International Journal of Artificial Intelligence and machine learning in Engineering
The International Journal of Artificial Intelligence and Machine Learning in Engineering is an international, peer-reviewed journal focused on research at the intersection of artificial intelligence (AI), machine learning (ML), and engineering disciplines. It aims to provide a scholarly platform where researchers, engineers, practitioners, and academicians can share original work that advances both the theoretical foundations and practical applications of AI/ML in engineering contexts. The journal accepts original research papers, high-quality review articles, and technical notes that explore innovative algorithms, system designs, and real-world implementations in engineering problems. It is published from the city Detroit, Michigan, United States. Publisher Name: Render Publications Frequency of publishing: Annual Publication Charges: Nil (Zero)
Artificial Intelligence-Driven Framework for Enhancing Business Process Intelligence and Operational Efficiency
Artificial Intelligence has emerged as a transformative technology for improving Business Process Intelligence and operational efficiency. This research proposes an AI-driven framework that integrates predictive analytics, intelligent automation, and data-driven decision support to optimize organizational processes. The framework enables proactive identification of inefficiencies, improved resource utilization, and enhanced operational decision-making. Through secondary analysis, the study examines AI capabilities and their contribution to intelligent process management. The proposed approach supports sustainable digital transformation by enabling adaptive, efficient, and intelligent business operations.
EVIDENCE-GROUNDED RAG SYSTEMS FOR HEALTHCARE POLICY RETRIEVAL AND CLAIMS VALIDATION
Healthcare payer organizations rely on extensive policy documents, reimbursement guidelines, clinical protocols, regulatory standards, and coding manuals to validate healthcare claims accurately. Conventional claims validation systems primarily depend on static rule engines and manual document review, resulting in inconsistent decisions, delayed claim adjudication, increased operational costs, and policy interpretation errors. Recent advances in RetrievalAugmented Generation (RAG), Large Language Models (LLMs), semantic search, vector databases, and enterprise knowledge retrieval enable evidence-grounded healthcare decision support with improved factual accuracy and explainability. This paper proposes an evidence-grounded RetrievalAugmented Generation framework for healthcare policy retrieval and automated claims validation by integrating enterprise policy repositories, vector databases, machine learning, cloud-native deployment, and intelligent document retrieval. The proposed framework continuously retrieves authoritative healthcare policies, coding guidelines, reimbursement regulations, and clinical documentation before generating evidencesupported claims validation recommendations. Experimental analysis demonstrates significant improvements in retrieval precision, claims validation accuracy, operational efficiency, regulatory compliance, and enterprise scalability compared with conventional rulebased healthcare claims systems. Keywords: Retrieval-Augmented Generation, Healthcare Policy Retrieval, Claims Validation, Large Language Models, Vector Database, Enterprise AI, Healthcare Automation, Semantic Search, Machine Learning, CloudNative Computing.
MULTI-AGENT GENERATIVE AI ARCHITECTURE FOR END-TO-END HEALTHCARE ADMINISTRATIVE AUTOMATION
Healthcare administrative operations involve complex workflows including patient registration, eligibility verification, prior authorization, medical coding, claims adjudication, payment processing, denial management, provider communication, and regulatory compliance. Conventional administrative systems depend heavily on manual intervention, rule-based automation, and disconnected enterprise applications, resulting in delayed processing, increased operational costs, administrative errors, and inconsistent decision-making. Recent advancements in Multi-Agent Generative Artificial Intelligence (GenAI), Large Language Models (LLMs), RetrievalAugmented Generation (RAG), autonomous intelligent agents, and cloud-native computing enable collaborative healthcare workflow automation with explainable decision support. This paper proposes a Multi-Agent Generative AI architecture for end-to-end healthcare administrative automation by integrating autonomous AI agents, enterprise knowledge retrieval, machine learning, predictive analytics, and cloud-native orchestration. The proposed framework continuously coordinates specialized intelligent agents responsible for patient services, claims processing, coding validation, prior authorization, provider communication, compliance monitoring, and revenue cycle optimization. Experimental analysis demonstrates significant improvements in workflow efficiency, operational scalability, decision consistency, administrative productivity, and healthcare service quality compared with conventional administrative automation systems. Keywords: Multi-Agent AI, Generative AI, Healthcare Administration, Large Language Models, Retrieval-Augmented Generation, Intelligent Agents, Healthcare Claims, Revenue Cycle Management, Enterprise AI, CloudNative Computing.
SECURE AND EXPLAINABLE RETRIEVAL-AUGMENTED GENERATION FOR HEALTHCARE DECISION SUPPORT
Healthcare organizations increasingly rely on Artificial Intelligence (AI) to support clinical decision-making, healthcare claims processing, prior authorization, policy interpretation, and patient care coordination. However, conventional Large Language Models (LLMs) often suffer from hallucinations, limited explainability, outdated knowledge, and security concerns when deployed in healthcare environments. Retrieval-Augmented Generation (RAG) addresses these limitations by combining enterprise knowledge retrieval with generative AI to produce context-aware and evidence-supported responses. This paper proposes a secure and explainable RetrievalAugmented Generation framework for healthcare decision support by integrating enterprise healthcare knowledge repositories, vector databases, explainable AI, secure cloudnative infrastructure, machine learning, and intelligent retrieval mechanisms. The proposed framework continuously retrieves authoritative clinical evidence, healthcare policies, reimbursement guidelines, and regulatory documentation before generating transparent healthcare recommendations. Experimental analysis demonstrates significant improvements in retrieval precision, decision accuracy, explainability, regulatory compliance, operational efficiency, and enterprise scalability compared with conventional healthcare AI systems. Keywords: Retrieval-Augmented Generation, Explainable AI, Healthcare Decision Support, Large Language Models, Healthcare Security, Vector Database, Enterprise AI, Semantic Search, Machine Learning, Cloud-Native Computing.
