International Journal of Computer Science and Engineering Innovations
type: channel
description: International Journal of Computer Science and Engineering Innovations (IJCSEI) is a double-blind scholarly peer reviewed journal publishes original research, review articles, survey, short communications, technical reports in the all areas of computer science and engineering. The core computing and innovations in engineering research are the most received concepts the journal compromises, emphasizing modern scientific computing and technology, while also embracing cutting-edge advancements in computational techniques, system designs, and engineering solutions that address global challenges and drive technological progress across diverse industries. The novel insights keeps on moving the research to the next level of knowledge in technical core, where the IJCSEI wishes to surface the modern and scientific knowledge for the gradual progress. The journal follows the stringent peer review criteria, and review process and guarantees the publication of high quality articles suitable to the IJCSEI.
language: en
publication name: International Journal of Computer Science and Engineering Innovations
items: Autonomous Enterprise Intelligence Platforms Enabled by Collaborative Multi-Agent Large Language Models
items: Resource Management and Deployment Blind Spots in FastAPI
items: Deep Learning-Enabled Industrial IoT Framework for Autonomous Fault Forecasting in Connected Diesel Engine Systems
items: Autonomous Financial DevOps Using Agentic AI in Cloud-Native Enterprise Environments
items: Developing a Scalable SQL-Based Framework for Ingesting and Analyzing Aerospace Simulation Output Data
items: Foundation Model-Based Predictive Analytics for Intelligent Transportation Systems
items: Federated Explainable AI Framework for Cross-Domain Medical Image Analysis
items: AI-Enabled Autonomous Network Slicing Optimization for 6G Communication Systems
items: Explainable Machine Learning for Software Defect Prediction in Large-Scale Code Repositories Using Code Embeddings and Repository-Level Knowledge Graphs
items: Federated Learning Approach for Cross-Enterprise HR Analytics in Oracle HCM
items: AI-Driven API Architectures for Multi-Cloud Enterprises: A Comparative Study of Centralized, Distributed, and Hybrid Deployment Models
items: AI-Driven Compliance and Configuration Intelligence at Scale: An Explainable, Human-Centered Framework for Enterprise Infrastructure
items: Enhancing Software Delivery Performance through AI-Driven Observability and Intelligent Automation
items: Optimized Deep Learning-Based Image Compression Using Convolutional Autoencoders
items: Architecting Secure Big Data Platforms: Insights from a Big Data and IT Security Specialist
items: Beyond Logs: Building Intelligent Monitoring Systems with Splunk Clusters
items: Enterprise Content Management in the Cloud Era: An AEM and DevOps Perspective
items: Building and Supporting Cloud-Native Architectures on AWS: Real-World Engineering Practice
items: Building Robust Salesforce Platforms: Architecture Patterns and Real-World Experience
items: Modern TechOps Architecture: Integrating AI, DevOps, and Observability at Enterprise Scale
items: Designing and Managing High-Availability Databases in Global Financial Systems
items: Scaling Software Quality: Real-World Experiences from Enterprise QA Management
items: Bridging Datacenters and Cloud: Insights from a Senior Cloud Systems Engineer at INFOR
items: Building Enterprise Grade CRM Systems: The Professional Journey of a Microsoft Dynamics Architect
items: Transforming Healthcare IT with Cloud Engineering and DevOps Automation
items: Ten Years of Engineering Excellence: My Journey as a Senior Developer in Enterprise and Data Platforms
items: Engineering Resilient Enterprise Solutions: A Senior Developer’s Perspective from the Airline Industry
items: From Microarchitecture to Silicon: Building High-Performance CPUs across Intel and Qualcomm
items: Engineering Reliable Healthcare Platforms with Java and ETL Technologies
items: From Code to Strategy: A Senior Technical Manager’s Journey in Cloud Native Banking Systems

Autonomous Enterprise Intelligence Platforms Enabled by Collaborative Multi-Agent Large Language Models
type: item
description: Enterprise Intelligence provides decision-making assistance to all levels of managers throughout an organization, leveraging data, models, and computation. Enterprise Agility facilitates rapid self-diagnosis and reconfiguration of operations to align with constantly changing governing conditions. Self-Evolving Enterprise Intelligence Platforms are enabled through collaboration among several Multi-Agent LLM systems. Collaboration involves negotiation to resolve conflicts and make trade-offs, ultimately achieving a shared purpose. The Enterprise Intelligence and Enterprise Agility concepts serve as a foundation to explore Architectural Paradigms that enable Self-Evolving Platforms. Agent-based decomposition is proposed to delineate roles, interfaces, modular boundaries, and interaction protocols, thereby enhancing scalability and fault tolerance. The mechanisms for evolution in LLM-driven systems are outlined, followed by a description of collaboration frameworks among the agents. Self-evolution in self-evolving platforms is examined via three mechanisms: continuous learning and adaptation, negotiation and trade-offs, and Multi-Agent strategy. Data and experience ingress continuously, meeting the requirements of LLM systems. New training sets are formed, model updates undertaken, and permanent feedback loops instituted under neural architecture search and safe reinforcement learning principles. The drift inherent in LLM systems necessitates vigilant monitoring. Change is inevitable; adaptation can minimize harm and capitalise on opportunities. The foregoing mechanisms jointly impact all aspects of a platform, yet the implementation of Multi-Agent strategies is essential to acquire true Self-Evolution.
subject: Articles
  topic: section
subject: Multi-Agent Large Language Models
  topic: keywords
subject: Self-Evolving Enterprise Intelligence
  topic: keywords
subject: Enterprise Decision Intelligence
  topic: keywords
subject: Collaborative AI Agents
  topic: keywords
subject: Large Language Model Orchestration
  topic: keywords
subject: Autonomous Enterprise Systems
  topic: keywords
subject: Agentic Artificial Intelligence
  topic: keywords
subject: Adaptive Knowledge Integration
  topic: keywords
subject: Intelligent Workflow Automation
  topic: keywords
subject: Human–AI Collaborative Decision-Making
  topic: keywords
creator: DHANARAJ SATHIRI
rights: Copyright (c) 2026
date: 2026-08-02
publication date: 2026-08-02
starting page: 35
ending page: 46
doi: 10.64137/31079458/IJCSEI-V2I3P103

Resource Management and Deployment Blind Spots in FastAPI
type: item
description: FastAPI is climbing the ladder to become the main framework for building cloud-native applications and is fast becoming one of the favorites thanks to its high performance, async features, developer-friendly design, and native integration with Python. More and more businesses are planning to use FastAPI to build scalable APIs and microservices capable of efficiently supporting real-time data processing, AI systems, and distributed applications. On the contrary, deployment environments sometimes face hidden operational challenges with FastAPI, which often go unnoticed during development and early production stages despite the framework's growing popularity. This research dives into the major problem of resource management and deployment blind spots in FastAPI-based systems. Firstly, it discusses the consequences of neglecting asynchronous execution, memory, CPU, connection pooling, and background task scheduling on application stability and scalability. Then it addresses deployment-related issues, including inefficiencies in container orchestration, limitations in Kubernetes scaling, dependency conflicts, gaps in observability, poor logging strategies, and failures in monitoring, which all negatively impact reliability in production environments. Through the study of real deployment cases and cloud-native infrastructures, the paper reveals that these hidden blind spots cause performance degradation, downtime risks, unpredictable latency, and rising operational costs. A well-organized methodology combining performance benchmarking, deployment analysis, and case-study evaluation is suggested to explore these issues in a more structured way. Besides, the study stresses the need for preparatory observability practices, fine-tuned asynchronous workflows, good resource allocation, and fault-tolerant deployment pipelines for enhancing system reliability. Results from the case study indicate that those who have put into practice advanced monitoring, smarter orchestration str…
subject: Articles
  topic: section
subject: FastAPI
  topic: keywords
subject: Resource Management
  topic: keywords
subject: Deployment Automation
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: Asynchronous Processing
  topic: keywords
subject: Cloud-Native Applications
  topic: keywords
subject: DevOps
  topic: keywords
subject: Scalability
  topic: keywords
creator: MADHURIMA KOMMURU
creator: SRUJANA PULIPAKA
rights: Copyright (c) 2026
date: 2026-06-13
publication date: 2026-06-13
starting page: 41
ending page: 50
doi: 10.64137/31079458/IJCSEI-V2I2P106

