International Journal of Innovation in Electronics and Computer Information Technology
The International Journal of Innovation in Electronics and Computer Information Technology (IJIE-CIT) is a peer-reviewed, open access scholarly publication dedicated to advancing research and innovation in the fields of electronics, computer science, and information technology. The journal serves as a dynamic platform for researchers, engineers, academicians, and industry professionals worldwide to present their cutting-edge findings, theoretical developments, and practical applications. With an emphasis on both foundational and emerging topics, IJIE-CIT fosters interdisciplinary dialogue and promotes the exchange of high-quality scientific knowledge. It is published from the city Detroit, Michigan, United States. Publisher Name: Nougate Publications Frequency of publishing: Annual Publication Charges: Nil (Zero)
LEGACY MAINFRAME MODERNIZATION THROUGH MACHINE LEARNING OPERATIONS AND INTELLIGENT DATA PIPELINES
Legacy mainframe systems continue to support critical enterprise operations across banking, insurance, healthcare, government, and largescale organizations due to their reliability, security, and transaction processing capabilities. However, aging architectures, limited scalability, complex maintenance requirements, and integration challenges restrict their ability to support modern digital transformation initiatives. This paper proposes a legacy mainframe modernization framework through Machine Learning Operations (MLOps) and intelligent data pipelines by integrating cloud computing, artificial intelligence, automated data engineering, API-based integration, containerized deployment, and predictive analytics. The proposed methodology enables automated data extraction, transformation, migration, intelligent workload analysis, model deployment, and continuous optimization of modernized enterprise applications. Experimental evaluation demonstrates improvements in data processing efficiency, system scalability, operational automation, migration reliability, performance monitoring, and decision-making capability. The proposed framework provides a scalable and intelligent approach for transforming traditional mainframe environments into modern cloud-enabled enterprise platforms while preserving business continuity and operational reliability. Keywords— Mainframe Modernization, MLOps, Intelligent Data Pipelines, Cloud Migration, Machine Learning, Enterprise Transformation, Data Engineering, Legacy Systems.
CLOUD-NATIVE MLOPS FRAMEWORK FOR PRODUCTIONSCALE HEALTHCARE CLAIMS ANALYTICS
Healthcare claims analytics has become increasingly complex due to the massive growth of digital healthcare data, evolving reimbursement policies, fraud risks, and the need for accurate financial and operational decisionmaking. Traditional analytics approaches often struggle with scalability, model deployment, continuous monitoring, and real-time processing of large-scale healthcare claims datasets. This paper proposes a cloud-native MLOps framework for production-scale healthcare claims analytics by integrating machine learning, cloud computing, container orchestration, automated machine learning pipelines, data engineering, predictive analytics, and continuous model governance. The proposed methodology enables automated data ingestion, feature engineering, model training, deployment, monitoring, and lifecycle management for healthcare claims intelligence. Experimental evaluation demonstrates improvements in claims prediction accuracy, fraud detection capability, scalability, deployment efficiency, operational reliability, and healthcare decision support. The proposed framework provides a secure and scalable solution for intelligent healthcare claims management while supporting modern healthcare organizations in achieving data-driven transformation. Keywords— Cloud-Native MLOps, Healthcare Claims Analytics, Machine Learning, Predictive Analytics, Kubernetes, Model Governance, Healthcare AI, Data Engineering.
DIGITAL THREAD ARCHITECTURE FOR TRACEABLE AND INTELLIGENT ADDITIVE MANUFACTURING SYSTEMS
Additive manufacturing has become a critical technology for Industry 4.0 by enabling flexible production, customized component fabrication, and reduced material consumption. However, maintaining complete traceability, interoperability, and intelligent decision-making across the manufacturing lifecycle remains a significant challenge. This paper proposes a Digital Thread architecture for traceable and intelligent additive manufacturing systems by integrating Industrial Internet of Things (IIoT), Digital Twin technology, machine learning, cloud computing, manufacturing execution systems, blockchain-based traceability, and intelligent analytics. The proposed methodology establishes a continuous data flow across design, simulation, production, inspection, maintenance, and lifecycle management stages. The framework enables real-time monitoring, quality tracking, predictive analysis, process optimization, and secure information exchange throughout the manufacturing lifecycle. Experimental evaluation demonstrates improvements in traceability, data interoperability, quality assurance, operational visibility, production efficiency, and decision-making accuracy. The proposed architecture provides a scalable and intelligent solution for next-generation additive manufacturing environments supporting sustainable Industry 4.0 transformation. Keywords— Digital Thread, Additive Manufacturing, Traceability, Digital Twin, IIoT, Intelligent Manufacturing, Machine Learning, Industry 4.0.
REAL-TIME MELT POOL MONITORING AND DEFECT PREDICTION USING INTELLIGENT SENSOR ANALYTICS
Metal additive manufacturing has become an essential technology for advanced industrial production due to its ability to manufacture complex components with improved design flexibility and reduced material waste. However, controlling melt pool dynamics and preventing defect formation remain critical challenges affecting manufacturing quality and reliability. This paper proposes a real-time melt pool monitoring and defect prediction framework using intelligent sensor analytics by integrating Industrial Internet of Things (IIoT), thermal imaging, optical sensing, machine learning, Digital Twin technology, multi-sensor data fusion, and predictive analytics. The proposed methodology continuously captures melt pool characteristics, analyzes process variations, predicts defect formation, and supports adaptive process control for improved manufacturing quality. Experimental evaluation demonstrates improvements in defect prediction accuracy, process stability, monitoring efficiency, surface quality, and production reliability. The proposed framework provides a scalable and intelligent solution for real-time quality assurance in Industry 4.0-enabled metal additive manufacturing environments. Keywords— Melt Pool Monitoring, Defect Prediction, Intelligent Sensor Analytics, Additive Manufacturing, Machine Learning, IIoT, Digital Twin, Smart Manufacturing.
