Risk assessment for commercial crime and legal protection based on modularity-optimized LM and SCA-optimized CNN.

ABSTRACT: With the Internet of Things (IoT) making significant strides in recent years, the challenges associated with data collection and analysis have emerged as a pressing concern in public security. When employed to tackle extensive criminal networks, the conventional deep learning model encounters issues such as heightened computational complexity, sluggish operational efficiency, and even system failures. Consequently, this research article introduces an intricately devised framework for detecting commercial offenses, employing a modularity-optimized Louvain-Method (LM) algorithm. Additionally, a convolutional neural networks (CNN)-based model is formulated to determine the feasibility of extending legal aid, wherein feature transformation is facilitated by utilizing TFIDF and Word2vec algorit

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10703012 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature