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: Secure and Zero-Trust Middleware Architectures for Real-Time Enterprise Data Exchange
items: Social Media Bot Fraud and Automated Abuse: Detection, Risk Scoring, and Real-Time Mitigation Frameworks
items: Distributed RAG Architecture for Enterprise Knowledge QA on Databricks
items: AI-Driven Real-Time Decision Support in Arthroscopic Procedures Using Computer Vision
items: Integrating Generative AI with Real-Time Data Pipelines for Operational Decision Intelligence
items: Governance-in-the-Loop: Runtime Policy Enforcement for Autonomous and Distributed AI Systems
items: Evaluating the Role of Contact Center Applications in Enhancing Customer Experience in Banks
items: Architecting Large-Scale Identity Governance Frameworks for Zero Trust Enterprises
items: QoS aware routing in Software Defined Networks
items: Modernizing Anti-Money Laundering (AML) Data Pipelines Using Cloud-Native Architectures on Azure and Databricks
items: Clean Before Predict: A Governance-First Methodology for High-Stakes AI Systems
items: A Hybrid Push–Pull Notification Model for Maritime Android Devices with Intermittent Satellite Links
items: A Data-Driven Framework for Intelligent Procurement Decision-Making Using Machine Learning and Predictive Analytics in Global Supply Chain Networks
items: Performance Evaluation and Testing Optimization Techniques for Cloud-Native Systems in Edge-Cloud Continuum
items: Architecting Autonomous Testing Pipelines for Cloud-Native Systems Using AI-Driven Fault Injection and Predictive Analytics
items: CareerMate - One Stop Solution for Job Preparation
items: Intelligent Predictive Maintenance through Machine Learning in Industry 4.0 Environments: A Review of Methods and Applications
items: Developer Productivity in Platform Engineering: Anchoring Large-Scale Migrations and Onboarding Strategies for Diverse Business Units
items: Automated Remediation Guardrails: A Risk-Aware Framework for Validating AI-Generated Production Scripts in Regulated Financial Infrastructure
items: Legacy System Decomposition Strategies for Cloud Modernization in Global Enterprises
items: A Multi-Agent Reinforcement Learning System for Autonomous Optimization of Web Infrastructure and Services
items: AI-Driven Performance Monitoring and Optimization in SAP BW/4HANA And AWS Hybrid Architectures
items: AI/ML-Driven Predictive Analytics on SAP BW/4HANA Using AWS Athena and Delta Lake for Scalable Financial and Operational Intelligence
items: Guardrails in Generative AI for Retail Financial Planning
items: Privacy-Preserving Personalization Using Federated Learning in AEM
items: AI-Driven Predictive Deployment Pipeline for AEM as a Cloud Service
items: Reinforcement-Learning-Based Personalization Engine for Adobe Experience Clouds
items: AI-Driven Forecasting in Dynamics 365 Sales: What Businesses Need to Know
items: How Enterprises are Achieving 360° Customer Views with Dynamics 365
items: Retail Reimagined: How Dynamics 365 Commerce Is Driving Omnichannel Experiences

Secure and Zero-Trust Middleware Architectures for Real-Time Enterprise Data Exchange
type: item
description: Modern enterprise systems are no longer confined to a single data center or a predictable network boundary. They sprawl across hybrid cloud platforms, edge nodes, and SaaS (Software as a Service) ecosystems, stitched together by middleware that moves sensitive data at high speed and high volume. For years, securing that middleware meant securing what surrounded it firewalls, API gateways, and identity brokers positioned at the perimeter. That model has not held up well. When an attacker or a compromised internal service is already inside the integration pipeline, perimeter controls offer little protection. Zero-Trust Architecture, formalized through NIST guidance, rejects the idea that network location confers trust and demands continuous verification of every transaction, every time. Bringing that principle into the middleware runtime itself rather than delegating it to external controls is the central challenge this article addresses. The Zero-Trust Middleware Architecture explained here includes features like checking who is sending messages, evaluating rules based on context, giving permission for each transaction, encrypting messages from start to finish, and monitoring. The practical domains where this matters most include financial settlement platforms, cross-institutional healthcare data exchange, enterprise API ecosystems, and any regulated environment where real-time data movement cannot be separated from real-time security enforcement. Rather than treating Zero-Trust as a network-layer concern, this framework extends it into the integration fabric where enterprise data actually flows.
subject: Articles
  topic: section
subject: Zero-Trust Architecture
  topic: keywords
subject: Middleware Security
  topic: keywords
subject: Real-Time Enterprise Integration
  topic: keywords
subject: Contextual Policy Enforcement
  topic: keywords
subject: Message-Level Cryptographic Integrity
  topic: keywords
creator: Suman Neela
rights: Copyright (c) 2026
date: 2024-12-30
publication date: 2024-12-30
starting page: 220
ending page: 227
doi: 10.63282/3050-9416.IJAIBDCMS-V5I4P124

Social Media Bot Fraud and Automated Abuse: Detection, Risk Scoring, and Real-Time Mitigation Frameworks
type: item
description: The exponential growth of social media platforms has transformed global communication while simultaneously expanding the digital attack surface for malicious activities. Modern platforms that rely on user-generated content and engagement-driven algorithms have become increasingly vulnerable to bot-driven fraud, coordinated manipulation, and impersonation attacks. These threats undermine information integrity, distort public discourse, and expose users to phishing, spam, and large-scale abuse. This study presents a comprehensive detection framework that integrates multi-dimensional feature engineering with hybrid machine learning and graph-based analytics to address the evolving complexity of automated attacks. The proposed approach captures account-level attributes, behavioral patterns, content characteristics, and network interactions to distinguish between legitimate users and coordinated malicious entities. In addition, a dynamic behavioral risk scoring mechanism is introduced to evaluate the likelihood of automation and malicious intent in real time, enabling adaptive and prioritized response strategies. Temporal analysis is incorporated to identify synchronized activities and burst patterns commonly associated with coordinated bot campaigns. Experimental evaluation demonstrates improved detection accuracy and enhanced visibility into coordinated attack structures compared to traditional approaches. The findings highlight the effectiveness of combining behavioral analytics with network intelligence in identifying sophisticated bot activities. The proposed framework offers a scalable and explainable solution for modern content moderation systems, supporting timely intervention while maintaining transparency and fairness in automated decision-making processes.
subject: Articles
  topic: section
subject: Bot Detection
  topic: keywords
subject: Social Media Fraud
  topic: keywords
subject: Coordinated Behavior
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Network Analysis
  topic: keywords
subject: Anomaly Detection
  topic: keywords
creator: Sameer Halbe
rights: Copyright (c) 2026
date: 2026-04-19
publication date: 2026-04-19
starting page: 105
ending page: 106
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P118

