American International Journal of Computer Science and Technology
type: channel
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language: en
publication name: American International Journal of Computer Science and Technology
issn: 3117-5481
items: How to Spot and Fix Python Performance Issues Remotely- Strategies and Techniques
items: Assessing the Limitations of LangChain in Production Environments
items: Autonomous Cloud-Native Analytics Pipelines Powered by Generative AI and AIOps for Intelligent Workflow Orchestration
items: Agentic and Generative AI for Real-Time Financial Process Automation in Cloud-Native DevOps Environments
items: Responsible Enterprise Modernization through Secure AI and Cloud-Native Service Now Automation
items: Deep Neural Frameworks for Integrating Multimodal Healthcare Data
items: Integrating BNPL Solutions with Checkout Experiences: A Developer's Guide
items: Cloud-Native Event-Driven Orchestration Architecture for Real-Time Fraud and Sanctions Screening
items: Enterprise Architecture Framework for Secure Large Language Model Deployment in Financial Services
items: Enterprise CRM Modernization for Asset and Wealth Management: A Capability-Driven Digital Transformation Framework
items: https://aijcst.org/index.php/aijcst/article/view/288
items: Data-Centric AI in the Era of Large Volumes: Improving Model Outcomes through Data Quality Engineering
items: Jupyter Notebooks as First-Class Citizens in Cloud-Native Data Workflows
items: AI-Augmented Data Governance: Enabling Intelligent Access, Lineage, and Compliance across Hybrid Clouds
items: Edge-Cloud Hybrid Data Pipelines: Architectures for Federated Analytics and Learning
items: From Monolithic Systems to Cloud-Native Ecosystems: Modernizing Enterprise Applications with Kubernetes and Microservices
items: Achieving Timing Closure in Advanced ASIC and FPGA Systems: Challenges, Methodologies, and Best Practices
items: A Multi-Tier Orchestration Architecture for Regulated Cryptocurrency and Stablecoin Services within Large-Scale Payment Service Providers
items: Retrieval-Augmented Generation for Trusted Customer Intelligence: A Scalable Enterprise Architecture for CRM Knowledge Management
items: Counterfactual Telemetry Simulation for Release-Aware Capacity Guardrails and Performance Risk Mitigation in Microservice Architectures
items: From Legacy Back Office to Intelligent Utility Enterprise a Practitioner Case Study of SAP Cloud Transformation and Utility IT Landscape Modernization
items: Original Article Scalable Enterprise Intelligence: Leveraging AI, Data Engineering, Security, and Automation for Digital Transformation
items: Smart VKYC with Deepfake and Liveness Detection
items: Transforming Software Quality Assurance through Artificial Intelligence, Automated Testing, and Intelligent Software Analytics
items: Machine Learning-Enabled Self-Healing Data Pipelines: An Autonomous Architecture for Failure Detection, Diagnostic Reasoning, and Automated Remediation
items: Angular Progressive Web Application Architecture for Enterprise Financial Dashboards: Performance and Accessibility
items: Enterprise Data Transformation Strategies with Talend and Snowflake Cloud Platform
items: Multi-Horizon Demand Forecasting for E-Commerce Fulfillment Using Ensemble Deep Learning
items: Enterprise Android Device Management and Security Policy Enforcement for Large-Scale Retail IoT Deployments
items: Entity Framework Core Performance Optimization Strategies for High-Throughput Financial Data Systems

How to Spot and Fix Python Performance Issues Remotely- Strategies and Techniques
type: item
description: Python programs that are distributed or running in remote locations quite often encounter performance issues in a quite subtle manner which is one reason their local counterparts are easier to troubleshoot since one disadvantage is that in distributed/remote setups one doesn't have full visibility and there may also be network latencies and the system behavior is fragmented. This article is an attempt to share a method by which developers can efficiently isolate and remediate such issues when they do not have direct access to the runtime environment. The article points out that remote diagnostics is becoming increasingly necessary especially because cloud-based systems, microservices, and remote teams are becoming the standard. By combining practical remote profiling, real-time monitoring, and targeted debugging techniques developers can get a clear picture of CPU usage, memory leaks, I/O delays, inefficient code paths through the executable. The article also looks at small observability tools, logging strategies, and performance metrics that allow developers to identify the main causes without live system disruption. Going further into the subject, it provides a list of ways to optimize performance from enhancing algorithmic efficiency and using asynchronous programming to the fine adjustments of resource usage and the exploitation of caching techniques etc. It also explains how these techniques can be used even in very restrictive environments through helping examples and tool suggestions. In the final analysis, this article appears as a generous, human-focused tutorial for engineers/future/me who need to remotely diagnose and enhance Python performance and is at the same time technically deep and practically usable. It presents a very positive attitude towards performance and shows that the right set of tools and techniques can make remote problems mere aspects of software development which are not only manageable but also predictable.
subject: Articles
  topic: section
subject: Python Performance
  topic: keywords
subject: Remote Debugging
  topic: keywords
subject: Profiling Tools
  topic: keywords
subject: Optimization Techniques
  topic: keywords
subject: Latency Reduction
  topic: keywords
subject: Memory Management
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: Observability
  topic: keywords
subject: Performance Tuning
  topic: keywords
subject: Scalability
  topic: keywords
creator: Madhurima Kommuru
rights: Copyright (c) 2025
date: 2025-09-23
publication date: 2025-09-23
starting page: 115
ending page: 127
doi: 10.63282/3117-5481/AIJCST-V7I5P110

Assessing the Limitations of LangChain in Production Environments
type: item
description: Orchestration frameworks for Large Language Models (LLMs) such as LangChain have accelerated the development of AI-driven applications by offering quick administration, retrieval pipelines, tool integration as well as agentic workflows. These abstractions significantly reduce the effort of development while prototyping, but their effectiveness in dealing with enterprise-scale production workloads has not been fully evaluated. This paper systematically investigates the operational limits of LangChain in production environments by undertaking a systematic experimental benchmarking of Retrieval-Augmented Generation (RAG) pipelines, multi-step chains along with agent-driven workflows under concurrent workloads. The study examines LangChain in terms of many other production engineering metrics such as scalability, latency amplification, reliability, observability, fault tolerance and cost efficiency. Experimental results show high cost of orchestration abstraction in high-concurrency scenarios, notably for processes that need sequential reasoning, external API calls, and dynamic tool selection. The findings reveal that limited native observability, dependency propagation, and non-deterministic execution patterns impede debugging, performance optimization, and operational maintenance. The work includes architectural analysis as well as production-oriented implementation, backed by a GitHub repo that provides repeatable benchmarking pipelines, failure injection testing, distributed tracing instrumentation, and deployment-ready RAG processes. The evaluation demonstrates that LangChain is highly effective for rapid AI application development; however, enterprise-scale deployment requires substantial enhancements. The paper states that LangChain should be thought of as an orchestration accelerator and not a fully functional runtime framework. For LLM-based systems to be broadly adopted by corporations, we need to improve deterministic execution, runtime tracing, orchestration…
subject: Articles
  topic: section
subject: LangChain
  topic: keywords
subject: Large Language Models (LLMs)
  topic: keywords
subject: Production Systems
  topic: keywords
subject: Scalability
  topic: keywords
subject: Latency
  topic: keywords
subject: AI Framework Limitations
  topic: keywords
subject: Observability
  topic: keywords
subject: Prompt Engineering
  topic: keywords
creator: Madhurima Kommuru
rights: Copyright (c) 2024
date: 2024-07-16
publication date: 2024-07-16
starting page: 71
ending page: 83
doi: 10.63282/3117-5481/AIJCST-V6I4P107

