as networking and communication technology becomes more widespread, thequantity and impact of system attackers have been increased rapidly. themethodology of intrusion detection (ids) is generally classified into two broadcategories according to the detection approaches: misuse detection and anomalydetection. in misuse detection approach, abnormal system behavior is defined atfirst, and then an...

Text Classification is an important research field in information retrieval and text mining. The main task in text classification is to assign text documents in predefined categories based on documents’ contents and labeled-training samples. Since word detection is a difficult and time consuming task in Persian language, Bayesian text classifier is an appropriate approach to deal with different...

breast cancer is the second largest cause of cancer deaths among women. at the same time, it is also among the most curable cancer types if it can be diagnosed early. this paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. the proposed method includes three main modules: the feature extraction module, the classifier module and the optimization module. in t...

Text Classification is an important research field in information retrieval and text mining. The main task in text classification is to assign text documents in predefined categories based on documents’ contents and labeled-training samples. Since word detection is a difficult and time consuming task in Persian language, Bayesian text classifier is an appropriate approach to deal with different...

This study explores a semi-supervised classification approach using random forest as a base classifier to classify the low-back disorders (LBDs) risk associated with the industrial jobs. Semi-supervised classification approach uses unlabeled data together with the small number of labelled data to create a better classifier. The results obtained by the proposed approach are compared with those o...

Concept drift detection algorithms have historically been faithful to the aged architecture of forcefully resetting base classifiers for each detected drift. This approach prevents underlying becoming outdated as distribution a data stream shifts from one concept another. In situations where both and temporal dependence are present within stream, forced can cause complications in classifier eva...

Abstract Quantum computing opens exciting opportunities for kernel-based machine learning methods, which have broad applications in data analysis. Recent works show that quantum computers can efficiently construct a model of classifier by engineering the interference effect to carry out kernel evaluation parallel. For practical these an important issue is minimize size circuits. We present simp...