Identifying Culprits Through Deep Deterministic Policy Gradient Deep Learning Investigation

TL;DR

This paper presents a novel application of Deep Deterministic Policy Gradient (DDPG) deep learning to criminal identification, achieving 95% accuracy in complex datasets, surpassing existing methods.

Contribution

The work introduces the use of DDPG for criminal identification, demonstrating its effectiveness over traditional approaches in complex data scenarios.

Findings

DDPG achieved 95% accuracy in identifying criminals.

The proposed method outperforms several existing techniques.

Effective in handling complex datasets with noise and irrelevant data.

Abstract

In the world of AI and advanced technologies investigation aspects identification of a crime or criminal plays a major problem. In this research we focus on a Conventional ways of implicating criminal investigations usually rely on limited data analysis. Finding an optimal and efficient method that will effectively identify criminals from complex datasets and minimise false positives and false negatives is the considered as a challenge. The main novelty approach of this work is based on the deep learning algorithm Deep Deterministic Policy Gradient (DDPG) is presented in this paper. We train the DDPG model with a dataset of crime scene material, witness statements and suspect profiles. The algorithm uses features to maximise the likelihood of identifying the offender while minimising the noise impact and irrelevant data. We show the efficacy of the proposed method, where DDPG identified criminals with an amazing accuracy of 95% than other several existing methods.