Outcome-based education (OBE) is a well-proven teaching strategy based upon a predefined set of expected outcomes. The components of OBE are Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes (COs). These latter are assessed at the end of each course and several recommended actions can be proposed by faculty members' to enhance the quality of courses and therefore the overall educational program. Considering a large number of courses and the faculty members' devotion, bad actions could be recommended and therefore undesirable and inappropriate decisions may occur. In this paper, a recommender system, using different machine learning algorithms, is proposed for predicting suitable actions based on course specifications, academic records, and course learning outcomes' assessments. We formulated the problem as a multi-label multi-class binary classification problem and the dataset was translated into different problem transformation and adaptive methods such as one-vs.-all, binary relevance, label powerset, classifier chain, and ML-KNN adaptive classifier. As a case study, the proposed recommender system is applied to the college of Computer and Information Sciences, Jouf University, Kingdom of Saudi Arabia (KSA) for helping academic staff improving the quality of teaching strategies. The obtained results showed that the proposed recommender system presents more recommended actions for improving students' learning experiences.
The immune system has received a special attention as a potential source of inspiration for innovative approaches to solve database security issues and build artificial immune systems. Database security issues need to be correctly identified to ensure that suitable responses are taken. This paper proposes an immunity-based error containment algorithm for providing optimum response in detected intrusions. The objective of the proposed algorithm is to reduce the false positive alarms to the minimum since not all the incidents are malicious in nature. The proposed algorithm is based on apoptotic and necrotic signals which are parts of the immunity structure in human immune system. Apoptotic signals define low-level alerts that could result from legitimate users but could be also the prerequisites for an attack, while necrotic signals define highlevel alerts that result from actual successful attacks.
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