Gliomas are among the most serious brain tumor types due to their aggressive behavior. Recent advances in automated methods for early brain tumor detection that analyze whole-slide images of brain tissue enable rapid and precise diagnosis. Therefore, we propose a hybrid approach combining a convolutional neural network and a vision transformer for deep analysis of whole-slide patches. This hybrid model employs a two-stage classification strategy to address highly imbalanced class distributions. The first stage uses a binary-label classifier to label patches as either normal or abnormal. The second stage employs a multi-label classifier to classify abnormal patches into their corresponding glioma subtypes. We evaluated the two-stage hybrid model on an imbalanced test set, achieving the highest balanced accuracy (95.31%), macro-averaged precision (96.08%), and macro-averaged F1-score (95.51%). The two-stage classification approach addressed class imbalance. Furthermore, the hybrid model, which combines a convolutional neural network and a vision transformer, captured deeper features, achieving superior performance and outperforming state-of-the-art methods.

Educational success is a critical component of societal development, yet increasing student dropout rates present ongoing challenges. While supervised learning models are commonly used for dropout prediction, they rely on manually labeled data, a process that is time-consuming and dependent on expert annotation. Unsupervised learning models, clustering approaches, have been explored as an alternative; however, existing methods typically group students based on activity patterns without generating binary outcome labels such as dropout or success. Furthermore, their effectiveness often depends heavily on the quality of the selected features, and most current solutions utilize only limited or pre-structured subsets of institutional data. This paper addresses these challenges and proposes EHCFE (Enhanced Hierarchical Clustering with Feature Engineering), to automatically generate binary labels from unlabeled educational datasets. EHCFE applies feature engineering by generating new features from the top-ranked features identified during feature selection while retaining the original feature set, thereby improving the quality of the labeling outcomes. The approach is evaluated on three datasets and compared with current and state-of-the-art models using several evaluation metrics, including F1 score, area under the receiver operating characteristic curve (AUC), and silhouette coefficient. Experimental results show that EHCFE achieves the highest F1 score (0.709 and 0.28) and AUC values (0.766 and 0.81) on two datasets. A ranking analysis across six evaluation metrics demonstrates that EHCFE outperforms existing models, achieving the highest average ranks of 1.50 and 1.83 on two datasets and a competitive rank of 1.92 on the third.

Wireless sensor networks consist of a set of smart sensors with limited memory and wireless communication capabilities. These sensors get data from the environment and send them to an application center. However, data loss has happened due to the characteristics of sensors, which negatively affect the accuracy of applications. To solve this problem, we need to estimate the missing data for applications that depend on accurate data collecting. In this study, we present an algorithm that uses the most significant historical data to estimate the missing data based on spatial and temporal correlations. In the proposed algorithm, we combine the spatial correlation by using data from the closest sensor based on the missing pattern and the temporal correlation by referring to the closest data prior to the missing instance. The experimental results demonstrate that the proposed algorithm lowers estimation errors when compared to current algorithms for a variety of missing data patterns.