REINFORCEMENT LEARNING-BASED ADAPTIVE RETRIEVAL FOR ENTERPRISE HEALTHCARE KNOWLEDGE SYSTEMS
Enterprise healthcare knowledge systems manage vast collections of clinical guidelines, payer policies, reimbursement regulations, Electronic Health Records (EHRs), medical literature, provider contracts, and regulatory documents that continuously evolve over time. Conventional retrieval systems typically employ static ranking algorithms and predefined retrieval strategies, limiting their ability to adapt to changing user contexts, knowledge repositories, and healthcare workflows. Recent advances in Reinforcement Learning (RL), Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), semantic search, and cloud-native computing enable adaptive knowledge retrieval through continuous interaction and learning from enterprise feedback. This paper proposes a Reinforcement Learning-based adaptive retrieval framework for enterprise healthcare knowledge systems by integrating intelligent retrieval agents, vector databases, machine learning, enterprise knowledge management, and cloud-native deployment. The proposed framework continuously optimizes retrieval strategies based on user interactions, retrieval relevance, clinical feedback, and organizational objectives to improve knowledge accessibility and healthcare decision support. Experimental analysis demonstrates significant improvements in retrieval precision, response relevance, knowledge utilization, operational efficiency, and enterprise scalability compared with conventional static retrieval systems. Keywords: Reinforcement Learning, Adaptive Retrieval, Retrieval-Augmented Generation, Healthcare Knowledge Systems, Large Language Models, Vector Database, Enterprise AI, Semantic Search, Machine Learning, Cloud-Native Computing.
CONTEXT-AWARE RAG OPTIMIZATION USING DYNAMIC QUERY AND DOCUMENT RELEVANCE FEEDBACK
Retrieval-Augmented Generation (RAG) has become a fundamental technology for enterprise knowledge management by integrating Large Language Models (LLMs) with external knowledge repositories to generate accurate and evidence-based responses. However, conventional RAG systems typically employ static query generation and fixed retrieval strategies that cannot effectively adapt to changing user intent, document relevance, and contextual information. Consequently, retrieval quality may degrade, resulting in reduced response accuracy and increased retrieval latency. Recent advances in context-aware computing, dynamic query optimization, relevance feedback, Reinforcement Learning, semantic search, and cloud-native architectures enable adaptive retrieval mechanisms capable of continuously improving enterprise knowledge access. This paper proposes a context-aware RetrievalAugmented Generation optimization framework using dynamic query reformulation and document relevance feedback by integrating enterprise knowledge repositories, vector databases, machine learning, semantic retrieval, and intelligent feedback mechanisms. The proposed framework continuously refines retrieval strategies based on contextual information, user interactions, and document relevance to improve response quality and enterprise knowledge utilization. Experimental analysis demonstrates significant improvements in retrieval precision, response relevance, operational efficiency, contextual understanding, and enterprise scalability compared with conventional static RetrievalAugmented Generation systems. Keywords: Retrieval-Augmented Generation, Context-Aware Computing, Dynamic Query Optimization, Relevance Feedback, Large Language Models, Semantic Search, Vector Database, Machine Learning, Enterprise AI, Cloud-Native Computing.
SUSTAINABLE GITOPS ARCHITECTURE FOR CARBONCONSCIOUS APPLICATION DEPLOYMENT
GitOps has become a widely adopted operational model for managing cloud-native applications through declarative infrastructure, automated deployment, and continuous synchronization. However, conventional GitOps workflows primarily focus on deployment automation, scalability, and operational consistency without explicitly considering energy efficiency and carbon emissions during application deployment. As enterprise cloud infrastructures continue to expand, sustainable GitOps architectures have become essential for reducing environmental impact while maintaining deployment reliability and application performance. This paper proposes a sustainable GitOps architecture for carbon-conscious application deployment by integrating Kubernetes, GitOps automation, machine learning, predictive analytics, cloud-native monitoring, and green computing. The proposed framework continuously evaluates infrastructure utilization, renewable energy availability, regional carbon intensity, workload characteristics, and deployment priorities before initiating application deployment. Experimental analysis demonstrates significant improvements in deployment efficiency, infrastructure utilization, energy savings, carbon emission reduction, and sustainable cloud-native operations compared with conventional GitOps deployment strategies. Keywords: GitOps, Sustainable Computing, Carbon-Conscious Deployment, Kubernetes, Cloud-Native Applications, Green Computing, Predictive Analytics, Machine Learning, Continuous Deployment, Enterprise Cloud Platforms.
MACHINE LEARNING-BASED ENERGY OPTIMIZATION FOR CONTAINERIZED CLOUD WORKLOADS
Containerized cloud workloads have become the foundation of modern enterprise cloudnative applications because they provide scalability, portability, fault tolerance, and efficient resource utilization. However, increasing deployment density and dynamic workload behavior have significantly increased data center energy consumption, making energy optimization an important challenge for cloud service providers. Conventional resource allocation approaches primarily optimize computational performance and application availability without explicitly considering energy efficiency or environmental sustainability. This paper proposes a machine learning-based energy optimization framework for containerized cloud workloads by integrating Kubernetes orchestration, predictive analytics, cloud-native monitoring, machine learning, and green computing. The proposed framework continuously analyzes workload characteristics, infrastructure utilization, application demand, energy consumption, and Quality of Service (QoS) metrics to generate intelligent resource optimization strategies. Experimental analysis demonstrates significant improvements in energy efficiency, infrastructure utilization, workload balancing, operational cost reduction, and sustainable cloud operations compared with conventional cloud resource management approaches. Keywords: Machine Learning, Energy Optimization, Containerized Workloads, Kubernetes, Cloud-Native Computing, Predictive Analytics, Green Computing, Resource Optimization, Sustainable Cloud, Enterprise Cloud Platforms.
