Identification of underground formation lithology from well-log data is an important task in petroleum exploration and engineering. Due to the cost or imprecision of some methods applied in this activity, there is a need to automate the procedure of reservoir characterization. Machine learning techniques can be efficient alternatives to lithology identification. To acquire proper performance, usually, some parameters of these techniques should be adjusted, and this can become a hard task depending on the complexity of the underlying problem. This letter integrates the gradient boosting (GB) with a differential evolution (DE) for formation lithology identification using data from the Daniudui gas field and the Hangjinqi gas field. This letter's contributions include the use of an evolutionary algorithm to adjust optimally the hyperparameters of the GB, and the results show improvements when compared with those obtained in the literature.

With the advent of modern educational technologies, detecting student engagement has emerged as a crucial tool for enhancing educational effectiveness and classroom management. To improve the accuracy and efficiency of existing engagement detection systems, this study presents an enhanced model utilizing the YOLOv8 architecture. The proposed framework incorporates the cascading of two YOLOv8 models to refine the search for faces in images, employs a modified ResNet50 for feature extraction at different engagement levels, and finally uses a Support Vector Machine (SVM) classifier to accurately detect student engagement levels based on the features extracted by the modified ResNet50. The integration of additional convolutional layers and fully connected layers within the YOLOv8 architecture optimizes detection capabilities and classification accuracy. For training, we used a comprehensive dataset encompassing various scenarios of student engagement to ensure the robustness of the detection system. Experimental results highlight the superior performance of the model, with a precision of 94.5%, a recall of 92.3%, an mAP0.5 of 92.7%, and an mAP0.5:0.95 of 66.3%, outperforming conventional models such as YOLOv5, ViT, RT-DETR, Faster-RCNN, and ResNet. This framework not only demonstrates significant potential for real-time applications but also represents a considerable advancement in the field of educational monitoring through artificial intelligence. Future research will explore the integration of Explainable Artificial Intelligence (XAI) techniques to further enhance the transparency and interpretability of the model’s decisions, thereby increasing the confidence and acceptance of these technologies in educational settings.

This paper presents the development of hybrid machine learning models to forecast the natural flows of water bodies. Five models were considered under the analysis: Extreme Gradient Boosting (XGB), Extreme Learning Machines (ELM), Support Vector Regression (SVR), Elastic Net linear model (EN), and Multivariate Adaptive Regression Splines (MARS). Grey Wolf Optimization Algorithm (GWO) optimised all of the models’ internal parameters. A feature selection approach was embedded in the hybrid model to reduce the number of input variables. The hybrid model performed the forecasts considering 1, 3, 5 and 7 days ahead on data collected from Cahora Bassa dam, Mozambique. In the experiments conducted in this paper, XGB outperformed EN, ELM, MARS, and SVR, presenting lower prediction error and uncertainty. The proposed XGB model arises as an alternative to help with flow prediction, which is crucial for hydroelectric power plant activity.