such as secure aggregation, differential privacy, encrypted model updates, and model auditing. It also discusses key challenges, including non-identically distributed data, communication overhead, model poisoning, inference attacks, regulatory compliance, and explainability. The article argues that federated learning can improve collaborative fraud intelligence while maintaining stronger data protection, provided that technical safeguards, governance structures, and human oversight are properly implemented. The study contributes to the growing discussion on privacy-preserving artificial intelligence in financial crime detection and digital banking security.
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Financial Institutions
  topic: keywords
subject: Privacy-Preserving Machine Learning
  topic: keywords
subject: Secure Aggregation
  topic: keywords
subject: Differential Privacy
  topic: keywords
subject: Financial Crime Analytics
  topic: keywords
subject: Digital Banking Security
  topic: keywords
subject: Collaborative Intelligence
  topic: keywords
subject: Financial Technology
  topic: keywords
creator: Sakthi Sankara Balaji Sathyamurthy
rights: Copyright (c) 2025
date: 2025-12-30
publication date: 2025-12-30
starting page: 319
ending page: 335
doi: 10.63282/3050-9416.IJAIBDCMS-V6I4P138

Model Context Protocol Servers as Orchestration Layers for Generative AI Agents in Enterprise Software Delivery
type: item
description: Model Context Protocol (MCP) is now a standardised interface for interaction between AI agents, opening new avenues for enterprise software delivery automation. To speed up software engineering processes, intelligent automation, continuous integration and continuous delivery (CI/CD), DevOps pipelines, cloud-native architecture, and large language model (LLM) assistants are becoming more prevalent in modern software firms. The crosscutting issues of communication, inconsistent context, and limited interoperability across development, test, deployment, monitoring, and governance environments pose a major challenge to the integration of heterogeneous AI agents. In this paper, authors provide a formal tool for delivering MCP servers as a context-aware orchestration layer in enterprise CI/CD environments. The proposed architecture places MCP servers between generative AI agents and enterprise software systems, facilitating the exchange of customizable context, coordination of dynamic workflows, intelligent decision support, and secure interactions among various delivery stakeholders. The framework builds on concepts from context-aware computing, agent-based software engineering, semantic orchestration, and service-oriented integration to create a common layer of communication for enterprise development ecosystems. The study explores the benefits of providing context persistence, workflow intelligence, automated task delegation, and real-time decision support via the orchestration using MCP, to enhance software delivery efficiency. It integrates the repository management systems, issue tracking systems, testing frameworks, deployment pipelines, monitoring systems, and knowledge repositories into a shared context. The proposed framework is built on structured workflow analysis and shows improvement of the requirement traceability, deployment reliability, development productivity and operational governance. Moreover, MCP servers give centralized context management that decr…
subject: Articles
  topic: section
subject: Model Context Protocol
  topic: keywords
subject: AI Agents
  topic: keywords
subject: Enterprise Software Delivery
  topic: keywords
subject: CI/CD Orchestration
  topic: keywords
subject: Generative AI
  topic: keywords
subject: Agentic Systems
  topic: keywords
subject: Spring Boot
  topic: keywords
subject: Intelligent Automation
  topic: keywords
subject: Context-Aware Computing
  topic: keywords
subject: Software Engineering
  topic: keywords
creator: Gnana Nishitha Chowdary Aluri
creator: Venkatesh Manohar
rights: Copyright (c) 2026
date: 2026-06-02
publication date: 2026-06-02
starting page: 337
ending page: 345
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P144

Enterprise Mobile Payment Gateway Architecture on IoT Handheld Devices: Security and Reliability Design
type: item
description: The deployment of mobile payment gateways on retail handheld devices connected to the IoT network adds a new security and reliability challenge that is unique to mobile payment deployments. This paper describes a complete enterprise mobile payment gateway deployment architecture based on the production deployment experience on Zebra Technologies handheld devices in a large scale retail environment. Digital payment systems have revolutionized the retail sector, providing real-time transactions, mobility, convenience and efficiency. The addition of payment to the functionality of the handheld IoT devices, however, increases the attack surface and presents several device authentication, transaction confidentiality, network reliability, certificate management, regulatory compliance issues. The proposed architecture mitigates these concerns by using a layered security strategy which includes Point-to-Point Encryption (P2PE), Transport Layer Security (TLS 1.3), secure certificate lifecycle management, tokenization services, device attestation mechanisms and enterprise-wide identity management. Another key aspect of the architecture that adds redundancy, failover services, transaction recoverability and distributed gateway to support HA and operation continuity in the retail market. Special attention is given to meeting Payment Card Industry Data Security Standard (PCI DSS) requirements and ensuring scalability from geographically disparate retail sites. The research explores the ability of android based enterprise handheld devices to handle payment transactions securely, with low latency and high reliability. Analysis performed at device, network, gateway and cloud service layers. Transaction success rates, service availability measures, recovery time objective, and fault tolerance are evaluated for reliability mechanisms. The study shows that layered encryption and certificate-based trust models are key to significantly lower security threats associated with payments, an…
subject: Articles
  topic: section
subject: Mobile Payment Gateway
  topic: keywords
subject: IoT
  topic: keywords
subject: Handheld Devices
  topic: keywords
subject: PCI DSS
  topic: keywords
subject: Enterprise Security
  topic: keywords
subject: Point-to-Point Encryption
  topic: keywords
subject: Certificate Management
  topic: keywords
subject: Retail Technology
  topic: keywords
subject: Android
  topic: keywords
subject: Payment Systems
  topic: keywords
creator: Chandra K Movva
creator: Gnana Nishitha Chowdary Aluri
rights: Copyright (c) 2025
date: 2025-04-02
publication date: 2025-04-02
starting page: 232
ending page: 241
doi: 10.63282/3050-9416.IJAIBDCMS-V6I1P124

Azure DevOps CI/CD Pipeline Design for Regulated Financial Software Deployment Environments
type: item
description: Continuous integration and continuous delivery in regulated financial software environments must satisfy requirements that extend beyond standard software engineering practice, including change management controls, deployment audit trails, segregation of duties enforcement, and rollback capability within regulatory timeframes. This paper presents a design framework for Azure DevOps CI/CD pipelines that satisfy these requirements while maintaining deployment velocity in enterprise financial software organizations.
subject: Articles
  topic: section
subject: Azure DevOps
  topic: keywords
subject: CI/CD
  topic: keywords
subject: Financial Software
  topic: keywords
subject: Regulated Environments
  topic: keywords
subject: Compliance Engineering
  topic: keywords
subject: Deployment Automation
  topic: keywords
subject: Change Management
  topic: keywords
subject: Audit Trails
  topic: keywords
subject: Software Delivery
  topic: keywords
subject: Pipeline Architecture
  topic: keywords
creator: Hari Krishna Mupparapu
rights: Copyright (c) 2023
date: 2023-12-30
publication date: 2023-12-30
starting page: 209
ending page: 219
doi: 10.63282/3050-9416.IJAIBDCMS-V4I4P121

Spring Batch Framework Patterns for Large-Scale Data Processing in Cloud-Native Microservices: A GCP Deployment Study
type: item
description: Although Spring Batch is a commonly cited framework for large-scale batch data processing in enterprise Java applications, using it in conjunction with cloud-native microservices architectures on Google Cloud Platform (GCP) brings design challenges that are just as significant as those faced in on-premises deployment scenarios with regards to job partitioning, fault handling, event scales and observability. The aim of this paper is to perform a systematic investigation of the four patterns of the Spring Batch framework specifically tailored for cloud-native microservices running on GCP: job partitioning patterns to achieve distributed computing, remote chunking patterns to achieve horizontal scaling, Cloud Pub/Sub integration patterns for event-driven batch triggering, and Stackdriver-based patterns for bull batch monitoring in production. Benefits of using enterprise data processing workloads are clearly illustrated through a series of case studies that show measurable gains in throughput and operational reliability in workloads when compared to the traditional batch deployment model.
subject: Articles
  topic: section
subject: Spring Batch
  topic: keywords
subject: Cloud-Native
  topic: keywords
subject: GCP
  topic: keywords
subject: Microservices
  topic: keywords
subject: Batch Processing
  topic: keywords
subject: Distributed Computing
  topic: keywords
subject: Java
  topic: keywords
subject: Cloud Pub/Sub
  topic: keywords
subject: Data Processing
  topic: keywords
subject: Enterprise Architecture
  topic: keywords
creator: Gnana Nishitha Chowdary Aluri
creator: Hari Krishna Mupparapu
rights: Copyright (c) 2021
date: 2021-12-30
publication date: 2021-12-30
starting page: 130
ending page: 142
doi: 10.63282/3050-9416.IJAIBDCMS-V2I4P113