PHYSICS-INFORMED MACHINE LEARNING FOR PREDICTIVE QUALITY CONTROL IN METAL ADDITIVE MANUFACTURING
Metal additive manufacturing has transformed advanced production by enabling the fabrication of complex components with high design flexibility and reduced material waste. However, maintaining consistent quality remains challenging due to complex physical interactions among thermal behavior, material properties, process parameters, and defect formation mechanisms. This paper proposes a PhysicsInformed Machine Learning (PIML) framework for predictive quality control in metal additive manufacturing by integrating physical process knowledge with machine learning, Industrial Internet of Things (IIoT), Digital Twin technology, multi-sensor data fusion, and predictive analytics. The proposed methodology combines sensor-driven manufacturing data with physics-based constraints to improve defect prediction, process understanding, and adaptive quality control. Experimental evaluation demonstrates improvements in defect prediction accuracy, model reliability, process stability, parameter optimization, and manufacturing efficiency. The proposed framework provides a trustworthy and scalable solution for intelligent additive manufacturing by combining data-driven intelligence with physical process awareness for Industry 4.0-enabled production environments. Keywords— Physics-Informed Machine Learning, Additive Manufacturing, Predictive Quality Control, Digital Twin, Machine Learning, IIoT, Smart Manufacturing, Industry 4.0.
MULTI-OBJECTIVE OPTIMIZATION OF ADDITIVE MANUFACTURING QUALITY, ENERGY, AND PRODUCTION EFFICIENCY
Additive manufacturing has emerged as an advanced production technology capable of fabricating complex components with reduced material waste and enhanced design flexibility. However, achieving optimal manufacturing performance requires balancing multiple conflicting objectives including product quality, energy consumption, production speed, and operational efficiency. This paper proposes a multi-objective optimization framework for additive manufacturing by integrating machine learning, Industrial Internet of Things (IIoT), Digital Twin technology, predictive analytics, multi-sensor data fusion, cloud computing, and intelligent optimization algorithms. The proposed methodology continuously analyzes manufacturing conditions, predicts quality outcomes, optimizes process parameters, and identifies optimal trade-offs between quality improvement, energy reduction, and production efficiency. Experimental evaluation demonstrates improvements in defect reduction, surface quality, energy efficiency, production throughput, process stability, and resource utilization. The proposed framework provides a scalable and intelligent solution for sustainable Industry 4.0- enabled additive manufacturing by supporting autonomous decision-making and optimized production operations. Keywords— Multi-Objective Optimization, Additive Manufacturing, Machine Learning, Energy Efficiency, Production Optimization, Digital Twin, IIoT, Smart Manufacturing.
DEEP LEARNING-BASED SURFACE AND INTERNAL DEFECT CLASSIFICATION IN METAL ADDITIVE MANUFACTURING
Metal additive manufacturing has gained significant attention in advanced industrial applications due to its ability to fabricate complex components with high design flexibility and reduced material waste. However, surface and internal defects such as cracks, pores, lack of fusion, inclusions, and dimensional irregularities remain major challenges affecting component reliability and production quality. This paper proposes a deep learning-based defect classification framework for metal additive manufacturing by integrating convolutional neural networks, computer vision, Industrial Internet of Things (IIoT), thermal imaging, Digital Twin technology, multi-sensor data fusion, and predictive analytics. The proposed methodology analyzes surface images, thermal signatures, and process monitoring data to automatically classify manufacturing defects and support real-time quality assurance. Experimental evaluation demonstrates improvements in defect classification accuracy, inspection efficiency, process monitoring reliability, and manufacturing productivity. The proposed framework provides a scalable and intelligent solution for automated defect inspection and quality enhancement in Industry 4.0-enabled metal additive manufacturing environments. Keywords— Deep Learning, Metal Additive Manufacturing, Defect Classification, Computer Vision, CNN, IIoT, Digital Twin, Smart Manufacturing.
ADAPTIVE PROCESS PARAMETER CONTROL USING MACHINE LEARNING IN ADDITIVE MANUFACTURING
Additive manufacturing has emerged as a revolutionary production technology for fabricating complex components with reduced material waste, enhanced design flexibility, and accelerated manufacturing cycles. However, achieving consistent product quality remains challenging due to complex relationships between process parameters, material behavior, thermal conditions, and manufacturing defects. This paper proposes an adaptive process parameter control framework using machine learning for additive manufacturing by integrating Industrial Internet of Things (IIoT), Digital Twin technology, multi-sensor data fusion, predictive analytics, cloud computing, and intelligent optimization techniques. The proposed methodology continuously analyzes manufacturing data, predicts process deviations, dynamically adjusts critical parameters, and improves production quality through closed-loop control. Experimental evaluation demonstrates improvements in defect reduction, parameter optimization accuracy, surface quality, production stability, energy efficiency, and operational reliability. The proposed framework provides a scalable and intelligent solution for autonomous additive manufacturing systems, supporting Industry 4.0 transformation and sustainable smart factory implementation. Keywords— Adaptive Process Control, Additive Manufacturing, Machine Learning, Parameter Optimization, Digital Twin, IIoT, Smart Manufacturing, Industry 4.0.
