The aim of this study is to investigate the effect of educational technologies on the Corporate Social Responsibility (CSR) perception of tourism students and their intention to work in the tourism business industry. By improving education programs with an investment in educational technologies, both universities and firms are believed to benefit from growing CSR initiatives, as well as potential young talents for their future business activities. Four-dimensional (economic, legal, ethical and philanthropic dimensions) model of CSR perception is followed. M-learning and E-learning platforms are compared as moderators to ensure the most effective platform for CSR education among the students. The study is conducted with data which is gathered from a total of 397 students who continue their bachelor and associate degrees in different universities in the Gulf nations. It is found that there is a positive relationship between students’ intention to work in the industry and the sub-dimensions of CSR, namely ethical responsibilities, legal responsibilities, and economical responsibilities. Conversely, philanthropic responsibilities had no effect on working intention. In addition, gender difference had no significant impact on working intention of the students in tourism industry. Moreover, it is revealed that e-learning tools are more effective in CSR education.

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Product performance satisfaction level can be referred to as the satisfaction level of mutual fund investors in terms of return, transparency, safety, liquidity, service quality, fund management and the overall performance of the mutual fund products. Here, the researcher attempted to analyze the satisfaction level of mutual fund investors concerning different funds/schemes opted by the investors. Based on these objectives seven parameters of satisfaction of mutual fund investors including the overall performance of the fund has been taken into consideration. The findings of the standardized regression weight of product performance satisfaction level and the Chi-square test reveals that there is a significant difference in the product performance satisfaction level of mutual fund investors about the funds opted by them (except in case of balanced-fund).

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Tumor segmentation is the primary and tedious task for the clinical experts. Computer Aided Design is the only solution which identifies the tumor very accurately with less time. Deep learning models such as the convolutionary neural network have been widely used in 3D biomedical segmentation and have achieved state-of-the-art performance.In this research, saliency based deep features are extracted from MRI. Then Support Vector Machine is used for classifying deep features. The proposed method is tested on BRATS 2015 dataset and it is compared with state-of-methods and recent methods. The proposed method achieves 0.94, 0.93 and 0.9 as dice score, precision and sensitivity respectively which is greater than other methods.