In the modern Digital Era, Data Mining is the powerful area for analyzing the large data sets to get unexpected relationships (models). The analysis of statistical data on sequential data points measured at regular time interval over a period of time is time series analysis. Time series analysis is used in predicting future occurrence of a time based event. One of the main areas where time seri...

in recent years, the needs of the internet are felt in lives of all people.  accordingly, many studies have been done on security in virtual environment. old technics such as firewalls, authentication and encryption could not provide internet security completely; so, intrusion detection system is created as a new solution and a defense wall in cyber environment. many studies were performed on d...

internet applications spreading and its high usage popularity result insignificant increasing of cyber-attacks. consequently, network security has becomea matter of importance and several methods have been developed for these attacks.for this purpose, intrusion detection systems (ids) are being used to monitor theattacks occurred on computer networks. data mining techniques, machinelearning, ne...

porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. nowadays, using intelligent techniques has become a popular method for porosity estimation. support vector machine (svm) a new intelligent method with a great generalization potential of modeling non-linear relationships has been introduced fo...

different approaches have been proposed for feature selection to obtain suitable features subset among all features. these methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. the objective functions are divided into two main groups: filter and wrapper methods.  in filter methods, features subsets are selected due to some measu...

segmentation and three‑dimensional (3d) visualization of teeth in dental computerized tomography (ct) images are of dentists’ requirements for both abnormalities diagnosis and the treatments such as dental implant and orthodontic planning. on the other hand, dental ct image segmentation is a difficult process because of the specific characteristics of the tooth’s structure. this paper presents ...

Each year, some 30 percent of global deaths are caused by cardiovascular diseases. This figure is worsening due to both the increasing elderly population and severe shortages of medical personnel. The development of a cardiovascular diseases classifier (CDC) for auto-diagnosis will help address solve the problem. Former CDCs did not achieve quick evaluation of cardiovascular diseases. In this l...

In this paper, we have developed a robust Support Vector Machines (SVM) scheme of classifying imbalanced and noisy data using the principles of Robust Optimization. Uncertainty is prevalent in almost all datasets and has not been addressed efficiently by most data mining techniques, as these are based on deterministic mathematical tools. Imbalanced datasets exist while performing analysis of ra...

This paper discusses about combining Support Vector Machine and decision trees for multi class text classification. Support Vector Machines are trained on each class at each level of the tree and the SVM which is more successful in predicting a class at that level is selected as the decision in that node. Thus a tree is constructed with different SVM in each node. And the tree constructed is us...

The Support Vector Machine (SVM) typically outperforms other algorithms on text classification problems, but requires training time roughly quadratic in the number of training documents. In contrast, linear time algorithms like Naive Bayes have lower performance, but can easily handle huge training sets. In this paper, we describe a technique that creates a continuum of classifiers between the ...