Vol. 8 No. 08 (2026): Volume 08 Issue 08

The increasing complexity of global supply chain operations has created an urgent need for intelligent decision-support systems capable of processing heterogeneous operational data and delivering accurate, real-time insights. Conventional supply chain management approaches often rely on single-source structured data and traditional analytical techniques, limiting their ability to capture the dynamic relationships among procurement, inventory, transportation, warehousing, supplier performance, and customer demand. To address these challenges, this study proposes a comprehensive Multimodal Artificial Intelligence (AI) and Data Analytics framework for intelligent supply chain decision-making. The proposed framework integrates numerical, categorical, temporal, and engineered business features extracted from an open-source supply chain dataset obtained from the Kaggle repository. A systematic methodology involving data preprocessing, feature extraction, feature engineering, model development, and model evaluation was implemented to develop robust predictive models. Eight machine learning and deep learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, XGBoost, LightGBM, CatBoost, and a proposed Multimodal Deep Neural Network (MDNN), were trained and evaluated using identical experimental settings. The experimental results demonstrate that the proposed MDNN significantly outperformed all benchmark models, achieving an accuracy of 98.43%, precision of 98.5%, recall of 98.3%, F1-score of 98.4%, AUC-ROC of 0.995, and Matthews Correlation Coefficient (MCC) of 0.968, indicating exceptional predictive capability and generalization performance. Furthermore, Explainable Artificial Intelligence (XAI) techniques, including SHAP and LIME, were incorporated to enhance model interpretability and support transparent managerial decision-making. The proposed framework provides a scalable and industry-ready solution that can be integrated with Enterprise Resource Planning (ERP), Internet of Things (IoT), cloud computing, and real-time analytics platforms to improve supply chain resilience, operational efficiency, inventory optimization, logistics planning, and risk management. The findings demonstrate that multimodal AI substantially enhances intelligent supply chain decision-making compared with conventional machine learning approaches and provides a practical pathway toward the realization of Industry 4.0 and Supply Chain 5.0.

The rapid evolution of cloud computing has transformed digital infrastructures from passive data storage environments into intelligent computational ecosystems capable of autonomous decision-making, adaptive optimization, and cognitive interaction. However, contemporary cloud systems frequently emphasize computational efficiency while lacking deeper conceptual models for knowledge interpretation, reasoning, and value-oriented intelligence. This research proposes a Philosophical Cognitive Computing Model (PCCM) that integrates Platonic conceptual frameworks with modern cloud intelligence mechanisms to enhance knowledge optimization, adaptive reasoning, and intelligent system governance. The study develops a theoretical and functional framework by examining philosophical foundations of knowledge, cognitive modeling, and scientific reasoning and mapping them into cloud-based computational architectures. The proposed model introduces cognitive knowledge layers, philosophical reasoning mechanisms, adaptive intelligence modules, and optimization strategies for intelligent cloud environments. Through analytical synthesis of existing philosophical and cognitive perspectives, the research demonstrates that integrating philosophical reasoning principles can improve interpretability, decision consistency, and human-centered intelligence in cloud computing systems. The findings suggest that philosophy-driven cognitive architectures provide a promising direction for developing next-generation intelligent cloud platforms capable of combining computational power with conceptual understanding.

Agile software development emphasizes rapid iteration, continuous integration, frequent releases, and incremental delivery, making regression testing a central software quality challenge. Conventional regression testing approaches often depend on manually selected test suites, static prioritization rules, and repeated execution of tests that provide limited incremental fault-detection value. This paper develops a conceptual intelligent regression testing framework that applies artificial intelligence (AI) techniques to test selection, prioritization, execution, failure classification, and continuous learning within Agile development pipelines. The methodological foundation combines supervised learning, representation learning, historical test-result analysis, change-impact assessment, and feedback-driven optimization. Because the supplied literature primarily concerns AI-based detection and classification in biomedical signal-processing applications rather than software testing, the paper explicitly treats these studies as methodological evidence for transferable AI patterns rather than direct empirical evidence for regression testing. The framework consequently emphasizes feature extraction, automated classification, adaptive prediction, and real-time decision support. A conceptual evaluation indicates that AI-assisted regression testing can improve the alignment between code changes and test execution priorities, reduce redundant execution, and create feedback loops capable of adapting to changing Agile projects. However, model drift, insufficient historical data, explainability, false prioritization, and integration complexity remain significant constraints. The analysis positions intelligent regression testing as an adaptive decision-support layer rather than a complete replacement for conventional testing practices.