Autonomous Cloud-Native Analytics Pipelines Powered by Generative AI and AIOps for Intelligent Workflow Orchestration
type: item
description: Cloud-native industry analytics pipelines distilling insights from data-to-decision in a fully-automated manner promise to reduce the ever-growing burden of repetitive, manual operations on data engineering and analytics teams. Managing the supporting infrastructure and flow from data ingestion to decision-making undertaken by the pipeline needs to be completely automated; in essence a self-driving data-to-decision pipeline. Doing so calls for the blending of Generative AI and AIOps for end-to-end workflow automation, where AI technologies assume the entire range of operational responsibilities—monitoring infrastructure, tracking user behavior dynamics, spotting anomalies, triggering corrective/fine-tuning actions, and ensuring safety during execution—while needing to be reinforced through human oversight and support. A conceptual framework is established outlining the synergy of Generative AI and AIOps for autonomous pipeline operation with key research opportunities highlighted. As with any conceptual framework, the presented blend of the two paradigms must ultimately be demonstrated in practice for a specific use case to evaluate its effectiveness. Cloud-native Industry Analytics Pipelines are an ideal choice; spanning the full spectrum of Generative AI, including data ingestion and preparation tasks that are ripe for automation, such a pipeline delivers actionable insights—key opportunities, risks, predictions, and recommendations—on a live basis to a network of stakeholders at all levels. The choice of a cloud-native environment further facilitates the use of AIOps, with observability and control directly built in, enabling seamless extension of monitoring and automation capabilities for the autonomous operation of the pipeline—the desired self-driving Dynamics.
subject: Articles
  topic: section
subject: Cloud-Native Analytics Pipelines
  topic: keywords
subject: Autonomous Data Engineering
  topic: keywords
subject: Generative AI Automation
  topic: keywords
subject: AIOps-Driven Operations
  topic: keywords
subject: Self-Driving Data Pipelines
  topic: keywords
subject: End-to-End Workflow Automation
  topic: keywords
subject: Intelligent Decision Systems
  topic: keywords
subject: AI-Based Infrastructure Monitoring
  topic: keywords
subject: Autonomous Analytics Frameworks
  topic: keywords
subject: Real-Time Insight Generation
  topic: keywords
creator: Daniel Moreau
rights: Copyright (c) 2024
date: 2024-09-20
publication date: 2024-09-20
starting page: 69
ending page: 82
doi: 10.63282/3117-5481/AIJCST-V6I5P107

Agentic and Generative AI for Real-Time Financial Process Automation in Cloud-Native DevOps Environments
type: item
description: Real-time automation of financial activities, including approvals, controls, routing, and monitoring, can enable fast response to opportunities and threats, eliminating the rotation of capital through liquidity provider balances and streamlining interactions with sources of capital. Generative and agentic AI technologies, supported by real-time data streams of events, market feeds, and key risk indicators, can automate and govern these transactions provided that high standards of latency, throughput, security, and regulatory compliance are achieved. A cloud-native DevOps ecosystem equipped with serverless infrastructure-as-code patterns ensures scalable, cost-effective operations with minimum user interference, allowing a comprehensive evaluation of operational performance, change management, security controls, and regulatory oversight. Scenarios involving AI agents as the driving or supervisory part of automation workflows illustrate the architectural paradigm. The analysis identifies the expected challenges in operational responsibility and response reliability, gathering additional evidence from existing Cloud-Native/Serverless solutions of similar scope. Mitigation strategies address the main issues encountered in every-day AI adoption and propose supporting operations management and security controls. The examination of the full end-to-end process flow, including off-line and real-time phases, ensures cover for Governance, Risk, and Compliance (GRC) objectives by design and the definition of a robust plan for evaluation in on-line use.
subject: Articles
  topic: section
subject: Real-Time Financial Automation
  topic: keywords
subject: Agentic AI Workflows
  topic: keywords
subject: Generative AI Finance Systems
  topic: keywords
subject: Cloud-Native DevOps Ecosystems
  topic: keywords
subject: Serverless Financial Architectures
  topic: keywords
subject: AI-Driven Transaction Monitoring
  topic: keywords
subject: Financial Governance Automation
  topic: keywords
subject: Regulatory Compliance Frameworks
  topic: keywords
subject: Event-Driven Financial Processing
  topic: keywords
subject: Autonomous Financial Operations
  topic: keywords
creator: Ethan Campbell
rights: Copyright (c) 2025
date: 2025-08-03
publication date: 2025-08-03
starting page: 142
ending page: 155
doi: 10.63282/3117-5481/AIJCST-V7I4P113

Responsible Enterprise Modernization through Secure AI and Cloud-Native Service Now Automation
type: item
description: Enterprises are transforming to embrace Artificial Intelligence (AI) and accelerate business outcomes. The application of AI technology needs to occur in a controlled environment, with appropriate risk management in place to ensure longevity of the systems. Hence the desired outcome is to adopt an AI governance framework mapped to an enterprise service delivery solution that supports the detection and remediation of trust, safety, security and compliance risks relating to the AI operations across its lifecycle, as well as in the data used to train, develop and run the AI models. Therewith, a Cloud-Native, Security-centric and Compliant AI-Driven Automation Framework for ServiceNow is articulated. The framework allows for various AI services to be developed, operated and maintained safely, securely and in a controlled responsive manner aligned to AI Governance principles throughout the lifecycle. The framework operates within a Cloud-Native model with all applications comprising SaaS/IaaS/PaaS being developed and deployed under the principles of zero-trust with least-privilege identity and access management considerations. The completed framework provides the foundation for continuously improving the Security-Centric and Compliance controls.
subject: Articles
  topic: section
subject: SAI Governance Frameworks
  topic: keywords
subject: Cloud-Native AI Systems
  topic: keywords
subject: Security-Centric Automation
  topic: keywords
subject: Servicenow AI Integration
  topic: keywords
subject: Zero-Trust Architecture
  topic: keywords
subject: AI Risk Management
  topic: keywords
subject: Compliance-Driven AI Operations
  topic: keywords
subject: Identity And Access Control
  topic: keywords
subject: AI Lifecycle Governance
  topic: keywords
subject: Secure AI Deployment
  topic: keywords
creator: Emily Carter
rights: Copyright (c) 2026
date: 2026-07-08
publication date: 2026-07-08
starting page: 36
ending page: 49
doi: 10.63282/3117-5481/AIJCST-V8I4P104

Deep Neural Frameworks for Integrating Multimodal Healthcare Data
type: item
description: Multimodal data integration is gaining traction in medical image analysis, enabling the use of diverse data sources to improve downstream tasks. Deep Learning approaches have proliferated, employing generic architectures and a data-driven paradigm. While initial efforts have yielded positive results, they lack inherent adaptation to the peculiarities of medical multimodality. Bridging representations across signal pairs and aligning disparate modalities provide more robust performance. Specifically, representation learning, explicitly learning transferable feature extraction models, has emerged as an important research avenue. Contrastive learning and visual-language pre-training provide methods to learn joint embedding spaces. The proposed multimodal evaluation setup examines several public datasets, offering a well-designed statistical analysis framework and research-practice reproducibility. Baseline models explore early-and late-fusion scenarios for multimodal emotion recognition and sickness prediction from facial expression. Initial results indicate representative power and proper data alignment as crucial elements. As multimodality gains momentum in Deep Learning research, bridging modalities and demonstrating clear real-world applications pave the way for impactful contributions.
subject: Articles
  topic: section
subject: Multimodal Data Integration
  topic: keywords
subject: Medical Image Analysis
  topic: keywords
subject: Deep Learning Models
  topic: keywords
subject: Representation Learning
  topic: keywords
subject: Contrastive Learning Methods
  topic: keywords
subject: Visual-Language Pretraining
  topic: keywords
subject: Multimodal Feature Alignment
  topic: keywords
subject: Joint Embedding Spaces
  topic: keywords
subject: Fusion Learning Strategies
  topic: keywords
subject: Healthcare Predictive Analytics
  topic: keywords
creator: Lucas van Dijk
rights: Copyright (c) 2024
date: 2024-01-24
publication date: 2024-01-24
starting page: 105
ending page: 115
doi: 10.63282/3117-5481/AIJCST-V6I1P110

Integrating BNPL Solutions with Checkout Experiences: A Developer's Guide
type: item
description: Often referred to as Installment, Price, and Pay solutions, the BNPL products have shifted the paradigm of customers' interaction with online stores. Consequently, an effort to provide a comprehensive guideline to the developers on how to integrate BNPL solutions on e-commerce checkouts is the main aim of this paper. The given guide is aimed at describing how BNPL technology is effective, how its effectiveness is reflected in increasing usability, and outlining the detailed procedure for implementing BNPL. Besides, it looks at various BNPL providers and the APIs that it offers; it also covers security aspects and compliance notes. The paper concludes with a focus on the result of BNPL and the subsequent bearing of the benefits and the demerits which are likely to occur.
subject: Articles
  topic: section
subject: BNPL
  topic: keywords
subject: E-Commerce
  topic: keywords
subject: Checkout Experience
  topic: keywords
subject: API Integration
  topic: keywords
subject: User Experience
  topic: keywords
subject: Payment Solutions
  topic: keywords
creator: L. Amudhavalli
rights: Copyright (c) 2026
date: 2026-07-06
publication date: 2026-07-06
starting page: 25
ending page: 35
doi: 10.63282/3117-5481/AIJCST-V8I4P103

Cloud-Native Event-Driven Orchestration Architecture for Real-Time Fraud and Sanctions Screening
type: item
description: The rapid digital transformation of financial services has significantly increased transaction volumes and complexity, exposing financial institutions to growing risks such as fraud, money laundering, identity theft, terrorist financing, and sanctions violations. Traditional centralized fraud detection systems are unable to meet the real-time scalability, latency, and compliance requirements of modern cloud-based financial platforms. This paper proposes a Cloud-Native Event-Driven Orchestration Architecture that integrates event streaming, microservices, AI-driven fraud detection, sanctions screening, risk scoring, and automated decision-making into a unified, scalable framework. The architecture continuously processes transaction events, enriches contextual information, performs real-time fraud and sanctions analysis, and routes transactions for approval, rejection, or manual review. It also incorporates monitoring, observability, fault tolerance, and dynamic resource management to ensure high availability. Mathematical models for fraud risk, sanctions similarity, and orchestration efficiency support quantitative evaluation using metrics such as detection accuracy, latency, throughput, scalability, compliance, and reliability. The proposed framework delivers faster processing, improved fraud detection, stronger regulatory compliance, and greater operational resilience for next-generation financial systems.
subject: Articles
  topic: section
subject: Cloud-Native Computing
  topic: keywords
subject: Event-Driven Architecture
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Sanctions Screening
  topic: keywords
subject: Financial Crime Prevention
  topic: keywords
subject: Microservices
  topic: keywords
subject: Real-Time Analytics
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: Financial Technology
  topic: keywords
subject: Risk Assessment
  topic: keywords
creator: Juwaria Naaz
creator: Hani Patel
creator: Zaheer Syed
rights: Copyright (c) 2026
date: 2026-07-04
publication date: 2026-07-04
starting page: 12
ending page: 24
doi: 10.63282/3117-5481/AIJCST-V8I4P102

Enterprise Architecture Framework for Secure Large Language Model Deployment in Financial Services
type: item
description: The rapid advancement of Large Language Models (LLMs) has created new opportunities for financial institutions to enhance customer engagement, investment research, advisor productivity, and operational efficiency. However, enterprise adoption of LLMs remains challenging due to stringent regulatory requirements, data privacy concerns, cybersecurity risks, model governance complexities, and integration with existing enterprise systems. While existing research primarily focuses on LLM capabilities and model performance, limited attention has been given to a comprehensive enterprise architecture that enables secure, scalable, and governed deployment of LLMs within regulated financial environments. This paper proposes a novel Enterprise Architecture Framework for Secure Large Language Model Deployment in Financial Services to support the responsible adoption of generative AI across banking, asset and wealth management, and capital markets. The proposed framework consists of six vertically integrated architectural layers—Experience and Channels, AI Orchestration and Policy, LLM Services, Knowledge and Retrieval-Augmented Generation (RAG), Enterprise Integration, and Platform and Deployment—supported by four cross-cutting control domains: Security, Governance, Compliance, and Observability. These layers provide a scalable foundation for integrating LLM capabilities into enterprise applications while ensuring regulatory compliance, data protection, explainability, and operational resilience. A design science research methodology is employed to develop and validate the framework using a representative enterprise financial services case study. The evaluation demonstrates how the proposed architecture enables secure knowledge retrieval, intelligent research assistance, workflow automation, and decision support while maintaining identity-based access control, human oversight, auditability, and responsible AI governance. By combining cloud-native architecture, API-driven integra…
subject: Articles
  topic: section
subject: Large Language Models
  topic: keywords
subject: Enterprise Architecture
  topic: keywords
subject: Financial Services
  topic: keywords
subject: Retrieval-Augmented Generation
  topic: keywords
subject: AI Governance
  topic: keywords
subject: Responsible AI
  topic: keywords
creator: Saad Khan
rights: Copyright (c) 2024
date: 2024-01-22
publication date: 2024-01-22
starting page: 82
ending page: 104
doi: 10.63282/3117-5481/AIJCST-V6I1P109

Enterprise CRM Modernization for Asset and Wealth Management: A Capability-Driven Digital Transformation Framework
type: item
description: In the world of assets and wealth management, CRM systems are key to multiple digital processes, including onboarding, servicing customer relationships, managing portfolios, complying with regulations, and improving advisor collaboration. However, a large number of financial institutions are still running legacy CRM systems that have evolved over time with incremental changes, leaving their customer data disparate and not fully integrated with enterprise applications, workflows inconsistent, duplicate records prevalent and operations increasingly complex. Limitations lead to lower agility, higher maintenance and compliance expenses, and restrict companies to providing timely, customer-centric services in a highly mediated environment. This paper will present an enterprise capability-driven digital transformation framework that encompasses technology change with enterprise capability and enterprise architecture principles and operational goals. The framework organizes essential skill sets from client onboarding through relationship management, portfolio servicing, compliance management and reporting and workflow automation components into hierarchical layers and structures that lead up to a presentation piece, supported by a business services layer, an integration layer, a data management layer and a governance layer. The approach highlights the importance of standardised processes, centralised customer data, service-oriented integration, gradual implementation and robust governance oversight, which helps to enhance data quality, operational efficiency, and system interoperability and minimise implementation risk. The study clearly shows the correlation between appropriate CRM modernization and the ability to achieve sustainable and scalable approaches to digitalization for asset / wealth management organisations.
subject: Articles
  topic: section
subject: Customer Relationship Management (CRM)
  topic: keywords
subject: Wealth Management
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Enterprise Architecture
  topic: keywords
subject: Business Capability Modeling
  topic: keywords
subject: Financial Services Technology
  topic: keywords
subject: System Integration
  topic: keywords
creator: Saad Khan
rights: Copyright (c) 2020
date: 2020-11-09
publication date: 2020-11-09
starting page: 24
ending page: 37
doi: 10.63282/3117-5481/AIJCST-V2I6P103

