Modern criminal investigations include various cutting-edge scientific methods, including hybrid models, game theory, and machine learning. Criminal investigations consist of the examination of data that is utilized as evidence. In this sense, many significant and innovative studies on the crime uncertainty research of criminals have been carried out in the past, and one method for resolving this ambiguity is called Neutrosophic logic. The criminal in the crime application platform's initial inquiry may be a new involved or a previous offender. In this paper, the framework describes identifying offenders in a criminal investigation. Crime data for a wide range of offences were collected. This approach uses a game theory model to compare the present crime to previous crimes committed by the same individual. Making the right decision about the crime and exercising sound judgment depend on identifying the actual offender. When assessing the empirical data, taking in the incident's location and early criminal prediction is essential.

Agriculture faces significant challenges due to climate change and unpredictable environmental factors, which impact crop yields and threaten food security. This study proposes a novel approach to crop yield prediction and risk assessment using neutrosophic logic and least squares regression. By integrating these methods, we aim to improve accuracy in predicting crop losses under uncertain conditions. The model classifies crops based on profitability and environmental risks, utilizing the independence test to evaluate the relationships between crop attributes. Our approach leverages deep learning techniques, such as restricted Boltzmann machines (RBM), to enhance the analysis of crop data and provide farmers with actionable insights for decision-making. The results demonstrate that the proposed method effectively estimates crop yield damage with the ability to assess risk under varying conditions, such as extreme weather. This approach can help farmers optimize crop management strategies, minimize losses, and improve overall productivity. Future work will explore the application of this method to other crops and farming systems to expand its utility.