Distributed RAG Architecture for Enterprise Knowledge QA on Databricks
type: item
description: Organizations rely on centralized knowledge repositories like Salesforce Knowledge to support internal teams and external teams. The traditional keyword/rule-based search often doesn’t provide accurate, up-to-date information at scale. This paper presents a unified data-centric and model-centric Retrieval-Augmented Generation (RAG) architecture implemented on Databricks using Apache Spark. The architecture integrates large-scale ETL processing with efficient model execution for vector embedding generation within a single distributed framework. The proposed solution employs open and modular design with separate components for data extraction, data cleansing, workflow orchestration, embedding generation, vector retrieval and LLM inference. In the proposed approach, knowledge articles are extracted by executing Salesforce Object Query Language (SOQL) queries against a Salesforce Knowledge object REST API endpoint and stored in a Databricks Lakehouse. The RAG pipeline employs stateful model reuse and fine-grained resource allocation coupled with concurrent multithreaded encoding within Databricks to perform large-scale vector embedding generation. The generated vector embeddings from the extracted content are stored in a vector database. At inference time, user queries from the chatbot are converted into vectors and matched against the vector database to find most relevant information chunks which are then sent along with prompts to an LLM to generate responses. This design ensures that answers are traceable to authoritative knowledge sources, reduces hallucinations, and supports continuous updates as knowledge articles evolve.The RAG pipeline implemented on a Databricks cluster demonstrated 100% CPU utilization with high execution efficiency on a vector embedding pipeline as shown in the experimentation. The proposed architecture provides fine-grained control over resource allocation, concurrency, and batching strategies, enabling enterprises to generate high quality e…
subject: Articles
  topic: section
subject: Databricks
  topic: keywords
subject: Vector Databases
  topic: keywords
subject: Llm
  topic: keywords
subject: Ray
  topic: keywords
subject: Rag
  topic: keywords
subject: Chatbot
  topic: keywords
subject: Vector Embeddings
  topic: keywords
subject: Salesforce
  topic: keywords
subject: Soql
  topic: keywords
creator: Vamshi Krishna Malthummeda
rights: Copyright (c) 2026
date: 2026-04-17
publication date: 2026-04-17
starting page: 98
ending page: 104
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P117

AI-Driven Real-Time Decision Support in Arthroscopic Procedures Using Computer Vision
type: item
description: The clinical efficacy of arthroscopic repair remains heavily dependent on a surgeon's spatial orientation and real-time interpretation of constrained visual fields. While intraoperative assistance is evolving, current platforms often struggle with high-latency processing and poor anatomical differentiation. We developed a high-performance computer vision framework that synchronizes anatomical segmentation with instrument tracking to provide instantaneous surgical guidance. By deploying hybrid CNN-Transformer architecture via edge computing, the system achieves sub-30ms latency meeting the strict requirements for fluid, real-time feedback in the operating room. Our evaluation across multi-institutional datasets shows a significant reduction in procedural deviation and improved accuracy in identifying critical structures like ligaments and meniscal boundaries.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Computer Vision
  topic: keywords
subject: Arthroscopy
  topic: keywords
subject: Real-Time Systems
  topic: keywords
subject: Surgical Decision Support
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Minimally Invasive Surgery
  topic: keywords
creator: Sayed Rafi Basheer
rights: Copyright (c) 2026
date: 2026-04-17
publication date: 2026-04-17
starting page: 92
ending page: 97
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P116

Integrating Generative AI with Real-Time Data Pipelines for Operational Decision Intelligence
type: item
description: This paper presents a comprehensive conceptual framework for integrating generative artificial intelligence (AI) with real-time data pipelines to enable operational decision intelligence. As organizations increasingly adopt AI-driven solutions, the ability to leverage real-time data streams with generative AI models becomes crucial for timely, informed decision-making. We propose a multi-layered architecture that encompasses data ingestion, AI processing, and decision intelligence generation, complemented by continuous feedback loops for model improvement. The framework addresses key challenges including latency management, model deployment strategies, and ethical considerations in AI-driven operations. Through examination of existing literature and emerging best practices, we demonstrate how this integration can enhance operational agility and decision quality across diverse domains. The paper discusses implementation considerations, trade-offs between performance metrics, and governance requirements. Finally, we outline future research directions including advanced interpretability methods, federated learning approaches, and multi-model orchestration strategies. This work contributes to the growing body of knowledge on AI systems architecture and provides practitioners with actionable insights for building intelligent operational systems.
subject: Articles
  topic: section
subject: Generative AI
  topic: keywords
subject: real-time data pipelines
  topic: keywords
subject: decision intelligence
  topic: keywords
subject: streaming architecture
  topic: keywords
subject: AI operations
  topic: keywords
subject: machine learning engineering
  topic: keywords
subject: data-driven decision making
  topic: keywords
creator: Stewyn Chaudhary
rights: Copyright (c) 2026
date: 2026-04-16
publication date: 2026-04-16
starting page: 85
ending page: 91
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P115

Governance-in-the-Loop: Runtime Policy Enforcement for Autonomous and Distributed AI Systems
type: item
description: AI governance mechanisms today are predominantly procedural. Documentation standards, audits, and risk assessments improve transparency but do not constrain runtime behavior. As AI systems evolve into autonomous, distributed platforms that invoke tools, spawn sub-agents, and operate across services, governance violations manifest as execution events rather than documentation failures. This structural mismatch prevents existing approaches from providing enforceable guarantees. We introduce Governance-in-the-Loop (GiL), a runtime architecture that embeds non-bypassable policy enforcement directly into AI execution primitives. GiL integrates Governance Enforcement Points (GEPs) into schedulers, model invocation paths, and inter-service communication layers. Policies support three outcomes: permit, deny, and modify – unlike deny, which cascades into workflow failure, modify preserves system availability by transforming the action into a policy-compliant alternative before execution. Each decision is bound to a verifiable audit artifact. We formalize governance as a complete mediation problem over distributed execution traces, define enforcement invariants, formally argue two core safety properties, and demonstrate differentiation from existing policy enforcement systems. The central argument is that enforceable AI governance requires architectural embedding, not procedural overlay.
subject: Articles
  topic: section
subject: AI Governance
  topic: keywords
subject: Runtime Enforcement
  topic: keywords
subject: Reference Monitor
  topic: keywords
subject: Distributed AI
  topic: keywords
subject: Policy Enforcement
  topic: keywords
subject: Complete Mediation
  topic: keywords
subject: Autonomous Agents
  topic: keywords
subject: Delegation Safety
  topic: keywords
subject: Action Modification
  topic: keywords
subject: Modify Outcome
  topic: keywords
creator: Ayush Jain
rights: Copyright (c) 2026
date: 2026-04-15
publication date: 2026-04-15
starting page: 79
ending page: 84
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P114

Evaluating the Role of Contact Center Applications in Enhancing Customer Experience in Banks
type: item
description: In the digital banking era, customer experience (CX) has become a primary determinant of competitive differentiation and long-term profitability. Contact center applications have evolved from operational support units into strategic customer engagement platforms integrating artificial intelligence (AI), omnichannel communication, analytics, and core banking systems. This study evaluates the role of contact center technological capability in enhancing customer experience and customer loyalty in banking institutions. Drawing upon Service Quality Theory, the Technology Acceptance Model, and the Resource-Based View, the paper proposes and develops a structural equation model (SEM) linking technological capability to operational efficiency, service quality, customer experience, and customer loyalty. Human-centric system architecture is presented to illustrate technological integration across layers. The study contributes theoretically by positioning contact center capability as a strategic digital resource and empirically by proposing measurable constructs suitable for Q1/Q2 journal evaluation. Managerial implications emphasize strategic IT investment alignment for sustainable competitive advantage.
subject: Articles
  topic: section
subject: Contact Center Applications
  topic: keywords
subject: Customer Experience
  topic: keywords
subject: Banking Technology
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Structural Equation Modeling
  topic: keywords
subject: Omnichannel Banking
  topic: keywords
creator: Hemalatha Murugesan
rights: Copyright (c) 2026
date: 2026-04-14
publication date: 2026-04-14
starting page: 76
ending page: 78
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P113

Architecting Large-Scale Identity Governance Frameworks for Zero Trust Enterprises
type: item
description: Identity governance has emerged as the foundational control plane of modern enterprise security. As organizations increasingly operate across hybrid and multi-cloud environments, traditional perimeter-based security models have proven inadequate. This paper examines the architectural principles, design patterns, and operational frameworks required to implement large-scale Identity Governance and Administration (IGA) within a Zero Trust paradigm. We analyze the convergence of identity lifecycle management, least-privilege enforcement, continuous verification, and AI-augmented risk intelligence. Drawing on current industry frameworks including NIST SP 800-207 and NIST SP 800-63 [8], we propose a five-layer reference architecture for scalable, policy-driven identity governance that spans human and non-human identities across enterprise-scale deployments. Empirical data from industry research underscores the urgency of this transition and informs our proposed design guidance.
subject: Articles
  topic: section
subject: Identity Governance and Administration
  topic: keywords
subject: Zero Trust Architecture
  topic: keywords
subject: Identity Lifecycle Management
  topic: keywords
subject: Least Privilege
  topic: keywords
subject: Privileged Access Management
  topic: keywords
subject: NIST SP 800-207
  topic: keywords
subject: Role-Based Access Control
  topic: keywords
subject: Continuous Verification
  topic: keywords
subject: Identity Threat Detection and Response
  topic: keywords
subject: Policy-as-Code
  topic: keywords
creator: Thrinaath Vajragowni
rights: Copyright (c) 2026
date: 2026-04-13
publication date: 2026-04-13
starting page: 70
ending page: 75
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P112

QoS aware routing in Software Defined Networks
type: item
description: The growing demand for bandwidth-intensive applications such as video streaming, multimedia services, and Internet of Things (IoT) deployments has intensified the need for efficient resource management that secures network operations without compromising Quality of Service (QoS). Addressing these challenges requires a comprehensive and centralized view of network resources. Software Defined Networking (SDN), as an emerging paradigm, offers centralized control and programmability, allowing administrators to dynamically manage network behavior and optimize routing. This paper introduces a QoS-aware routing scheme for SDN that considers parameters such as available bandwidth, packet delay, and packet loss to determine the most efficient routing paths. Optimal paths are selected based on predefined threshold criteria to meet users’ QoS expectations. Furthermore, we explore the integration of SDN with the Service-Oriented Architecture (SOA) model, emphasizing a user-centric approach to traffic engineering and Quality of Experience (QoE) enhancement. QoS guarantees remain essential for optimizing performance metrics and ensuring reliability, especially under constrained network conditions. Controllers dynamically identify optimal routes to meet clients’ core QoS requirements, highlighting QoS-aware delivery as a cornerstone for next-generation network design.
subject: Articles
  topic: section
subject: Qos Optimization
  topic: keywords
subject: Communication
  topic: keywords
subject: Traffic Engineering
  topic: keywords
subject: Quality Of Experience (Qoe)
  topic: keywords
subject: Quality Of Service (Qos)
  topic: keywords
subject: Service Level Agreements (SLA)
  topic: keywords
subject: Software-Defined Network (SDN)
  topic: keywords
subject: Application-Aware Routing
  topic: keywords
subject: Service Oriented Architecture (SOA)
  topic: keywords
subject: Qsroute (Qos Aware Routing)
  topic: keywords
creator: Shimul Shah
rights: Copyright (c) 2026
date: 2026-04-12
publication date: 2026-04-12
starting page: 65
ending page: 69
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P111

Modernizing Anti-Money Laundering (AML) Data Pipelines Using Cloud-Native Architectures on Azure and Databricks
type: item
description: Financial institutions must analyze large volumes of transactional data while complying with strict regulatory requirements aimed at preventing financial crime. Many existing Anti-Money Laundering (AML) systems rely on legacy infrastructures that use batch-oriented ETL pipelines and static rule-based monitoring, resulting in limited scalability and delayed fraud detection. This paper proposes a cloud-native AML data pipeline architecture implemented using Microsoft Azure and Azure Databricks. The framework integrates real-time data streaming, scalable data lake storage, and machine learning–based analytics to detect suspicious financial behavior. Data governance, lineage tracking, and automated compliance checks are embedded within the pipeline. The proposed approach enhances processing throughput, improves analytical accuracy, and enables financial institutions to respond more effectively to emerging financial crime patterns.
subject: Articles
  topic: section
subject: Anti-Money Laundering (AML)
  topic: keywords
subject: Cloud-Native Architecture
  topic: keywords
subject: Azure Databricks
  topic: keywords
subject: Delta Lake
  topic: keywords
subject: Real-Time Streaming
  topic: keywords
subject: Financial Compliance
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Data Governance
  topic: keywords
creator: Mukesh Kumar Mishra
rights: Copyright (c) 2026
date: 2026-04-11
publication date: 2026-04-11
starting page: 61
ending page: 64
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P110

Clean Before Predict: A Governance-First Methodology for High-Stakes AI Systems
type: item
description: Artificial intelligence (AI) systems, especially high-stakes ones, particularly in clinical domains, require not only predictive accuracy but also robustness, fairness and reliability. Conventional machine learning pipelines are mainly concerned with optimization of prediction, and usually fail to consider data quality, bias and risk-related matters that may result in unsafe or unreliable results. To overcome this shortcoming, this paper suggests a Governance-First, Clean-Before-Predict (CFP) model that re-organizes the traditional pipeline by imposing data cleaning and governance limitations before model training. The suggested methodology includes the four phases of data cleaning and quality checks, implementation of governance according to fairness and compliance indicators, risk-sensitive predictive modeling, and overall assessment. The experiments on the MIMIC-III clinical data with Logistic Regression, Random Forest and XGBoost show that CFP framework can be as effective as baseline models in terms of Accuracy, F1-score, and ROC-AUC, with the added benefit of increasing data reliability and decreasing the impact of noisy and biased samples. It is worth noting that XGBoost performs better in CFP setting. These findings suggest that the suggested solution increases stability and reliability without affecting the predictive accuracy of the tool considerably, which is why it can be used in high-stakes AI systems.
subject: Articles
  topic: section
subject: Governance-First AI
  topic: keywords
subject: Clean-Before-Predict (CFP)
  topic: keywords
subject: High-Stakes AI Systems
  topic: keywords
subject: Healthcare AI
  topic: keywords
subject: Data Quality
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Fairness In AI
  topic: keywords
subject: Risk-Aware Machine Learning
  topic: keywords
subject: Trustworthy AI
  topic: keywords
subject: Clinical Decision Support
  topic: keywords
subject: Bias Mitigation
  topic: keywords
subject: MIMIC-III Dataset
  topic: keywords
creator: Nidhi Singh
rights: Copyright (c) 2026
date: 2026-04-10
publication date: 2026-04-10
starting page: 54
ending page: 60
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P109

A Hybrid Push–Pull Notification Model for Maritime Android Devices with Intermittent Satellite Links
type: item
description: Android applications deployed in maritime environments such as cruise ships, offshore vessels, and research platforms operate under fundamentally different network constraints than terrestrial mobile systems. Connectivity is typically provided via satellite links characterized by high latency, limited bandwidth, intermittent availability, and asymmetric up-link–downlink behavior. Traditional mobile notification systems, which rely heavily on continuous push-based delivery models, degrade severely under these conditions, resulting in delayed, dropped, or inconsistent notification delivery. This paper introduces a Hybrid Push–Pull Notification Model (HPPNM) designed specifically for Android devices operating over intermittent satellite links. The proposed model combines opportunistic push delivery during periods of link availability with adaptive pull-based synchronization during offline or de-graded connectivity windows. By decoupling notification intent from immediate delivery and dynamically switching between push and pull modes based on network conditions, HPPNM ensures reliable, timely, and bandwidth-efficient notification de-livery in maritime environments. The framework operates entirely at the application layer and integrates with Android notification APIs, local persistence, lifecycle-aware execution, and constrained network schedulers. Through architectural design and experimental evaluation un-der simulated satellite conditions, this paper demonstrates that HPPNM significantly improves notification reliability, reduces bandwidth waste, and preserves user trust compared to push-only approaches.
subject: Articles
  topic: section
subject: Android Notifications
  topic: keywords
subject: Satellite Connectivity
  topic: keywords
subject: Maritime Systems
  topic: keywords
subject: Hybrid Push–Pull
  topic: keywords
subject: Offline-First Communication
  topic: keywords
subject: Mobile Resilience
  topic: keywords
creator: Varun Reddy Guda
rights: Copyright (c) 2026
date: 2026-04-08
publication date: 2026-04-08
starting page: 43
ending page: 53
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P108

A Data-Driven Framework for Intelligent Procurement Decision-Making Using Machine Learning and Predictive Analytics in Global Supply Chain Networks
type: item
description: Global supply chains are concurrently facing mounting demands, uncertain suppliers, price fluctuations, and low real-time visibility in procurement, making this a major challenge that leads to lack of efficiency in young decisions and an increasing cost of doing business. Conventional systems, which are based on rules, are reactive, and do not use the increasing amount of enterprise and external data to provide predictive information. The paper suggests a machine learning (ML)-based predictive analytics-powered framework to make intelligent procurement decisions. This framework has a modular structure that includes the data ingestion, preprocessing and feature engineering, and a decision engine powered by machine learning. It uses regression and ensemble cost and demand forecasts, and time-series models like LSTM and ARIMA to capture time variation. Supplier evaluation is done using multi-criteria decision-making (MCDM). Experimental findings on real and simulated data show that the accuracy of forecasting, cost reduction in procurement and selection of suppliers are better than traditional methods. The suggested framework opens the prospects of decisions that are proactive and real-time as well as enhances resilience in the supply chain. All in all, the study provides a scalable and extending answer to data-driven procurement to aid greater efficiency and strategic decision-making in intricate global supply chain settings.
subject: Articles
  topic: section
subject: Intelligent Procurement
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Demand Forecasting
  topic: keywords
subject: Supplier Selection
  topic: keywords
creator: Venkata Sathya Kumar Koppisetti
rights: Copyright (c) 2026
date: 2026-03-23
publication date: 2026-03-23
starting page: 306
ending page: 316
doi: 10.63282/3050-9416.IJAIBDCMS-V7I1P145

Performance Evaluation and Testing Optimization Techniques for Cloud-Native Systems in Edge-Cloud Continuum
type: item
description: The concept of cloud-native systems has transformed the manner in which application is deployed today utilizing microservices, containerization, and orchestration frameworks to provide scalability, resilience and agility. As the edge-cloud continuum rapidly emerges, with the computational resources being spread across centralized cloud data centers and decentralized edge nodes, new issues emerge in performance evaluation and testing optimization. The paper provides a detailed analysis of the performance assessment strategies and testing optimization tricks that are designed to suit cloud-native systems working in a heterogeneous edge-cloud setup. The edge-cloud continuum presents latency, bandwidth, resource availability, and workload distribution variability dynamically, thus requiring conventional performance testing methodologies to be inadequate. The study examines the innovative testing systems that integrate real-time telemetry, distributed tracing, and predictive analytics powered by AI in order to improve performance visibility. The paper also highlights the significance of workload modelling, chaos engineering and adaptive testing pipelines in the reliability of systems in different operational contexts. The performance metrics, including response time, throughput, resource utilization, and fault tolerance are analyzed in a comprehensive way. The paper offers a hybrid testing architecture that brings continuous testing practices and edge-aware optimization strategies together. Experimental evidence shows that optimized testing pipelines can enhance the efficiency of system performance up to 35 percent and reduce the latency by 20 percent in edge-intensive workloads. Moreover, this paper identifies how container orchestration platforms facilitate effective resource distribution and scaling processes at edge and cloud layers. The suggested methodology includes automated test case generation, dynamic provisioning of resources and optimization loops run by feed…
subject: Articles
  topic: section
subject: Cloud-Native Systems
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Performance Evaluation
  topic: keywords
subject: Testing Optimization
  topic: keywords
subject: Microservices
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: Edge-Cloud Continuum
  topic: keywords
subject: Distributed Systems
  topic: keywords
creator: Pranay Kale
rights: Copyright (c) 2025
date: 2025-05-27
publication date: 2025-05-27
starting page: 119
ending page: 126
doi: 10.63282/3050-9416.IJAIBDCMS-V6I2P114

Architecting Autonomous Testing Pipelines for Cloud-Native Systems Using AI-Driven Fault Injection and Predictive Analytics
type: item
description: The rapid adoption of cloud-native architectures, characterized by microservices, containerization, and dynamic orchestration, has significantly increased the complexity of software systems. Conventional methods of testing do not always suffice to tackle the issues related to distributed environments, scaling dynamically and deploying continuously. In this paper, the author introduces an autonomous testing pipeline architecture which combines AI-based fault injection and predictive analytics to promote the reliability, resilience, and performance of a cloud-native system. The suggested model establishes a smart fault injection engine that can emulate realistic and adaptive failure modes according to past system dynamics and run-time environment. Simultaneously, a predictive analytics engine is used to predict possible failures, high-risk components, and prioritize test execution using machine learning models. These modules are easily incorporated in CI/CD pipelines by the use of a centralized test orchestrator that facilitates the execution of continuous, automated, and proactive tests in dynamic settings. Also, the architecture includes a monitoring and observability layer and a feedback and learning module that creates a closed-loop system that recursively optimizes testing strategies and promotes self-healing. Experimental evidence shows that it has a large improvement on fault-detection rates, prediction accuracy, and system resilience, without incurring a large performance overhead. The study will help to improve the intelligent DevOps in the future by facilitating self-adaptive and scalable efficient testing pipelines, which will lead to the development of robust and reliable delivery of cloud-native applications.
subject: Articles
  topic: section
subject: Cloud-Native Systems
  topic: keywords
subject: Autonomous Testing
  topic: keywords
subject: AI-Driven Fault Injection
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: CI/CD Pipelines
  topic: keywords
subject: Resilience Engineering
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: DevOps
  topic: keywords
creator: Pranay Kale
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 207
ending page: 216
doi: 10.63282/3050-9416.IJAIBDCMS-V6I1P121

CareerMate - One Stop Solution for Job Preparation
type: item
description: CareerMate AI is an integrated solution designed to assist students in the entire job preparation pro-cess by bringing together assessment, practice, feed-back, and resume-building activities in one system. Current job preparation solutions work in a discon-nected manner, requiring students to work on differ-ent systems that lack integrated performance mea-surement and individualized feedback, making it challenging to track progress and adjust job prepa-ration approaches accordingly. The proposed solu-tion will be developed by bringing together four AI-based modules: adaptive MCQ-based skill assess-ment modules, an AI-based mock interview mod-ule that offers structured feedback on answers, a smart resume builder module that produces ATS-compliant resumes, and an automated resume re-view module that analyzes the relevance of content and keyword alignment for particular job roles. Nat-ural Language Processing methods will be applied to assess text-based answers, improve resume con-tent, and offer feedback, and adaptive logic will be applied to adjust question difficulty levels based on user performance. The four modules will be inte-grated into a single workflow system that allows for systematic assessment and progress measurement at various stages of job preparation.
subject: Articles
  topic: section
subject: Job Preparation
  topic: keywords
subject: Career Guidance
  topic: keywords
subject: Interview Preparation
  topic: keywords
subject: Resume Building
  topic: keywords
subject: Placement Training
  topic: keywords
subject: Aptitude Training
  topic: keywords
subject: Mock Interviews
  topic: keywords
subject: Career Development
  topic: keywords
subject: Soft Skills Training
  topic: keywords
subject: Communication Skills
  topic: keywords
subject: Technical Skills
  topic: keywords
subject: Problem Solving
  topic: keywords
subject: Logical Reasoning
  topic: keywords
subject: Coding Practice
  topic: keywords
subject: Time Management
  topic: keywords
creator: Amit Korade
creator: Sanskruti Adap
creator: Soham Babshetye
creator: Parth Dhanorkar
creator: Reshma Malik
rights: Copyright (c) 2026
date: 2026-04-06
publication date: 2026-04-06
starting page: 34
ending page: 42
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P107

