f) 2019-Scopus Open Access (PDF)

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    WEB DATA CLASSIFICATION USING SUPPORT VECTOR MACHINE BACK PROPAGATION NEURAL NETWORK
    (Blue Eyes Intelligence Engineering and Sciences Publication, 2019-09) Arunpriya, C
    These days, the development of World Wide Web has surpassed a lot with extra desires. Extraordinary arrangement of content reports, transmission records and pictures were reachable inside the web it’s as yet expanding in its structures. Information handling is that the style of removing information’s realistic inside the web. Web mining could be a piece of information preparing that identifies with differed examination networks like data recovery, bearing frameworks and artificial insight. The data’s in these structures are very much organized from the beginning. This web mining receives a great deal of the date mining procedures to discover most likely supportive data from web substance. The ideas of web mining with its classifications were examined. The paper chiefly focused on the web Content mining undertakings along the edge of its procedures and calculations. In this paper we proposed AI calculation based order .SVM_BPM calculation grouped the web content information and thought about existing calculations our proposed arrangement calculation is high effective and less time calculation.
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    A DEEP LEARNING OF AUTISM SPECTRUM DISORDER USING NAÏVE BAYES, IBK AND J48 CLASSIFIERS
    (Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP), 2019-07) Gomathi, S
    Deciding the right classification algorithm to classify and predict the disease is more important in the health care field. The eminence of prediction depends on the accuracy of the dataset and the machine learning method used to classify the dataset. Predicting autism behaviors through laboratory or image tests is very time consuming and expensive. With the advancement of machine learning (ML), autism can be predicted in the early stage. The main objective of the paper is to analyze the three classifiers Naïve Bayes, J48 and IBk (k-NN). An Autism Spectrum Disorder (ASD) diagnosis dataset with 21 attributes is obtained from the UCI machine learning repository. The attributes have experimented with the three classifiers using WEKA tool. 10-fold cross validation is used in all three classifiers. In the analysis, J48 shows the best accuracy compared with the other two classifiers. The architecture diagram is shown to depict the flow of the analysis. The Confusion matrix with other relevant results and figures are shown.