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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The principal intention of this work is to compare the performance of the supervised brain tumour segmentation methods. These segmentation methods are based on machine learning. First, the input MR brain image is denoised by employing the adaptive bilateral filter, and the image contrast is enhanced employing the histogram equalization. Then we retrieve the features from the pre-processed image. Among several feature extraction methods, this work uses the shape, intensity, and texture feature extractors. Subsequent to removing these three types of features, fragment the tumor dependent on these recovered segments. The supervised segmentation approach is used for this. Among several supervised segmentation methods, this work uses three machine learning methods, namely Probabilistic Neural Network (PNN), Artificial Neural Network (ANN), and Convolution Neural Network (CNN). Finally, the retrieved features are feed into these machine learning methods to segment the brain tumour regions. To find out the best machine learning approach, the performance of these three supervised machines learning methods is evaluated by four performance metrics. Based on these evaluations, the best segmentation approach is discovered. Four execution boundaries are utilized, in particular, Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV), Jaccard list (JI), and Sensitivity (SEN) to analyze the presentation of the AI strategy. The experimental outputs exposed that the CNN makes greater than other methods.

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