Quantum-Classical Hybrid Algorithms for Complex Query Planning: A Scalable Computational Framework for High-Dimensional Decision Optimization in Heterogeneous Data Ecosystems
type: item
description: The rapidly increasing number of heterogeneous data ecosystems, such as structured databases, semi-structured data lakes, real-time streams, distributed knowledge graphs, has further complicated the issue of query planning and optimization in modern information systems extensively. The optimization of queries is essentially an NP-hard combinatorial problem especially when based on multi-join queries, distributed execution plan, dynamic cost model, and uncertainty in the data access patterns. Even decades of concept and engineering advances, classical database optimizers are rapidly becoming unable to offer a globally optimal execution plan when faced with high-dimensional data, high data volatility, with a very tight latency constraint. Good news Recent breakthroughs in quantum computing propose promising computational paradigms that can be used to solve combinatorial optimization problems via quantum parallelism, superposition, and entanglement. Nevertheless, even the near-term quantum hardware is limited in the number of available qubits, noise, and decoherence, making it impractical to achieve fully quantum database systems in any perceived future. This is also the reason why quantum-classical hybrid algorithms are being developed: designed to add quantum optimization subroutines to classical database architectures is guaranteed without breaking their compatibility with existing execution engines. The following paper presents a framework of scalable quantum-classical hybrid computing of complex query planning in heterogeneous data ecosystems. The framework breaks down query optimization into classical and quantum-amenable optimization, which allows NP-hard subproblems, such as join ordering, operator placement, and cost minimization, to be transformed into quantum optimization models such as Quadratic Unconstrained Binary Optimization (QUBO) and Ising Hamiltonians. Classical elements handle query parsing, semantic analysis, constraint verification, and orchestrat…
subject: Articles
  topic: section
subject: Quantum-Classical Hybrid Computing
  topic: keywords
subject: Query Optimization
  topic: keywords
subject: Np-Hard Problems
  topic: keywords
subject: Quantum Annealing
  topic: keywords
subject: Variation Quantum Algorithms
  topic: keywords
subject: Heterogeneous Data Ecosystems
  topic: keywords
subject: Database Systems
  topic: keywords
subject: Decision Optimization
  topic: keywords
creator: Parameswara Reddy Nangi
creator: Chaithanya Kumar Reddy Nala Obannagari
rights: Copyright (c) 2026
date: 2026-05-31
publication date: 2026-05-31
starting page: 328
ending page: 336
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P143

Intelligent Risk Assessment and Classification in Project Management Using Data-Driven AI Models
type: item
description: The rapid evolution of globalization, digitalization, and technological innovations has led to an increase in complexity of the project management domain; therefore, assessing and classifying the risks involved becomes increasingly complicated. The conventional techniques of risk management, which include risk identification, analysis, and forecasting, cannot adequately detect and control complex risks related to finance, operations, technology, strategy, and cybersecurity. In view of the aforementioned difficulties, the use of “Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), predictive analytics, and big data” has been identified as a smart way to improve the management of the project risks. This study is intended to review the contribution of AI-based solutions for risk assessment and classification in project management settings. This study discusses several types of AI algorithms and predictive models, including “Decision Tree, Random Forest, Support Vector Machine, Artificial Neural Networks, Recurrent Neural Network, and Long Short-Term Memory Models”, focusing on their uses, strengths, and weaknesses in project risk management. AI enhances the accuracy of predictions and decisions, enabling real-time monitoring and planning by uncovering patterns of risks and developing measures to mitigate them. Yet, several barriers remain that prevent the implementation of intelligent risk management practices, including data integrity problems, cybersecurity concerns, ethical issues, AI bias, lack of transparency, and absence of real-life validation studies. This paper concludes that the implementation of Explainable AI (XAI), real-time analysis capabilities, cloud computing technologies, and digital twins can improve the performance of intelligent risk management systems.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Project Management
  topic: keywords
subject: Risk Classification
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Data-Driven AI Models
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Fuzzy Logic
  topic: keywords
subject: Hybrid AI Systems
  topic: keywords
creator: Susanna Pabbineedi
creator: Aravindh Balan
rights: Copyright (c) 2026
date: 2026-05-29
publication date: 2026-05-29
starting page: 319
ending page: 327
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P142

