نوع مقاله : مقاله پژوهشی
نویسنده
استادیار گروه حقوق، دانشکده ادبیات و علوم انسانی، دانشگاه ملی زابل، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسنده [English]
.Prevention of crimes related to counterfeit drugs will not have a clear perspective due to the technologies used in the production and distribution of these drugs with traditional methods such as field monitoring. Therefore, adopting appropriate preventive measures requires the use of new technologies with the ability to detect these crimes on a large scale and with high accuracy. In this regard, artificial intelligence neural networks such as recurrent neural networks, random generating neural network and convolutional neural network are able to discover these crimes by taking inspiration from the structure of the human brain. However, each of these networks has disadvantages that the legal system faces difficulties in preventing these crimes. Therefore, the present research with the case study method is an attempt to identify the most efficient neural network to prevent these crimes. The output of this research shows that the legislator has paid special attention to the monitoring technique in the field of situational prevention; But the tool has not defined this monitoring. However, the deputy food and drug department uses Titek system (tracking code) to identify and discover crimes in this area. Despite this, this system will not be able to detect all forms of fraud due to the unintelligent nature of the system; Therefore, it seems that the simultaneous use of three networks (recurrent neural networks, random generative neural network and convolutional neural network) in the form of a hybrid neural network will improve the realization of drug crime detection on a large scale.
کلیدواژهها [English]
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