introduction: raman spectroscopy, that is a spectroscopic technique based on inelastic scattering of monochromatic light, can provide valuable information about molecular vibrations, so using this technique we can study molecular changes in a sample. material and methods: in this research, 153 raman spectra obtained from normal and dried skin samples. baseline and electrical noise were eliminat...

Heart diseases are among the most important causes of mortality in the world, especially in industrial countries. Using heart sounds and the features extracted from them are among the non-aggressive diagnosis and prognosis methods for heart diseases. In this study, the time-scale, Cepstral, frequency, temporal and turbulence features are saved and extracted from the heart sounds, and then they ...

Introduction: To develop different radiomic models based on radiomic features and machine learning methods to predict early intensity modulated radiation therapy (IMRT) response.   Materials and Methods: Thirty prostate patients were included. All patients underwent pre ad post-IMRT T2 weighted and apparent diffusing coefficient (ADC) magnetic resonance imagi...

Background: Selection of human spermatozoa prior to assisted reproduction is currently based on motility and morphology. Recently, Zeta method has been introduced for normal sperm selection based on sperm surface charges. In addition, one of the features of apoptosis is the externalization of phosphatidylserine (PS), which are normally present on the inner leaflet of the sperm plasma membrane. ...

Variable (feature) selection has attracted much attention in contemporary statistical learning and recent scientific research. This is mainly due to the rapid advancement in modern technology that allows scientists to collect data of unprecedented size and complexity. One type of statistical problem in such applications is concerned with modeling an output variable as a function of a sma...

Feature selection for various applications has been carried out for many years in many different research areas. However, there is a trade-off between finding feature subsets with minimum length and increasing the classification accuracy. In this paper, a filter-wrapper feature selection approach based on fuzzy-rough gain ratio is proposed to tackle this problem. As a search strategy, a modifie...

Feature selection methods identify subsets of features in large datasets. Such methods have become popular in data-intensive areas, and performing feature selection prior tomodel constructionmay reduce the computational cost and improve the model quality. Monte Carlo Feature Selection (MCFS) is a feature selection method aimed at finding features to use for classification. Here we suggest a str...