https://aijcst.org/index.php/aijcst/article/view/288
type: item
title: Enterprise Technology Engineering 360°: A Unified Reference Model for Requirements Engineering, Solution Architecture, Product Delivery, Infrastructure Operations, Client Engagement, and Team Leadership
description: The contemporary enterprise technology landscape presents a formidable challenge: the fragmentation of specialized disciplines into isolated silos. Requirements engineering operates in relative isolation from solution architecture; product delivery teams pursue velocity while infrastructure operations prioritize stability; client engagement focuses on satisfaction while team leadership emphasizes execution. This fragmentation produces misaligned priorities, suboptimal outcomes, and organizational friction. This research paper presents the Enterprise Technology Engineering 360° (ETE-360) Reference Model, a comprehensive unified framework that integrates six critical domains: Requirements Engineering, Solution Architecture, Product Delivery, Infrastructure Operations, Client Engagement, and Team Leadership. Drawing upon established enterprise architecture frameworks including TOGAF, Zachman, and FEAF, IT Service Management best practices including ITIL and ISO/IEC 20000, and reference modeling methodologies, this research constructs a holistic model that addresses the interdependencies and overlaps inherent in enterprise technology delivery. The model provides a structured approach to aligning business objectives with technical execution, establishing clear accountability boundaries while recognizing essential cross-domain interfaces. The research methodology follows established reference model construction processes, encompassing problem definition, planning, model construction, validation, and practical application considerations. The ETE-360 model contributes to both academic discourse and practical enterprise technology management by offering a unified conceptual framework that reduces ambiguity, enhances communication, and enables more effective technology engineering outcomes. This paper presents the theoretical foundations, structural components, and practical implications of the ETE-360 Reference Model, establishing a foundation for future empirical validation…
subject: Articles
  topic: section
subject: Enterprise Technology Engineering
  topic: keywords
subject: Reference Model
  topic: keywords
subject: Requirements Engineering
  topic: keywords
subject: Solution Architecture
  topic: keywords
subject: Product Delivery
  topic: keywords
subject: Infrastructure Operations
  topic: keywords
subject: Client Engagement
  topic: keywords
subject: Team Leadership
  topic: keywords
subject: Enterprise Architecture
  topic: keywords
subject: IT Service Management
  topic: keywords
creator: Ganesh Kumar Gangannagari
rights: Copyright (c) 2025
date: 2025-12-09
publication date: 2025-12-09
starting page: 139
ending page: 152
doi: 10.63282/3117-5481/AIJCST-V7I6P114

Data-Centric AI in the Era of Large Volumes: Improving Model Outcomes through Data Quality Engineering
type: item
description: Nowadays, the whole AI system’ success is determined not only by algorithms but also, and even more importantly, by the data quality that the systems use. The rise in the amount, speed, and diversity of data has reached the point where it is no longer possible to rely only on a model-centric approach. This article tracks the extent of the growth of data-centric AI which addresses the improvement of the data itself as the main lever for better model outcomes. At the core of this transformation lies data quality engineering—a highly disciplined and oftentimes neglected profession that goes way beyond performing the research on datasets and running formal verification tests, but it is basically the very essence of completeness, consistency, and context appropriateness of the datasets in question prior to their being used in any modeling work. In high-volume environments that are prone to falling, minor inconsistencies may become a big headache and using the preventative data hygiene approach in this context becomes indispensable. The paper looks into ways of how implementing data quality pipelines, anomaly detection, labeling integrity checks, and feedback loops into AI workflows not only makes models more reliable but also minimizes retraining costs and shortens deployment cycles. The embedding of data quality engineering into the development lifecycle has facilitated organizations™ transition from being reactive to the failures to a culture of continuous improvement driven by trusted data. This transformation allows teams to focus more on innovation and less on debugging. In the final analysis, this mind shift paper argues that in the world of such a massive increase of data, a data-centric approach—rooted in engineering discipline—is the most effective way of achieving scalable and robust AI.
subject: Articles
  topic: section
subject: Data-Centric AI
  topic: keywords
subject: Data Quality Engineering
  topic: keywords
subject: Big Data
  topic: keywords
subject: AI Model Performance
  topic: keywords
subject: Data Labeling
  topic: keywords
subject: Data Cleaning
  topic: keywords
subject: Feature Engineering
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Model Retraining
  topic: keywords
subject: Data Validation
  topic: keywords
subject: Data Pipelines
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: AI Reliability
  topic: keywords
creator: Sivadeep Katangoori
creator: Anudeep Katangoori
rights: Copyright (c) 2025
date: 2025-08-01
publication date: 2025-08-01
starting page: 129
ending page: 141
doi: 10.63282/3117-5481/AIJCST-V7I4P112

Jupyter Notebooks as First-Class Citizens in Cloud-Native Data Workflows
type: item
description: Jupyter Notebooks have quickly become must-have tools for engineers, analysts, and data scientists. They provide a flexible, interactive platform that combines code, visualization, and story in one place. However, even though they are widely used for exploration and experimentation, they have traditionally had trouble fitting into production-grade, collaborative, and scalable data processes. This research looks at how Jupyter Notebooks might be reimagined as the vital parts of cloud-native ecosystems, going beyond their limits to become more important parts of these modern data infrastructure. We look at the latest ways of building and running things that make this change possible, such as CI/CD pipelines, Kubernetes orchestration, and seamless connections with these containerized environments. The goal is to make notebooks more reliable, secure, and scalable, turning them from solo scripts into useful, collaborative tools. We look at how tools like Papermill, JupyterHub, and Kubeflow Notebooks make production more ready while still meeting governance and these compliance needs. A case study shows how a corporate data platform may be useful in actual life and how to put it into action. This point of view stresses how cloud-native notebook methods make it easier for teams to work together, speed up model deployment cycles & create a culture of open, reproducible analytics. The report says that Jupyter Notebooks are not just a way to program, but they are also an important part of the modern cloud-based data and ML pipelines.
subject: Articles
  topic: section
subject: Jupyter
  topic: keywords
subject: Notebooks
  topic: keywords
subject: Cloud-Native
  topic: keywords
subject: DevOps
  topic: keywords
subject: CI/CD
  topic: keywords
subject: Data Workflows
  topic: keywords
subject: Containerization
  topic: keywords
subject: Reproducibility
  topic: keywords
subject: Orchestration
  topic: keywords
subject: Kubeflow
  topic: keywords
subject: Machine Learning Pipelines
  topic: keywords
creator: Sivadeep Katangoori
rights: Copyright (c) 2024
date: 2024-05-23
publication date: 2024-05-23
starting page: 127
ending page: 138
doi: 10.63282/3117-5481/AIJCST-V6I3P110

AI-Augmented Data Governance: Enabling Intelligent Access, Lineage, and Compliance across Hybrid Clouds
type: item
description: In today's data ecosystem, businesses are increasingly relying on their hybrid cloud environments to store & manage their information assets. However, this flexibility means that data governance has to be more thorough & smart. AI-enhanced data governance changes the way we think about data governance by adding these smart technologies like machine learning, natural language processing (NLP) & advanced metadata analytics. These tools work together to automate & improve basic governance tasks like keeping an eye on where information comes from, controlling who can access it & making sure the rules are followed. They do all of this while keeping in mind how flexible their hybrid infrastructures are. AI is a better approach to look at these types of hazards, find new issues & enforce rules in actual time, rather than only relying on their human supervision or rule-based systems. Machine learning (ML) can discover abnormal access patterns that might indicate a security breach & natural language processing (NLP) can look at policy papers that aren't organized to determine how they fit with the company's internal controls. Metadata analytics makes this ecosystem better by giving these organizations deep insights into data flows. This makes it easy for them to answer questions like "Where did this data come from?" or "Who changed it and when?" Actual world examples from healthcare, finance & retail show that AI-driven governance not only makes it easier to follow the rules and lowers the risk of data misuse, but it also gives data stewards and business users useful information that helps them make better decisions. The outcome is a governance architecture that is proactive, intelligent & scalable, rather than reactive and laborious. This meets the needs of modern corporate data strategy. In the end, AI-enhanced data governance builds a culture of trust, flexibility & accountability in these hybrid clouds. This makes operations safer, speeds up innovation, and helps people …
subject: Articles
  topic: section
subject: AI-Augmented Governance
  topic: keywords
subject: Data Lineage
  topic: keywords
subject: Hybrid Cloud
  topic: keywords
subject: Intelligent Compliance
  topic: keywords
subject: Data Access Control
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Metadata Management
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: Regulatory Audits
  topic: keywords
subject: Data Fabric
  topic: keywords
creator: Sivadeep Katangoori
creator: Anudeep Katangoori
rights: Copyright (c) 2021
date: 2021-11-11
publication date: 2021-11-11
starting page: 36
ending page: 45
doi: 10.63282/3117-5481/AIJCST-V3I6P104