MULTI-SENSOR FUSION FOR EARLY DEFECT DETECTION IN METAL 3D PRINTING PROCESSES
Metal 3D printing has become a critical manufacturing technology for producing complex components with improved design flexibility, reduced material waste, and accelerated production cycles. However, early detection of defects such as porosity, cracks, incomplete fusion, thermal distortion, and surface irregularities remains a major challenge affecting product reliability and manufacturing efficiency. This paper proposes a multi-sensor fusion framework for early defect detection in metal 3D printing processes by integrating Industrial Internet of Things (IIoT), machine learning, thermal imaging, optical monitoring, acoustic sensing, vibration analysis, Digital Twin technology, and cloud-based predictive analytics. The proposed methodology continuously collects heterogeneous manufacturing data, performs intelligent feature extraction, detects early defect signatures, and supports adaptive process control for improved production quality. Experimental evaluation demonstrates improvements in defect detection accuracy, process stability, manufacturing reliability, energy efficiency, and operational performance. The proposed framework provides a scalable solution for intelligent quality assurance in Industry 4.0- enabled additive manufacturing environments. Keywords— Multi-Sensor Fusion, Metal 3D Printing, Defect Detection, Machine Learning, IIoT, Digital Twin, Smart Manufacturing, Industry 4.0.
EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR PROCESS DEFECT DIAGNOSIS IN ADDITIVE MANUFACTURING
Additive manufacturing has become a key enabling technology for Industry 4.0 by supporting the production of complex components with high design flexibility and reduced material waste. However, manufacturing defects such as porosity, lack of fusion, cracking, residual stresses, and dimensional inaccuracies continue to affect product quality and process reliability. This paper proposes an Explainable Artificial Intelligence (XAI)-based framework for process defect diagnosis in additive manufacturing by integrating machine learning, explainable AI, Industrial Internet of Things (IIoT), multi-sensor data fusion, cloud computing, digital twin technology, predictive analytics, and adaptive process control. The proposed methodology continuously monitors manufacturing operations, diagnoses process defects, explains prediction outcomes, and recommends optimized manufacturing parameters for quality improvement. Experimental evaluation demonstrates improvements in defect diagnosis accuracy, model interpretability, production stability, equipment utilization, surface quality, and manufacturing efficiency. The proposed framework provides a scalable and intelligent solution for transparent, trustworthy, and sustainable additive manufacturing under Industry 4.0 environments. Keywords— Explainable Artificial Intelligence, Additive Manufacturing, Defect Diagnosis, Machine Learning, Digital Twin, IIoT, Smart Manufacturing, Industry 4.0.
DIGITAL TWIN-ENABLED QUALITY ASSURANCE FOR REAL-TIME METAL ADDITIVE MANUFACTURING
Digital Twin technology has become a transformative solution for intelligent manufacturing by enabling real-time synchronization between physical production systems and virtual process models. In metal additive manufacturing, Digital Twins facilitate continuous quality monitoring, predictive analytics, defect detection, and adaptive process optimization, thereby improving production efficiency and component reliability. This paper proposes a Digital Twin-enabled quality assurance framework integrating Industrial Internet of Things (IIoT), machine learning, multi-sensor data fusion, cloud computing, predictive analytics, adaptive process control, and manufacturing execution systems. The proposed methodology continuously monitors manufacturing operations, predicts quality deviations, optimizes process parameters, and supports automated quality assurance throughout the production lifecycle. Experimental evaluation demonstrates improvements in defect detection accuracy, dimensional precision, production stability, equipment utilization, material efficiency, and manufacturing productivity. The proposed framework provides a scalable and intelligent solution for Industry 4.0-enabled metal additive manufacturing while supporting sustainable production, real-time quality assurance, and autonomous manufacturing optimization.
MACHINE LEARNING-BASED DEFECT PREDICTION AND PARAMETER OPTIMIZATION IN METAL ADDITIVE MANUFACTURING
Metal additive manufacturing has revolutionized industrial production by enabling complex geometries, reduced material waste, and rapid prototyping. However, defects such as porosity, cracks, lack of fusion, and dimensional inaccuracies continue to limit part quality and production reliability. This paper proposes a machine learning-based framework for defect prediction and process parameter optimization in metal additive manufacturing by integrating Industrial Internet of Things (IIoT), multi-sensor data acquisition, cloud-based analytics, digital twin technology, predictive modeling, and adaptive process control. The proposed methodology continuously analyzes manufacturing data, predicts defect formation, optimizes laser power, scan speed, hatch spacing, and layer thickness, and supports real-time quality improvement. Experimental evaluation demonstrates improvements in defect prediction accuracy, dimensional precision, surface quality, process stability, production efficiency, and material utilization. The proposed framework provides a scalable and intelligent solution for smart metal additive manufacturing, supporting Industry 4.0 implementation and sustainable digital manufacturing. Keywords— Metal Additive Manufacturing, Machine Learning, Defect Prediction, Process Optimization, Digital Twin, IIoT, Smart Manufacturing, Industry 4.0.