Intelligent Predictive Maintenance through Machine Learning in Industry 4.0 Environments: A Review of Methods and Applications
type: item
description: Rapid advancements in industrial automation and digital transformation have underscored the significance of intelligent predictive maintenance (Pd.M.) in ensuring the durability, efficacy, and reliability of industrial assets. It is common for reactive and preventative maintenance procedures to lead to expensive downtime, wasteful use of resources, and unforeseen breakdowns. As an alternative, Pd.M. anticipates equipment failures using machine learning techniques, sensor fusion, and real-time data analytics, which minimizes operational disturbances and permits proactive decision-making. This paper offers a thorough analysis of ML-driven Pd.M., highlighting significant methodologies for industrial equipment health assessment, anomaly prediction, and Fault detection using deep learning, supervised learning, reinforcement learning, and unsupervised learning. Furthermore, the research delves into the most significant obstacles related to cybersecurity, scalability, feature selection, data quality, model interpretability, and Eliot system integration. The impact of ML-driven Pd.M. on industrial maintenance, including its ability to optimize asset performance, lower upkeep expenses, and boost operational effectiveness, is demonstrated through a thorough examination of real-world case studies and developing trends. The findings underscore the transformative impact of intelligent maintenance solutions and emphasize the need for continuous advancements in ML techniques to further enhance predictive capabilities in complex industrial environments.
subject: Articles
  topic: section
subject: Industry 4.0
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
subject: Cyber-Physical Systems (CPS)
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Prognostics and Health Management (PHM)
  topic: keywords
creator: Vishnu Vardhan Chakravaram
rights: Copyright (c) 2022
date: 2022-09-30
publication date: 2022-09-30
starting page: 128
ending page: 136
doi: 10.63282/3050-9416.IJAIBDCMS-V3I3P115

Developer Productivity in Platform Engineering: Anchoring Large-Scale Migrations and Onboarding Strategies for Diverse Business Units
type: item
description: As cloud-native platforms evolve, the ”Human Bottleneck” the friction associated with migrating diverse business units to standardized infrastructure becomes the primary inhibitor of enterprise velocity. Traditional” Force-Majeure” migrations lead to technical debt and developer burnout. In this research, I propose a Migration Anchoring Framework that utilizes Productive Friction and Self-Service Abstractions to accelerate onboarding. I introduce a formal model for the Migration Friction Coefficient (Cf ) and demonstrate, through a multi-year study of a 2,000-engineer organization, how “Golden Path” engineering can reduce onboarding time by 72% while maintaining 100% compliance with platform standards.
subject: Articles
  topic: section
subject: Developer Productivity
  topic: keywords
subject: Platform Engineering
  topic: keywords
subject: Large-Scale Migration
  topic: keywords
subject: Application Modernization
  topic: keywords
subject: Enterprise Transformation
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Microservices Architecture
  topic: keywords
subject: DevOps
  topic: keywords
subject: CI/CD Pipelines
  topic: keywords
subject: Infrastructure As Code (IaC)
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: Containerization
  topic: keywords
subject: API Management
  topic: keywords
creator: Anupam Ojha
rights: Copyright (c) 2025
date: 2025-12-30
publication date: 2025-12-30
starting page: 309
ending page: 311
doi: 10.63282/3050-9416.IJAIBDCMS-V6I4P136

Automated Remediation Guardrails: A Risk-Aware Framework for Validating AI-Generated Production Scripts in Regulated Financial Infrastructure
type: item
description: The integration of AI coding assistants into DevOps and site reliability engineering workflows has accelerated infrastructure automation, enabling engineers to generate Terraform configurations, shell scripts, and deployment parameter files at speeds previously requiring significantly more time. However, AI-generated infrastructure artifacts introduce a novel category of operational risk: code that is syntactically valid but semantically incorrect in the specific production context, hallucinated API calls, or configurations that create valid infrastructure with unintended security or reliability implications. Research has shown that approximately 40% of programs generated by AI assistants contain exploitable vulnerabilities, and that iterative AI code generation without human validation can increase critical vulnerabilities by as much as 37.6%. In regulated financial services environments where infrastructure changes require documented change control and SOC 2 audit evidence, deploying AI-generated production scripts without a structured validation framework creates compliance gaps that outweigh the productivity benefit. This paper presents a risk-aware remediation guardrail framework developed and deployed at NCR/Candescent for validating AI-generated infrastructure artifacts before their application to production banking environments. The framework implements a four-tier risk classification system based on blast radius analysis, reversibility assessment, and scope validation against existing Terraform state. It integrates with the Jira DCR change control workflow through Model Context Protocol tooling, ensuring every AI-generated artifact modifying production infrastructure is associated with an approved change record. Evaluated against a production corpus of 108 AI-generated artifacts, the automated tier classifier achieved 91% accuracy against expert human review as ground truth, with a 4.6% false negative rate in the safety-critical undertierring direction. The…
subject: Articles
  topic: section
subject: AI-Generated Code
  topic: keywords
subject: Remediation Guardrails
  topic: keywords
subject: Blast Radius Analysis
  topic: keywords
subject: Terraform Validation
  topic: keywords
subject: SOC 2 Compliance
  topic: keywords
subject: Model Context Protocol
  topic: keywords
subject: Agentic AI
  topic: keywords
subject: Infrastructure as Code
  topic: keywords
subject: Change Control
  topic: keywords
subject: Risk Classification
  topic: keywords
subject: Financial Services SRE
  topic: keywords
creator: Ajay Devineni
rights: Copyright (c) 2025
date: 2025-05-25
publication date: 2025-05-25
starting page: 113
ending page: 118
doi: 10.63282/3050-9416.IJAIBDCMS-V6I2P113

