electroencephalogram (eeg) is one of the useful biological signals to distinguish different brain diseases and mental states. in recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from eeg signals. in this research, we introduce an emot...

Objective: Nowadays, financial distress prediction is one of the most important research issues in the field of risk management that has always been interesting to banks, companies, corporations, managers and investors. The main objective of this study is to develop a high performance predictive model and to compare the results with other commonly used models in financial distress prediction M...

breast cancer is the most common type of cancer among women. the important key to treat the breast cancer is early detection of it because according to many pathological studies more than 80% of all abnormalities are still benign at primary stages; so in recent years, many studies and extensive research done to early detection of breast cancer with higher precision and accuracy.infra-red breast...

background: in this paper we compare a highly accurate supervised to an unsupervised technique that uses breast thermal images with the aim of assisting physicians in early detection of breast cancer. methods: first, we segmented the images and determined the region of interest. then, 23 features that included statistical, morphological, frequency domain, histogram and gray-level co-occurrence ...

Machine learning-based classification techniques provide support for the decision making process in the field of healthcare, especially in disease diagnosis, prognosis and screening. Healthcare datasets are voluminous in nature and their high dimensionality problem comprises in terms of slower learning rate and higher computational cost. Feature selection is expected to deal with the high dimen...

biomedical datasets usually include a large number of features relative to the number of samples. however, some data dimensions may be less relevant or even irrelevant to the output class. selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. to this end, this paper presents a hybrid method of filter and wr...

in this study, a prediction model based on support vector machines (svm) improved by introducing a volume weighted penalty function to the model was introduced to increase the accuracy of forecasting short term trends on the stock market to develop the optimal trading strategy. along with vw-svm classifier, a hybrid feature selection method was used that consisted of f-score as the filter part ...

Feature selection is a critical procedure in many pattern recognition applications. There are two distinct mechanisms for feature selection namely the wrapper methods and the filter methods. The filter methods are generally considered inferior to wrapper methods, however wrapper methods are computationally more demanding than filter methods. A novel filter feature selection method based on mutu...