MULTI-OBJECTIVE RESOURCE SCHEDULING FOR PERFORMANCE AND CARBON EFFICIENCY IN KUBERNETES
Kubernetes has become the leading container orchestration platform for cloud-native enterprise applications because it enables scalable resource management, automated workload scheduling, and high application availability. However, conventional Kubernetes schedulers primarily optimize computational performance and resource utilization without explicitly considering carbon emissions and energy efficiency. As enterprise cloud infrastructures continue to expand, balancing application performance with environmental sustainability has become an important operational challenge. This paper proposes a multi-objective resource scheduling framework for Kubernetes by integrating machine learning, predictive analytics, cloud-native monitoring, carbon-aware computing, and intelligent resource orchestration. The proposed framework continuously evaluates workload characteristics, cluster utilization, application priorities, renewable energy availability, carbon intensity, and Quality of Service (QoS) requirements to generate optimal scheduling decisions. Experimental analysis demonstrates significant improvements in workload performance, infrastructure utilization, energy efficiency, carbon emission reduction, and sustainable cloud operations compared with conventional Kubernetes scheduling approaches. Keywords: Kubernetes, Multi-Objective Scheduling, Carbon Efficiency, Machine Learning, Predictive Analytics, Cloud-Native Computing, Green Computing, Resource Optimization, Sustainable Cloud, Container Orchestration.
PRODUCTION-READY RETRIEVAL-AUGMENTED GENERATION FOR AUTOMATED HEALTHCARE CLAIMS INTELLIGENCE
Healthcare payer organizations process millions of medical claims daily, requiring accurate coding validation, policy compliance verification, fraud detection, prior authorization support, and regulatory adherence. Conventional claims processing systems often depend on rule-based engines and manual review, resulting in delayed adjudication, inconsistent decisions, and increased operational costs. Recent advancements in Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), vector databases, and enterprise knowledge retrieval enable intelligent claims automation while maintaining factual accuracy and explainability. This paper proposes a production-ready Retrieval-Augmented Generation framework for automated healthcare claims intelligence by integrating enterprise knowledge repositories, vector search, machine learning, cloud-native deployment, and intelligent document retrieval. The proposed framework continuously retrieves relevant payer policies, coding guidelines, clinical documentation, and regulatory knowledge before generating explainable recommendations for claims processing. Experimental analysis demonstrates significant improvements in claims accuracy, retrieval precision, processing efficiency, decision consistency, and operational scalability compared with conventional rule-based claims processing systems. Keywords: Retrieval-Augmented Generation, Healthcare Claims, Large Language Models, Vector Database, Claims Intelligence, Machine Learning, Enterprise AI, Healthcare Automation, Cloud-Native Computing, Prior Authorization.
AGENTIC ARTIFICIAL INTELLIGENCE FRAMEWORK FOR INTELLIGENT PRIOR AUTHORIZATION WORKFLOW AUTOMATION
Prior authorization is one of the most resourceintensive administrative processes in healthcare payer organizations, requiring continuous verification of clinical documentation, payer policies, medical necessity criteria, provider eligibility, and regulatory compliance. Conventional prior authorization workflows depend heavily on manual review, rule-based systems, and fragmented enterprise knowledge, resulting in delayed approvals, inconsistent decisions, increased operational costs, and provider dissatisfaction. Recent advances in Agentic Artificial Intelligence, RetrievalAugmented Generation (RAG), Large Language Models (LLMs), machine learning, and cloud-native computing enable autonomous healthcare workflow orchestration with explainable decision-making. This paper proposes an Agentic Artificial Intelligence framework for intelligent prior authorization workflow automation by integrating autonomous AI agents, enterprise knowledge retrieval, predictive analytics, cloud-native deployment, and healthcare policy intelligence. The proposed framework continuously analyzes authorization requests, retrieves relevant clinical evidence, evaluates medical necessity, and coordinates multi-stage approval workflows while maintaining regulatory compliance. Experimental analysis demonstrates significant improvements in authorization accuracy, workflow efficiency, operational scalability, decision consistency, and healthcare automation compared with conventional prior authorization systems. Keywords: Agentic Artificial Intelligence, Prior Authorization, Healthcare Automation, Retrieval-Augmented Generation, Large Language Models, Machine Learning, Intelligent Agents, Healthcare Claims, Enterprise AI, Cloud-Native Computing.
SECURE API LIFECYCLE GOVERNANCE FOR CLOUDNATIVE ENTERPRISE APPLICATIONS
Cloud-native enterprise applications increasingly rely on Application Programming Interfaces (APIs) for communication among distributed microservices, external business partners, mobile applications, and cloud platforms. As API ecosystems expand, ensuring secure lifecycle governance has become critical for protecting enterprise data, maintaining regulatory compliance, and ensuring operational reliability. This paper proposes a secure API lifecycle governance framework integrating API security, DevSecOps, machine learning-assisted governance, cloud-native architecture, Industrial API management, continuous monitoring, and automated compliance validation for enterprise applications. The proposed methodology provides end-to-end governance covering API design, implementation, deployment, monitoring, version management, security enforcement, auditing, and retirement. Automated policy validation, intelligent anomaly detection, and continuous lifecycle monitoring improve API reliability while reducing security risks and governance complexity. Experimental evaluation demonstrates improvements in API security, governance efficiency, deployment reliability, compliance management, and enterprise scalability. The proposed framework provides a production-ready solution for secure API lifecycle governance in modern cloud-native enterprise environments. Keywords— API Lifecycle Governance, API Security, Cloud-Native Applications, DevSecOps, API Management, Enterprise Integration, OpenAPI, RAML.
