ARTIFICIAL INTELLIGENCE TECHNIQUES APPLICATION IN THE MOBILE DEVICE DATA ANALYSIS TO IDENTIFY PERSON-OF-INTEREST

The methods for identifying persons of interest (POI) based on mobile device data has been considered. The problem is relevant and unresolved in the activities of law enforcement intelligence and other agencies involved in operational search activities due to the large amount of data stored on mobile devices. Given the complexity and volume of mobile data traditional analysis methods are often insufficiently effective. The authors propose use of artificial intelligence (AI) including machine learning and natural language processing to improve the efficiency and speed of mobile device data analysis. This approach aims to overcome the limitations of manual data analysis and enhance the process of identifying POIs while adhering to the principles of forensic integrity. The research specifically demonstrates how machine learning can be utilized to search for persons of interest in WhatsApp messenger data. A method has been developed for decentralized control of adaptive data collection processes using the principle of equilibrium and reinforcement learning using the normalized exponential function method. The developed method allows for efficient operation of autonomous distributed systems in conditions of dynamic changes in the number of data collection processes and limited information interaction between them. The results of the experiment indicate that using artificial intelligence for facial recognition may result in false positive outcomes implying that humans cannot be entirely replaced at the current stage of AI evolution. However the application of deep learning showed an 88% success rate in facial recognition. These findings underscore the transformative potential of artificial intelligence in mobile forensics highlighting its capacity to enhance the accuracy and efficiency of data analysis in mobile devices. Key words: artificial intelligence mobile forensics data analysis ios whatsapp