Deep Learning-Enabled Industrial IoT Framework for Autonomous Fault Forecasting in Connected Diesel Engine Systems
type: item
description: Diesel engines power 80% of commercial traffic and 80% of the world's merchant fleet. Almost all ship propulsion engines are diesel. Because even unnoticed faults can lead to considerable losses, Predictive Maintenance (PdM) applications are needed to guarantee Availability, Reliability, and Safety. The proliferation of sensors surrounding these machines and their connectivity in the Industrial IoT (IIoT) creates a new ecosystem for Condition-Based Maintenance (CBM). Autonomously monitoring the data generated within newly connected diesel engine ecosystems would guarantee a faster response to detected symptoms. Data from analogue sensors, digital binary sensors, and telemetry lines would act as predictors to feed a fault classification engine. This would allow faster decision-making by maintenance teams and ultimately reduce repair costs and machine downtime. Connectivity enhances intelligent systems equipped with Artificial Intelligence (AI) algorithms capable of autonomously processing and analyzing the generated data. A variety of Data Challenge tasks, offered in competitions on crowdsourcing platforms like Kaggle, could be solved using the same principles. In these competitions, databases often link telemetric information of diesel engine vehicles with driving qualities like Comfort or Safety, or with the Failure classification of electric components. Such competitions generally seek to predict the Failure code.
subject: Articles
  topic: section
subject: Predictive Maintenance Systems
  topic: keywords
subject: Diesel Engine Analytics
  topic: keywords
subject: Industrial IoT Monitoring
  topic: keywords
subject: Condition-Based Maintenance
  topic: keywords
subject: Autonomous Fault Detection
  topic: keywords
subject: Sensor Data Intelligence
  topic: keywords
subject: Telemetry-Based Diagnostics
  topic: keywords
subject: AI-Driven Maintenance
  topic: keywords
subject: Failure Prediction Models
  topic: keywords
subject: Intelligent Engine Monitoring
  topic: keywords
creator: LUCAS VAN DIJK
rights: Copyright (c) 2025
date: 2025-12-31
publication date: 2025-12-31
starting page: 38
ending page: 47
doi: 10.64137/31079458/IJCSEI-V1I2P106

Autonomous Financial DevOps Using Agentic AI in Cloud-Native Enterprise Environments
type: item
description: Modern cloud-native Financial DevOps should harness the full predictive and optimizing power of data and analytics. Organizations need sufficient and suitably specialized expertise and experience to implement Financial DevOps that are agentic, autonomous, and orchestrated in support of a senior business executive, while ensuring observability and risk management. These capabilities allow for the migration away from traditional budgeting cycles and resource-allocation models towards the establishment of cloud-native consumption-based FinOps at the enterprise level. A framework for Risk-based Reliability Engineering supports quantifying and mitigating the operational risk introduced by autonomy. Ethical, legal, and governance considerations relevant to implementation define an agentic fiduciary office responsible for ensuring suitable design and implementation balanced with sufficient and suitably specialized expertise and experience. While many technical stakeholders in cloud-native FinOps teams are naturally drawn to Cloud FinOps principles and processes, benefits would accrue from having others take on distinct business-facing Agentic AI-Driven Autonomous Financial DevOps responsibilities, thus allowing the business and technical sides of the organization to communicate more easily around financial topics. Achieving seamless Cloud Financial Operations (FinOps) delivery requires the establishment of sound Data Governance, Security, and Compliance capabilities; permanent focus on sufficient Data Quality; harmonious complementarity of the Cloud FinOps technical stack with Data Analytics and decision-making tools; a Clear Decision-Making Framework suited to Data-driven Digital Business Operations; incorporation of Risk-based Reliability Engineering within DevOps; and Agentic AI-Driven Autonomous Financial DevOps.
subject: Articles
  topic: section
subject: Cloud-Native Financial DevOps
  topic: keywords
subject: Agentic FinOps Systems
  topic: keywords
subject: Autonomous Financial Operations
  topic: keywords
subject: Risk-Based Reliability Engineering
  topic: keywords
subject: AI-Driven FinOps Analytics
  topic: keywords
subject: Cloud Cost Optimization
  topic: keywords
subject: Financial Governance Frameworks
  topic: keywords
subject: Data-Driven Decision Systems
  topic: keywords
subject: DevOps Risk Management
  topic: keywords
subject: Enterprise FinOps Architecture
  topic: keywords
creator: OLIVIA BENNETT
rights: Copyright (c) 2026
date: 2026-07-04
publication date: 2026-07-04
starting page: 23
ending page: 34
doi: 10.64137/31079458/IJCSEI-V2I3P102

Developing a Scalable SQL-Based Framework for Ingesting and Analyzing Aerospace Simulation Output Data
type: item
description: Typical aircraft external loads processes were originally developed for smaller models under legacy computational constraints. While these processes have been adapted to handle modern model sizes, they were not designed to scale efficiently with the growing volume and complexity of simulation data. As a result, downstream plotting and critical case selection workflows remain inefficient and lack consistent traceability across aircraft and analysis loops. To address these limitations, a relational SQL-based framework was developed to structure and scale external loads data across millions of records and multiple analysis types into a unified and structured dataset. This framework enables automated post-processing tools for envelope visualization and comparison, along with a reduction methodology that identifies the smallest subset of critical cases required to preserve structural design conditions within a specified tolerance. The database supports efficient extraction of one-dimensional envelope extrema and optional two-dimensional envelope boundaries using convex hull methods. These envelope requirements are used to define a constraint set, which is solved using a greedy set-cover algorithm with a user-defined tolerance (e.g., 3%) to determine the minimal set of representative conditions. SQL enables efficient filtering, aggregation, and envelope extraction over large datasets, while higher-order geometric and combinatorial operations are performed in Python after data reduction. Application of the framework to a representative dataset containing 4,914 flight conditions produced approximately 696 one-dimensional envelope requirements, which were further reduced to a final subset of 82–94 critical cases depending on the inclusion of multidimensional constraints. The reduction process maintained envelope fidelity within the specified tolerance and completed within practical computational time. This approach consolidates large-scale external loads data into a single, …
subject: Articles
  topic: section
subject: Relational Database
  topic: keywords
subject: Aerospace Data
  topic: keywords
subject: Data Ingestion
  topic: keywords
subject: Data Reduction
  topic: keywords
subject: Simulated Flight Cas
  topic: keywords
subject: Structural Load
  topic: keywords
subject: Flight Conditions
  topic: keywords
subject: External Loads
  topic: keywords
creator: DWIGHT RANSOM
creator: SIKHA BAGUI
rights: Copyright (c) 2026
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 22
doi: 10.64137/31079458/IJCSEI-V2I3P101