SPECIFICATION-FIRST API ENGINEERING USING RAML AND OPENAPI FOR ENTERPRISE INTEGRATION
Specification-first API engineering has emerged as a fundamental approach for developing scalable, secure, and interoperable enterprise integration solutions. Unlike code-first development, specification-driven methodologies define application programming interfaces before implementation, enabling consistent API design, improved collaboration, automated documentation, and standardized governance across distributed enterprise systems. This paper proposes a specification-first API engineering framework integrating RAML, OpenAPI Specification (OAS), API lifecycle management, machine learning-assisted validation, cloud-native deployment, DevSecOps, and enterprise governance for intelligent system integration. The proposed methodology supports automated API design validation, version management, security policy enforcement, contract testing, and continuous deployment while ensuring interoperability across heterogeneous enterprise applications. Experimental evaluation demonstrates improvements in API consistency, development productivity, governance efficiency, deployment reliability, and enterprise scalability. The proposed framework provides a standardized and production-ready solution for enterprise API engineering within modern cloud-native and microservices-based integration environments. Keywords— Specification-First API, RAML, OpenAPI Specification, Enterprise Integration, API Governance, DevSecOps, Cloud-Native Architecture, API Lifecycle Management.
DATA-DRIVEN DECISION SUPPORT FOR ADAPTIVE TURNING PROCESS OPTIMIZATION
Adaptive turning process optimization has become increasingly important in intelligent manufacturing because machining quality, production efficiency, and operational sustainability depend on timely and accurate process decisions. Conventional turning parameter selection is generally based on static machining guidelines that cannot effectively respond to changing machining conditions during production. This paper proposes a data-driven decision support framework integrating machine learning, predictive analytics, Industrial Internet of Things (IIoT), Digital Twin technology, cloud manufacturing, intelligent sensors, and adaptive process control for optimizing turning operations in real time. The proposed methodology continuously analyzes machining data, predicts machining performance, and recommends optimal cutting parameters to improve surface quality, reduce tool wear, minimize energy consumption, and increase productivity. Experimental evaluation demonstrates improvements in machining quality, production efficiency, decision accuracy, operational reliability, tool utilization, and manufacturing sustainability. The proposed framework provides an intelligent, scalable, and Industry 4.0-ready solution for adaptive turning process optimization.
REAL-TIME MACHINING QUALITY CONTROL THROUGH DIGITAL TWIN AND PREDICTIVE ANALYTICS
Real-time machining quality control has become essential for modern manufacturing because product quality, dimensional accuracy, and production efficiency depend on continuous monitoring of machining conditions. Conventional quality inspection techniques are primarily performed after machining, resulting in delayed defect detection, increased production cost, and material wastage. This paper proposes a real-time machining quality control framework integrating Digital Twin technology, predictive analytics, Industrial Internet of Things (IIoT), machine learning, intelligent sensors, and cloud manufacturing for continuous monitoring and adaptive optimization of machining processes. The proposed methodology synchronizes physical machining operations with virtual Digital Twin models capable of predicting machining quality and recommending corrective actions before defects occur. Experimental evaluation demonstrates improvements in surface quality, dimensional accuracy, machining stability, tool utilization, production efficiency, predictive maintenance, and operational reliability. The proposed framework provides an intelligent, scalable, and Industry 4.0-ready solution for real-time machining quality control and sustainable manufacturing. Keywords— Digital Twin, Predictive Analytics, Machining Quality Control, Intelligent Manufacturing, Machine Learning, Industrial Internet of Things, Smart Manufacturing, Adaptive Process Control.
ENERGY-AWARE MACHINING PARAMETER OPTIMIZATION FOR QUALITY AND TOOL LIFE IMPROVEMENT
Energy-efficient machining has become a critical objective in modern manufacturing because energy consumption directly influences production cost, environmental sustainability, and operational efficiency. Conventional machining parameter optimization primarily focuses on surface quality or productivity while neglecting energy utilization and tool life. This paper proposes an energy-aware machining parameter optimization framework integrating machine learning, predictive analytics, Industrial Internet of Things (IIoT), cloud manufacturing, intelligent sensors, and adaptive process monitoring for simultaneously improving machining quality, reducing energy consumption, and extending tool life. The proposed methodology optimizes cutting speed, feed rate, and depth of cut using multi-objective decisionmaking while continuously monitoring machining performance through real-time sensor data. Experimental evaluation demonstrates improvements in surface finish, tool utilization, machining stability, energy efficiency, production productivity, and manufacturing sustainability. The proposed framework provides an intelligent, scalable, and Industry 4.0-ready solution for energy-aware machining optimization in advanced manufacturing environments. Keywords— Energy-Aware Machining, Cutting Parameter Optimization, Machine Learning, Tool Life, Surface Quality, Industrial Internet of Things, Predictive Analytics, Sustainable Manufacturing.
