International Journal of Scientific & Technology Research, 2019
This paper aims at combining machine learning techniques with Smartphone sensors (i.e. accelerome... more This paper aims at combining machine learning techniques with Smartphone sensors (i.e. accelerometer sensor) to develop a smart model capable of classifying vehicle driving style into (Excellent, good or weak) categories. In this paper, we use several machine learning algorithms (Neural network, KNN, Naïve Bayes and Random forest tree) to train and test data extracted from Smartphone sensors. The results indicate the possibility to exploit Smartphone sensor readings in the design of a reliable model capable of identifying the driving style based on accelerometer readings. All examined machine learning algorithms maintain high accuracy in classifying the vehicle's driving class; however, the neural network classifiers achieve the highest accuracy ratio reaches (99.9%).
Programming is a major challenge faced by universities students at different levels. A significan... more Programming is a major challenge faced by universities students at different levels. A significant learning outcome that emerged in recent years is working in teams, wherein two programmers can engage in pair programming to work on a single task as a group. This study examined the usability and effectiveness of using pair programming. We also evaluated the effects of pair programming on student results, time consumed, and number of errors, Big-O notation, and time complexity. The mixed method approach was applied by formulating a questionnaire and three different projects. We applied a novel strategy for creating pairs of students from different levels and courses in Tafila Technical University. Results indicated that pair programming is feasible and effective for educational purposes. Positive results were also obtained for programming learning, time, performance, and code quality.
International Journal of Scientific & Technology Research, 2019
This paper aims at combining machine learning techniques with Smartphone sensors (i.e. accelerome... more This paper aims at combining machine learning techniques with Smartphone sensors (i.e. accelerometer sensor) to develop a smart model capable of classifying vehicle driving style into (Excellent, good or weak) categories. In this paper, we use several machine learning algorithms (Neural network, KNN, Naïve Bayes and Random forest tree) to train and test data extracted from Smartphone sensors. The results indicate the possibility to exploit Smartphone sensor readings in the design of a reliable model capable of identifying the driving style based on accelerometer readings. All examined machine learning algorithms maintain high accuracy in classifying the vehicle's driving class; however, the neural network classifiers achieve the highest accuracy ratio reaches (99.9%).
Programming is a major challenge faced by universities students at different levels. A significan... more Programming is a major challenge faced by universities students at different levels. A significant learning outcome that emerged in recent years is working in teams, wherein two programmers can engage in pair programming to work on a single task as a group. This study examined the usability and effectiveness of using pair programming. We also evaluated the effects of pair programming on student results, time consumed, and number of errors, Big-O notation, and time complexity. The mixed method approach was applied by formulating a questionnaire and three different projects. We applied a novel strategy for creating pairs of students from different levels and courses in Tafila Technical University. Results indicated that pair programming is feasible and effective for educational purposes. Positive results were also obtained for programming learning, time, performance, and code quality.
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Papers by Zaid Halhouli