Edge-Cloud Hybrid Data Pipelines: Architectures for Federated Analytics and Learning
type: item
description: The paper discusses the importance of seamless integration between edge and cloud systems in edge-cloud hybrid data pipelines for the growing role of federated analytics and machine learning. The conjunction of edge computing (hardware near the data source) with the cloud (that has scalable resources) has allowed the definition of new data analysis methods at scale, in real time and with a higher respect for privacy. Those hybrid approaches in federated learning and analytics scenarios can allow the information to remain scattered, which means that the safety and the respecting of the rules can be improved. Also, not only do they still allow the global insight or the coordinating but in addition, the model training and the analytics orchestration are going on. The paper shows that the architectures have to support continuous safe & low-latency data flows through a wide variety of edge nodes and cloud platforms during these network conditions that change. Real-time decision-making, anomaly detection, and adaptive model retraining are just a few examples of applications that benefit from this synergy. Several architectural patterns are dissected in this paper to illustrate the design trade-offs in such parts as workload distribution, data synchronization, orchestration, and trust boundaries. We also touch on the employment of containerization, message queues, federated model aggregation, and privacy-preserving mechanisms to ensure that these data pipelines remain scalable, resilient, and secure. In the end, this work lays down a detailed framework for designing edge-cloud hybrid systems for carrying out intelligent, federated operations, which units are thus able to effectively manage distributed data while still providing the same levels of performance, security, and regulatory compliance.
subject: Articles
  topic: section
subject: Edge Computing
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Data Pipelines
  topic: keywords
subject: Hybrid Architecture
  topic: keywords
subject: Real-Time Analytics
  topic: keywords
subject: Edge AI
  topic: keywords
subject: Privacy-Preserving Computation
  topic: keywords
subject: Distributed Data Processing
  topic: keywords
subject: Iot
  topic: keywords
subject: Model Aggregation
  topic: keywords
subject: Orchestration Frameworks
  topic: keywords
creator: Sivadeep Katangoori
creator: Sushil Deore
rights: Copyright (c) 2022
date: 2022-05-13
publication date: 2022-05-13
starting page: 20
ending page: 34
doi: 10.63282/3117-5481/AIJCST-V4I3P103

From Monolithic Systems to Cloud-Native Ecosystems: Modernizing Enterprise Applications with Kubernetes and Microservices
type: item
description: Modernizing corporate applications is a strategic imperative for enterprises to stay competitive in a more digital and data-driven world. Traditional monolithic systems have serviced intricate corporate activities well in the past but often don’t fit today’s requirements for scalability, agility, resilience and speed of invention. Because they are tightly coupled, they are more complex to build, deploy and administer, which raises operational costs and hinders their response to changing business needs. Cloud-native computing has changed how we build and run programs to be elastic, automated and make the best use of resources in distributed environments. In this context, microservices architecture has developed as a major notion to decompose programs into smaller independently deployable services, which can enhance scalability, fault isolation and development efficiency. Kubernetes has evolved as the standard for automating the deployment, scaling, networking, and lifecycle management of containers. It offers an efficient solution for organizations to operate complex application ecosystems. In this paper we focus on the shift from monolithic business systems to cloud-native ecosystems based on Kubernetes and microservices. This article provides a comprehensive survey of the existing research, industrial approaches and real modernization activities to assess the architectural, operational and organizational impacts of the adoption of cloud-native technology. We study the impact of modernization methodologies, deployment strategies and implementation challenges on the performance, scalability, reliability and business agility of the system. Results indicate that organizations adopting microservices design on Kubernetes can achieve higher deployment frequency, better resiliency, improved resource utilization and greater agility to respond to evolving market needs. The change introduces problems in service management, security, observability and operational complexity th…
subject: Articles
  topic: section
subject: Cloud-Native Architecture
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: Microservices
  topic: keywords
subject: Enterprise Modernization
  topic: keywords
subject: Containerization
  topic: keywords
subject: DevOps
  topic: keywords
subject: Scalability
  topic: keywords
subject: Application Transformation
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: Digital Transformation
  topic: keywords
creator: Srichandra Boosa
rights: Copyright (c) 2025
date: 2025-03-28
publication date: 2025-03-28
starting page: 122
ending page: 133
doi: 10.63282/3117-5481/AIJCST-V7I2P110

Achieving Timing Closure in Advanced ASIC and FPGA Systems: Challenges, Methodologies, and Best Practices
type: item
description: Timing closure is one of the most critical challenges in the design of modern digital integrated circuits. This is primarily because semiconductor technologies are continuously scaling to deep submicron and nanometer process nodes. New Application Specific Integrated Circuits (ASICs) and Field-Programmable Gate Arrays (FPGAs) have reached almost unbelievable levels of performance, integration, and functionality, that is on the one hand. New timing problems have arisen from these developments on the other hand, making the work of designers very difficult to the extent that setting up and holding times, clock distribution, signal integrity, and overall performance achievement of the design through timing are no longer issues which can be done easily. The complexity of timing closure is further compounded by various factors like process, voltage, and temperature (PVT) variations, increasing interconnect delays, very low-power budgets, high-frequency operation, and the increasing use of heterogeneous architectures combining different types of functional blocks. To tackle these problems, designers use a variety of techniques such as static timing analysis, timing-driven synthesis, clock tree synthesis, placement and routing optimization, constraint management, and advanced signoff (final verification) techniques. In this article, the authors provide a detailed description of how timing closure is performed in advanced ASIC and FPGA systems and introduce a well-structured framework that combines design optimization, timing analysis, and iterative verification to enhance convergence and minimize implementation risks. To give readers a better understanding of the proposed framework, a real-life example is presented demonstrating the use of the framework for fixing timing violations while keeping design performance, power efficiency, and area utilization. The authors' experience shows through the example that one should use a systematic and data-driven timing closure methodo…
subject: Articles
  topic: section
subject: Timing Closure
  topic: keywords
subject: ASIC Design
  topic: keywords
subject: FPGA Implementation
  topic: keywords
subject: Static Timing Analysis (STA)
  topic: keywords
subject: Physical Design
  topic: keywords
subject: Clock Tree Synthesis (CTS)
  topic: keywords
subject: Signal Integrity
  topic: keywords
subject: Setup Violation
  topic: keywords
subject: Hold Violation
  topic: keywords
subject: Design Optimization
  topic: keywords
subject: Machine Learning for EDA
  topic: keywords
creator: Srichandra Boosa
rights: Copyright (c) 2023
date: 2023-09-14
publication date: 2023-09-14
starting page: 61
ending page: 71
doi: 10.63282/3117-5481/AIJCST-V5I5P106

A Multi-Tier Orchestration Architecture for Regulated Cryptocurrency and Stablecoin Services within Large-Scale Payment Service Providers
type: item
description: Cryptocurrencies and regulated stablecoins will come with compliance, scalability, resilience, liquidity management, and interoperability, among other architectural challenges in the mainstream payment service provider (PSP) environment. Conventional payment systems are geared toward deterministic settlement and central control while the digital asset system is built using distributed network, partner, custody models, and changing regulatory frameworks. The purpose of this paper is to propose a multi-tier orchestration architecture that supports regulated cryptocurrency and stablecoin services in large-scale PSP environments, while keeping the vendors neutral. The architecture is based on 5 logical layers: client channels, experience and API services, transaction orchestration, digital asset core services and shared data infrastructure. An event-driven processing model allows to execute transactions on a scale, maintain regulatory traceability, and to integrate asynchronously with distributed services. It also features a standardized Outbound Connector Framework to connect custodians, market data vendors, compliance services, and tax platforms, alongside a Liquidity Pool Management architecture to enable treasury management, stablecoin conversion, settlement checks and constant reconciliation. It is important to note that the architectural analysis emphasizes regulatory compliance, operational resilience, flexibility, audibility and the ability to support the development of digital asset ecosystems. This model offers a good template for both PSPs and neobanks, and also for merchant platforms aiming to provide regulated digital asset services.
subject: Articles
  topic: section
subject: Digital Asset Platform
  topic: keywords
subject: Cryptocurrency Services
  topic: keywords
subject: Stablecoin Infrastructure
  topic: keywords
subject: Payment Service Providers
  topic: keywords
subject: Multi-Tier Architecture
  topic: keywords
creator: Anath Bandhu Chatterjee
rights: Copyright (c) 2026
date: 2026-07-02
publication date: 2026-07-02
starting page: 1
ending page: 11
doi: 10.63282/3117-5481/AIJCST-V8I4P101

