Automating Forensic Analysis: Integrating Machine Learning Algorithms for Forensic Evidence and Serial Killer Profiling

Serial murder investigations pose significant challenges due to the complexity of criminal patterns and behaviors. This study presents an integrated forensic analysis system using advanced artificial intelligence (AI) techniques to enhance the efficiency and accuracy of criminal investigations, particularly in serial murder cases. By applying various machine learning algorithms, including logistic regression, support vector machines (SVM), K-nearest neighbors (KNN), convolutional neural networks (CNN), and generative adversarial networks (GAN), the system analyzes key forensic evidence such as serial killer patterns, fingerprint recognition, face sketching, bloodstain analysis, and autopsy reports. Each module in the system contributes to narrowing down suspect profiles and linking potential cases using AI-driven pattern recognition and data analysis. The system demonstrated high performance across multiple forensic tasks, with logistic regression and SVM achieving 85% accuracy in serial killer pattern recognition, GAN achieving 99% accuracy in face recognition, and LSTM achieving 99.3% accuracy in bloodstain analysis. The integration of these AI techniques ensures robust, unbiased, and efficient forensic analysis, significantly reducing the time and labor required in traditional investigative methods. The findings of this study underscore the transformative potential of AI in forensic science, providing law enforcement with powerful tools to profile serial killers, analyze crime scenes, and identify critical evidence with precision. This unified AI-based approach holds promise for improving investigative outcomes and enhancing public safety by assisting in the timely resolution of complex murder cases.