A Conceptual Framework for Training Effectiveness through AI-enabled Digital Learning in Luxury Automotive Retail
type: item
description: The luxury automobile retailing industry is witnessing a paradigm shift owing to digitalization, changing customer needs, connectivity of vehicles, advancements in electric vehicles, and the integration of Artificial Intelligence (AI) technologies into different business operations. In this backdrop, the training of employees has gained importance and become a key driver of service delivery, customer satisfaction, brand image, and competitive advantage for organizations. In this regard, the present review paper aims to develop a conceptual model to improve training performance through digital learning using AI technologies in luxury automobile retailing. For the purpose of this research, the exploratory approach will be used along with a conceptual review method based on literature reviews on artificial intelligence, digital learning, organizational learning, adaptive learning systems, employee training, and automotive retailing innovations. The conceptual model under investigation shows that artificial intelligence-enabled learning systems serve as major catalysts to ensure efficient training with engagement, learning satisfaction, motivation, and knowledge retention acting as crucial intermediary variables. Based on the results obtained, AI-based learning environments could have a substantial impact on the development of workers' competencies, fast learning processes, high-quality customer service, organizational efficiency, and competitive advantages in luxury automobile retail settings. Nonetheless, issues of algorithmic discrimination, employee privacy protection, ethical management, and maintenance of human elements in premium customer communications should not be underestimated. Therefore, the research comes to the conclusion that the future of training programs in luxury car dealerships lies in combining artificial intelligence technologies and human-based methods in education.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Digital Learning
  topic: keywords
subject: Training Effectiveness
  topic: keywords
subject: Luxury Automotive Retail
  topic: keywords
subject: Organizational Learning
  topic: keywords
subject: Learning Analytics
  topic: keywords
subject: Adaptive Learning
  topic: keywords
subject: Employee Performance
  topic: keywords
subject: Customer Experience
  topic: keywords
subject: Conceptual Framework
  topic: keywords
subject: Human-Ai Collaboration
  topic: keywords
subject: Workforce Development
  topic: keywords
creator: Mohit Kohli
rights: Copyright (c) 2026
date: 2026-05-27
publication date: 2026-05-27
starting page: 311
ending page: 318
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P141

Impact of IoT and Digital Technologies on Project Management Efficiency: A Survey Study
type: item
description: The Internet of Things (IoT) and digital technologies have made a significant impact on project management, enhancing efficiency, communication, monitoring, and decision-making. Some of the traditional methods of project management can sometimes have problems with poor communication, understated estimation, quality of resources usage, and lack of risk management. Advanced digital solutions, including AI, Cloud computing, Big data analytics, Blockchain technology and IoT devices, hold the promise of delivering innovative solutions to these challenges through automation, real-time monitoring, predictive analysis, and intelligent decision-making. The study is a survey research that explores the effect of Digital Technologies and IoT on the efficiency of the project management, specifically on the efficiency of the following project management tasks: planning, monitoring, communicating, managing risk, and optimizing resources. This research also evaluates the added value of digital tools to increase productivity, reduce operating costs, enable collaboration and support data-driven project management. Additionally, it outlines the critical implementation issues, such as cybersecurity concerns and deployment expenses, technical skill gaps, and integration complexities. The study highlights growing importance of digital transformation in project management and provides recommendations for future studies to recognize impact of implementing digital transformation to increase project efficiency in various industries through a comprehensive literature review.
subject: Articles
  topic: section
subject: Internet of Things (IoT)
  topic: keywords
subject: Digital Technologies
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subject: Project Management
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subject: Artificial Intelligence (AI)
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subject: Big Data Analytics
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creator: Aravindh Balan
rights: Copyright (c) 2026
date: 2026-05-25
publication date: 2026-05-25
starting page: 303
ending page: 310
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P140

Examining the Role of Adaptive Policy Frameworks and Organisational Sustainability in the Indian Manufacturing Industry under Uncertain Business Conditions
type: item
description: The Indian manufacturing industry is experiencing a radical change as a result of globalization, digitization, environmental factors, technological disruption, and business uncertainties. Supply chain disruptions, fluctuations in prices of raw materials, geopolitical issues, climate change regulations, and the emergence of Industry 4.0 technology are some of the factors responsible for creating operational and strategic issues for manufacturing firms. The traditional rigid governance strategies are becoming obsolete in managing such uncertain scenarios and, therefore, require new policy strategies that enhance adaptability and sustainability. In this research, an explorative methodology will be used through literature analysis concerning adaptive governance, organizational sustainability, Industry 4.0, and sustainable manufacturing. This study explores the role played by the mechanisms of adaptive governance, which include value chain flexibility, real-time data integration, monitoring mechanisms, and organizational culture with a focus on sustainability, in facilitating organizational resilience, efficient use of resources, innovations, and competitiveness. In addition, the study evaluates the key driving forces and the key hindrances to the uptake of sustainability in India. These factors include institutional pressure, competitive pressure, technical hurdles, financial limitations, and supply-related obstacles. It has been established that manufacturing companies which adopt adaptive governance mechanisms and sustainable practices have better chances of managing uncertainties and enhancing resilience.
subject: Articles
  topic: section
subject: Adaptive Policy Frameworks
  topic: keywords
subject: Organizational Sustainability
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subject: Indian Manufacturing Industry
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subject: Industry 4.0
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subject: Business Uncertainty
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subject: Sustainable Manufacturing
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subject: Organizational Resilience
  topic: keywords
subject: ESG
  topic: keywords
subject: Dynamic Capabilities
  topic: keywords
subject: Green Manufacturing
  topic: keywords
creator: Sandile Khoza
rights: Copyright (c) 2026
date: 2026-05-23
publication date: 2026-05-23
starting page: 294
ending page: 302
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P139