Foundation Model-Based Predictive Analytics for Intelligent Transportation Systems
type: item
description: Intelligent Transportation Systems (ITS) are undergoing a paradigm shift driven by the unprecedented volume of multi-modal data and the necessity for highly accurate, real-time predictive analytics. Traditional machine learning and deep learning architectures, while effective for localized tasks, often struggle with spatial-temporal distribution shifts, cross-city generalization, and the integration of heterogeneous data sources such as traffic cameras, GPS trajectories, weather reports, and textual incident logs. This paper investigates the application of Foundation Models (FMs) large-scale architectures pre-trained on massive datasets and fine-tuned for downstream tasks as the core engine for next-generation ITS predictive analytics. The article presents a generic framework which exploits the zero-shot, few-shot and multi-modal capabilities of foundation models to forecast traffic flow, predict traffic incidents and improve public transit schedules. This study uses large-scale comparisons and empirical simulations to evaluate the structural benefits, integration techniques, and performance trade-offs of FMs against common deep learning frameworks. The results show that foundation model-based methods achieve better generalization on unseen urban networks and high robustness towards the noisy or missing sensor data. Lastly, we identify some of the principal two-sided challenges including computational overhead, latency constraints in edge deployment and privacy protection, also discuss a few strategies for addressing these issues and direction towards new research and deployable practices within smart city infrastructures.
subject: Articles
  topic: section
subject: Foundation Models
  topic: keywords
subject: Intelligent Transportation Systems
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Spatial-Temporal Modeling
  topic: keywords
subject: Traffic Flow Forecasting
  topic: keywords
subject: Multi-Modal Data Fusion
  topic: keywords
creator: T. L. WEERAWARDHANA
rights: Copyright (c) 2026
date: 2026-06-10
publication date: 2026-06-10
starting page: 35
ending page: 40
doi: 10.64137/31079458/IJCSEI-V2I2P105

Federated Explainable AI Framework for Cross-Domain Medical Image Analysis
type: item
description: The integration of deep learning architectures into clinical workflows has catalyzed unprecedented advancements in automated medical image analysis. However, the deployment of these centralized models faces severe impediments due to data privacy mandates, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), alongside the pervasive "black-box" nature of deep neural networks. To reconcile the tension between collaborative machine learning, data sovereignty, and clinical interpretability, this paper introduces a novel Federated Explainable Artificial Intelligence (Fed-XAI) framework tailored for cross-domain medical image analysis. The proposed architecture enables multi-institutional collaboration by training robust deep learning models locally across heterogeneous healthcare domains without centralizing raw patient data. To overcome the specific challenge of domain shift—arising from variations in imaging protocols, manufacturer hardware, and patient demographics—we incorporate an adaptive, domain-agnostic aggregation protocol alongside localized feature alignment layers. Crucially, the framework embeds post-hoc interpretability mechanisms, utilizing federated gradient-based attribution and attention map aggregation, to provide clinicians with transparent, pixel-level justifications for automated diagnostic outputs. We evaluate our Fed-XAI framework across a multi-institutional dataset consisting of chest X-rays, histopathology slides, and magnetic resonance imaging (MRI) scans distributed across four simulated distinct hospital domains. The empirical results demonstrate that our framework achieves diagnostic performance metrics comparable to centralized training paradigms while maintaining strict privacy boundaries. Furthermore, the qualitative and quantitative evaluations of the generated explanations verify that the framework identifies genuine pathological biomarkers rather than exploiting spur…
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Explainable Artificial Intelligence (XAI)
  topic: keywords
subject: Medical Image Analysis
  topic: keywords
subject: Domain Adaptation
  topic: keywords
subject: Deep Learning Privacy
  topic: keywords
subject: Trustworthy AI
  topic: keywords
creator: D. D. T. K. KULATHUNGA
rights: Copyright (c) 2026
date: 2026-06-08
publication date: 2026-06-08
starting page: 28
ending page: 34
doi: 10.64137/31079458/IJCSEI-V2I2P104

AI-Enabled Autonomous Network Slicing Optimization for 6G Communication Systems
type: item
description: The advent of sixth-generation (6G) wireless communication networks introduces unprecedented demands for ultra-high data rates, near-zero latency, massive device connectivity, and hyper-reliability. To satisfy these heterogeneous service requirements simultaneously, network slicing has emerged as a foundational architectural paradigm. However, the static and reactive resource allocation strategies utilized in legacy generations are insufficient to cope with the highly dynamic, multi-dimensional, and time-varying nature of 6G traffic demands. This paper presents a comprehensive framework for artificial intelligence (AI)-enabled autonomous network slicing optimization in 6G systems. We investigate the application of advanced machine learning paradigms specifically deep reinforcement learning (DRL), federated learning (FL), and generative AI to orchestrate dynamic resource provisioning, cross-slice isolation, and proactive SLA (Service Level Agreement) enforcement. Through a robustly designed methodology combining centralized global coordination with decentralized edge execution, this study provides an optimized, self-healing, and intent-driven network orchestration architecture. The experimental evaluation illustrates that the proposed AI-driven framework outperforms conventional heuristics and static optimization models across critical performance metrics, including spectrum efficiency, SLA satisfaction rate, and computational overhead. The paper concludes by defining prominent research gaps and future directions toward standardizing fully autonomous, zero-touch 6G network management.
subject: Articles
  topic: section
subject: 6G Communication Systems
  topic: keywords
subject: Network Slicing
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Deep Reinforcement Learning
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Zero-Touch Network Management
  topic: keywords
creator: P. J. NARAYANAN
creator: JEEVAJOTHI
rights: Copyright (c) 2026
date: 2026-06-06
publication date: 2026-06-06
starting page: 23
ending page: 27
doi: 10.64137/31079458/IJCSEI-V2I2P103

Explainable Machine Learning for Software Defect Prediction in Large-Scale Code Repositories Using Code Embeddings and Repository-Level Knowledge Graphs
type: item
description: Software defect prediction has moved from static metric classification toward learning-based quality intelligence that must operate across millions of functions, commits, tests, services, and developer interactions. This paper proposes an explainable machine learning framework for large-scale defect prediction that fuses contextual code embeddings, repository-level knowledge graphs, and governance-aware explanation mechanisms into a single lifecycle architecture. The core premise is that source code tokens alone cannot represent the socio-technical context in which defects emerge: dependency changes, test failures, build instability, ownership patterns, issue histories, deployment topology, and review signals also shape risk. Accordingly, the proposed framework encodes functions and patches with transformer-based code representations, constructs a typed repository graph from version-control and DevOps artifacts, and combines these representations through a graph-aware fusion layer that predicts file-, function-, and commit-level defect risk. The design builds on advances in pre-trained programming-language models [1]. It also aligns with lifecycle governance work that connects defect prediction, automated testing, and architecture-centered delivery controls [2]. For regulated and high-assurance domains, the model must produce explanations that are traceable enough for review triage, audit, and risk acceptance rather than merely producing probability scores [3]. The explainability layer, therefore, integrates local feature attribution, graph rationales, counterfactual repository edits, and confidence calibration so that predictions can be inspected by developers, testers, release managers, and compliance reviewers. A reproducible experimental protocol is specified for temporal validation, cross-project evaluation, ablation analysis, effort-aware ranking, and explanation faithfulness. The paper contributes a rigorous blueprint for defect prediction systems that are se…
subject: Articles
  topic: section
subject: Software Defect Prediction
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Code Embeddings
  topic: keywords
subject: Repository Knowledge Graph
  topic: keywords
subject: Codebert
  topic: keywords
subject: Graphcodebert
  topic: keywords
subject: Just-in-Time Prediction
  topic: keywords
subject: Devops Governance
  topic: keywords
creator: KARTIK KUMARAN
rights: Copyright (c) 2026
date: 2026-06-04
publication date: 2026-06-04
starting page: 11
ending page: 22
doi: 10.64137/31079458/IJCSEI-V2I2P102