HUMAN–AI COLLABORATIVE FRAMEWORK FOR EVALUATING EMERGING AUTONOMOUS CYBER RISKS
The growing adoption of autonomous artificial intelligence across enterprise environments has introduced complex cybersecurity risks that require transparent, collaborative, and trustworthy evaluation mechanisms. Human expertise remains essential for validating AIgenerated assessments, interpreting complex security events, and ensuring ethical governance throughout the cybersecurity lifecycle. This paper proposes a Human–AI collaborative framework integrating machine learning, explainable artificial intelligence, behavioral analytics, cloudnative DevSecOps, Zero-Trust architecture, threat intelligence, continuous monitoring, and enterprise governance. The proposed methodology combines automated AI-driven cybersecurity analytics with human expert validation to improve threat assessment accuracy, governance consistency, operational reliability, and regulatory compliance. Experimental evaluation demonstrates improvements in defensive decision quality, threat detection accuracy, explainability, organizational resilience, and enterprise cybersecurity performance. The proposed framework provides a scalable and production-ready solution for evaluating emerging autonomous cyber risks while supporting responsible AI deployment, ethical decision-making, and secure enterprise digital transformation. Keywords— Human–AI Collaboration, Explainable Artificial Intelligence, Cyber Risk Assessment, Behavioral Analytics, DevSecOps, Zero-Trust Security, Enterprise Governance, Threat Intelligence.
REINFORCEMENT LEARNING-BASED CYBER DEFENSE AGAINST ADAPTIVE MACHINE-GENERATED ATTACKS
The rapid evolution of artificial intelligence has significantly enhanced cybersecurity capabilities while simultaneously introducing adaptive machine-generated threats that continuously evolve in response to defensive mechanisms. Conventional rule-based security solutions often struggle to counter dynamic threat behaviors in modern cloud-native enterprise environments. This paper proposes a reinforcement learningbased cyber defense framework integrating machine learning, reinforcement learning, behavioral analytics, Zero-Trust architecture, cloud-native DevSecOps, explainable artificial intelligence, continuous monitoring, and enterprise governance. The proposed methodology enables defensive systems to continuously learn from operational telemetry, optimize security policies, improve threat detection, and automate defensive decisionmaking within controlled environments. Experimental evaluation demonstrates improvements in threat detection accuracy, adaptive defense performance, governance efficiency, operational reliability, and enterprise cyber resilience. The proposed framework provides a scalable and production-ready solution for strengthening enterprise cybersecurity against adaptive machine-generated attacks while supporting responsible artificial intelligence deployment and regulatory compliance. Keywords— Reinforcement Learning, Cyber Defense, Machine Learning, Behavioral Analytics, Zero-Trust Security, DevSecOps, Explainable AI, Enterprise Governance.
EXPLAINABLE THREAT INTELLIGENCE FOR AUTONOMOUS ADVERSARIAL AI CAPABILITY ASSESSMENT
The rapid advancement of autonomous artificial intelligence has increased the need for transparent and trustworthy cybersecurity assessment frameworks capable of evaluating AI-related security risks while supporting responsible deployment. Explainable threat intelligence enables security analysts to understand the reasoning behind AI-assisted threat assessments, improve defensive decision-making, and strengthen organizational cyber resilience. This paper proposes an explainable threat intelligence framework integrating machine learning, explainable artificial intelligence (XAI), behavioral analytics, cloud-native DevSecOps, Zero-Trust architecture, continuous monitoring, human-in-the-loop validation, and enterprise governance. The proposed methodology continuously analyzes AI-related threat indicators, evaluates defensive readiness, generates interpretable risk assessments, and supports adaptive security policy refinement while maintaining regulatory compliance. Experimental evaluation demonstrates improvements in threat assessment transparency, behavioral analysis accuracy, governance efficiency, operational reliability, and enterprise cyber resilience. The proposed framework provides a scalable and production-ready solution for explainable AI-assisted threat intelligence within modern enterprise cybersecurity environments. Keywords— Explainable Artificial Intelligence, Threat Intelligence, Machine Learning, Behavioral Analytics, Cybersecurity, DevSecOps, Zero-Trust Security, Enterprise Governance.
DATA-DRIVEN VULNERABILITY ASSESSMENT AND REMEDIATION PRIORITIZATION FOR MODERN SOFTWARE APPLICATIONS
Modern software applications are continuously exposed to evolving cybersecurity threats due to increasing system complexity, cloud adoption, third-party dependencies, and rapid software delivery cycles. Traditional vulnerability assessment approaches often generate large volumes of security findings without considering contextual factors required for effective remediation prioritization. This paper proposes a Data-Driven Vulnerability Assessment and Remediation Prioritization Framework for Modern Software Applications by integrating machine learning, security analytics, vulnerability intelligence, software metrics, and intelligent risk assessment techniques. The proposed framework analyzes vulnerability characteristics, source code information, application behavior, dependency relationships, historical remediation data, and business impact factors to identify and prioritize critical security risks. Machine learning models classify vulnerabilities based on severity, exploitability, and operational impact, enabling optimized remediation strategies. The framework supports continuous security monitoring through DevSecOps integration and automated vulnerability management workflows. Experimental evaluation demonstrates improvements in vulnerability assessment accuracy, remediation prioritization efficiency, risk visibility, and secure software management. Keywords— Data-Driven Security, Vulnerability Assessment, Remediation Prioritization, Machine Learning, Software Security, Risk Analytics, DevSecOps, Vulnerability Management.
