The Industrial Internet of Things has grown significantly in recent years. While implementing industrial digitalization, automation, and intelligence introduced a slew of cyber risks, the complex and varied industrial Internet of Things environment provided a new attack surface for network attackers. As a result, conventional intrusion detection technology cannot satisfy the network threat discovery requirements in today’s Industrial Internet of Things environment. In this research, the authors have used reinforcement learning rather than supervised and unsupervised learning, because it could very well improve the decision-making ability of the learning process by integrating abstract thinking of complete understanding, using deep knowledge to perform simple and nonlinear transformations of large-scale original input data into higher-level abstract expressions, and using learning algorithm or learning based on feedback signals, in the lack of guiding knowledge, which is based on the trial-and-error learning model, from the interaction with the environment to find the best good solution. In this respect, this article presents a near-end strategy optimization method for the Industrial Internet of Things intrusion detection system based on a deep reinforcement learning algorithm. This method combines deep learning’s observation capability with reinforcement learning’s decision-making capability to enable efficient detection of different kinds of cyberassaults on the Industrial Internet of Things. In this manuscript, the DRL-IDS intrusion detection system is built on a feature selection method based on LightGBM, which efficiently selects the most attractive feature set from industrial Internet of Things data; when paired with deep learning algorithms, it effectively detects intrusions. To begin, the application is based on GBM’s feature selection algorithm, which extracts the most compelling feature set from Industrial Internet of Things data; then, in conjunction with the deep learning algorithm, the hidden layer of the multilayer perception network is used as the shared network structure for the value network and strategic network in the PPO2 algorithm; and finally, the intrusion detection model is constructed using the PPO2 algorithm and ReLU (R). Numerous tests conducted on a publicly available data set of the Industrial Internet of Things demonstrate that the suggested intrusion detection system detects 99 percent of different kinds of network assaults on the Industrial Internet of Things. Additionally, the accuracy rate is 0.9%. The accuracy, precision, recall rate, F1 score, and other performance indicators are superior to those of the existing intrusion detection system, which is based on deep learning models such as LSTM, CNN, and RNN, as well as deep reinforcement learning models such as DDQN and DQN.

Computer programming is considered as a difficult area of study for novices. One of the reasons is the main focus of the curriculum presented in an introductory programming (IP) course which emphasizes more on the programming knowledge (syntax and semantic) of the programming language. This study introduced a new teaching curriculum in the IP course which focuses on different skills required by the novices. We compared the IP course materials based on the traditional and new approaches against five categories. The result shows that the new approach encourages both the programming knowledge and problem solving strategies, and promotes deep learning. Furthermore, it discourages programming shortcut (Problem statement → Code), and support three-step approach (Problem statement → Solution Plans → Code) in solving a problem statement. The new approach also promotes algorithmic thinking in the IP course by paying equal attention on the problem solving strategies.

<p>E-learning systems installed in educational institutions have increased the efficiency of scholarly processes over the years. E-learning, however, has faced many factors affecting the continuous intention of teachers and students to use e-learning, such as student satisfaction, productivity, and academic success. Therefore to improve academic success, there is a need for institutions to enhance their e-learning programs. Thus the primary objective of this thesis is to construct a composite. This research focuses on the advancement of e-learning to boost students' continuous trying to use e-learning to increase students' level of understanding and academic performance. Data were collected using questionnaires returned to determine their e-learning feedback by 295 undergraduates from four universities in Oman, after which (PLS-SEM) used e-learning to assess their ongoing trial of using e-learning. The facts demonstrate that the variables are essential for the continuing decision to use e-learning.</p>

Most novice programmers consider learning to program as a difficult and challenging field of study for them. As a result, high dropout and failure rates in programming one courses reported. One of the reasons is that most programming (1) courses don’t give equal attention to syntax and semantics of programming language and algorithmic thinking. In this study, a web application based on Problem Analysis and Algorithmic Model (PAAM) was prepared and acquainted in the programming (1) course. The application focuses on problem analysis and algorithmic thinking. The influence on genders' opinion after offering the PAAM model in the programming (1) course determined by organizing a survey. The mean values of the male and female survey respondents compared by performing the T-test. The purpose is to determine if there is any significant difference between the mean values. Results show that students appreciated the web application in the programming (1) course. Male students discerned more positive responses in the survey questions compared to female students. The T-test result shows a significant difference between the values of respondents because the p-value for equal variances assumed is (.000), which is less than p = 0.05. The application encourages a new approach based on four steps (problem statement → problem analysis → problem-solving skills → code) for novices. The application helps students to understand programming structures, program design and comprehension.

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