Federated Learning Approach for Cross-Enterprise HR Analytics in Oracle HCM
type: item
description: Human Resource (HR) analytics is a decision-making and strategic capability that assists organizations in managing workforce, talent acquisition, employee retention etc. using data-driven insights. In today's world Human Capital Management (HCM) platforms, like Oracle HCM Cloud, are entwined up to the core of every modern enterprise with global business units as workforce data is scattered wide across geographical boundaries. Yet, with the increasing adoption of HR analytics, arise significant issues about employee privacy, protecting sensitive data, ensuring compliance with regulatory requirements and sharing data across organizations. Traditional centralized analytics architectures also involve enterprises centralizing sensitive employee data into a shared repository, raising major privacy risks as well as governance issues. Federated Learning (FL) has shown to be a powerful machine learning paradigm for distributed model training without the need of exchanging raw data. Federated learning enables local model training, where sensitive workforce information does not get transmitted from one organization to another, but rather only the local models (parameters or gradients) are communicated with a centralized aggregation server. This angle is perfectly in line with recent privacy regulations like GDPR (General Data Protection Regulation), CCPA (California Consumer Privacy Act), enterprise governance frameworks. Cooperative analytics using federated learning and Oracle Human Capital Management (Oracle HCM) systems creates great potential for new cross-enterprise HR analytics by enabling an organization to extract common intelligence while maintaining data privacy. Cross-Enterprise HR Analytics: A Federated Learning Framework for Oracle HCM The framework combines Oracle HCM Cloud modules, federated learning, privacy-preserving techniques, secure parameter aggregation and predictive analytics. The evaluation of the framework uses multiple HR analytics use cases such as…
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Oracle HCM
  topic: keywords
subject: Human Resource Analytics
  topic: keywords
subject: Workforce Intelligence
  topic: keywords
subject: Privacy-Preserving Machine Learning
  topic: keywords
subject: Cross-Enterprise Analytics
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Talent Management
  topic: keywords
subject: Data Governance
  topic: keywords
subject: HR Technology
  topic: keywords
creator: A. BASHEER AHAMED
rights: Copyright (c) 2026
date: 2026-04-11
publication date: 2026-04-11
starting page: 1
ending page: 10
doi: 10.64137/31079458/IJCSEI-V2I2P101

AI-Driven API Architectures for Multi-Cloud Enterprises: A Comparative Study of Centralized, Distributed, and Hybrid Deployment Models
type: item
description: Multi-cloud enterprises increasingly operate across heterogeneous environments that include public cloud platforms, private data centers, Kubernetes clusters, serverless functions, legacy middleware, partner integrations, and regulated data domains. In such conditions, the conventional distinction between an API gateway and a service mesh is no longer sufficient. API gateways remain essential for north-south traffic, external API exposure, consumer onboarding, throttling, authentication, and monetization. Service meshes remain important for east-west service communication, mutual TLS, observability, traffic shifting, policy enforcement, and runtime resilience. However, modern enterprise integration requires an API mesh architecture that combines gateway-level governance with mesh-level service connectivity and platform-independent policy control. This paper proposes a vendor-agnostic architectural comparison of service-mesh, API gateway, and hybrid API-mesh patterns for multi-cloud enterprises. The study presents a layered reference model, evaluates tradeoffs across security, latency, observability, operational complexity, governance, failure isolation, and migration feasibility, and introduces a decision framework for selecting appropriate patterns across enterprise scenarios. The paper argues that mature multi-cloud organizations should not treat API gateway and service mesh as competing technologies. Instead, they should design a hybrid control plane that separates business API management from service-to-service communication while maintaining consistent identity, telemetry, policy, and lifecycle governance. The proposed API mesh approach supports gradual modernization from monoliths to microservices, improves distributed system visibility, strengthens zero-trust adoption, and reduces integration fragility across cloud-native and legacy estates.
subject: Articles
  topic: section
subject: API Mesh
  topic: keywords
subject: Service Mesh
  topic: keywords
subject: API Gateway
  topic: keywords
subject: Multi-Cloud Architecture
  topic: keywords
subject: Hybrid Cloud
  topic: keywords
subject: Microservices
  topic: keywords
subject: Zero Trust
  topic: keywords
subject: Traffic Management
  topic: keywords
subject: Observability
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: Cloud Native Systems
  topic: keywords
subject: Enterprise Integration
  topic: keywords
creator: RAKESH REDDY THALAKANTI
rights: Copyright (c) 2026
date: 2026-02-21
publication date: 2026-02-21
starting page: 60
ending page: 67
doi: 10.64137/31079458/IJCSEI-V2I1P108

AI-Driven Compliance and Configuration Intelligence at Scale: An Explainable, Human-Centered Framework for Enterprise Infrastructure
type: item
description: Enterprise IT environments increasingly span heterogeneous on-premises, cloud, and hybrid platforms, where configuration changes occur continuously and at scale. Ensuring configuration and security compliance under these conditions remains a persistent challenge, particularly in regulated domains where transparency, auditability, and human accountability are mandatory. Traditional rule-based compliance mechanisms, while effective for baseline enforcement, struggle to provide timely detection of configuration drift, contextual risk assessment, and decision support. This paper proposes an AI-driven, explainable compliance and configuration intelligence framework designed to augment existing configuration management and governance tooling. Rather than replacing deterministic controls, the framework introduces data-driven risk inference, temporal drift analysis, and human-in-the-loop validation. We present a reference architecture derived from large-scale enterprise deployments and align it with established research in configuration management, explainable artificial intelligence (XAI), and human-AI interaction. The results demonstrate how explainable AI can enhance situational awareness, reduce compliance overhead, and improve governance without compromising control or trust.
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Machine Learning (ML)
  topic: keywords
subject: Explainable AI (XAI)
  topic: keywords
subject: Human-Centered AI
  topic: keywords
subject: Intelligent Automation
  topic: keywords
subject: Decision Intelligence
  topic: keywords
creator: Nadeem Siddiqui
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 123
ending page: 127
doi: 10.64137/3107-9458/ICCSEMTI26-121

Enhancing Software Delivery Performance through AI-Driven Observability and Intelligent Automation
type: item
description: Modern software delivery operates under simultaneous pressure for speed, stability, security, and cost efficiency. Although DevOps and continuous delivery practices have improved release velocity, many organizations still struggle to translate raw operational telemetry into timely decisions that reduce change failure, shorten recovery windows, and improve delivery throughput. This paper develops a Q1-style conceptual research framework that positions AI-driven observability as the missing decision layer between software delivery instrumentation and intelligent automation. Using a structured synthesis of forty references spanning software delivery performance, AIOps, defect prediction secure microservices, cloud-native monitoring, and cross-domain AI automation, the paper derives a reference architecture that integrates telemetry collection, dependency-aware context modeling, anomaly and risk prediction, policy-aware orchestration, and human-centered decision support. The framework links observability signals such as logs, metrics, traces, pipeline events, configuration changes, and service dependencies to delivery outcomes measured through throughput and stability metrics. In contrast to narrow monitoring or isolated machine learning approaches, the proposed model treats software delivery as a closed feedback system in which prediction, explanation, and automated action must remain aligned with governance, security, and service objectives. The paper contributes three outcomes: a reference architecture for AI-driven observability and intelligent automation, a mechanism-level explanation of how that architecture can improve delivery performance, and an evaluation model grounded in software delivery metrics, automation quality measures, and socio-technical controls. The result is a practical research agenda for building delivery systems that are not only observable, but also increasingly adaptive, trustworthy, and operationally efficient.
subject: Articles
  topic: section
subject: AI-Driven Observability
  topic: keywords
subject: Software Delivery Performance
  topic: keywords
subject: Devops
  topic: keywords
subject: AIops
  topic: keywords
subject: Intelligent Automation
  topic: keywords
subject: Continuous Delivery
  topic: keywords
subject: Dora Metrics
  topic: keywords
subject: Cloud-Native Systems
  topic: keywords
creator: MANISH VENKATARAMAN
creator: KAVYA MENON
creator: ARJUN SENAPATI
rights: Copyright (c) 2026
date: 2026-02-18
publication date: 2026-02-18
starting page: 52
ending page: 59
doi: 10.64137/31079458/IJCSEI-V2I1P107

