Integrating Explainable Machine Learning with Swarm-Optimized Cloud Intelligence: A Multicriteria Approach to Digital Forensics, Risk Mitigation, and Software Development Using the Weighted Product Method
DOI:
https://doi.org/10.15662/IJARCST.2021.0402001Keywords:
explainable machine learning, swarm intelligence, cloud intelligence, digital forensics, risk mitigation, software development, weighted product method, multicriteria decision-making, explainable AI, PSO, ACO, DevSecOps, model interpretability, cloud optimizationAbstract
The growing complexity of digital ecosystems demands transparent, adaptive, and intelligent systems that can support forensic analysis, risk mitigation, and software development decision-making. This research presents an integrated framework combining Explainable Machine Learning (XML) and Swarm-Optimized Cloud Intelligence (SOCI) within a multicriteria decision-making (MCDM) paradigm using the Weighted Product Method (WPM). The proposed architecture leverages explainable AI models—such as SHAP, LIME, and attention-based networks—to enhance interpretability and traceability in digital forensic investigations, ensuring transparency in predictive outcomes and anomaly detection. Swarm intelligence techniques, including Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), are deployed in a cloud environment to optimize resource allocation, incident response, and task scheduling across distributed forensic and software development workflows. The Weighted Product Method serves as the decision layer, balancing performance, security, cost, and reliability metrics to support informed, risk-aware operational strategies. Experimental validation across cloud-based forensic datasets and DevSecOps pipelines demonstrates improved model transparency, computational efficiency, and decision accuracy. The study contributes a holistic framework that bridges explainability, optimization, and intelligent automation—advancing the state of digital forensics and secure software engineering in cloud-driven environments.
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