Legacy System Decomposition Strategies for Cloud Modernization in Global Enterprises
type: item
description: The modernization of legacy information technology systems has emerged as a central strategic imperative for global enterprises seeking to maintain competitive advantage in an increasingly digitized marketplace. As traditional monolithic architectures reach their functional limits, the transition to cloud-native microservice architectures offers the promise of enhanced scalability, resilience, and deployment velocity. However, the process of decomposing established legacy systems often characterized by significant technical debt and undocumented complexities presents a myriad of architectural and operational challenges. This paper provides a comprehensive investigation into legacy system decomposition strategies, analyzing the technical mechanisms, economic implications, and regulatory constraints inherent in large-scale cloud modernization. By synthesizing current research on architectural patterns such as the Strangler Fig, Branch by Abstraction, and the Sidecar pattern, the study delineates a framework for incremental transformation that minimizes business disruption. Furthermore, the role of Domain-Driven Design (DDD) is explored as a foundational methodology for identifying service boundaries and maintaining domain integrity during the transition. The research also addresses the critical impact of global data sovereignty and residency laws on architectural decisions, particularly in highly regulated sectors. Through an analysis of quantitative modularity metrics and the identification of common anti-patterns such as the distributed monolith, this paper offers an expert-level roadmap for navigating the complexities of transitional-state architectures in the global enterprise context.
subject: Articles
  topic: section
subject: Legacy Systems
  topic: keywords
subject: Cloud Modernization
  topic: keywords
subject: Microservices
  topic: keywords
subject: Strangler Fig Pattern
  topic: keywords
subject: Domain-Driven Design
  topic: keywords
subject: Technical Debt
  topic: keywords
subject: Data Sovereignty
  topic: keywords
subject: Service Decomposition
  topic: keywords
subject: Enterprise Architecture
  topic: keywords
creator: Pradeep Kachakayala
rights: Copyright (c) 2021
date: 2021-06-30
publication date: 2021-06-30
starting page: 139
ending page: 145
doi: 10.63282/3050-9416.IJAIBDCMS-V2I2P115

A Multi-Agent Reinforcement Learning System for Autonomous Optimization of Web Infrastructure and Services
type: item
description: The exponential growth of web-based applications and cloud-native services has introduced unprecedented complexity in managing modern web infrastructure. Traditional rule-based and heuristic-driven optimization approaches are increasingly inadequate to handle dynamic workloads, heterogeneous environments, and real-time service demands. This paper presents a comprehensive framework for a Multi-Agent Reinforcement Learning (MARL) system designed for the autonomous optimization of web infrastructure and services. The proposed system leverages distributed intelligent agents that collaboratively learn optimal strategies for resource allocation, traffic routing, load balancing, and service orchestration. Reinforcement learning (RL), particularly in multi-agent settings, offers a promising paradigm for adaptive decision-making under uncertainty. Unlike centralized optimization models, MARL enables decentralized agents to interact with both the environment and each other, facilitating scalable and resilient infrastructure management. Each agent in the proposed architecture is responsible for a specific subsystem—such as compute resource management, network routing, or service scaling—and learns policies through continuous interaction with the environment using reward signals derived from performance metrics like latency, throughput, and cost efficiency. The architecture integrates advanced techniques including Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and cooperative learning mechanisms such as centralized training with decentralized execution (CTDE). The system also incorporates state representation models capturing real-time metrics, action spaces defined by infrastructure control parameters, and reward functions designed to balance multiple objectives such as performance, reliability, and cost. To validate the effectiveness of the proposed approach, simulations are conducted on a cloud-based web service environment with varying workloads and traffic patt…
subject: Articles
  topic: section
subject: Multi-Agent Reinforcement Learning
  topic: keywords
subject: Web Infrastructure Optimization
  topic: keywords
subject: Autonomous Systems
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Deep Reinforcement Learning
  topic: keywords
subject: Resource Allocation
  topic: keywords
subject: Load Balancing
  topic: keywords
subject: Service Orchestration
  topic: keywords
creator: Raju Dandigam
rights: Copyright (c) 2023
date: 2023-09-30
publication date: 2023-09-30
starting page: 146
ending page: 154
doi: 10.63282/3050-9416.IJAIBDCMS-V4I3P115

AI-Driven Performance Monitoring and Optimization in SAP BW/4HANA And AWS Hybrid Architectures
type: item
description: The accelerated development of enterprise data ecosystems has led to the need to combine the sophisticated analytics systems with scalable cloud platforms. Next-generation data warehousing solution SAP BW/4HANA provides more in-memory processing and performance, whereas Amazon Web Services (AWS) offers distributed, elastic, cloud computing abilities. Nevertheless, hybrid implementations that use on-premise SAP systems with AWS environments are subject to introducing complexities in terms of performance monitoring, workload optimization and resource allocation. The conventional monitoring devices that are mostly rule-based and reactive are not adequate to deal with the dynamic and heterogeneous character of such systems.The current paper features an AI-based performance monitoring and optimization framework in an SAP BW/4HANA environment deployed on the AWS hybrid environments. The solution proposed depends on machine learning algorithms (supervised regression models, data clustering, and reinforcement learning approaches) to make predictive analytics, anomaly detection, and automated optimization possible. The system will combine SAP application server, SAP HANA database, and Amazon EC2, S3, and CloudWatch telemetry data.Multi-layered architecture is presented, which comprises of data acquisition, feature engineering, model training and decision orchestration layers. The KPIs of seek response time, CPU response and memory response and the I/O throughput are constantly monitored and analyzed. The system bottlenecks are predicted using the predictive models and resource provisioning and query execution strategy are dynamically changed as part of the optimization policy.The study measures the performance of the suggested framework through simulated workload of enterprises. Findings show that there have been great advancements in the performance of systems which are lower latency, improved throughput, and better resource utilization. Proactive system control with the co…
subject: Articles
  topic: section
subject: SAP BW/4HANA
  topic: keywords
subject: AWS
  topic: keywords
subject: Hybrid Architecture
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Performance Monitoring
  topic: keywords
subject: Cloud Optimization
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Resource Management
  topic: keywords
subject: Enterprise Systems
  topic: keywords
subject: SAP BW
  topic: keywords
subject: performance optimization
  topic: keywords
creator: Sumit Sachdeva
rights: Copyright (c) 2025
date: 2025-08-08
publication date: 2025-08-08
starting page: 119
ending page: 126
doi: 10.63282/3050-9416.IJAIBDCMS-V6I3P114

AI/ML-Driven Predictive Analytics on SAP BW/4HANA Using AWS Athena and Delta Lake for Scalable Financial and Operational Intelligence
type: item
description: The rapid data volume increase in any enterprise has resulted in the need to build data analytics frameworks that are scalable, efficient, and intelligent enough to help convert raw data into actionable insights. Existing organizations are becoming more dependent on sophisticated analytics solutions as a competitive edge, process optimization, and monetary decision-making. The paper will provide a full architecture of AI/ML-based predictive analytics with SAP BW/4HANA, using AWS Athena and Delta Lake to facilitate scale in financial and operational intelligence. SAP BW/4HANA is an effective enterprise data warehouse that is in-memory computing optimized and allows real-time analytics and data modeling speedy. Nevertheless, classic BW systems are challenged with the capabilities of processing unstructured information, processing at scale on distributed computers and machine learning applications. In order to overcome them, in this study, a hybrid architecture, which is a combination of SAP BW/4HANA and AWS-native cloud-native services (aws athena as the serverless querying engine and Delta Lake as trusted data lakes with ACID transactions), will be suggested. The suggested framework allows the easy data extraction of SAP BW/4HANA into a scaled data lake setting, in which Delta Lake is utilized to guarantee the integrity of data, enforce data schemas, and provide time travel. The AWS Athena supports a cost-effective, rapid querying of large data sets in the Amazon S3 storage, without the need to provide infrastructure. The combination of AI/ML models allows making financial forecasting, anomaly detection, and optimization of operation. The current study proposes a multi-layered architecture that includes the data ingestion, storage, processing and analytics as well as visualization layers. Regression, time-series forecasting, and classification algorithms based on machine learning are used to predict financial and operational data. To guarantee accuracy and scalabilit…
subject: Articles
  topic: section
subject: SAP BW/4HANA
  topic: keywords
subject: AWS Athena
  topic: keywords
subject: Delta Lake
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: SAP BW
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Financial Intelligence
  topic: keywords
subject: Operational Intelligence
  topic: keywords
subject: Data Lake
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Big Data Analytics
  topic: keywords
creator: Sumit Sachdeva
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 198
ending page: 206
doi: 10.63282/3050-9416.IJAIBDCMS-V6I1P120