PREDICTIVE TOOL CONDITION MONITORING USING INDUSTRIAL IOT AND MULTI-SENSOR DATA FUSION
Tool condition monitoring is essential for ensuring machining quality, production efficiency, equipment reliability, and manufacturing sustainability in modern smart factories. Conventional tool monitoring techniques rely on periodic inspections or singlesensor measurements that often fail to capture the complex behavior of machining processes. This paper proposes a predictive tool condition monitoring framework integrating Industrial Internet of Things (IIoT), multi-sensor data fusion, machine learning, predictive analytics, cloud manufacturing, and intelligent decision support for real-time monitoring of machining systems. The proposed methodology combines vibration, cutting force, acoustic emission, temperature, spindle power, and tool displacement measurements to accurately predict tool health and remaining useful life. Adaptive predictive maintenance and intelligent process control enable timely intervention before catastrophic tool failure occurs. Experimental evaluation demonstrates improvements in prediction accuracy, machining stability, tool utilization, maintenance scheduling, production efficiency, and operational reliability. The proposed framework provides a scalable, intelligent, and Industry 4.0-ready solution for predictive tool condition monitoring in advanced manufacturing environments. Keywords— Tool Condition Monitoring, Industrial Internet of Things, Multi-Sensor Data Fusion, Predictive Maintenance, Machine Learning, Smart Manufacturing, Intelligent Sensors, Predictive Analytics.
HYBRID OPTIMIZATION OF CUTTING SPEED, FEED RATE, AND DEPTH OF CUT FOR SUSTAINABLE MACHINING
Sustainable machining has become an important objective in modern manufacturing by balancing machining quality, production efficiency, energy consumption, tool life, and environmental impact. Conventional machining parameter optimization often focuses on a single performance objective without considering the complex trade-offs among productivity, surface quality, tool wear, and sustainability. This paper proposes a hybrid optimization framework integrating machine learning, multi-objective optimization, predictive analytics, Industrial Internet of Things (IIoT), cloud manufacturing, and intelligent process monitoring for optimizing cutting speed, feed rate, and depth of cut in sustainable machining operations. The proposed methodology simultaneously minimizes surface roughness, tool wear, energy consumption, and production cost while maximizing machining efficiency and material removal rate. Experimental evaluation demonstrates improvements in machining quality, tool utilization, energy efficiency, operational stability, production productivity, and manufacturing sustainability. The proposed framework provides an intelligent, adaptive, and Industry 4.0-ready solution for sustainable machining optimization. Keywords— Sustainable Machining, Hybrid Optimization, Cutting Speed, Feed Rate, Depth of Cut, Machine Learning, IIoT, Smart Manufacturing.
EXPLAINABLE MACHINE LEARNING FOR SURFACE ROUGHNESS PREDICTION AND CUTTING PARAMETER OPTIMIZATION
Surface roughness is a critical quality indicator in precision machining because it directly influences product performance, dimensional accuracy, wear resistance, fatigue strength, and manufacturing reliability. Conventional machine learning models can accurately predict surface roughness but often operate as black-box systems, limiting their adoption in industrial decision-making where transparent reasoning is essential. This paper proposes an Explainable Machine Learning (XML) framework integrating intelligent sensors, predictive analytics, Industrial Internet of Things (IIoT), explainable artificial intelligence, cloud manufacturing, and adaptive machining optimization for surface roughness prediction and cutting parameter optimization. The proposed methodology combines predictive learning with explainability techniques to identify the influence of machining parameters on surface quality while recommending optimal cutting conditions. Experimental evaluation demonstrates improvements in prediction accuracy, machining quality, interpretability, tool utilization, production efficiency, and manufacturing sustainability. The proposed framework provides an intelligent, transparent, and Industry 4.0-ready solution for explainable machining optimization in advanced manufacturing environments.
SENSOR-DRIVEN TOOL WEAR PREDICTION AND ADAPTIVE CONTROL IN SMART MACHINING SYSTEMS
Tool wear is one of the most significant factors affecting machining quality, dimensional accuracy, production efficiency, and manufacturing cost in modern machining operations. Conventional tool wear assessment relies on periodic manual inspection, which often results in unexpected tool failures and production interruptions. This paper proposes a sensordriven tool wear prediction and adaptive control framework integrating intelligent sensors, Industrial Internet of Things (IIoT), machine learning, predictive analytics, cloud manufacturing, and real-time process monitoring for smart machining systems. The proposed methodology continuously acquires machining signals including cutting force, vibration, acoustic emission, temperature, spindle power, and tool displacement to predict tool wear and automatically optimize machining parameters. Adaptive process control dynamically adjusts machining conditions to extend tool life while maintaining product quality and operational stability. Experimental evaluation demonstrates improvements in prediction accuracy, machining reliability, tool utilization, production efficiency, predictive maintenance, and manufacturing sustainability. The proposed framework provides an intelligent and scalable solution for smart machining environments.
PRODUCTION-READY RETRIEVAL-AUGMENTED GENERATION FRAMEWORK FOR HEALTHCARE CLAIMS DECISION SUPPORT
Healthcare claims processing involves complex clinical documentation, medical coding standards, payer policies, and regulatory compliance requirements. Conventional decision support systems primarily rely on predefined business rules and keyword-based information retrieval, often limiting their ability to provide accurate, context-aware recommendations for claims adjudication and prior authorization. Retrieval-Augmented Generation (RAG) has emerged as an effective artificial intelligence paradigm that combines large language models with external enterprise knowledge repositories to generate trustworthy, evidence-based responses. This paper proposes a ProductionReady Retrieval-Augmented Generation framework that integrates enterprise document indexing, semantic search, vector databases, knowledge grounding, automated model governance, and MLOps pipelines for healthcare claims decision support. The proposed framework enables intelligent retrieval of payer policies, coding guidelines, clinical documentation, and historical claims while minimizing hallucinations through retrievalbased evidence generation. Experimental evaluation demonstrates improvements in retrieval accuracy, decision consistency, response latency, operational efficiency, and regulatory compliance. The proposed architecture provides a scalable and production-ready solution for intelligent healthcare claims management. Keywords— Retrieval-Augmented Generation, Healthcare Claims, Decision Support, Large Language Models, Vector Database, Semantic Search, MLOps, Healthcare AI.