Optimized Deep Learning-Based Image Compression Using Convolutional Autoencoders
type: item
description: This study presents an optimized image compression framework based on deep learning, specifically a convolutional autoencoder. Traditional compression methods such as JPEG and PNG rely on fixed mathematical transformations, which often lead to quality degradation at high compression ratios. To address these limitations, the proposed system adopts a data-driven approach that learns compact and efficient image representations through end-to-end training. The framework integrates convolutional encoding, latent space quantization, entropy coding, and decoding mechanisms to ensure efficient storage and transmission. Experimental evaluation was conducted using benchmark datasets, and performance was assessed using metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and compression ratio. The results demonstrate that the model achieves a compression ratio exceeding 133:1 with minimal perceptual quality loss, maintaining stable PSNR and SSIM values across varying compression strategies. Furthermore, the model exhibits fast convergence, strong generalization capability, and real-time processing performance, making it suitable for deployment in resource-constrained environments. Despite challenges such as computational training cost and minor loss of high-frequency details, the proposed approach significantly improves compression efficiency and visual quality. These findings highlight the potential of deep learning–based methods in advancing next-generation image compression systems.
subject: Articles
  topic: section
subject: Deep Learning
  topic: keywords
subject: Image Compression
  topic: keywords
subject: Autoencoder
  topic: keywords
subject: PSNR
  topic: keywords
subject: SSIM
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
creator: OLUBORODE KAYODE OLADIPUPO
creator: ZAYYANU YUNUSA
creator: LAWAL SAIDI OLALEKAN
creator: ABUBAKAR BELLO
rights: Copyright (c) 2026
date: 2026-02-16
publication date: 2026-02-16
starting page: 41
ending page: 51
doi: 10.64137/31079458/IJCSEI-V2I1P106

Architecting Secure Big Data Platforms: Insights from a Big Data and IT Security Specialist
type: item
description: Nowadays organizations gather process and derive value from massive volumes of structured and unstructured data differently due to the rapid expansion of big data platforms. Organizations that are increasingly operating in distributed ecosystems, consisting of cloud infrastructures, data lakes, real-time analytics pipelines, and third-party integrations, are facing security and privacy risks of big data and these risks have been increased drastically. For example, unauthorized access, data leakage, insider attacks, and compliance violations, among other threats, are making it so that perimeter-based security models are no longer able to protect sensitive data. The central message of this article is that instead of adding security afterward, we need to be embedding security mechanisms directly into the core of big data platforms in a security-by-design architecture. Considering the input of a Big Data and IT Security Specialist, the paper develops a secure big data architecture that, inter alia, includes data encryption, fine-grained access control, identity and key management, secure data ingestion, and continuous monitoring as part of this comprehensive framework. The case of practical implementation featuring an enterprise-scale big data deployment served as the ground for the application of the proposed model, wherein the security controls were related to the working of practical operational workflows as well as regulatory requirements. Among the most important conclusions drawn is that applying security-by-design principles to data protection and compliance not only leads to better security and compliance but also improves the platform's reliability and stakeholder trust. The paper wraps up by highlighting the practical implications for architects and security practitioners, basically providing doable tools for them to construct strong, secure, and scalable big data platforms in today's enterprise environments.
subject: Articles
  topic: section
subject: Big Data Security
  topic: keywords
subject: Secure Data Architecture
  topic: keywords
subject: IT Security
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: Data Privacy
  topic: keywords
subject: Zero Trust
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: Compliance
  topic: keywords
creator: Sivadeep Katangoori
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 116
ending page: 122
doi: 10.64137/3107-9458/ICCSEMTI26-120

Beyond Logs: Building Intelligent Monitoring Systems with Splunk Clusters
type: item
description: Modern businesses go above and beyond just recording information and addressing problems as they arise. When collecting data from a large number of workstations, centralized logging solutions shine. However, when it comes to distributed, cloud-based, and microservices-based systems, they may not hold up as well. The proliferation of hybrid clouds, containers, and edge systems has led to a dramatic increase in the volume, velocity, and diversity of operational data. A system's security, speed, and reliability should not be assumed only because it offers static dashboards and keyword-based searches. Interaction between smart monitoring systems is essential for their ability to collect, measure, and store data in real time. They should also have the ability to anticipate problems and address them promptly. It is necessary to make this modification in Splunk clusters. The multi-site architectures, indexer clustering, and search head clustering that make up Splunk's business observability solutions make them very dependable and error-tolerant. In this post, we'll look at how Splunk clusters may help businesses create robust monitoring systems that can do more than just centralize log storage. Topics covered include automated frameworks, AI-driven operational insights, clustering approaches, scalable architecture, and advanced analytics.
subject: Articles
  topic: section
subject: Splunk Clusters
  topic: keywords
subject: Intelligent Monitoring
  topic: keywords
subject: Observability
  topic: keywords
subject: Log Analytics
  topic: keywords
subject: SIEM
  topic: keywords
subject: DevOps
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: SRE
  topic: keywords
subject: AIOps
  topic: keywords
creator: Hitesh Allam
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 110
ending page: 115
doi: 10.64137/3107-9458/ICCSEMTI26-119

Enterprise Content Management in the Cloud Era: An AEM and DevOps Perspective
type: item
description: Enterprise Content Management (ECM) aside from being just a traditional on-premise document repository is now an experience-driven platform that has the ability to power highly personalized omnichannel digital journeys. Organizations are expected to meet customers' ever-increasing demand for faster response times, scalable solutions, and constant customer engagement which is why ECM is in the process of radically changing into a cloud-native architecture becoming that essential core to the delivery of digital experience while at the same time moving beyond storage and workflow automation. Adobe Experience Manager (AEM) is one of the leading platforms in this cloud world which integrates content management, digital asset management, and experience orchestration, thus enabling enterprises to provide the same content regardless of channel be it web, mobile, or even the new emerging channels. Nevertheless, merely acquiring AEM will not suffice; the triumph of the contemporary ECM is thus largely determined by the manner in which content and code flow from inception to production. It is here that DevOps plays a crucial role, bringing in automation, collaboration, and reliability within the ECM lifecycle. When DevOps practices such as CI/CD pipelines, infrastructure as code, automated testing, and monitoring are combined, they make for a safer deployment environment, a faster release process, and a more stable platform these being very instrumental in the case of intricate AEM environments that have multiple teams, integrations, and a stringent governance requirement. The focus of this paper is on the nexus between cloud-native ECM, the capabilities of AEM, and the DevOps-led delivery models. It offers a down-to-earth view of how businesses can modernize their content operations while at the same time staying secure, compliant, and efficient. This study leverages a comparative review of the trends in ECM transformation, a detailed architectural examination of AEM in cloud…
subject: Articles
  topic: section
subject: Enterprise Content Management (ECM)
  topic: keywords
subject: Adobe Experience Manager (AEM)
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Devops
  topic: keywords
subject: CI/CD
  topic: keywords
subject: Cloud Manager
  topic: keywords
subject: Digital Experience Platform (DXP)
  topic: keywords
subject: Content Governance
  topic: keywords
subject: Microservices
  topic: keywords
subject: Automation
  topic: keywords
creator: Siva Sai Krishna Suryadevara
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 104
ending page: 109
doi: 10.64137/3107-9458/ICCSEMTI26-118

Building and Supporting Cloud-Native Architectures on AWS: Real-World Engineering Practice
type: item
description: Cloud-native architectures represent a transformative evolution from traditional monolithic systems, enabling organizations to develop scalable, resilient, and agile applications through microservices, containerization, and automated orchestration. Amazon Web Services (AWS) has emerged as a leading platform for enabling this transformation by offering a comprehensive ecosystem of infrastructure, platform, and managed services. However, a gap persists between theoretical cloud-native design principles and practical implementation realities, where engineering teams must address operational complexity, reliability challenges, security governance, cost optimization, and organizational readiness. This study examines real-world engineering practices for designing and supporting cloud-native architectures on AWS through production deployment analysis, operational case studies, and implementation-driven insights. It focuses on distributed observability, resilience engineering, DevSecOps integration, automated CI/CD pipelines, and cost optimization in dynamic cloud environments. The findings present actionable architectural patterns, operational strategies, and governance frameworks that help organizations successfully adopt and sustain cloud-native systems, bridging the divide between conceptual design and operational execution.
subject: Articles
  topic: section
subject: Cloud-Native Architecture
  topic: keywords
subject: AWS
  topic: keywords
subject: Microservices
  topic: keywords
subject: Devops
  topic: keywords
subject: Site Reliability Engineering
  topic: keywords
subject: Infrastructure As Code
  topic: keywords
subject: Observability
  topic: keywords
subject: Resilience Engineering
  topic: keywords
subject: Cost Optimization
  topic: keywords
creator: Shiva Santosh Allenki
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 98
ending page: 103
doi: 10.64137/3107-9458/ICCSEMTI26-117

