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The increase of malware that are exploiting the Internet daily has become a serious threat. The manual heuristic inspection of malware analysis is no longer considered effective and efficient compared against the high spreading rate of malware. Hence, automated behavior-based malware detection using machine learning techniques is considered a profound solution. The behavior of each malware on an emulated...
Forecasting applications on the stock market attract much interest from researchers in the artificial intelligence field. The problem tackled in this study concerns predicting the direction of change of stock price indices, formulated in terms of binary classification. We use gene expression programming to evolve pools of binary classifiers and investigate several approaches to construct ensembles...
Accurate classification of caller interactions within Interactive Voice Response systems would assist corporations to determine caller behavior within these telephony applications. This paper details the development of such a classification system for a pay beneficiary application. Fuzzy Inference Systems, Multi-Layer Perceptron, Support Vector Machine and ensemble of classifiers were developed. Accuracy,...
In this paper, computer aided diagnosis is applied to the brain CT image processing. We compared performance of morphological operations in extracting three types of features i.e. gray scale, symmetry and texture. SVM, MLPNN and RBFNN are used to build classifiers for normal and abnormal brain CT image. It shows that morphological operation can improve the accuracy. Moreover, method of SVM has better...
Meteorological conditions are crucial for the agricultural production. Rainfall, in particular, can be cited as the most influential by having direct relation with hydric balance. Meteorological satellites that cover the whole earth have been extensively used for the development of statistical and artificial intelligence models for rainfall estimation. However, some of these techniques have flaws...
Feature selection and ensemble classification increase system efficiency and accuracy in machine learning, data mining and biomedical informatics. This research presents an analysis of the effect of removing irrelevant and redundant features with ensemble classifiers using two datasets from UCI machine learning repository. Accuracy and computational time were evaluated by four base classifiers; NaiveBayes,...
This paper presents the results of the application of a feature selection procedure to an automatic music genre classification system. The classification system is based on the use of multiple feature vectors and an ensemble approach, according to time and space decomposition strategies. Feature vectors are extracted from music segments from the beginning, middle and end of the original music signal...
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