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Email spam is an increasing problem because it disrupting and time consuming for user, since the easy and cheap of sending email. Email Spam filtering can be done with a binary classification with machine learning as classifier. To date, email spam detection still challenging since the email spam still happens a lot and the detection still need improvement. Decision Tree (DT) is one of famous classifier...
The previous models or methods almost adopted static measure, and filters need be updated and maintained frequently, so they can not adapt to dynamic spam and lack of self-adaptation. In this paper, An Immunological approach to filtering junk Email has been built. The experiment shows that this model can efficiently raise both the recall ratio and precision ratio, and enhance the ability of self-adaptation...
This paper presents a spam immune method to effectively filter the unsolicited email messages. The idea is to classify messages using the trained antibodies, which are produced by the artificial immune system based on the gene fragment library. The approach focuses on the libraries used to generate the detectors, the training of antibody and the filtering application. The experimental results show...
How to effectively monitor water quality of Coastal and inland waters by optical remote sensing has always been a difficulty. This study develops a neural network model to improve the accuracy in monitoring water quality of Lake Taihu, China, a large shallow subtropical lake. A three-layer back-propagation neural network is built up to estimate concentrations of chlorophyll-a, total suspended matter...
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