| 摘要: | Over the last decade, the eSports industry has experienced significant growth. World-class eSports players now enter contracts with a team, follow a strict training regimen, and compete in tournaments. Just like conventional athletes, most eSports competitors suffer injuries that deeply affect their performance or would prevent them from training or competing. Moreover, the accuracy of performance predictions in existing studies is often constrained by insufficient prediction models. Thus, in this work, an advanced optimization-enabled deep learning approach is modeled for player re-identification and skill prediction. Primarily, the data collected from the eSports Sensor Dataset undergoes a feature selection process. This feature selection is done by using Serial Exponential-Ring TossGame-Based Optimization (SeEXP-RTGBO) method, which combines Serial Exponential Moving Average concept with the Ring Toss Game-Based Optimization (RTGBO) technique. Then, feature fusion is executed by using Morisita's overlap index and Bi-LSTM attention. The main aim of feature fusion is to enhance model performance by combining complementary information from multiple features, which leads to more accurate and robust predictions. Subsequently, skill predictions and player re-identification are established by using Deep Maxout Network (DMN), which is optimized using the proposed SeEXP-RTGBO. Ultimately, an empirical assessment is conducted by evaluating various metrics, including accuracy, sensitivity, and specificity. In this context, the presented approach achieved an accuracy of 0.959, sensitivity of 0.961, and specificity of 0.956, thereby demonstrating superiority over previously established models. |
