IoT applications have been used in many contexts, especially applications on mobile devices. This work presents an attendance checking system by identifying and recognizing human faces on mobile devices using a transfer learning approach. This system includes a mobile application and a web application. These two applications are communicated by using APIs. The mobile application detects human faces by using the camera on that mobile device, then, the face features are extracted using the FaceNet model, and finally, the attendees are identified by computing similarity with existing faces in the database. The proposed system was tested on a dataset with 358 images of 52 employees in our office. Results show that the accuracy is about 93.46% on the test set and 97.06% in the real environment. Thus, this system could be used for checking attendances in several real contexts.

This work proposes a novel approach - personalized forecasting - to take into account the sequential effect in predicting student performance (PSP). Instead of using all historical data as other methods in PSP, the proposed methods only use the information of the individual students for forecasting his/her own performance. Moreover, these methods also encode the "student effect" (e.g. how good/clever a student is, in performing the tasks) and "task effect" (e.g. how difficult/easy the task is) into the models. Experimental results show that the proposed methods perform nicely and much faster than the other state-of-the-art methods in PSP.

Recently, researchers have shown that the Area Under the ROC Curve (AUC) has a serious deficiency since it implicitly uses different misclassification cost distributions for different classifiers. Thus, using the AUC can be compared to using different metrics to evaluate different classifiers [1]. To overcome this incoherence, the H measure was proposed, which uses a symmetric Beta distribution to replace the implicit cost weight distribution in the AUC. When learning from imbalanced data, misclassifying a minority class example is much more serious than misclassifying a majority class example. To take different misclassification costs into account, we propose using an asymmetric Beta distribution (B42) instead of a symmetric one. Experimental results on 36 imbalanced data sets using SVMs and logistic regression show that B42 is a good choice for evaluating on imbalanced data sets because it puts more weight on the minority class. We also show that balanced random undersampling does not work for large and highly imbalanced data sets, although it has been reported to be effective for small data sets.

Class imbalance is one of the challenging problems for machine learning algorithms. When learning from highly imbalanced data, most classifiers are overwhelmed by the majority class examples, so the false negative rate is always high. Although researchers have introduced many methods to deal with this problem, including resampling techniques and cost-sensitive learning (CSL), most of them focus on either of these techniques. This study presents two empirical methods that deal with class imbalance using both resampling and CSL. The first method combines and compares several sampling techniques with CSL using support vector machines (SVM). The second method proposes using CSL by optimizing the cost ratio (cost matrix) locally. Our experimental results on 18 imbalanced datasets from the UCI repository show that the first method can reduce the misclassification costs, and the second method can improve the classifier performance.

This paper introduces and compares some techniques used to predict the student performance at the university. Recently, researchers have focused on applying machine learning in higher education to support both the students and the instructors getting better in their performances. Some previous papers have introduced this problem but the prediction results were unsatisfactory because of the class imbalance problem, which causes the degradation of the classifiers. The purpose of this paper is to tackle the class imbalance for improving the prediction/classification results by over-sampling techniques as well as using cost-sensitive learning (CSL). The paper shows that the results have been improved when comparing with only using baseline classifiers such as Decision Tree (DT), Bayesian Networks (BN), and Support Vector Machines (SVM) to the original datasets.