MACHINE LEARNING-BASED SURFACE QUALITY PREDICTION IN INTELLIGENT TURNING OPERATIONS
Surface quality is one of the most important indicators of machining performance in precision turning operations because it directly influences dimensional accuracy, fatigue strength, wear resistance, corrosion behavior, and product reliability. Conventional surface quality assessment relies on post-process inspection, which increases production time and manufacturing costs. Machine learning provides an intelligent approach for predicting surface quality using real-time machining data before the completion of machining operations. This paper proposes a machine learning-based framework integrating intelligent sensors, predictive analytics, Industrial Internet of Things (IIoT), cloud-based manufacturing analytics, and adaptive process monitoring for accurate surface quality prediction in turning operations. The proposed methodology utilizes machining parameters, sensor measurements, and historical production data to develop predictive models capable of estimating surface roughness under varying machining conditions. Experimental evaluation demonstrates improvements in prediction accuracy, machining stability, tool utilization, production efficiency, and manufacturing quality. The proposed framework provides a scalable and intelligent solution for predictive surface quality monitoring in Industry 4.0 manufacturing environments. Keywords— Machine Learning, Surface Quality Prediction, Turning Operations, Surface Roughness, Intelligent Manufacturing, Predictive Analytics, IIoT, Smart Manufacturing.
MULTI-OBJECTIVE OPTIMIZATION OF TURNING PARAMETERS FOR SURFACE ROUGHNESS AND TOOL WEAR REDUCTION
Turning is one of the most widely used machining operations in modern manufacturing industries, where achieving superior surface quality while minimizing tool wear is essential for improving productivity and reducing production costs. Conventional optimization techniques often focus on a single machining objective and fail to balance multiple conflicting performance criteria simultaneously. This paper proposes a multiobjective optimization framework for turning operations by integrating machining parameter optimization, predictive analytics, machine learning, real-time process monitoring, and intelligent decision support. The proposed methodology simultaneously optimizes cutting speed, feed rate, depth of cut, and tool geometry to minimize surface roughness and tool wear while maximizing machining efficiency. The framework employs data-driven optimization, continuous process monitoring, and adaptive parameter selection to improve machining performance under varying operating conditions. Experimental evaluation demonstrates improvements in surface finish, tool life, machining stability, material removal efficiency, and production productivity. The proposed framework provides a scalable and intelligent solution for advanced turning process optimization in smart manufacturing environments.
PRODUCTION MLOPS ARCHITECTURE FOR SCALABLE DEPLOYMENT OF GENERATIVE AI IN HEALTHCARE PAYER SYSTEMS
Healthcare payer organizations increasingly deploy Generative Artificial Intelligence to automate claims adjudication, prior authorization, policy interpretation, customer support, and revenue cycle management. However, production deployment of Generative AI requires robust MLOps infrastructure capable of ensuring scalability, reliability, security, governance, and continuous model lifecycle management. Conventional AI deployment strategies frequently encounter challenges related to model drift, governance, monitoring, deployment automation, and regulatory compliance. This paper proposes a Production MLOps architecture integrating cloud-native deployment, Retrieval-Augmented Generation, enterprise knowledge management, automated model governance, continuous integration and deployment, monitoring, and scalable inference services for healthcare payer systems. The proposed framework enables reliable deployment, version management, model monitoring, prompt management, security governance, and automated optimization throughout the AI lifecycle. Experimental evaluation demonstrates improvements in deployment reliability, operational scalability, inference performance, governance effectiveness, regulatory compliance, and administrative productivity. The proposed architecture provides a secure, explainable, and production-ready foundation for enterprise Generative AI deployment in healthcare payer organizations. Keywords— MLOps, Generative Artificial Intelligence, Healthcare Payer Systems, Cloud Computing, Retrieval-Augmented Generation, Model Governance, Enterprise AI, Healthcare Claims
SUSTAINABLE DEVOPS FRAMEWORK FOR ENERGY-AWARE CONTAINERIZED APPLICATION DEPLOYMENT
Containerized applications have become the foundation of modern cloud-native software development due to their scalability, portability, and efficient resource utilization. However, the rapid growth of container orchestration platforms has significantly increased energy consumption in cloud data centers, necessitating sustainable deployment strategies. DevOps automation provides continuous integration and continuous deployment (CI/CD) capabilities but often prioritizes deployment speed over energy efficiency. This paper proposes a Sustainable DevOps Framework for Energy-Aware Containerized Application Deployment that integrates GitOps principles, Kubernetes orchestration, Infrastructure as Code (IaC), predictive autoscaling, carbon-aware scheduling, and continuous energy monitoring. The framework dynamically allocates workloads based on infrastructure utilization, renewable energy availability, and application demand while maintaining deployment reliability and service quality. Experimental evaluation demonstrates improvements in deployment efficiency, resource utilization, energy consumption, infrastructure automation, and carbon emission reduction compared with conventional DevOps deployment models. The proposed framework provides a practical solution for implementing environmentally sustainable cloud-native application deployment without compromising operational performance or scalability. Keywords— Sustainable DevOps, Green Cloud Computing, Containerized Applications, Kubernetes, GitOps, Energy-Aware Scheduling, CI/CD, Infrastructure as Code, Carbon-Aware Deployment.
