Design of an Integrated Model with Contrastive Predictive Coding and Model-Agnostic Meta-Learning for Adaptive and Explainable Mobile Forensic Analysis

Handling multimodal & multiactivity data samples has been one of the major challenges in mobile forensic analysis that includes handling diverse, sequential data types like call logs, messages, and GPS records. Traditional supervised models struggle with dynamic and unlabeled mobile data and require large amounts of labeled datasets and time-consuming retraining as data patterns evolve. The integration of CPC, MAML, and SHAP for an advanced self-supervised learning framework is proposed in this paper to surpass the drawbacks mentioned above. This proposed architecture is structured such that it capitalizes on CPC's ability to draw high-quality temporal representations from unlabeled data by encoding sequential dependencies in mobile data, making it apt for capturing the nuanced behavioral patterns. After CPC, MAML provides rapid adaptability with learning initialization parameters generalizing across forensic tasks using minimal retraining, thus being important for quick adaptation in an evolving data environment. SHAP improves the transparency of models by giving feature importance scores that would help forensic analysts understand and validate model predictions. These methods, taken together, provide a robust pipeline which addresses the quality of representation as well as the interpretability of a model, making the framework flexible to complex real-world mobile forensic data with a minimal amount of dependency on labeled samples. Empirical results have shown a significant improvement, where CPC enhanced the quality of the representation up to 10%, while MAML accelerated the adaptation speed by 20% to 25%, and SHAP reached up to 90% interpretability of model decisions. It will bring a transformative approach toward the mobile forensic analysis that significantly improves the ability to extract accurate and explainable insights in the support of high-stakes evidence validation process.