The students’ performance prediction (SPP) problem is a challenging problem that managers face at any institution. Collecting educational quantitative and qualitative data from many resources such as exam centers, virtual courses, e-learning educational systems, and other resources is not a simple task. Even after collecting data, we might face imbalanced data, missing data, biased data, and different data types such as strings, numbers, and letters. One of the most common challenges in this area is the large number of attributes (features). Determining the highly valuable features is needed to improve the overall students’ performance. This paper proposes an evolutionary-based SPP model utilizing an enhanced form of the Whale Optimization Algorithm (EWOA) as a wrapper feature selection to keep the most informative features and enhance the prediction quality. The proposed EWOA combines the Whale Optimization Algorithm (WOA) with Sine Cosine Algorithm (SCA) and Logistic Chaotic Map (LCM) to improve the overall performance of WOA. The SCA will empower the exploitation process inside WOA and minimize the probability of being stuck in local optima. The main idea is to enhance the worst half of the population in WOA using SCA. Besides, LCM strategy is employed to control the population diversity and improve the exploration process. As such, we handled the imbalanced data using the Adaptive Synthetic (ADASYN) sampling technique and converting WOA to binary variant employing transfer functions (TFs) that belong to different families (S-shaped and V-shaped). Two real educational datasets are used, and five different classifiers are employed: the Decision Trees (DT), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and LogitBoost (LB). The obtained results show that the LDA classifier is the most reliable classifier with both datasets. In addition, the proposed EWOA outperforms other methods in the literature as wrapper feature selection with selected transfer functions.
Two series of novel 5-arylazo-3-cyano-2-(2″,3″,4″,6″-tetra-O-acetyl-β-d-galacto pyranosyloxy) pyridines and 3-cyano-2-(2″,3″,4″,6″-tetra-O-acetyl-β-d-galactopyranosyloxy) pyridines were synthesized in high yields utilizing a microwave-assisted synthesis tool guided by the principles of green chemistry. The chemical structures of the new substances were confirmed on the basis of their elemental analysis and spectroscopic data (FT-IR, 1D, 2D-NMR). Activity against different bacterial strains was studied. The anticancer potential of the new compounds is also discussed. Molecular docking was used as a tool in this research work to get better insight into the possible interactions, affinities, and expected modes of binding of the most promising derivatives of the potential chemotherapeutic target (DHFR).
Many real world RDF collections are large compared with other real world data structures. Such large RDF collections evolve in a distributed environment. Therefore, these changes between RDF versions need to be detected and computed in order to synchronize these changes to the other users. To cope with the evolving nature of the semantic web, it is important to understand the costs and benefits of the different change detection techniques. In this paper, we experimentally provide a detailed analysis of the overall process of RDF change detection techniques namely: explicit change detection, forward-inference change detection, backward-inference change detection and backward-inference and pruning change detection. The results show that pruning is relatively expensive by comparison with inferencing
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