부산대학교 도서관

The criminal justice system faces the challenge of effectively combating crime while respecting the rights of individuals. In recent years, machine learning has emerged as a powerful tool to assist law enforcement agencies, legal professionals, and criminal justice systems in addressing this challenge. This research study explores the application of machine learning techniques to various aspects of criminal analysis, including predictive policing, crime forecasting, suspect identification, and risk assessment. In this study, different data sources are used to train and evaluate the learning model, including criminal history data, image analysis, and social media content. The study uses a variety of algorithms, from statistical models to deep learning, to extract insights and patterns from data. This research focuses on the technological aspects of machine learning and decision-making in criminal investigations. The results of our analysis demonstrate the potential of machine learning to increase the effectiveness and efficiency of criminal justice. Predictive police models identify crime hot spots with a high degree of accuracy, allowing law enforcement to allocate resources. Crime prediction models provide insight into preventing future crimes. Crime prediction models provide insight into preventing future crimes. Suspect detection and risk assessment models assist in crime investigation and offender rehabilitation. This research study also emphasizes the importance of ethical considerations in deploying machine learning solutions within the criminal justice system. The study discusses the issues related to bias, fairness, and privacy, emphasizing the need for transparent and accountable algorithms.