Guardrails in Generative AI for Retail Financial Planning
type: item
description: Generative Artificial Intelligence (GenAI) is increasingly being adopted in financial planning systems that assist retail investors with investment decisions, budgeting, and long‑term wealth management. While large language models can interpret financial data and provide personalized recommendations, they also introduce risks such as hallucinated advice, regulatory non‑compliance, and biased financial recommendations. Guardrail mechanisms are therefore essential to ensure that AI‑generated financial advice remains safe, compliant, and aligned with financial best practices. This research paper explores the role of guardrails in GenAI‑driven financial planning systems for retail consumers. The study presents the architecture of a guardrail‑enabled financial planning system, discusses datasets used in financial planning models, and explains the calculation logic that supports responsible investment recommendations. Real‑world case studies illustrate how guardrails can prevent harmful outputs and maintain regulatory compliance. The paper also analyzes the integration of policy enforcement layers, risk scoring models, and validation mechanisms in financial AI systems. The findings demonstrate that guardrail frameworks significantly improve reliability and trustworthiness in AI‑driven financial advisory systems. The paper concludes by highlighting the importance of combining machine learning, financial analytics, and governance frameworks to build safe and trustworthy AI‑powered financial planning platforms.
subject: Articles
  topic: section
subject: Generative Artificial Intelligence (Genai)
  topic: keywords
subject: Financial Planning Systems
  topic: keywords
subject: Retail Investors
  topic: keywords
subject: AI Guardrails
  topic: keywords
subject: Responsible AI
  topic: keywords
subject: Financial Advisory Automation
  topic: keywords
subject: Risk Management
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
subject: Bias Mitigation
  topic: keywords
subject: Hallucination Prevention
  topic: keywords
subject: Investment Recommendation Systems
  topic: keywords
subject: Personal Finance Management
  topic: keywords
subject: Wealth Management
  topic: keywords
subject: Explainable AI (XAI)
  topic: keywords
subject: Policy Enforcement Layers
  topic: keywords
subject: Risk Scoring Models
  topic: keywords
subject: Financial Data Analytics
  topic: keywords
subject: AI Governance Frameworks
  topic: keywords
subject: Model Validation
  topic: keywords
subject: Ethical AI In Finance
  topic: keywords
creator: Ratna Raja Kumar Jambi
rights: Copyright (c) 2026
date: 2026-04-04
publication date: 2026-04-04
starting page: 31
ending page: 33
doi: 10.63282/3050-9416.IJAIBDCMS-V7I2P106

Privacy-Preserving Personalization Using Federated Learning in AEM
type: item
description: Adobe Experience Manager (AEM) relies heavily on personalized digital experiences, as they are a key enabler of brands distributing the right, timely, and user-friendly content that matches each individual customer's needs. On the other hand, reaching that high level of personalization frequently entails the gathering and processing of sensitive data located on the client side, thereby posing privacy problems of user tracking, data centralization, and conformity with rules such as GDPR and CCPA. Consequently, Federated Learning (FL) stands out as an excellent alternative in that it permits the training of machine-learning models to be done on the user's device without having to send the raw personal data to a central server. This paper devises a privacy-respecting personalization mechanism that uses FL to communicate with AEM's personalization engine, consequently allowing a company to obtain behavioral insights without violating the privacy of the users. Herein, the AEM-managed digital touchpoints receive the deployment of lightweight FL models that learn from on-device interactions, and at regular intervals, they send only the encrypted model updates to the server, thus ensuring privacy preservation. From there, AEM aggregates these updates to fine-tune global personalization models that are sent back to the devices for further training. As such, the method retains the sumptuousness of AEM personalization features like content targeting, segmentation, and predictive recommendations while simultaneously achieving a significant cut in privacy risk. The suggested remedy achieves refined model metrics, augmented user engagement, and shorter personalization response time due to execution at the local level, all happening concurrently with the maintenance of data secrecy. The potential of such a solution includes the optimization of e-commerce, the scoring of content relevance, real-time audience segmentation, and adaptive customer journeys across websites and mobile ap…
subject: Articles
  topic: section
subject: Privacy-Preserving Personalization
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: AEM
  topic: keywords
subject: Adobe Experience Manager
  topic: keywords
subject: Edge AI
  topic: keywords
subject: User Data Privacy
  topic: keywords
subject: GDPR Compliance
  topic: keywords
subject: On-Device Learning
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Digital Experience Platforms
  topic: keywords
creator: Siva Sai Krishna Suryadevara
creator: Santosh Nakirikanti
rights: Copyright (c) 2023
date: 2023-12-30
publication date: 2023-12-30
starting page: 190
ending page: 199
doi: 10.63282/3050-9416.IJAIBDCMS-V4I4P119

AI-Driven Predictive Deployment Pipeline for AEM as a Cloud Service
type: item
description: Adobe Experience Manager as a Cloud Service (AEMaaCS) has changed the way businesses build & deliver these digital experiences. However, its highly automated, cloud-native architecture makes CI/CD more difficult because it requires frequent code deployments, strict deployment schedules, continuous platform updates & complicated dependency behaviors that can make these deployment cycles unpredictable. Conventional pipelines usually only fix problems after they happen, which slows down release speed because of delays, rollbacks, and many other inefficiencies. This paper introduces an AI-driven predictive deployment pipeline designed to improve their intelligence, foresight, and adaptive decision-making in AEMaaCS release processes. Before the deployment is run, the pipeline uses machine-learning models to look at historical deployment logs, code modification metadata, AEM health indicators & environmental trends to figure out how likely it is that the build will succeed, the content will regress, the performance will drop, or the environment will become unstable. It works seamlessly with existing DevOps workflows, adding predictive scoring, automatic risk classification, and dynamic deployment strategies like auto-pausing, optimizing rollout timings, or proposing code changes. The experimental setup tests the system with actual deployment datasets and simulated cloud service environments. It shows huge improvements in deployment reliability, fewer failed builds, faster recovery times. The results suggest that forecasting scoring can help teams stay clear of high-risk releases with a precision of up to 85% and decrease the number of pipeline failures by recognizing problems early on. The proposed approach improves AEMaaCS DevOps through shifting how they handle implementations from a reactive to a more proactive way of making decisions using the information they have. The contributions include the latest predictive architecture made for AEMaaCS, a whole approach for fo…
subject: Articles
  topic: section
subject: AEM as a Cloud Service
  topic: keywords
subject: CI/CD
  topic: keywords
subject: Predictive Deployment
  topic: keywords
subject: AI-powered DevOps
  topic: keywords
subject: Adobe Experience Manager
  topic: keywords
subject: Machine Learning for Deployment
  topic: keywords
subject: Cloud Automation
  topic: keywords
subject: Drift Detection
  topic: keywords
subject: Risk Scoring
  topic: keywords
subject: Release Optimization
  topic: keywords
creator: Siva Sai Krishna Suryadevara
rights: Copyright (c) 2022
date: 2022-06-30
publication date: 2022-06-30
starting page: 178
ending page: 191
doi: 10.63282/3050-9416.IJAIBDCMS-V3I2P116