ADAPTIVE MACHINE LEARNING FOR CONTINUOUS SECURITY TESTING IN CLOUD-NATIVE SOFTWARE SYSTEMS
Cloud-native software systems have transformed modern application development through scalable architectures, microservices, containerization, and continuous delivery practices. However, the dynamic nature of cloud environments introduces complex security challenges, including evolving vulnerabilities, misconfigurations, dependency risks, and rapidly changing attack surfaces. Traditional security testing approaches often lack adaptability and struggle to provide continuous protection in highly dynamic cloud-native ecosystems. This paper proposes an Adaptive Machine Learning Framework for Continuous Security Testing in Cloud-Native Software Systems by integrating adaptive learning algorithms, DevSecOps practices, automated security testing, vulnerability intelligence, and cloud-native monitoring mechanisms. The proposed framework continuously analyzes application behavior, source code changes, infrastructure configurations, security events, and testing outcomes to detect emerging security risks. Adaptive machine learning models dynamically update their knowledge based on new threats and system changes. Experimental evaluation demonstrates improvements in vulnerability detection accuracy, threat adaptation capability, continuous monitoring efficiency, and cloudnative security resilience. The proposed framework provides an intelligent approach for maintaining secure cloud-native software environments. Keywords—Adaptive Machine Learning, CloudNative Security, Continuous Security Testing, DevSecOps, Vulnerability Detection, Automated Testing, Artificial Intelligence, Software Security.
ENSEMBLE LEARNING-BASED DETECTION OF HIGH-RISK SOFTWARE DEFECTS IN ENTERPRISE APPLICATIONS
Enterprise applications are increasingly vulnerable to software defects due to growing system complexity, rapid development cycles, cloud adoption, and extensive dependency usage. High-risk software defects can introduce security weaknesses, operational failures, and reliability issues that negatively impact business continuity. Traditional defect detection approaches based on static analysis, rule-based techniques, and manual inspection often face limitations in identifying complex defect patterns and prioritizing critical issues. This paper proposes an Ensemble Learning-Based Detection Framework for HighRisk Software Defects in Enterprise Applications by integrating ensemble machine learning, software metrics analysis, vulnerability intelligence, and automated software testing. The proposed framework combines multiple predictive models to analyze source code characteristics, historical defect data, testing outcomes, dependency information, and software quality indicators. Ensemble learning improves defect classification accuracy by leveraging the strengths of multiple algorithms. The framework identifies high-risk defects, prioritizes remediation efforts, and supports continuous software quality improvement. Experimental evaluation demonstrates improvements in defect prediction accuracy, risk classification, falsepositive reduction, and enterprise software reliability.
RISK-AWARE SOFTWARE TESTING FRAMEWORK FOR EARLY IDENTIFICATION OF SECURITY VULNERABILITIES
The increasing complexity of modern software systems and the rapid adoption of continuous development practices have intensified the challenge of identifying security vulnerabilities at early stages of the software lifecycle. Traditional software testing approaches primarily focus on functional correctness and often provide limited capabilities for assessing security risks during development. This paper proposes a Risk-Aware Software Testing Framework for Early Identification of Security Vulnerabilities by integrating machine learning, security analytics, software testing metrics, vulnerability intelligence, and risk assessment techniques. The proposed framework analyzes source code characteristics, software dependencies, testing outcomes, historical defect information, and security indicators to identify potential vulnerabilities before deployment. A risk-aware prioritization mechanism evaluates vulnerabilities based on severity, exploitability, application importance, and business impact. The framework enables continuous security testing within DevSecOps pipelines and supports proactive vulnerability management. Experimental evaluation demonstrates improvements in early vulnerability detection, risk classification accuracy, testing efficiency, and secure software development practices. The proposed approach provides an intelligent solution for reducing security risks during software engineering processes. Keywords— Risk-Aware Testing, Software Vulnerability Detection, Machine Learning, Security Testing, DevSecOps, Vulnerability Assessment, Secure Software Development, Risk Analytics.
GRAPH-BASED MACHINE LEARNING FOR SOURCE CODE VULNERABILITY DETECTION AND SECURE SOFTWARE TESTING
The increasing complexity of software applications and the rapid adoption of interconnected digital systems have created significant challenges in identifying source code vulnerabilities effectively. Traditional vulnerability detection techniques based on static analysis and predefined security rules often fail to capture complex relationships between software components, dependencies, and execution behaviors. This paper proposes a Graph-Based Machine Learning Framework for Source Code Vulnerability Detection and Secure Software Testing by integrating graph representation learning, machine learning, software dependency analysis, and automated security testing. The proposed framework converts source code into graph-based representations by modeling functions, variables, control flows, data dependencies, and program interactions. Graphbased learning models analyze these representations to identify vulnerable patterns and classify security weaknesses. The framework integrates continuous software testing practices to provide early vulnerability detection throughout the development lifecycle. Experimental evaluation demonstrates improvements in vulnerability detection accuracy, code analysis efficiency, false-positive reduction, and secure software testing capabilities. The proposed approach provides an intelligent solution for scalable software security management. Keywords— Graph Neural Networks, Machine Learning, Source Code Analysis, Vulnerability Detection, Secure Software Testing, Software Security, DevSecOps, Artificial Intelligence.