KNOWLEDGE GRAPH-ENHANCED RETRIEVALAUGMENTED GENERATION FOR HEALTHCARE REVENUE CYCLE OPTIMIZATION
Healthcare revenue cycle management requires accurate interpretation of payer policies, clinical documentation, coding standards, provider contracts, and reimbursement regulations to optimize financial performance and reduce claim denials. Conventional Retrieval-Augmented Generation (RAG) systems rely primarily on semantic similarity retrieval and often overlook complex relationships among healthcare entities. Knowledge Graph-Enhanced RetrievalAugmented Generation combines semantic retrieval with structured knowledge graphs to improve contextual understanding, evidence retrieval, and decision support. This paper proposes a Knowledge Graph-Enhanced RAG framework integrating semantic search, healthcare knowledge graphs, vector databases, enterprise knowledge management, cloud-native MLOps, and automated governance for intelligent healthcare revenue cycle optimization. The proposed architecture retrieves semantically relevant documents together with graph-based relationships among diagnoses, procedures, policies, providers, and reimbursement rules to generate evidence-grounded recommendations. Experimental evaluation demonstrates improvements in retrieval precision, claims validation accuracy, decision consistency, operational efficiency, regulatory compliance, and revenue cycle performance. The proposed framework provides a scalable, explainable, and production-ready solution for intelligent healthcare revenue cycle optimization. Keywords— Knowledge Graph, RetrievalAugmented Generation, Healthcare Revenue Cycle Management, Semantic Retrieval, Large Language Models, Healthcare Claims, MLOps, Explainable AI
EXPLAINABLE MACHINE LEARNING FOR QUALITY PREDICTION AND PROCESS OPTIMIZATION IN ADVANCED MANUFACTURING
Advanced manufacturing systems are undergoing rapid transformation through the adoption of Industry 4.0 technologies, including Artificial Intelligence (AI), Machine Learning (ML), Industrial Internet of Things (IIoT), Cyber-Physical Systems (CPS), cloud computing, digital twins, and big data analytics. These technologies generate enormous volumes of manufacturing data that can be utilized to improve product quality, optimize production processes, reduce operational costs, and support intelligent decision-making. Machine learning algorithms have demonstrated exceptional capabilities in predicting manufacturing quality, detecting process anomalies, forecasting equipment failures, and optimizing production parameters. However, many highperformance machine learning models, particularly deep learning architectures and ensemble learning techniques, operate as black-box systems that provide highly accurate predictions without offering transparent explanations for their decisions. The lack of interpretability limits industrial acceptance because manufacturing engineers require understandable and trustworthy explanations before implementing critical process modifications. Consequently, Explainable Machine Learning (XML) has emerged as an essential research area that combines predictive accuracy with model transparency to support reliable decisionmaking in advanced manufacturing environments. This research proposes an Explainable Machine Learning framework for intelligent quality prediction and process optimization in advanced manufacturing systems. The proposed framework integrates Industrial Internet of Things (IIoT)-enabled sensor data, Enterprise Resource Planning (ERP) information, Manufacturing Execution System (MES) records, and machine learning algorithms with Explainable Artificial Intelligence (XAI) techniques to provide accurate, transparent, and interpretable manufacturing predictions. Real-time production data are collected from multiple manufacturing resources, including production equipment, quality inspection systems, environmental monitoring devices, machine vision systems, vibration sensors, temperature sensors, energy meters, and process control systems. The integrated manufacturing dataset undergoes preprocessing, feature engineering, normalization, anomaly detection, and feature selection before being supplied to machine learning models such as Random Forest, Support Vector Machine (SVM), Gradient Boosting, Extreme Gradient Boosting (XGBoost), Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM) networks for quality prediction and process analysis.
SENSOR-DRIVEN PROCESS INTELLIGENCE FOR REALTIME DEFECT DETECTION IN METAL ADDITIVE MANUFACTURING
Metal Additive Manufacturing (MAM) has revolutionized advanced manufacturing by enabling the fabrication of highly complex metallic components with exceptional design flexibility, reduced material waste, and shortened production cycles. However, ensuring consistent product quality remains a significant challenge because the manufacturing process involves complex thermal, mechanical, and metallurgical interactions that are highly sensitive to process parameter variations. Defects such as porosity, lack of fusion, keyhole instability, cracking, balling, residual stresses, and dimensional inaccuracies frequently occur due to unstable process conditions, reducing component reliability and increasing production costs. Conventional quality assurance methods primarily rely on post-process inspection techniques, which are incapable of detecting defects during fabrication and often result in expensive rework or component rejection. Recent advancements in Industrial Internet of Things (IIoT), intelligent sensor networks, artificial intelligence, and real-time manufacturing analytics have enabled continuous monitoring of manufacturing processes through sensor-driven process intelligence. This research proposes a comprehensive sensor-driven process intelligence framework for real-time defect detection in metal additive manufacturing by integrating multi-sensor data acquisition, edge computing, machine learning, Digital Twin technology, and adaptive process optimization within a unified intelligent manufacturing architecture. The proposed framework continuously collects thermal images, melt pool characteristics, acoustic emissions, vibration signals, laser power measurements, environmental conditions, and machine operating parameters using IIoT-enabled sensors. The acquired data are processed through feature engineering and analyzed using advanced machine learning algorithms, including Random Forest, Support Vector Machine, Extreme Gradient Boosting, Artificial Neural Networks, Convolutional Neural Networks, and Long ShortTerm Memory networks, to predict manufacturing defects in real time. Intelligent optimization algorithms subsequently recommend corrective process adjustments through a closed-loop feedback mechanism to maintain stable manufacturing conditions. Experimental evaluation demonstrates that the proposed framework achieves defect detection accuracy exceeding 98.5%, reduces manufacturing defects by approximately 44%, improves process stability by 37%, decreases inspection time by 58%, and enhances overall production productivity by nearly 40% compared with conventional manufacturing quality monitoring approaches. The proposed sensordriven process intelligence framework provides an effective, scalable, and intelligent solution for autonomous defect detection and quality assurance in Industry 4.0-enabled metal additive manufacturing systems.
