Abstract:Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting Machine, was investigated for the prediction of biological activity based on quantitative description of the compound's molecular structure. Seven datasets, well known in the literature were used in this paper and experimental results show that Xgboost can outperform machine learning algorithms like Random Forest (RF), Support Vector Machines (LSVM), Radial Basis Function Neural Network (RBFN) and Naïve Bayes (NB) for the prediction of biological activities. In addition to its ability to detect minority activity classes in highly imbalanced datasets, it showed remarkable performance on both high and low diversity datasets.

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The global pandemic forced the closure of learning institutions and an abrupt switch from physical (face-to-face) learning to e-learning. The Academic Staff Union of University postulates that e-learning will not work during the period. This paper evaluates the attitude of engineering students in a Nigerian private university to e - learning during the period of national lockdown in Nigeria. A questionnaire was designed to collect students’ attitudes about learning efficiency, quality, and associated cost. Ease or difficulty of the transition to e-learning, digital skills requirement, commitment to e-learning, digital skills improvement, and preferred test mode were studied. In addition, the relationship between gender and preferred test mode was examined. 73 students responded to the questionnaire. A significantly lower percentage (4%) of the engineering students prefer the e-learning method, while a more significant percentage (62%) of the respondents prefer blended learning. Gender has no significant relationship with the preferred learning mode of the students. Moreover, the students found the e-learning approach to be expensive. Finally, there is still much to be done by Nigerian educational stakeholders to improve the experience of e-learning in Nigeria.

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Machine learning based flight delay prediction is one of the numerous real-life application domains where the problem of imbalance in class distribution is reported to affect the performance of learning algorithms. However, the fact that learning algorithms have been reported to perform well on some class imbalance problems posits the possibility of other contributing factors. In this study, we visually explore air traffic data after dimensionality reduction with t-Distributed Stochastic Neighbour Embedding. Our initial findings suggest a high degree of overlapping between the delayed and on-time class instances which can be a greater problem for learning algorithms than class imbalance.