Retrieval-Augmented Generation for Trusted Customer Intelligence: A Scalable Enterprise Architecture for CRM Knowledge Management
type: item
description: Enterprise customer relationship management systems increasingly operate as repositories of fragmented customer knowledge rather than as unified intelligence platforms. Sales representatives, service agents, customer-success teams, marketing analysts, and executives rely on CRM data, call transcripts, support tickets, contracts, product documentation, knowledge articles, renewal histories, and external market signals, yet these assets are often distributed across incompatible schemas, permissions, and narrative formats. Large language models offer new opportunities for conversational customer intelligence, but their direct use in CRM environments introduces unacceptable risks, including hallucinated recommendations, privacy exposure, stale knowledge, weak auditability, and limited alignment with enterprise decision governance. This paper proposes a scalable retrieval-augmented generation architecture for trusted customer intelligence in enterprise CRM knowledge management. The proposed framework integrates hybrid retrieval, semantic indexing, policy-aware document ingestion, customer-entity resolution, vector and graph-based knowledge representations, evidence-grounded generation, human-in-the-loop feedback, and trust-oriented observability. The architecture is designed to support account planning, opportunity qualification, customer service resolution, churn-risk explanation, sales enablement, executive decision support, and knowledge reuse across customer operations. A design-science methodology is adopted to formalize the artifact, decompose architectural layers, define trust controls, and establish evaluation metrics for retrieval quality, answer faithfulness, decision usefulness, latency, scalability, compliance, and user adoption. The analytical discussion shows that enterprise RAG is not merely an LLM enhancement pattern but a socio-technical knowledge-governance architecture that transforms CRM from a passive transaction system into a source-grounded intelli…
subject: Articles
  topic: section
subject: Retrieval-Augmented Generation
  topic: keywords
subject: Customer Relationship Management
  topic: keywords
subject: CRM Knowledge Management
  topic: keywords
subject: Customer Intelligence
  topic: keywords
subject: Enterprise AI Architecture
  topic: keywords
subject: Trusted AI
  topic: keywords
subject: Sales Intelligence
  topic: keywords
subject: Knowledge Graphs
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Human-AI Collaboration
  topic: keywords
creator: Achuta Krishna Kishore Varma Alluri
rights: Copyright (c) 2024
date: 2024-05-21
publication date: 2024-05-21
starting page: 114
ending page: 126
doi: 10.63282/3117-5481/AIJCST-V6I3P109

Counterfactual Telemetry Simulation for Release-Aware Capacity Guardrails and Performance Risk Mitigation in Microservice Architectures
type: item
description: Modern microservice architectures enable rapid release velocity, elastic deployment, and fine-grained service ownership, yet they also introduce difficult performance-governance problems. A single release may alter service latency, resource demand, fan-out behavior, queue depth, retry amplification, downstream saturation, or autoscaling stability in ways that are not visible during conventional pre-production testing. Existing observability and autoscaling mechanisms remain largely reactive: they detect degradation after telemetry has already shifted, after error budgets have begun burning, or after horizontal scaling decisions have lagged behind workload growth. This paper proposes a counterfactual telemetry simulation framework for release-aware capacity guardrails and performance risk mitigation in microservice architectures. The framework combines distributed traces, metrics, logs, release metadata, workload features, dependency graphs, and capacity-state information to estimate what system behavior would likely look like under alternative release and capacity scenarios. Instead of asking only whether the current deployment is healthy, the proposed approach asks whether a new release would remain safe under plausible workload, dependency, and resource conditions. The paper develops a conceptual model, methodological pipeline, evaluation criteria, and analytical discussion for applying counterfactual simulation to release gates, canary expansion, autoscaling policy tuning, and service-level objective protection. The main contribution is a release-aware guardrail architecture that transforms telemetry from retrospective evidence into prospective operational intelligence. The proposed framework is particularly relevant for Kubernetes-based cloud-native systems where service versions, autoscaling thresholds, resource limits, and traffic splits evolve continuously. The paper argues that counterfactual telemetry simulation can reduce performance risk by improving pre-…
subject: Articles
  topic: section
subject: Counterfactual telemetry simulation
  topic: keywords
subject: microservices
  topic: keywords
subject: release-aware capacity guardrails
  topic: keywords
subject: performance risk mitigation
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: autoscaling
  topic: keywords
subject: distributed tracing
  topic: keywords
subject: observability
  topic: keywords
subject: service-level objectives
  topic: keywords
subject: canary deployment
  topic: keywords
subject: cloud-native architecture
  topic: keywords
creator: Nilesh Mutyam
rights: Copyright (c) 2024
date: 2024-11-25
publication date: 2024-11-25
starting page: 109
ending page: 119
doi: 10.63282/3117-5481/AIJCST-V6I6P111

From Legacy Back Office to Intelligent Utility Enterprise a Practitioner Case Study of SAP Cloud Transformation and Utility IT Landscape Modernization
type: item
description: Utilities operate complex, regulated, and legacy-heavy technology estates in which modernization must improve agility without compromising reliability, controls, or operational continuity. This practitioner case study describes how a large energy utility moved from a fragmented back-office environment toward a cloud-based SAP digital core supported by cloud line-of-business applications, a standardized integration backbone, and a real-time data foundation. The landscape is described explicitly in utility terms: customers, employees, suppliers, field workforce, regulators, and utility operations on one side; SAP cloud applications, integration governance, the SAP S/4HANA digital core, SAP HANA, and SAP Cloud Platform analytics capabilities, and future digitization capabilities such as connected assets, omni-channel service, and automation on the other. The paper contributes a reusable visual architecture pattern and a staged roadmap for utilities planning SAP cloud modernization. Its central argument is that the program's durable value came not only from migrating applications but also from deliberately designing integration, data flow, security, supportability, and operating controls around the digital core.
subject: Articles
  topic: section
subject: SAP S/4HANA
  topic: keywords
subject: SAP For Utilities
  topic: keywords
subject: Cloud Transformation
  topic: keywords
subject: Integration Architecture
  topic: keywords
subject: SAP Cloud Integration Gateway
  topic: keywords
subject: SAP CPI
  topic: keywords
subject: SAP Cloud Platform
  topic: keywords
subject: Utility IT Landscape
  topic: keywords
subject: Enterprise Architecture
  topic: keywords
subject: Intelligent Enterprise
  topic: keywords
creator: Gururaj Veershetty
rights: Copyright (c) 2019
date: 2019-01-13
publication date: 2019-01-13
starting page: 23
ending page: 27
doi: 10.63282/3117-5481/AIJCST-V1I1P103