Building Robust Salesforce Platforms: Architecture Patterns and Real-World Experience
type: item
description: Salesforce has changed a lot and is now a very important enterprise platform that supports customer engagement, automation, analytics, and integration of different systems on a large scale. However, as companies grow across multiple clouds and business domains, their Salesforce environments become so customized and architecturally fragile. Uncontrolled development of Salesforce leads to performance bottlenecks, failure of integrations, gaps in governance, and accumulation of technical debt that weaken platform reliability and scalability. This article analyzes the use of field-tested architectural patterns and best practices in the creation of resilient Salesforce platforms by integrating enterprise architecture theory, cloud-native design principles, and Salesforce-specific frameworks. A structured approach is offered that is based on different aspects, such as modular domain-driven design, decoupled and event-driven integration, automation governance, DevOps-driven delivery, and observability-based reliability engineering. A real-world enterprise case study shows that intentional architectural transformation can lead to system stability, deployment reliability, and long-term maintainability. The paper points to the fact that creating a robust Salesforce platform is more of an architectural challenge than a tooling problem and it also points out some of the new trends such as AI-driven automation, multi-cloud orchestration, and low-code governance that will be major factors in shaping future enterprise Salesforce architectures.
subject: Articles
  topic: section
subject: Salesforce Architecture
  topic: keywords
subject: Enterprise CRM
  topic: keywords
subject: Platform Scalability
  topic: keywords
subject: Integration Patterns
  topic: keywords
subject: Devops
  topic: keywords
subject: Observability
  topic: keywords
subject: Technical Debt
  topic: keywords
subject: Multi-Cloud Systems
  topic: keywords
subject: Governance Frameworks
  topic: keywords
subject: Event-Driven Architecture
  topic: keywords
creator: Sai Veera Rupesh Shiramalla
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 92
ending page: 97
doi: 10.64137/3107-9458/ICCSEMTI26-116

Modern TechOps Architecture: Integrating AI, DevOps, and Observability at Enterprise Scale
type: item
description: Enterprise IT ecosystems are changing very fast with the help of cloud-native adoption, microservices expansion, hybrid and multi-cloud infrastructures, and increasing operational complexity that together challenge the traditional IT operations model to keep time and still maintain the reliability, scalability, and efficiency. To integrate AI, DevOps methodologies, and observability frameworks to set up adaptive and automated operational ecosystems, TechOps has been positioned as a next-generation operational paradigm. We suggest a combined architecture for TechOps that unites AI-powered automation, ongoing DevOps pipelines, and cutting-edge observability to significantly increase the reliability of the system at the enterprise scale, the efficiency of operations, and the effectiveness of incident management. The paper provides an assessment of the challenges of the current operational approaches and brings in a framework that is scalable and capable of enabling real-time monitoring, predictive failure detection, automated remediation, and continuous performance optimization. A case study based on an enterprise cloud infrastructure in the real world shows significant improvements in deployment speed, system resilience, and operational cost efficiency. The case study also demonstrates how integrated TechOps strategies help to reduce downtime, improve Mean Time to Resolution (MTTR), and strengthen proactive operational governance. Besides that, the suggested architecture solves other major problems of the industry such as tool fragmentation, data silos, and governance complexities, which typically stand in the way of digital transformation initiatives. Also, the paper contributes a formalized process of how to incorporate AI and observability into a DevOps-driven ecosystem and points out the next areas of work in autonomous infrastructure management, AI governance frameworks, and self-healing distributed systems.
subject: Articles
  topic: section
subject: TechOps
  topic: keywords
subject: AIOps
  topic: keywords
subject: DevOps
  topic: keywords
subject: Observability
  topic: keywords
subject: Enterprise Architecture
  topic: keywords
subject: Cloud-Native Operations
  topic: keywords
subject: Intelligent Automation
  topic: keywords
subject: Site Reliability Engineering
  topic: keywords
subject: Predictive Monitoring
  topic: keywords
subject: Self-Healing Systems
  topic: keywords
creator: Gaddam Rohit Reddy
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 85
ending page: 91
doi: 10.64137/3107-9458/ICCSEMTI26-115

Designing and Managing High-Availability Databases in Global Financial Systems
type: item
description: Designing and managing databases for global financial systems requires a lot more than quick queries and large storage capacity. Such banking systems need to be extremely reliable (almost 24/7), provide strong consistency guarantees, and be very resilient even when subjected to the highest operational stress. In the era of interlinked markets, payment platforms operate around the clock and coordinate real-time payments, trading, fraud detection, and regulatory reporting across time zones. So, even short downtime periods may turn into serious problems regarding lost revenue, customer dissatisfaction, regulatory breaches, and systemic risk. This paper studies the reasons for which high availability (HA) should be regarded as a must-have and how the main architectural principles borrowed from infosec are exploited to reach this goal. This research dives into HA from a fairly pragmatic standpoint: it addresses the most elementary components of a robust HA design multi-region replication strategies, automated failover mechanisms, disaster recovery planning (RPO/RTO targets), and how to strike a balance between consistency and performance in a distributed environment. The​‍​‌‍​‍‌ paper presents a method that integrates design review, reliability modeling, and a case study of a globally distributed and financially intensive workload to illustrate the reality of compromises and implementation choices. The paper's findings show that technology is only one factor in the equation of HA success; other essential factors are operational discipline, continuous monitoring, regular failover drills, and detailed recovery playbooks. The last section of the paper argues that carefully planned and well-executed HA partnerships are essential for maintaining transaction integrity, reducing the enterprise historical risk, and helping to keep the trust in the financial services of the ​‍​‌‍​‍‌world.
subject: Articles
  topic: section
subject: High Availability
  topic: keywords
subject: Financial Systems
  topic: keywords
subject: Database Replication
  topic: keywords
subject: Fault Tolerance
  topic: keywords
subject: Disaster Recovery
  topic: keywords
subject: Failover
  topic: keywords
subject: Consistency Models
  topic: keywords
subject: RPO
  topic: keywords
subject: RTO
  topic: keywords
subject: Multi-region Architecture
  topic: keywords
creator: Guruprasad Nookala
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 79
ending page: 84
doi: 10.64137/3107-9458/ICCSEMTI26-114

Scaling Software Quality: Real-World Experiences from Enterprise QA Management
type: item
description: The conventional quality assurance (QA) methods, usually manual, isolated, and reactive, are having a hard time keeping up with the needs of rapid delivery, continuous integration, and high reliability raised by the new environment. This article investigates the ways software quality can be effectively scaled through enterprise-level QA management and refers to the experiences of real-world transformations instead of only theoretical models. It outlines practical approaches, tools, and governance processes employed by large organizations to change QA from a mere testing function to a quality-driven engineering discipline. An extensive case study of a QA transformation initiative in an enterprise is at the core of this research. It shows how the use of standardized frameworks, test automation, metrics-driven decision-making and cross-functional collaboration came to be the solutions to quality bottlenecks on a large scale. The effects reveal that the measures taken have resulted in fewer defects through better defect prevention; thus, this is one of the ways performance was enhanced, and the rework and testing cost were significantly down, and the firmware update cycles were sped up, all of this happened without losing the quality of reliability. Instead of just presenting the outcomes, the article goes on to share the experiences, compromises, and organizational difficulties that were part of the journey to the new state of affairs. By marrying the theoretical aspects of software quality with the practical aspects of the enterprise, this paper serves to provide tangible value to such QA leaders, engineering managers, and practitioners who are looking to strike the right balance between quality, speed, and cost in large-scale software environments.
subject: Articles
  topic: section
subject: Enterprise Quality Assurance
  topic: keywords
subject: Software Quality Management
  topic: keywords
subject: Scalable QA
  topic: keywords
subject: Test Automation
  topic: keywords
subject: Continuous Testing
  topic: keywords
subject: DevOps
  topic: keywords
subject: Software Process Improvement
  topic: keywords
creator: Appala Nooka Kumar
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 72
ending page: 78
doi: 10.64137/3107-9458/ICCSEMTI26-113

