Automatic Decision Algorithm of Interpretation Power in Criminal Justice Based on Data Activity Consultant

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Abstract

In order to solve the problems existing in the current decision algorithm, such as poor data processing performance, low decision accuracy, and long decision time, an automatic decision algorithm of criminal justice interpretation right based on data activity consultant was designed. According to the requirements of reasonable design consultant system data activity and demanded data activity consultants provide data processing requirements and scope. Use the Scrapy web crawler framework to crawl data related to criminal justice interpretation and criminal law provisions from related websites, and clear and extract the collected data to realize data query. Based on the obtained data, the feature array of criminal law is designed, and the decision of criminal judicial interpretation right is made. The C4.5 decision tree algorithm is used to predict the correct rate of decision. The decision of criminal judicial interpretation right is adjusted constantly according to the prediction results to achieve the goal of the automatic decision of criminal judicial interpretation right. Experimental results show that the algorithm has superior data processing performance, high decision accuracy, and short decision time, which verifies the effectiveness of the algorithm.

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Is Open Access

Text Classification Using Novel Term Weighting Scheme-Based Improved TF-IDF for Internet Media Reports

Zhiying Jiang, Bo Gao, Yanlin He … (2021)

With the rapid development of the internet technology, a large amount of internet text data can be obtained. The text classification (TC) technology plays a very important role in processing massive text data, but the accuracy of classification is directly affected by the performance of term weighting in TC. Due to the original design of information retrieval (IR), term frequency-inverse document frequency (TF-IDF) is not effective enough for TC, especially for processing text data with unbalanced distributions in internet media reports. Therefore, the variance between the DF value of a particular term and the average of all DFs DF ¯ , namely, the document frequency variance (ADF), is proposed to enhance the ability in processing text data with unbalanced distribution. Then, the normal TF-IDF is modified by the proposed ADF for processing unbalanced text collection in four different ways, namely, TF-IADF, TF-IADF+, TF-IADFnorm, and TF-IADF+norm. As a result, an effective model can be established for the TC task of internet media reports. A series of simulations have been carried out to evaluate the performance of the proposed methods. Compared with TF-IDF on state-of-the-art classification algorithms, the effectiveness and feasibility of the proposed methods are confirmed by simulation results.