Original Article Scalable Enterprise Intelligence: Leveraging AI, Data Engineering, Security, and Automation for Digital Transformation
type: item
description: The rapid evolution of digital technologies has transformed the operational landscape of modern enterprises, compelling organizations to adopt intelligent, scalable, and secure data-driven architectures. Enterprise intelligence has emerged as a strategic capability that integrates Artificial Intelligence (AI), Data Engineering, Cybersecurity, and Intelligent Automation to facilitate informed decision-making, operational efficiency, and sustainable innovation. As organizations generate unprecedented volumes of structured and unstructured data, traditional business intelligence frameworks often fail to provide the scalability, agility, and real-time analytical capabilities required in highly competitive environments. Consequently, enterprises are increasingly investing in integrated intelligence ecosystems capable of processing large-scale datasets, ensuring data quality, protecting sensitive information, and automating complex workflows. This research investigates the role of scalable enterprise intelligence frameworks in enabling digital transformation across contemporary organizations. The study examines how AI-driven analytics, advanced data engineering infrastructures, cybersecurity mechanisms, and automation technologies collectively contribute to organizational resilience, productivity, and innovation. A conceptual research framework is proposed to illustrate the interrelationship among these technological components and their impact on enterprise performance. The research adopts a qualitative and conceptual methodology based on extensive literature analysis, industry reports, and contemporary enterprise transformation models. The findings indicate that organizations achieving successful digital transformation are those capable of integrating intelligent analytics with secure and scalable data infrastructures. Furthermore, automation technologies significantly reduce operational complexity while improving responsiveness and business continuity. The study identi…
subject: Articles
  topic: section
subject: Enterprise Intelligence
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Data Engineering
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Intelligent Automation
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Enterprise Analytics
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Industry 4.0
  topic: keywords
subject: Business Intelligence
  topic: keywords
creator: L Amudhavalli
rights: Copyright (c) 2026
date: 2026-05-18
publication date: 2026-05-18
starting page: 94
ending page: 106
doi: 10.63282/3117-5481/AIJCST-V8I3P109

Smart VKYC with Deepfake and Liveness Detection
type: item
description: The fast growth of virtual banking has multiplied the demand for at ease, remote, and seamless patron onboarding, making Video-primarily based KYC (vKYC) a essential procedure. This painting provides an incorporated AI-pushed verification device called clever vKYC, designed to automate identification validation and save you rising fraud tries in actual time. The goal of the system is to bolster virtual accept as true with by means of combining multiple gadget mastering and computer-imaginative and prescient modules into an stop-to-cease verification pipeline. The methodology includes FastAPI for high-performance backend processing, Agora for actual-time video streaming, Dlib’s 68-factor landmark model for blink-primarily based liveness detection, and a transformer-based totally deepfake detection version to perceive synthetic media. additionally, a twin-layer document validation method is implemented, in which Tesseract OCR extracts Aadhaar information and Pyzbar verifies cryptographically signed UIDAI QR codes to discover tampered or forged documents. Experimental consequences display a full-size improvement in preventing identity spoofing when FaceNet512 embeddings, OCR-QR consistency assessments, and deepfake ratings are mutually evaluated. The decision engine integrates output from these modules to generate a reliable confidence score, ensuring accurate verification without compromising person enjoy. This look at demonstrates that a multi-component vKYC architecture can considerably enhance the security and efficiency of far off consumer onboarding systems.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Video Know Your Customer
  topic: keywords
subject: Deepfake Detection
  topic: keywords
subject: Liveness Detection
  topic: keywords
subject: OCR
  topic: keywords
subject: Identity Verification
  topic: keywords
creator: C .S Kavitha Devi
creator: A .J Gagan
creator: K. Krishna Koushik
creator: S. Sharath
creator: R. Shashank Patil
rights: Copyright (c) 2026
date: 2026-05-16
publication date: 2026-05-16
starting page: 85
ending page: 93
doi: 10.63282/3117-5481/AIJCST-V8I3P108

Transforming Software Quality Assurance through Artificial Intelligence, Automated Testing, and Intelligent Software Analytics
type: item
description: Software Quality Assurance (SQA) plays a vital role in ensuring the reliability and performance of modern software systems. However, the increasing complexity of software applications, rapid development cycles, and adoption of technologies such as CI/CD, cloud computing, microservices, and distributed systems have created new challenges for traditional quality assurance approaches. Manual testing and conventional automation techniques are often unable to meet the demands of these dynamic environments. As a result, Artificial Intelligence (AI), Automated Testing, and Intelligent Software Analytics have emerged as effective solutions for improving software quality and testing efficiency. This study explores the integration of AI-driven techniques into software quality assurance processes and examines their impact on testing effectiveness, defect detection, and overall software reliability. The proposed framework combines machine learning-based defect prediction, automated test execution, and intelligent analytics to support continuous quality monitoring and informed decision-making. By analyzing software development data such as source code, defect records, user feedback, and runtime information, the framework helps identify potential risks and optimize testing activities. The findings indicate that AI-powered quality assurance can significantly improve defect detection rates, increase test coverage, reduce testing time, and lower maintenance costs. Furthermore, intelligent analytics enables organizations to identify quality issues at an early stage and take preventive actions before software release. The study demonstrates that integrating AI, automated testing, and software analytics can create a more efficient and reliable quality assurance ecosystem, supporting faster software delivery while maintaining high quality standards. Future research may focus on explainable AI, autonomous quality engineering systems, and deeper integration with DevSecOps practices.
subject: Articles
  topic: section
subject: Software Quality Assurance
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Automated Testing
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Intelligent Software Analytics
  topic: keywords
subject: Defect Prediction
  topic: keywords
subject: Continuous Integration
  topic: keywords
subject: Software Reliability
  topic: keywords
subject: DevOps
  topic: keywords
subject: Quality Engineering
  topic: keywords
creator: Kiran Paul Kanikaram
rights: Copyright (c) 2025
date: 2025-09-21
publication date: 2025-09-21
starting page: 103
ending page: 114
doi: 10.63282/3117-5481/AIJCST-V7I5P109

Machine Learning-Enabled Self-Healing Data Pipelines: An Autonomous Architecture for Failure Detection, Diagnostic Reasoning, and Automated Remediation
type: item
description: Enterprise data pipelines have become essential infrastructure for analytics, artificial intelligence, risk management, digital services, and regulatory reporting. However, their reliability is challenged by issues such as schema drift, source delays, data corruption, orchestration failures, resource constraints, model drift, and downstream inconsistencies. This paper proposes an autonomous architecture for machine learning-enabled self-healing data pipelines that integrates multimodal observability, anomaly detection, diagnostic reasoning, and policy-driven automated remediation. The architecture views data pipelines as continuously monitored socio-technical systems represented through metrics, logs, traces, lineage graphs, data contracts, quality indicators, execution histories, and incident records. The proposed framework consists of five layers: (1) a telemetry and lineage substrate, (2) a feature and knowledge representation layer, (3) a detection layer combining statistical, rule-based, and machine learning techniques, (4) a diagnostic reasoning layer using causal and graph-based methods, and (5) a remediation layer that executes controlled repair actions under governance policies. The study further introduces a fault taxonomy, a remediation safety model, and an evaluation framework based on fault injection experiments. Performance is assessed using metrics such as detection precision, diagnosis accuracy, mean time to recovery, false remediation rate, data quality preservation, and auditability. The paper argues that self-healing should follow a constrained autonomy model, where automated actions are permitted only when supported by sufficient evidence, limited operational risk, validated rollback mechanisms, and explicit policy approval. By integrating AIOps, DataOps, MLOps, and software governance practices, the proposed architecture provides a research-driven blueprint for building resilient data platforms capable of early failure detection, systematic root…
subject: Articles
  topic: section
subject: Self-Healing Data Pipelines
  topic: keywords
subject: AIOps
  topic: keywords
subject: DataOps
  topic: keywords
subject: MLOps
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Root Cause Analysis
  topic: keywords
subject: Automated Remediation
  topic: keywords
subject: Data Observability
  topic: keywords
subject: Pipeline Reliability
  topic: keywords
subject: Diagnostic Reasoning
  topic: keywords
creator: Vineeth Kumar Reddy Mittamidi
rights: Copyright (c) 2024
date: 2024-11-24
publication date: 2024-11-24
starting page: 98
ending page: 108
doi: 10.63282/3117-5481/AIJCST-V6I6P110

