Wavelet Neural Network configuration

Wavelet Neural Network configuration

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Prostate cancer is the century disease that endanger the life of men. The earlier to diagnose the disease, the probability of curing this disease is higher. Therefore, new approaches of diagnosis is required to effectively detect the prostate cancer in early stage compared to the traditional methods. Therefore, WNN is a new adopted approach in pros...

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... WNN configuration is presented and displayed in Figure 1. The significant structure of WNN is introduced as the following [16]: 1. WNN Parameters initialization The matrix that exists between the input and the hidden layer is í µí±Š = ( í µí±Š í µí±—í µí±˜ ) í µí±×í µí±š ...

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... Several researches were conducted on health information systems and medical data [9][10][11][12][13][14][15]. Some researchers worked on the classification of different diseases such as diabetics [11], Alzheimer [12], cancer [13,14] while others compared several classification and data mining algorithms on health data [15] whether these data were in English, Arabic [16], or multilingual [17,18]. ...
... Several researches were conducted on health information systems and medical data [9][10][11][12][13][14][15]. Some researchers worked on the classification of different diseases such as diabetics [11], Alzheimer [12], cancer [13,14] while others compared several classification and data mining algorithms on health data [15] whether these data were in English, Arabic [16], or multilingual [17,18]. There are many applications on topic modeling that were applied in different domains by different topic modeling approaches. ...
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The number of digital medical documents is increasing continuously; several medical websites share a lot of unclassified articles. These articles have very long texts that should be read to determine the topic of each document. The classification of these documents is important so researchers can use these documents easily and the effort and time in reading and searching for a specific topic will be reduced. Therefore, an automatic way to extract latent topics from these text documents is needed. Topic modeling is one of the techniques used to deal with this problem. In this paper, a medical collection of documents is used; this collection contains documents from three types of widespread diseases (Heart Diseases, Blood Pressure and Cholesterol). LDA topic modeling technique is applied to classify these documents into the previous mentioned topics. An evaluation of the algorithm’s results is done and the LDA shows a good level of classification accuracy.