Bridging Datacenters and Cloud: Insights from a Senior Cloud Systems Engineer at INFOR
type: item
description: Many businesses are undergoing profound transformations as they move their datacenter infrastructures to the cloud. The use of cloud computing has great potential for growth, increased dependability, and accelerated ideation. A transfer is much more complicated than just relocating mission-critical organizational systems. Working as a Senior Cloud Systems Engineer at INFOR, I see this change as a massive undertaking centering on hybrid designs that integrate traditional data centers with new cloud environments. Managing big hybrid enterprise systems is explored in this article from a technological, organizational, and operational perspective. Existing investments in on-premises infrastructure, regulations, and uptime expectations are examined to determine their influence on intents to transition to cloud computing. The primary challenges include creating effective hybrid connections, managing tasks that are sensitive to latency, ensuring that security and governance are consistent across the two settings, and adapting operational frameworks to include both legacy and new systems. In real-world circumstances, practical compromise is usually more important than architectural integrity, according to the results. It considers not just ideal state designs but also the practical procedures required for hybrid systems to operate, such as managing costs, monitoring, responding to incidents, and empowering teams. Anyone making changes similar to these platform developers, corporate architects, or IT managers should find this information useful. Making decisions based on facts and adhering to the law are two of the most important things this article stresses. In doing so, it exemplifies how companies may make prudent use of cloud technology to upgrade throughout data center-to-cloud migration while preserving stability, resilience, and operation.
subject: Articles
  topic: section
subject: Hybrid Cloud
  topic: keywords
subject: Datacenter Modernization
  topic: keywords
subject: Cloud Engineering
  topic: keywords
subject: Enterprise It
  topic: keywords
subject: Infor
  topic: keywords
subject: Infrastructure Transformation
  topic: keywords
subject: Cloud Operations
  topic: keywords
creator: Mallikarjun Vppalapati
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 65
ending page: 71
doi: 10.64137/3107-9458/ICCSEMTI26-112

Building Enterprise Grade CRM Systems: The Professional Journey of a Microsoft Dynamics Architect
type: item
description: Enterprise CRM solutions are crucial for gathering data, satisfying customers, and running a company. This means less time to prepare for and complete large-scale initiatives. As a business expands, its customer relationship management system must be capable of managing a large number of users, complex procedures, regulatory compliance, and integration with enterprise resource planning (ERP), analytics, self-service, and customer-facing applications. Microsoft Dynamics integrates extensively with comprehensive CRM solutions. It is well-known for its adaptability and compatibility with other Microsoft products and the cloud. Becoming a Microsoft Dynamics Architect requires more than just demonstrating proficiency in using the software to construct customer relationship management (CRM) solutions for businesses. Determine the true demands of the company and put in place effective control mechanisms. A Dynamics Architect's career prospects may improve if they assist companies in finding solutions to their challenges. It is important to strike a balance between customization and setup, construct systems with scalability, and adhere to safety and security regulations. In the end, it's certain that CRM systems aid in the company's long-term objective attainment. The goal of the training is to improve participants' performance on the job. Initially, architects oversaw issues. They are now in charge of specialized teams that advise customers on IT matters and educate them. This paper might be helpful for IT administrators, platform developers, and Microsoft Dynamics experts in creating solid customer relationship management solutions for big businesses. Expertise and a higher level of expertise are required for this position.
subject: Articles
  topic: section
subject: Enterprise CRM
  topic: keywords
subject: Microsoft Dynamics 365
  topic: keywords
subject: CRM Architecture
  topic: keywords
subject: Solution Design
  topic: keywords
subject: Power Platform
  topic: keywords
subject: Systems Integration
  topic: keywords
subject: CRM Governance
  topic: keywords
creator: Rajarshi Krishna Muppaneni
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 60
ending page: 64
doi: 10.64137/3107-9458/ICCSEMTI26-111

Transforming Healthcare IT with Cloud Engineering and DevOps Automation
type: item
description: Healthcare IT systems are under growing pressure to meet high security, availability, and compliance standards while offering the best possible patient experience. If your infrastructure is out of date, your apps aren't working effectively, and you have to manually install them, it may be tough to grow, stifling the execution of new ideas, and increasing operational risk. Healthcare firms can no longer depend on old IT procedures. They must implement data-driven care models, digital health platforms, and interoperable electronic health records. Cloud engineering and DevOps automation are two technologies that are currently useful in healthcare IT. Cloud-native designs allow you to easily add and remove resources as required, save money, and expand or shrink. DevOps, on the other hand, use automation, continuous integration and delivery (CI/CD), and infrastructure-like code to accommodate new ideas while ensuring stability. When combined, these technologies speed up app development, improve system dependability, and allow proactive security in line with HIPAA and GDPR, two healthcare-related laws. This article explains how cloud engineering and DevOps automation are changing the way hospital IT teams operate. It explores typical industry difficulties, presents examples of reference designs and automation phases, and investigates DevOps strategies that emphasize security and compliance. The paper offers practical advice and real-world examples for healthcare organizations wishing to continue offering scaled, patient-focused digital services while becoming more robust, adaptable, and legally compliant.
subject: Articles
  topic: section
subject: Healthcare IT
  topic: keywords
subject: Cloud Engineering
  topic: keywords
subject: DevOps Automation
  topic: keywords
subject: HIPAA Compliance
  topic: keywords
subject: CI/CD
  topic: keywords
subject: Healthcare Cloud
  topic: keywords
subject: Digital Health Transformation
  topic: keywords
creator: Lalith Sriram Datla
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 54
ending page: 59
doi: 10.64137/3107-9458/ICCSEMTI26-110

Ten Years of Engineering Excellence: My Journey as a Senior Developer in Enterprise and Data Platforms
type: item
description: Over the last ten years, enterprise software and data platforms have been drastically changed by the adoption of cloud computing, distributed architectures, and data-driven business strategies. This article is a reflection on a ten-year professional journey of a senior developer between large-scale enterprise applications and distributed data ecosystems. It delves into experiences of making resilient enterprise applications, scalable distributed data platforms, and cloud-native architectural patterns, besides it points out important engineering decisions, trade-offs, and operational challenges that have been faced and solved during system evolution. The conversation starts with the change from pure feature implementation to system engineering that puts scalability, reliability, maintainability, and business goals at the first place. Besides, the article uses real-life architectural patterns that have enabled sustainable platform growth, looks into failures and anti-patterns which have led to technical debt and discusses the evolving role of senior engineers in mentoring, cross-functional collaboration, and developing long-term platform strategy. By combining empirical lessons with standard engineering methods, this paper offers a compass to mid-to-senior engineers who find themselves in a technologically complex and organization-wise limited setting and still want to keep on the technical growth path.
subject: Articles
  topic: section
subject: Enterprise Software Engineering
  topic: keywords
subject: Data Platforms
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: System Architecture
  topic: keywords
subject: Scalability And Reliability
  topic: keywords
subject: Technical Leadership
  topic: keywords
subject: Cloud-Native Systems
  topic: keywords
subject: Engineering Best Practices
  topic: keywords
creator: Bapu Rao Srigadde
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 48
ending page: 53
doi: 10.64137/3107-9458/ICCSEMTI26-109

