Maximizing the use of explosives is crucial for optimizing blasting operations, significantly influencing productivity and cost-effectiveness in mining activities. This work explores the incorporation of machine learning methods to predict powder factor, a crucial measure for assessing the effectiveness of explosive deployment, using important rock characteristics. The goal is to enhance the accuracy of powder factor prediction by employing machine learning methods, namely decision tree models and artificial neural networks. The analysis finds key rock factors that have a substantial impact on the powder factor, hence enabling more accurate planning and execution of blasting operations. The analysis uses data from 180 blast rounds carried out at a dolomite mine in south-south Nigeria. It incorporates measures such as root mean square error (RSME), mean absolute error (MAE), R-squared (R²), and variance accounted for (VAF) to determine the best models for predicting powder factor. The results indicate that the decision tree model (MD4) outperforms alternative approaches, such as artificial neural networks and Gaussian Process Regression (GPR). In addition, the research presents an efficient artificial neural network equation (MD2) for estimating the values of optimum powder factor, demonstrating outstanding blasting fragmentation. In conclusion, this research provides significant information for improving the accuracy of powder factor prediction, which is especially advantageous for small-scale blasting operations.

The significance of human activity recognition (HAR) has increased in recent years because of its wide-ranging applications in areas such as healthcare, security and surveillance, entertainment, and intelligent settings. A significant challenge in computer vision is the automated and accurate recognition of human actions. This survey presents the last related work conducted over the 2015–2023 years in various areas of human activity recognition. It introduces the classification of HAR basic methodologies. In general, HAR approaches are categorized into two primary groups: sensor-based and vision-based HAR. This classification is based on the type of created data and the system environment itself. Based on our study, smart phones, smart watches, accelerometers, gyroscopes, and arm bands are all examples of sensor-based techniques. For the vision-based techniques, there are cameras, Microsoft Kinect, and thermal cameras. Also categorized the general datasets referenced in academic papers are based on the type of activity: individual actions, behaviors, interactions, and group activities. Subsequently, the preprocessing and feature engineering procedures are demonstrated. At long last, this review is able to offer some study concepts that investigate and analyze HAR.