MULTI-OBJECTIVE OPTIMIZATION OF ADDITIVE MANUFACTURING PARAMETERS FOR DEFECT AND ENERGY REDUCTION
Additive Manufacturing (AM) has emerged as one of the most transformative technologies in modern manufacturing due to its ability to fabricate complex geometries with minimal material waste, rapid prototyping capabilities, and high design flexibility. Technologies such as Selective Laser Melting (SLM), Laser Powder Bed Fusion (LPBF), Direct Metal Laser Sintering (DMLS), Electron Beam Melting (EBM), and Fused Deposition Modeling (FDM) are increasingly being adopted in aerospace, automotive, biomedical, energy, defense, and precision engineering industries. Despite these advantages, additive manufacturing processes often suffer from multiple qualityrelated challenges including porosity, lack of fusion, residual stresses, surface roughness, dimensional inaccuracies, cracking, and excessive energy consumption. These defects significantly influence the mechanical properties, reliability, production cost, and sustainability of manufactured components. Since process parameters such as laser power, scanning speed, hatch spacing, layer thickness, build orientation, nozzle temperature, print speed, and infill density simultaneously affect product quality and energy utilization, optimizing these parameters has become one of the most important research challenges in intelligent manufacturing. This research proposes a comprehensive multi-objective optimization framework for additive manufacturing that simultaneously minimizes manufacturing defects and energy consumption while maximizing part quality and production efficiency. The proposed framework integrates Internet of Things (IoT)-enabled manufacturing systems, machine learning models, real-time process monitoring, and advanced multi-objective optimization algorithms to determine the optimal combination of additive manufacturing process parameters. Manufacturing data are continuously collected from IoT-enabled sensors monitoring laser characteristics, melt pool temperature, chamber conditions, power consumption, build platform status, environmental parameters, and machine operating conditions. These real-time sensor data are integrated with historical production information to develop predictive models capable of estimating defect formation and energy requirements under different process conditions. The proposed framework employs advanced machine learning algorithms including Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN), Gradient Boosting, Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks to predict manufacturing defects, energy consumption, surface quality, dimensional accuracy, and production performance. The predictive models are coupled with multi-objective optimization techniques such as Non- dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Pareto-based optimization methods to identify optimal process parameter combinations that satisfy multiple manufacturing objectives simultaneously. Unlike conventional singleobjective optimization approaches, the proposed framework generates Paretooptimal solutions that provide manufacturers with flexible trade-offs between energy efficiency, production quality, productivity, and manufacturing cost. Experimental evaluation demonstrates that the proposed optimization framework significantly reduces porosity, residual stress, surface roughness, and dimensional deviation while simultaneously lowering energy consumption and improving machine utilization compared with traditional parameter selection methods. Continuous feedback from IoT sensors enables adaptive optimization by dynamically updating machine learning models and optimization strategies based on real-time manufacturing conditions. The proposed framework supports intelligent decision-making, sustainable manufacturing, predictive quality control, and autonomous process optimization within Industry 4.0 smart manufacturing environments. Consequently, the research contributes to the development of energy-efficient, high-quality, and sustainable additive manufacturing systems capable of meeting the increasing demands of modern industrial production. Keywords: Additive Manufacturing, MultiObjective Optimization, Process Parameter Optimization, Machine Learning, Internet of Things, Energy Consumption, Manufacturing Defects, Industry 4.0, Smart Manufacturing, Predictive Analytics.
DIGITAL TWIN-ASSISTED QUALITY MONITORING FOR INTELLIGENT ADDITIVE MANUFACTURING PROCESSES
Digital Twin technology has emerged as a transformative paradigm for realizing intelligent, adaptive, and autonomous manufacturing systems by establishing a real-time virtual representation of physical manufacturing assets. In additive manufacturing, maintaining consistent product quality remains a major challenge because of the highly dynamic interactions among process parameters, material characteristics, machine behavior, and environmental conditions. Conventional quality monitoring techniques primarily rely on offline inspection and post-process evaluation, making defect detection costly, time-consuming, and incapable of preventing quality degradation during production. The integration of Digital Twin technology with Industrial Internet of Things (IIoT), advanced sensing, artificial intelligence, and cloud computing provides an effective solution for continuous process monitoring, predictive quality assessment, and adaptive manufacturing control. This research proposes a Digital Twin-assisted quality monitoring framework for intelligent additive manufacturing processes that combines real-time sensor data acquisition, virtual process modeling, machine learning-based defect prediction, process simulation, and closed-loop feedback control to continuously evaluate manufacturing quality throughout the production lifecycle. The proposed framework synchronizes physical manufacturing equipment with its virtual counterpart using bidirectional data communication, enabling real-time visualization, process optimization, anomaly detection, and intelligent decision support. Multiple sensor streams, including thermal images, melt pool characteristics, acoustic emissions, vibration signals, environmental conditions, and machine operating parameters, are continuously integrated into the Digital Twin model for dynamic process analysis. Machine learning algorithms, including Random Forest, Support Vector Machine, Extreme Gradient Boosting, Artificial Neural Networks, and Long Short-Term Memory networks, are employed to predict quality deviations and manufacturing defects while optimization algorithms recommend corrective process adjustments. Experimental evaluation demonstrates that the proposed framework achieves a quality prediction accuracy exceeding 98%, reduces manufacturing defects by approximately 42%, improves process stability by 35%, decreases inspection time by 55%, and enhances overall production efficiency by nearly 38% compared with conventional quality monitoring approaches. The proposed Digital Twin-assisted framework provides a scalable, intelligent, and data-driven solution for achieving autonomous quality assurance in Industry 4.0- enabled additive manufacturing systems. Keywords: Digital Twin, Additive Manufacturing, Quality Monitoring, Machine Le