AUTOMATED SECURITY WEAKNESS DETECTION IN CONTINUOUS INTEGRATION PIPELINES USING PREDICTIVE MODELS
Continuous Integration (CI) pipelines have become essential components of modern software development by enabling rapid code integration, automated testing, and frequent software releases. However, accelerated development cycles also introduce security challenges, as vulnerabilities and security weaknesses may propagate into production environments if not detected early. Traditional security testing approaches often depend on static rules, manual reviews, and isolated vulnerability scanning, limiting their ability to identify complex security issues during continuous development workflows. This paper proposes an Automated Security Weakness Detection Framework in Continuous Integration Pipelines Using Predictive Models by integrating machine learning, predictive analytics, DevSecOps practices, automated testing, and intelligent security monitoring. The proposed framework analyzes source code changes, software metrics, dependency information, testing outcomes, and historical vulnerability patterns to predict potential security weaknesses. Predictive models classify security risks and provide early warnings during CI execution. Experimental evaluation demonstrates improvements in weakness detection accuracy, security automation, vulnerability prevention, and continuous software delivery reliability.
INTELLIGENT VULNERABILITY PRIORITIZATION USING MACHINE LEARNING AND SOFTWARE TESTING ANALYTICS
The increasing complexity of modern software systems and the continuous emergence of cyber threats have created significant challenges in identifying and prioritizing security vulnerabilities effectively. Traditional vulnerability management approaches often generate large numbers of security findings, making it difficult for organizations to determine which vulnerabilities require immediate attention. This paper proposes an Intelligent Vulnerability Prioritization Framework Using Machine Learning and Software Testing Analytics by integrating machine learning, software testing metrics, vulnerability intelligence, risk assessment, and automated security analytics. The proposed framework analyzes vulnerability characteristics, source code behavior, software testing outcomes, exploit information, and system impact factors to intelligently rank security risks. Machine learning models classify vulnerabilities according to severity, exploitability, and business impact, enabling efficient remediation planning. Software testing analytics provide additional insights from code coverage, defect history, and testing performance. Experimental evaluation demonstrates improvements in vulnerability ranking accuracy, remediation efficiency, risk visibility, and security resource optimization. The proposed framework supports proactive and intelligent vulnerability management in modern software environments. Keywords— Vulnerability Prioritization, Machine Learning, Software Testing Analytics, Risk Assessment, Cybersecurity, DevSecOps, Security Analytics, Intelligent Automation.
HYBRID DEEP LEARNING MODELS FOR SECURITY DEFECT DETECTION IN ENTERPRISE SOFTWARE SYSTEMS
Enterprise software systems are increasingly exposed to security defects due to complex architectures, large-scale codebases, third-party dependencies, and rapidly evolving cyber threats. Traditional security defect detection methods based on static analysis and manually defined rules often experience limitations in detecting sophisticated vulnerabilities and adapting to new attack patterns. This paper proposes a Hybrid Deep Learning Framework for Security Defect Detection in Enterprise Software Systems by integrating deep learning, machine learning, static code analysis, software metrics, vulnerability intelligence, and automated security testing. The proposed framework combines multiple deep learning models to analyze source code structures, execution patterns, dependency relationships, and historical vulnerability information for accurate defect identification. Hybrid learning mechanisms improve feature extraction, classification accuracy, and detection reliability. The framework continuously evaluates enterprise software components and prioritizes security defects based on severity and potential impact. Experimental evaluation demonstrates improvements in defect detection accuracy, vulnerability classification, falsepositive reduction, and continuous security monitoring. The proposed approach provides an intelligent solution for secure enterprise software development.
EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR AUTOMATED SOFTWARE VULNERABILITY PREDICTION AND RISK CLASSIFICATION
Software vulnerabilities have become a major security concern due to increasing software complexity, rapid development cycles, and the continuous evolution of cyber threats. Traditional vulnerability assessment approaches often rely on manual analysis and rule-based security tools, which may fail to identify complex vulnerabilities and provide limited insights into risk factors. This paper proposes an Explainable Artificial Intelligence Framework for Automated Software Vulnerability Prediction and Risk Classification by integrating machine learning, deep learning, explainable AI (XAI), static code analysis, software metrics, and intelligent security analytics. The proposed framework analyzes source code characteristics, software dependencies, historical vulnerability information, and security patterns to predict potential vulnerabilities and classify their associated risks. Explainability mechanisms provide transparent insights into model predictions, enabling developers and security analysts to understand vulnerability causes and prioritize remediation activities. Experimental evaluation demonstrates improvements in vulnerability prediction accuracy, risk classification performance, interpretability, and automated security assessment. The proposed framework provides a trustworthy solution for intelligent software security management.
MACHINE LEARNING-BASED SECURITY VULNERABILITY DETECTION FRAMEWORK FOR CONTINUOUS SOFTWARE TESTING
Software security vulnerabilities continue to pose significant challenges for modern software development due to increasing system complexity, frequent releases, and evolving cyber threats. Traditional vulnerability detection approaches based on manual code review, static analysis, and rule-based security testing often struggle to identify sophisticated vulnerabilities across large-scale software systems. This paper proposes a Machine Learning-Based Security Vulnerability Detection Framework for Continuous Software Testing by integrating machine learning, automated software testing, static and dynamic analysis, DevSecOps practices, and intelligent security analytics. The proposed framework continuously analyzes source code, software behavior, dependency information, and testing outcomes to identify potential security weaknesses throughout the software development lifecycle. Machine learning models classify vulnerabilities, detect anomalous coding patterns, and prioritize security risks based on severity and impact. Experimental evaluation demonstrates improvements in vulnerability detection accuracy, testing efficiency, false-positive reduction, and continuous security monitoring capability. The proposed framework provides a scalable solution for integrating intelligent vulnerability detection into modern software delivery pipelines.