Engineering Resilient Enterprise Solutions: A Senior Developer’s Perspective from the Airline Industry
type: item
description: Digital transformation is vital for the airline industry, which requires complex enterprise systems to be secure, scalable, and highly available in a constantly changing environment. From a senior developer's perspective, this research analyses how resilience can be deliberately designed in airline enterprise solutions to help operational continuity, regulatory compliance, and the changing needs of customers. The goal of this paper is to provide the practical insights that the authors have gained by being directly involved in the design, modernization, and support of large-scale airline systems, thus helping to close the gap between architectural theory and real-world practice. The key problems that were resolved include the limitations of the legacy systems, the integration of the different heterogeneous platforms, performance issues during peak load, and the need for the system to be backed up rapidly after a failure. The proposed approach puts major emphasis on modular architecture, cloud native design principles, automation, and being proactively monitored as the main facilitators of resilience. The paper presents a real-life case study from the airline industry which shows the application of these strategies and thus, the resulting measurable benefits in the system's stability, deployment agility, and incident response time. The research comes to the conclusion that resilient enterprise engineering should not only be regarded as a technical issue but also as a form of consciousness that is influenced by one's experience, teamwork, and continuous learning. Such a conception has repercussions for developers, architects, and organizations thus, it guides the digital transformation in the high stakes sectors like aviation.
subject: Articles
  topic: section
subject: Enterprise Architecture
  topic: keywords
subject: System Resilience
  topic: keywords
subject: Airline Industry
  topic: keywords
subject: Microservices
  topic: keywords
subject: Fault Tolerance
  topic: keywords
subject: High Availability
  topic: keywords
subject: Cloud-Native Systems
  topic: keywords
creator: Kavya Muppaneni
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 43
ending page: 47
doi: 10.64137/3107-9458/ICCSEMTI26-108

From Microarchitecture to Silicon: Building High-Performance CPUs across Intel and Qualcomm
type: item
description: This paper outlines the complete cycle of creating efficient central processing units (CPUs), starting from initial microarchitectural ideas to actual manufacturable silicon devices. It does so by focusing on two main industry paradigms: the Intel x86 ecosystem and Qualcomm's ARM-based architectures. In the scenario where modern computing workloads are increasingly demanding higher performance with limited power and thermal budgets, CPU architects are compelled to make a very complicated balancing act among instruction-set philosophy, pipeline depth, memory hierarchy, power management, and physical implementation. Thus, fundamental decisions on architectural trade-offs, validation methods, and the physical outcomes of the chips are discussed. The major problems addressed revolve around keeping the ramping up of the performance at par with the power density limits, designing scalable products for different market segments, handling the complexity of the design, and ensuring the chips can be produced at a high yield in advanced process nodes. The article puts forward a vendor-neutral methodology for design and verification, which is a combination of microarchitecture, power mgmt, and physical design constraints initially optimized together and then supported by iterative modeling and silicon-aware verification. A side-by-side comparison that takes a look into the CPU development stories of Intel and Qualcomm is provided through a case study where the focus is on the design decisions, the performance-per-watt metrics, and the post-silicon validation activities, including bring-up days. The paper finds that while the architectural philosophies are different, the converging practices—such as workload-driven designs, aggressive power optimizations, and close couplings of hardware/software co-design—are the common ingredients for the success in both ecosystems. Subsequently, the paper articulates the implications for the future of CPU architectures.
subject: Articles
  topic: section
subject: CPU Microarchitecture
  topic: keywords
subject: High-Performance Computing
  topic: keywords
subject: Silicon Design
  topic: keywords
subject: Intel Architecture
  topic: keywords
subject: Qualcomm Snapdragon
  topic: keywords
subject: Power–Performance Optimization
  topic: keywords
subject: Front-End And Back-End Design
  topic: keywords
creator: Jayanth M. Devaraju
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 37
ending page: 42
doi: 10.64137/3107-9458/ICCSEMTI26-107

Engineering Reliable Healthcare Platforms with Java and ETL Technologies
type: item
description: A reliable healthcare data platform cannot be compromised if healthcare services are to be safe, efficient, and compliant in a digital environment. However, creating such platforms has always been a challenge because of the vast amount, variation, and confidentiality of healthcare data. In this paper, we investigate the architectural, technological, and operational aspects of building trustworthy healthcare platforms with Java backend systems supplemented by ETL (Extract, Transform, Load) technologies. Today's healthcare ecosystems are continually generating large volumes of heterogeneous data in various formats from electronic health records, clinical devices, laboratory information systems, insurers, and patient-facing applications. To integrate these diverse data sources while preserving data correctness, ensuring high availability, security, and compliance with the law requires a very carefully designed system foundation. This research introduces a scalable and fault-tolerant platform architecture based on Java enterprise frameworks and distributed ETL pipelines, putting a major focus on the reliability engineering principles. Some of the critical design considerations were redundancy, transactional consistency, failure isolation, observability, and secure data processing, which are all consistent with such healthcare-specific requirements as privacy protection, interoperability standards, and audit readiness. The paper presents a case study that is inspired by the actual world, which demonstrates how the proposed architecture supports multi-source healthcare data integration. The illustrative use-case scenario confirms measurable improvements in data quality, system uptime, and processing latency when operating under realistic best-case scenarios. Moreover, the performance evaluation analyzes the impact of Java microservice architectural choices and ETL orchestration strategies on system reliability and operational stability. The results unveil trade-offs betwe…
subject: Articles
  topic: section
subject: Healthcare Platforms
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subject: Java Enterprise Systems
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subject: ETL Pipelines
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subject: Data Reliability
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subject: Health Data Integration
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subject: System Architecture
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subject: Fault Tolerance
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creator: Bhavitha Guntupalli
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 30
ending page: 36
doi: 10.64137/3107-9458/ICCSEMTI26-106

From Code to Strategy: A Senior Technical Manager’s Journey in Cloud Native Banking Systems
type: item
description: This essay looks at the career path of a senior technical manager who goes from a hands-on engineering job to a strategic leader who helps change cloud-native these banking systems. This look explores how early experiences with designing, building & running distributed systems affect their decision-making at both the architectural as well as organizational levels, especially in the banking sector, which is highly regulated and risk-sensitive. The abstract talks about how hard it is to update previous systems that weren't designed to be flexible, adapt very quickly, or be used all the time. It also discusses the choices that require to be taken to find a harmonious equilibrium among adherence to the laws, being stable, while remaining innovative. As banks shift from massive, on-premise systems to microservices-centered, cloud-native architectures, they continue to confront challenges with scalability, resilience, and value for money. The report says that higher-level technical jobs are now beginning to seem more and more like management positions. For instance, they need to make certain that their engineering groups are working toward corporate objectives, collaborate with those who benefit from risk, safety, and product, and convert tiny technical decisions into longer-term strategic advantages. The story additionally makes very clear that the way things are done in technology needs to fit with the scheme of things. It illustrates that cloud applications, automation, and platform engineering can be deployed as more than simply tools; they may be used as well to transform how things are done and how individuals act. This paper illustrates how astute technical leadership may expedite the digital transformation in cloud-based banking institutions by confronting actual-world obstacles and options while maintaining trust, dependability, and compliance with law. In the end, the cloud-native architecture is a technological and organizational catalyst that affects the manne…
subject: Articles
  topic: section
subject: Cloud-Native Banking
  topic: keywords
subject: Technical Leadership
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subject: Digital Transformation
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subject: Banking Architecture
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subject: Devops
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subject: Micro services
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subject: Enterprise Strategy
  topic: keywords
creator: Balkishan Arugula
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 24
ending page: 29
doi: 10.64137/3107-9458/ICCSEMTI26-105