Reinforcement-Learning-Based Personalization Engine for Adobe Experience Clouds
type: item
description: This research presents a Reinforcement Learning-Based Personalization Engine designed to enhance the provision of tailored digital experiences within Adobe Experience Cloud during customer journeys. Traditional rule-based or static machine-learning personalization techniques often struggle with changing their user behavior, limited information as well as shifting business goals. This means that these systems need to always be learning & changing in actual time. The proposed engine employs reinforcement learning as its principal decision-making structure, enabling the system to observe user interactions, predict intent & improve content selection through continuous feedback loops. The architecture works well with Adobe Experience Platform, Adobe Target along with Adobe Analytics. It uses customer profiles, event streams & content metadata to express state, and it sets rewards based on their engagement, conversion, and long-term value assessments. A multi-agent reinforcement learning architecture is used to balance exploration as well as exploitation among many other different audience groups, making sure that their personalization is both scalable & consistent. The methodology includes ways to encode states, deep reinforcement learning algorithms to optimize policies as well as an orchestration layer that makes sure that actual time decisions are in line with marketing goals & compliance requirements. A case study demonstrates the engine's successful utilization for tailored online and app experiences, highlighting improvements in click-through rates, dwell time as well as conversion rates compared to baseline models. Experimental results demonstrate that the RL agent proficiently adapts to evolving user behaviors, alleviates overfitting to ephemeral trends & delivers experiences that are more contextually aware. This study introduces a scalable, intelligent, and extensible customization technology that transforms Adobe Experience Cloud into a continuously evolving e…
subject: Articles
  topic: section
subject: Reinforcement Learning
  topic: keywords
subject: Adobe Experience Cloud
  topic: keywords
subject: Personalization Engine
  topic: keywords
subject: Customer Experience Optimization
  topic: keywords
subject: Multi-Armed Bandits
  topic: keywords
subject: Digital Experience Platform
  topic: keywords
subject: User Modeling
  topic: keywords
creator: Siva Sai Krishna Suryadevara
rights: Copyright (c) 2021
date: 2021-03-30
publication date: 2021-03-30
starting page: 111
ending page: 121
doi: 10.63282/3050-9416.IJAIBDCMS-V2I1P112

AI-Driven Forecasting in Dynamics 365 Sales: What Businesses Need to Know
type: item
description: AI-driven forecasting embedded in contemporary Customer Relationship Management (CRM) systems is the major factor in changing the way businesses predict sales outcomes, optimize decision-making and improve revenue accuracy. Using predictive analytics gives businesses the ability to handle large datasets, discover the hidden patterns, and come up with dependable sales projections that not only help strategic planning but also performance management. Microsoft Dynamics 365 Sales is the most prominent platform that goes hand in hand with advanced AI capabilities, offering such tools as predictive scoring, pipeline intelligence, and automated insights which are the main source of power for sales teams to invest their time in high-value opportunities. This paper is about the use and performance of AI-driven forecasting within Dynamics 365 Sales. It takes a mixed-method approach by combining quantitative analysis of forecast accuracy and qualitative assessment through business case studies. The results show that the integration of AI leads to the highest precision of prediction, the most efficient sales, and better customer engagement while at the same time reducing human bias in sales predictions. The case studies demonstrate the rise in conversion rates and resource optimization that is measurable and common to different sectors of the economy. The research points out that the strategic application of AI forecasting tools is a major factor in the coming of a more agile, data-driven decision-making process which is the main reason organizations can foresee market shifts and still be able to allocate resources effectively. In other words, AI-driven forecasting in corporate sales environments like Dynamics 365 Sales is a vital move forward in business intelligence, with the next changes being anticipated to involve increased automation, context-aware analysis and instant adaptability in sales ​‍​‌‍​‍‌management.
subject: Articles
  topic: section
subject: AI Forecasting
  topic: keywords
subject: Dynamics 365 Sales
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: CRM
  topic: keywords
subject: Sales Performance
  topic: keywords
subject: Demand Forecasting
  topic: keywords
subject: Microsoft Cloud
  topic: keywords
subject: Business Intelligence
  topic: keywords
creator: Rajarshi Krishna Muppaneni
rights: Copyright (c) 2026
date: 2023-03-30
publication date: 2023-03-30
starting page: 168
ending page: 176
doi: 10.63282/3050-9416.IJAIBDCMS-V4I1P117

How Enterprises are Achieving 360° Customer Views with Dynamics 365
type: item
description: Modern businesses are trying to get to know their customers better so they can provide them more individualized, persistent. & meaningful experiences every time they connect with them. Microsoft Dynamics 365 is a key part of this transition because it combines both CRM & ERP features with AI-driven insights & advanced analytics. This link lets organizations combine different types of user information from sales, marketing, service & activities into a unified, smart outline. This helps teams make conclusions faster & with more data. Getting a full picture of the customer is hard because of data silos, previous systems & information that isn't always accurate. These things might make it very hard to see & respond to customers. Dynamics 365 overcomes these problems by providing a single data model, a built-in cloud connection & built-in automation that ensures data consistency & availability across departments. This study investigates an organized approach that coalesce their information, automates processes & utilizes their machine learning inside these Dynamics 365 to get thorough buyer insight. A case study demonstrates how firms using these capabilities have improved customer settlement, decreased response times & increased operational efficiency. The findings demonstrate that acquiring a holistic customer view is not only a technology advancement but a strategic enabler of digital transformation enhancing engagement, cultivating loyalty & equipping firms for sustained growth in a customer-centric era.
subject: Articles
  topic: section
subject: 360° Customer View
  topic: keywords
subject: Dynamics 365
  topic: keywords
subject: Customer Relationship Management
  topic: keywords
subject: Data Integration
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Customer Insights
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Cloud CRM
  topic: keywords
subject: Predictive Analytics
  topic: keywords
creator: Rajarshi Krishna Muppaneni
rights: Copyright (c) 2026
date: 2021-06-30
publication date: 2021-06-30
starting page: 129
ending page: 138
doi: 10.63282/3050-9416.IJAIBDCMS-V2I2P114

Retail Reimagined: How Dynamics 365 Commerce Is Driving Omnichannel Experiences
type: item
description: This study examines the impact of Microsoft Dynamics 365 Commerce on merchandising by integrating the actual world & many online interactions to create a genuine omnichannel environment. The idea is to figure out how many firms can use this platform to provide each consumer a tailored, data-driven experience while yet keeping these things operating extremely efficiently. The method involves looking at the platform's main features, such as AI-driven product guidance, a coherent commerce architecture & integrated their analytics, to see how they affect customer engagement & make supply chains more efficient. Dynamics 365 Commerce connects all of these retail touchpoints, such as storefronts, online & tasks in the back office. This helps firms guess what customers want, better manage their inventories & respond rapidly to changes in the market. The findings show that this integrated ecosystem makes it easier to run a retail business & gives decision-makers useful information that helps growth & customer loyalty. The technology uses smart automation & predictive by these analytics to help shops provide customers consistent, personalized shopping experiences while cutting down on these expenses & inefficiencies. The research says that Dynamics 365 Commerce encourages retail innovation by bringing together traditional & also digital channels, changing how customers engage with businesses & getting businesses ready for long-term success in a world that is increasingly focused on their information.
subject: Articles
  topic: section
subject: Dynamics 365 Commerce
  topic: keywords
subject: Omnichannel Retail
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Customer Experience
  topic: keywords
subject: AI in Retail
  topic: keywords
subject: Cloud Commerce
  topic: keywords
subject: Microsoft Ecosystem
  topic: keywords
creator: Rajarshi Krishna Muppaneni
rights: Copyright (c) 2026
date: 2020-03-30
publication date: 2020-03-30
starting page: 49
ending page: 59
doi: 10.63282/3050-9416.IJAIBDCMS-V1I1P106