Construction of 2022 Qatar World Cup match result prediction model and analysis of performance indicators.

This research investigates the influence of performance metrics on match outcomes and constructs a predictive model using data from the Qatar World Cup. Employing magnitude-based decision and an array of machine learning algorithms, such as Decision Trees, Logistic Regression, Support Vector Machines, AdaBoost, Random Forests, and Artificial Neural Network, we examined data from 59 matches, excluding extra time. Fourteen performance indicators were integrated into the model, with two types of match outcomes-winning and non-winning-serving as the output variables.

Research on motion recognition based on multi-dimensional sensing data and deep learning algorithms.

Motion recognition provides movement information for people with physical dysfunction, the elderly and motion-sensing games production, and is important for accurate recognition of human motion. We employed three classical machine learning algorithms and three deep learning algorithm models for motion recognition, namely Random Forests (RF), K-Nearest Neighbors (KNN) and Decision Tree (DT) and Dynamic Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Compared with the Inertial Measurement Unit (IMU) worn on seven parts of body.