Automated Evidence Identification Techniques Transforming Digital Forensic Investigations

In digital forensic investigations, discovering proof fast and properly requires a complex framework with multiple algorithms. Neural network analysis, feature-based classification, clustering for outliers, natural language processing for text analysis, and time series models for time analysis are used in this study to create a full strategy. The Feature-Based Classification Algorithm collects features, creates vectors, and trains classifiers. This enables other algorithms. Neural network analysis improves pattern recognition with iterative refinement, whereas k-means clustering detects anomalies automatically. NLP for Text Analysis finds text proof via tokenization, mood analysis, and information extraction. Time series modeling helps understand data across time in temporal analysis. Ablation studies stress each strategy and illustrate how they work together to increase performance. The proposed method is superior at locating items, evaluating data fast, and handling more data. The visuals highlight how dependable, durable, and cost-effective the approach is, making it the best. Through effective algorithm integration, the proposed method changes automated evidence recognition. Each algorithm’s ongoing improvement and cooperation make it adaptable to new investigations.