Suchergebnisse

The purpose of this study is to examine the perception of business students, of the different Lebanese universities, of entrepreneurship. It focuses on students' career as entrepreneurs, their opinion as to entrepreneurial characteristics, and the external factors that influence such a choice. The main significance of this study is to fill the gap in the current literature about entrepreneurship in Lebanon, since few studies have been conducted based on the Lebanese culture. A questionnaire is given to a sample of 800 senior and graduate business students from six main universities. A Cronbach Alpha test is used to confirm the internal reliability of the questionnaire. The results of the implemented descriptive statistics and chi-square technique proved the formulated hypotheses, namely, 1) students perceive entrepreneurial career as a primary choice, and highly is preferred by male graduate and working students; 2) students prioritize the entrepreneurial characteristics in the following order: motivation to attain wealth, autonomy, achievement, locus of control and innovativeness, self-efficacy, risk-taking, and pro-activeness. Gender and parents' background proved to be similar among students in almost all characteristics, but not as to the level of education, working experience, and university type; 3) students perceive financial capital as the most important factor to entrepreneurship; and 4) they consider political and economic climate as barriers to entrepreneurship and business growth in Lebanon. This research ends with implications, including the significance of reformulating governmental policies to support new entrepreneurs' opening new ventures; and, universities' top administrators are to enhance curriculum by introducing courses in entrepreneurship and adopting training programs to stimulate entrepreneurial behavior.

This paper presents an efficient system using a deep learning algorithm that recognizes daily activities and investigates the worst falling cases to save elders during daily life. This system is a physical activity recognition system based on the Internet of Medical Things (IoMT) and uses convolutional neural networks (CNNets) that learn features and classifiers automatically. The test data include the elderly who live alone. The performance of CNNets is compared against that of state-of-the-art methods, such as activity windowing, fixed sample windowing, time-weighted windowing, mutual information windowing, dynamic windowing, fixed time windowing, sequence prediction algorithm, and conditional random fields. The results indicate that CNNets are competitive with state-of-the-art methods, exhibiting enhanced IoMT accuracy of 98.37%, which is the highest among the proposed solutions using the same dataset.