The present study aims to model systematic risk using financial and accounting variables. Accordingly, the data for 174 companies in Tehran Stock Exchange are extracted for the period of 2006 to 2016. First, the systematic risk index is estimated using the ARFIMA-FIGARCH model. Then, based on the research background, 35 affective financial and accounting variables are simultaneously used with t...

Abstract Introduction: Myocardial Infarction, also known as heart attack, normally occurs due to such causes as smoking, family history, diabetes, and so on. It is recognized as one of the leading causes of death in the world. Therefore, the present study aimed to evaluate the performance of classification models in order to predict Myocardial Infarction, using a feature selection method tha...

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...

This paper measures end-to-end (e2e) throughput of power beacon-assisted multi-hop decode-and-forward (DF) relaying scheme adopting non-orthogonal multiple access (NOMA) and transmit antenna selection (TAS)/selection combining (SC). Particularly, TAS/SC NOMA are adopted at each hop to relay different data a source destinations. Moreover, the transmitters including relays have harvest wireless e...

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 ...

Gaussian neural networks have always suffered from the curse of dimensionality; the number of weights needed increases exponentially with the number of inputs and outputs. Many methods have been proposed to solve this problem by optimally or sub-optimally selecting the weights or centers of the Gaussian neural network [1],[2]. However, most of these attempts are not suitable for online implemen...

This paper investigates the throughput performance of an incremental virtual full-duplex non-orthogonal multiple access (I-VFD-NOMA) communication system, where two energy harvesting (EH) enabled near users are willing to forward message for far user using decode-and-forward (DF) protocol. If direct link between source and exists, I-VFD-NOMA scheme is used save channel resources. does not exist...

This paper proposes a new way of using forward selection of explanatory variables in regression or canonical redundancy analysis. The classical forward selection method presents two problems: a highly inflated Type I error and an overestimation of the amount of explained variance. Correcting these problems will greatly improve the performance of this very useful method in ecological modeling. T...

Selecting appropriate features has become a key task when dealing with high-dimensional data. We present a new algorithm designed to find an optimal solution for classification tasks. Our approach combines forward selection, backward elimination and exhaustive search. We demonstrate its capabilities and limits using artificial and real world data sets. Regarding artificial data sets interleavin...