درخت حوزه‌های تخصصی

In this paper, we discuss a new type of Mann iterative algorithm for finding a common fixed point of two nonexpansive mappings in real Hilbert spaces, and we establish the weak convergence of this fixed point approach by considering new circumstances. We can also show the algorithm’s strong convergence by taking into account an extra requirement. Our results extend the previous results given by Kanzow and Shehu as well as by Cho at et. al. Also in the last section, some applications of the main results are shown, and by some examples, we show the generality of our main results.

The aim of present study was to investigate the fundamental power strategy in the stock market with the role of arbitrage cost versus feelings in companies listed on the Tehran Stock Exchange. The spatial domain of this research was the compa-nies listed on the Tehran Stock Exchange and the temporal domain was the years between 2014 and 2019. The present research is among the applied studies and it is descriptive in terms of nature and correlational in term of method. In this study, a library method was used to collect data and information. Based on the systemat-ic elimination method, 139 companies were selected as a statistical sample. De-scriptive and inferential statistics were used to describe and summarize the collect-ed data. To analyze the data, first the variance heterogeneity test, F-Limmer test, Hausman test and Jarque-Bera test were used. Then, multivariate regression test was used to confirm and reject the research hypotheses (Eviews software). The results showed that the fundamental power strategy could predict future returns, and shareholder feeling and arbitrage cost strengthen the relationship between the fundamental power strategy and stock returns.

Many studies have been conducted in the field of bankruptcy prediction; But in most of them only financial ratios are used. However, in Iran, many non-financial factors affect bankruptcy. The main purpose of this study is to develop a mathematical model in which financial and non-financial indicators such as management and economics factors are used to predict bankruptcy. In this study, 44 variables that had the greatest impact on bankruptcy forecast were selected and with confirmatory factor analysis, a questionnaire was developed and sent to experts in the fields of management, accounting and economics to rank the impact of these variables. The statistical sample of the study includes 200 bankrupt and non-bankrupt companies listed in the period 2009-2018. After collecting the questionnaires using the OLS regression estimation method, the variables that had a factor load of less than 0.5 were eliminated and in the final model 9 main variables. The research model identified 95% of bankrupt companies and 93% of non-bankrupt companies with 95.4% confidence. Then, for verification, two hypotheses were developed and the model of this research was compared with two existing models. The ability to distinguish bankrupt companies from non-bankrupt ones by our proposed model was 6% more accurate than the Pourheidari et al. model, and 9.4% more accurate than Altman’s model.

The human machine interface research in the light of modern fast computers and advanced sensors is taking new heights. The classification and processing of neural activity in the brain accessed by Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), functional Magnetic Resonance Imaging (fMRI), Electrocorticography (ECoG), EEG Electroencephalogram (EEG) etc., are peeling off new paradigms for pattern recognition in human brain-machine interaction applications. In the present paper, an effective novel scheme based upon a synergetic approach employing the Genetic Algorithm (GA), Support Vector Machine and Wavelet packet transform for motor imagery classification and optimal Channel selection is proposed. GA with SVM acting as the objective function is employed for simultaneous selection of features and channels optimally. The binary population of GA is uniquely represented in three-dimensional structure and a new cross-over operator for GA are introduced. The new modified cross-over operator is proposed for the modified three-dimensional population. The ‘data set I’ of ‘BCI Competition IV’ is taken for evaluation of the efficacy of the proposed scheme. For subject ‘a’ accuracy is 88.9 6.9 with 10 channels, for subject ‘b’ accuracy is 79.20±5.36with 11 channels, for subject ‘f’ accuracy is 90.50±3.56 with 13 channels, and for subject ‘g’ accuracy is 92.23±3.21with 12 channels. The proposed scheme outperforms in terms of classification accuracy for subjects ‘a, b, f, g’ and in terms of number of channels for subject ‘a’ and that for subject ‘b’ is same as reported earlier in literature. Therefore, proposed scheme contributes a significant development in terms of new three-dimensional representation of binary population for GA as well as significant new modification to the GA operators. The efficacy of the scheme is evident from the results presented in the paper for dataset under consideration.