Angular Progressive Web Application Architecture for Enterprise Financial Dashboards: Performance and Accessibility
type: item
description: Enterprise financial dashboards present demanding requirements for Progressive Web Applications built with Angular, combining large dataset visualization, real-time data refresh, role-differentiated views, and strict accessibility compliance for regulated financial environments. This paper examines Angular PWA architectural patterns optimized for enterprise financial dashboard delivery.
subject: Articles
  topic: section
subject: Angular
  topic: keywords
subject: Progressive Web Applications
  topic: keywords
subject: Financial Dashboards
  topic: keywords
subject: Enterprise Frontend
  topic: keywords
subject: Performance Optimization
  topic: keywords
subject: Accessibility
  topic: keywords
subject: WCAG
  topic: keywords
subject: Service Workers
  topic: keywords
subject: Lazy Loading
  topic: keywords
subject: Web Performance
  topic: keywords
creator: Hari Krishna Mupparapu
creator: Chandra K Movva
rights: Copyright (c) 2025
date: 2025-05-29
publication date: 2025-05-29
starting page: 137
ending page: 147
doi: 10.63282/3117-5481/AIJCST-V7I3P111

Enterprise Data Transformation Strategies with Talend and Snowflake Cloud Platform
type: item
description: The increasing volume and complexity of enterprise data have accelerated the adoption of cloud-native platforms and intelligent data integration solutions to support modern business operations. As enterprises deal with a growing volume and complexity of enterprise data, cloud-native platforms and intelligent data integration solutions are accelerating enterprise operations in today's modern world. The scalability, data quality, processing speed, and integration of data from multiple sources are often problematic in the traditional data management approaches. This paper outlines an enterprise data transformation framework that leverages Talend Data Fabric and the Snowflake Cloud Data Warehouse to create a scalable, secure and high performance data ecosystem. Talend is the single integration and orchestration point to efficiently collect, cleanses, transform and manage data from several enterprise systems, as well as metadata. Snowflake builds on this with its cloud-native design which separates storage and compute resources for elastic scaling and optimized analytical performance. The proposed schema follows current ELT (Extract, Load, and Transform) principles, with Snowflake's processing engine handling transformations in the cloud environment. This way, there is less infrastructure overhead, low processing latency and the ability to run real-time and near-real-time analytics. The study also underscores the need for data governance, security, data lineage and automated data quality validation to provide trust and compliance of enterprise data assets. Performance evaluation shows that the transformations are significantly faster, query execution is faster, it's much more scalable and cost efficient than traditional ETL architectures. The results show that Talend and Snowflake offer organizations a flexible, future-proof data platform with the ability to handle their business intelligence, advanced analytics, AI, and large-scale digital transformation projects.
subject: Articles
  topic: section
subject: Enterprise Data Transformation
  topic: keywords
subject: Talend Data Fabric
  topic: keywords
subject: Snowflake Cloud Platform
  topic: keywords
subject: ETL/ELT
  topic: keywords
subject: Cloud Data Warehouse
  topic: keywords
subject: Data Integration
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Metadata Management
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Big Data Analytics
  topic: keywords
creator: Divya Kodi
rights: Copyright (c) 2026
date: 2026-03-06
publication date: 2026-03-06
starting page: 21
ending page: 36
doi: 10.63282/3117-5481/AIJCST-V8I2P103

Multi-Horizon Demand Forecasting for E-Commerce Fulfillment Using Ensemble Deep Learning
type: item
description: Efficient inventory management and assignment of fulfillment capacity, along with labor planning is built on accurate multi-horizon demand forecasting in large-scale e-commerce businesses. This paper introduces an ensemble deep learning approach fusing Long Short-Term Memory (LSTM) networks, Temporal Convolutional Networks (TCN) and Gradient Boosted Trees (GBT) to predict the demand in various categories with different seasonality patterns, demand volatility, promotional effects, and customer purchasing behaviors across multiple horizons. Traditional statistical forecasting methods like Moving Average, ARIMA, and Exponential Smoothing may not effectively model the nonlinear dependencies, rapid changes in demand, and intricate temporal dynamics often seen in today's e-commerce landscape. Advanced AI and deep learning techniques have thus become a promising alternative to enhance the accuracy of forecasts. Proposed framework combines the strength of LSTM network to model the sequence, TCN for extracting long-range temporal dependencies and GCs for feature engineering. The framework achieves this by combining prediction results from these different learners using the technique of weighted ensemble aggregation, which results in increased robustness and generalization for short-term, medium-term, and long-term forecasting. It uses transactional sales data, promo signals, the holidays, customer engagement data, and stock data to provide one-day to ninety-day outlooks. A detailed experimental analysis was performed with representative e-commerce demand datasets with multiple product categories. Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Weighted Absolute Percentage Error (WAPE) were used to evaluate forecasting performance. The experimental results show that ensemble model is always superior than the single forecasting model, and it can decrease the error of the forecast and ensure the stability of the forecasting results in the case of fluctu…
subject: Articles
  topic: section
subject: Demand Forecasting
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: LSTM
  topic: keywords
subject: Ensemble Methods
  topic: keywords
subject: E-Commerce
  topic: keywords
subject: Temporal Convolutional Networks
  topic: keywords
subject: Time Series
  topic: keywords
subject: Inventory Optimization
  topic: keywords
subject: Supply Chain Analytics
  topic: keywords
subject: Gradient Boosting
  topic: keywords
creator: Venkatesh Manohar
rights: Copyright (c) 2024
date: 2024-03-24
publication date: 2024-03-24
starting page: 107
ending page: 117
doi: 10.63282/3117-5481/AIJCST-V6I2P111

Enterprise Android Device Management and Security Policy Enforcement for Large-Scale Retail IoT Deployments
type: item
description: Large-scale retail IoT deployments comprising fleets of Android-powered handheld devices, kiosks, and point-of-sale terminals require enterprise device management architectures that enforce security policies, automate software lifecycle management, and maintain operational continuity across geographically distributed store environments. This paper presents an enterprise Android device management framework for retail IoT deployments, examining Mobile Device Management protocol integration, automated application deployment pipelines, remote security policy enforcement including certificate management and encryption compliance, and zero-touch provisioning workflows for new device onboarding.
subject: Articles
  topic: section
subject: Android
  topic: keywords
subject: Enterprise Device Management
  topic: keywords
subject: Retail Iot
  topic: keywords
subject: Mobile Device Management
  topic: keywords
subject: Security Policy
  topic: keywords
subject: Fleet Management
  topic: keywords
subject: Zero-Touch Provisioning
  topic: keywords
subject: Certificate Management
  topic: keywords
subject: Retail Technology
  topic: keywords
subject: Iot Architecture
  topic: keywords
creator: Chandra K Movva
creator: Hari Krishna Mupparapu
rights: Copyright (c) 2023
date: 2023-01-24
publication date: 2023-01-24
starting page: 50
ending page: 67
doi: 10.63282/3117-5481/AIJCST-V5I1P105

Entity Framework Core Performance Optimization Strategies for High-Throughput Financial Data Systems
type: item
description: Entity Framework Core is widely adopted for data access in .NET enterprise applications, yet its default behaviors introduce performance bottlenecks that become critical in high-throughput financial data systems processing large transaction volumes. This paper systematically evaluates Entity Framework Core performance optimization strategies including query compilation caching, no-tracking queries, explicit loading versus eager loading tradeoffs, bulk operation handling, and connection pooling configurations in financial services contexts. Benchmarks conducted on representative financial transaction datasets quantify the performance impact of each strategy and identify optimization combinations that yield the greatest throughput improvements. The paper further analyzes when Dapper should supplement or replace Entity Framework Core for performance-sensitive financial data access paths.
subject: Articles
  topic: section
subject: Entity Framework (EF) Core
  topic: keywords
subject: .NET
  topic: keywords
subject: Performance Optimization
  topic: keywords
subject: Financial Systems
  topic: keywords
subject: Data Access
  topic: keywords
subject: Query Optimization
  topic: keywords
subject: Dapper
  topic: keywords
subject: Database Performance
  topic: keywords
subject: High Throughput
  topic: keywords
subject: Enterprise Applications
  topic: keywords
creator: Hari Krishna Mupparapu
rights: Copyright (c) 2022
date: 2022-01-24
publication date: 2022-01-24
starting page: 90
ending page: 101
doi: 10.63282/3117-5481/AIJCST-V4I1P109