This paper introduces the background and significance of building a CatBoost-based spam detection model and proposes a new research approach on the classical research model. Besides, a large number of network resources are occupied which makes 85% of the system resources of the mail server are used for the identification of spam. It is not only a waste of resources, but may even lead to network congestion and paralysis, affecting the normal business email communication of enterprises. In this study, we use the Enron-Spam dataset, which is currently the most publicly available dataset used in email-related research. First, we use word bagging processing and TF-IDF processing for feature extraction, and then CatBoost integration algorithm is used for training. The final accuracy of the model is more than 98%. Compared with the conventional model, the model has better performance, which can effectively improve the accuracy of spam detection and identification.
KEYWORDS: Data modeling, Machine learning, Education and training, Detection and tracking algorithms, Statistical modeling, Data processing, Support vector machines, Reflection, Model-based design, Matrices
At the time mobile devices and online payment make people’s life more convenient, they caused an increasing number of fraud cases in recent years. In this paper, we represented trading data as graphs by graph machine learning and setting up a high-performance model which could detect fraudulent transactions automatically. The datasets that the paper used were the fraudulent transactions dataset on Kaggle’s credit cards. By random under-sampling, it was processed and shown as bipartite graphs which were substituted into our training models after being processed by graph embedding algorithm. Finally, the optimal model was found by the coming out results. The result reveals that average embedder algorithm could detect fraud more precisely than the other three algorithms.
With the popularization and rapid development of the internet, the negative impact caused by negative comments is exacerbated. The increasingly common negative comments on various video websites and social networking sites have exercised a malign influence on public opinion and caused adverse social consequences. This paper uses LightGBM to establish a high-performance negative comment prediction model. The data set selected in this paper is from the Internet movie database IMDB, in which positive and negative comments have been marked for our training model. After Bag of Words processing and TF-IDF processing of the dataset and substituting it into our training model, the researchers obtained the final model with an accuracy of more than 95%, which has a more satisfactory performance compared with the classical integrated model and the traditional binary classification model. Thus, it can be seen that this model has an advantage in identifying spiteful comments online.
KEYWORDS: Deep learning, Data modeling, Education and training, Random forests, Performance modeling, Machine learning, Industry, Matrices, Mathematical modeling, Detection and tracking algorithms
With the changes of people’s consuming attitudes and the popularization of mobile payment, credit card seems increasingly indispensable in life. However, as the number of issued credit cards and credit lines is increasing, there emerges more and more fraud cases involving credit cards. Due to the rapid development of the Internet industry, the channels for capital flow have become unprecedentedly smooth, making it very difficult to prevent credit card fraud cases. If that continues, the development of banks and other financial institutions in the credit card field would be restricted, which might affect people's daily consumption and even the normal running of the society. The Bayesian Deep Learning method is used to quantify the uncertainty of credit card fraud prediction in this essay. Through experimental analysis, the accuracy of the model is over 99%. Compared with conventional classification models, this model has superior performance.
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