CLOUD-ENABLED ERP MODERNIZATION THROUGH INTELLIGENT DATA MAPPING AND MIGRATION OPTIMIZATION
Enterprise Resource Planning systems form the operational foundation of many organizations by integrating finance, procurement, inventory, manufacturing, human resources, supply chain, customer management, and reporting processes. However, a large number of enterprises continue to depend on legacy ERP platforms characterized by tightly coupled architectures, obsolete data models, duplicated records, proprietary interfaces, fragmented customizations, limited scalability, and expensive maintenance. Cloudenabled ERP modernization provides an opportunity to improve agility, interoperability, elasticity, analytics, and operational resilience, but migration from legacy environments remains complex because source and target systems frequently differ in schemas, semantics, identifiers, formats, business rules, master-data structures, and historical quality. This paper proposes a cloud-enabled ERP modernization framework based on intelligent data mapping and migration optimization. The framework integrates legacy source discovery, metadata extraction, data profiling, semantic schema matching, machine-learning-assisted field mapping, transformation rule generation, duplicate detection, quality scoring, dependencyaware migration sequencing, workload prediction, cloud resource optimization, secure API integration, secrets management, reconciliation, rollback control, and postmigration monitoring. The proposed methodology combines deterministic rules with similarity analysis, historical mapping knowledge, domain constraints, and human validation to identify reliable correspondences between heterogeneous ERP structures. Migration workloads are classified according to business criticality, data sensitivity, dependency, volume, transformation complexity, and downtime tolerance. An optimization layer schedules extraction, transformation, validation, and loading tasks across scalable cloud resources while controlling cost, energy consumption, and service disruption. Representative evaluation results indicate that the proposed framework can improve mapping accuracy, reduce manual mapping effort, decrease migration duration, lower transformation errors, improve duplicate detection, increase reconciliation success, reduce cloud resource waste, and strengthen rollback readiness compared with conventional manually driven migration approaches. The study provides a scalable foundation for enterprises seeking to modernize legacy ERP infrastructure through intelligent, secure, auditable, and cloud-aware data migration.
DATA QUALITY AND INTEGRITY MANAGEMENT FRAMEWORK FOR ENTERPRISE ERP MIGRATION PROJECTS
Enterprise Resource Planning migration projects are critical components of organizational digital transformation because they enable enterprises to replace fragmented legacy systems, modernize business processes, consolidate operational information, adopt cloud platforms, improve analytical capabilities, and establish integrated enterprise architectures. However, ERP migration initiatives frequently experience substantial difficulties due to incomplete records, duplicate entities, inconsistent master data, invalid formats, obsolete values, broken relationships, semantic mismatches, transformation errors, unauthorized modifications, missing lineage, reconciliation failures, and weak governance. Poor-quality or corrupted data can cause incorrect financial reporting, disrupted procurement, inaccurate inventory positions, payroll errors, customerservice failures, compliance violations, and reduced confidence in the migrated platform. This paper proposes a comprehensive Data Quality and Integrity Management Framework for Enterprise ERP Migration Projects. The framework integrates legacy data discovery, source-system profiling, business-critical data classification, metadata management, qualityrule definition, duplicate detection, master-data harmonization, schema mapping, semantic transformation, referential-integrity validation, migration-wave planning, secure extraction, staging-zone controls, transformation monitoring, reconciliation, lineage tracking, anomaly detection, role-based authorization, API governance, auditability, migration Digital Twins, rollback readiness, post-migration monitoring, and continuous quality improvement. The proposed methodology establishes a multistage migration lifecycle in which data is assessed before extraction, validated during transformation, reconciled after loading, and continuously monitored after production cutover. Quality dimensions including completeness, accuracy, consistency, validity, uniqueness, timeliness, conformity, and referential integrity are evaluated according to business-specific requirements. A migration Digital Twin maintains synchronized representations of source datasets, transformation states, target structures, quality exceptions, lineage relationships, and migration-wave progress. Machine-learningassisted anomaly analytics identify unusual records, transformation deviations, duplicate patterns, and integrity risks that may not be captured through static rules. OpenAPI-oriented service contracts support interoperability among migration tools, ERP applications, validation services, quality dashboards, and governance platforms, while secure lifecycle management protects migration credentials and sensitive enterprise data. A representative analytical evaluation compares conventional extracttransform-load migration with the proposed framework across data completeness, duplicate reduction, transformation accuracy, reconciliation success, referential-integrity violations, migration defects, exceptionresolution time, rollback readiness, cutover stability, and post-migration business incidents. The results indicate substantial improvements in migrated-data reliability, traceability, operational continuity, and governance. The study concludes that successful ERP modernization requires data quality and integrity to be managed as continuous lifecycle capabilities rather than as isolated cleansing activities performed immediately before cutover.
RISK-AWARE DATA MIGRATION ARCHITECTURE FOR LARGE-SCALE ENTERPRISE RESOURCE PLANNING MODERNIZATION
Large-scale Enterprise Resource Planning modernization has become a strategic requirement for organizations seeking to replace fragmented legacy applications, aging mainframe environments, isolated databases, inflexible business processes, and tightly coupled enterprise platforms with scalable cloud-enabled and service-oriented digital ecosystems. However, ERP modernization programs frequently encounter substantial migration risks arising from inconsistent master data, duplicate records, undocumented dependencies, schema incompatibility, incomplete historical information, data-quality defects, regulatory constraints, integration failures, security exposure, business downtime, reconciliation errors, and uncontrolled cutover activities. This paper proposes a risk-aware data migration architecture for large-scale Enterprise Resource Planning modernization. The proposed methodology integrates legacy landscape discovery, business data classification, migration dependency mapping, data-quality profiling, risk identification, risk prioritization, canonical data modeling, transformation governance, secure extraction, staging-zone protection, validation, reconciliation, phased migration, rollback readiness, continuous observability, and postmigration assurance. The architecture establishes a migration control plane that maintains end-toend visibility across source systems, transformation pipelines, target ERP modules, interfaces, business owners, migration waves, and risk states. Data entities are evaluated according to business criticality, regulatory sensitivity, quality condition, dependency complexity, migration volume, transformation intensity, and operational impact. High-risk data objects receive stronger validation, approval, reconciliation, and rollback controls, whereas lower-risk objects follow streamlined but governed migration paths. The framework further introduces risk-aware migration waves to reduce uncontrolled big-bang cutovers and supports automated evidence collection for auditability. A representative experimental evaluation demonstrates improvements in migration accuracy, reconciliation success, data-quality compliance, dependency visibility, defect detection, cutover predictability, rollback readiness, and post-migration stability compared with conventional migration practices. The findings establish that risk-aware data migration provides a practical foundation for reliable ERP modernization by combining technical transformation, business governance, security, observability, and continuous assurance.
INTELLIGENT LEGACY-TO-CLOUD ERP TRANSFORMATION USING AUTOMATED DATA MIGRATION AND VALIDATION
Enterprise Resource Planning systems form the operational foundation of many organizations by integrating finance, procurement, inventory, manufacturing, human resources, customer operations, supply-chain processes, and regulatory reporting. However, numerous enterprises continue to depend on legacy ERP platforms characterized by tightly coupled architectures, obsolete technologies, fragmented databases, customized schemas, manual interfaces, limited scalability, and expensive maintenance. Migrating these environments to cloud-based ERP platforms is complex because decades of operational data may contain inconsistent formats, duplicate entities, missing attributes, obsolete codes, undocumented dependencies, incompatible master-data structures, and business-critical historical records. This paper proposes an intelligent legacy-to-cloud ERP transformation framework using automated data migration and validation. The proposed methodology integrates legacysystem discovery, metadata extraction, dependency analysis, data profiling, schema mapping, semantic transformation, duplicate detection, anomaly identification, data-quality scoring, automated cleansing, incremental migration, reconciliation, business-rule validation, referential-integrity verification, APIbased integration, secure credential management, cloud deployment, and post-migration monitoring. Machine-learning techniques are incorporated to identify mapping candidates, anomalous records, duplicate entities, and highrisk migration objects. The framework maintains traceability between legacy source records and cloud targets so that transformed data can be audited and reconciled. A staged migration model combines pilot migration, historical backfill, incremental synchronization, controlled cutover, rollback readiness, and continuous validation. Representative evaluation indicates that the proposed framework can improve mapping accuracy, reduce duplicate records, increase validation coverage, decrease migration defects, shorten reconciliation effort, and reduce business disruption compared with predominantly manual migration approaches. The framework provides a practical foundation for ERP modernization, cloud transformation, intelligent data engineering, automated validation, enterprise integration, and scalable digital operations.
SMART DATA MIGRATION FRAMEWORK FOR SECURE AND EFFICIENT ERP MODERNIZATION
Enterprise Resource Planning systems form the operational backbone of contemporary organizations by integrating finance, procurement, inventory, manufacturing, human resources, customer management, supply chain operations, and enterprise reporting. However, many organizations continue to depend on aging ERP platforms characterized by fragmented databases, obsolete interfaces, duplicated records, inconsistent master data, tightly coupled customizations, limited scalability, weak interoperability, and increasing cybersecurity risks. ERP modernization requires the reliable transfer of large volumes of business-critical information from legacy platforms toward modern cloud, hybrid, service-oriented, or modular enterprise environments. Conventional migration approaches frequently emphasize bulk extraction and loading while providing insufficient support for semantic consistency, data quality, lineage, security, reconciliation, rollback, and business continuity. This paper proposes a smart data migration framework for secure and efficient ERP modernization. The proposed methodology integrates migration readiness assessment, source-system discovery, metadata extraction, automated data profiling, dependency analysis, business-rule identification, intelligent data classification, quality scoring, schema mapping, transformation governance, sensitive-data protection, API-based integration, staged migration, change data capture, reconciliation, lineage tracking, anomaly detection, rollback management, and postmigration observability. Data entities are classified according to business criticality, sensitivity, quality, dependency, transaction frequency, and migration complexity so that appropriate strategies can be selected for master, transactional, historical, reference, configuration, and unstructured information. Machine learningassisted anomaly detection identifies suspicious transformations and unexpected distribution changes, while rule-based validation protects deterministic business constraints. Security mechanisms incorporate controlled identity, encryption, secrets management, least-privilege access, immutable audit evidence, and integrity verification across migration pipelines. Representative experimental evaluation across a simulated multi-module ERP modernization environment demonstrates improvements in migration accuracy, duplicate reduction, reconciliation success, data quality, processing efficiency, security traceability, and rollback readiness compared with conventional extracttransform-load migration. The results further indicate that phased migration with continuous validation provides stronger reliability than single-event bulk transition. The proposed framework supports integration with governed APIs, cloud-native orchestration, MLOps services, Industrial Internet of Things platforms, Digital Twins, and enterprise analytics. The study demonstrates that smart, security-aware, and metadata-driven migration can provide a practical foundation for reliable ERP modernization while preserving operational continuity and enterprise trust.