A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems

A New Fuzzy-Based Classification Method for Use in Smart/Precision Medicine

The development of information technology has had a significant impact on various areas of human activity, including medicine. It has led to the emergence of the phenomenon of Industry 4.0, which, in turn, led to the development of the concept of Medicine 4.0. Medicine 4.0, or smart medicine, can be considered as a structural association of such areas as AI-based medicine, telemedicine, and precision medicine. Each of these areas has its own characteristic data, along with the specifics of their processing and analysis. Nevertheless, at present, all these types of data must be processed simultaneously, in order to provide the most complete picture of the health of each individual patient. In this paper, after a brief analysis of the topic of medical data, a new classification method is proposed that allows the processing of the maximum number of data types. The specificity of this method is its use of a fuzzy classifier. The effectiveness of this method is confirmed by an analysis of the results from the classification of various types of data for medical applications and health problems. In this paper, as an illustration of the proposed method, a fuzzy decision tree has been used as the fuzzy classifier. The accuracy of the classification in terms of the proposed method, based on a fuzzy classifier, gives the best performance in comparison with crisp classifiers.

Week-Wise Student Performance Early Prediction in Virtual Learning Environment Using a Deep Explainable Artificial Intelligence

Early prediction of students’ learning performance and analysis of student behavior in a virtual learning environment (VLE) are crucial to minimize the high failure rate in online courses during the COVID-19 pandemic. Nevertheless, traditional machine learning models fail to predict student performance in the early weeks due to the lack of students’ activities’ data in a week-wise timely manner (i.e., spatiotemporal feature issues). Furthermore, the imbalanced data distribution in the VLE impacts the prediction model performance. Thus, there are severe challenges in handling spatiotemporal features, imbalanced data sets, and a lack of explainability for enhancing the confidence of the prediction system. Therefore, an intelligent framework for explainable student performance prediction (ESPP) is proposed in this study in order to provide the interpretability of the prediction results. First, this framework utilized a time-series weekly student activity data set and dealt with the VLE imbalanced data distribution using a hybrid data sampling method. Then, a combination of convolutional neural network (CNN) and long short-term memory (LSTM) was employed to extract the spatiotemporal features and develop the early prediction deep learning (DL) model. Finally, the DL model was explained by visualizing and analyzing typical predictions, students’ activities’ maps, and feature importance. The numerical results of cross-validation showed that the proposed new DL model (i.e., the combined CNN-LSTM and ConvLSTM), in the early prediction cases, performed better than the baseline models of LSTM, support vector machine (SVM), and logistic regression (LR) models.

A Waste Classification Method Based on a Multilayer Hybrid Convolution Neural Network

With the rapid development of deep learning technology, a variety of network models for classification have been proposed, which is beneficial to the realization of intelligent waste classification. However, there are still some problems with the existing models in waste classification such as low classification accuracy or long running time. Aimed at solving these problems, in this paper, a waste classification method based on a multilayer hybrid convolution neural network (MLH-CNN) is proposed. The network structure of this method is similar to VggNet but simpler, with fewer parameters and a higher classification accuracy. By changing the number of network modules and channels, the performance of the proposed model is improved. Finally, this paper finds the appropriate parameters for waste image classification and chooses the optimal model as the final model. The experimental results show that, compared with some recent works, the proposed method has a simpler network structure and higher waste classification accuracy. A large number of experiments in a TrashNet dataset show that the proposed method achieves a classification accuracy of up to 92.6%, which is 4.18% and 4.6% higher than that of some state-of-the-art methods, and proves the effectiveness of the proposed method.