درخت حوزه‌های تخصصی

In the 21st century, the meaning of places is essential to people. Meanings have gained symbolic importance through tourism heritage. Tourism is a powerful force  in shaping the world and our understanding of different cultures, people, and  places. This research investigates the complexities of religious tourism and the  management of religious destinations. It obtains criteria for managing religious  goals with a detailed look and uses the descriptive-analytical method based on  library documents and the opinion of research experts. Then, using research experts  and ISM-PLS hybrid approach, a three-level framework for managing religious  goals in Yazd city has been presented and analyzed. The results showed that the  government's decisions and actions were the most effective criteria. The ultimate  goal was to create a sense of trust and security among religious tourists. It is hoped  that the findings of this research will be a starting point for managing religious  tourism destinations and a guide for examining the complexities of Islamic tourism  destinations and their management.

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.