Skill Prediction and Player Re-Identification Using Serial Exponential Ring Toss Game-Based Optimization Based Deep Maxout Network
學年 114
學期 1
出版（發表）日期 2025-11-17
作品名稱 Skill Prediction and Player Re-Identification Using Serial Exponential Ring Toss Game-Based Optimization Based Deep Maxout Network
作品名稱（其他語言）
著者 Tzu-Chia Chen
單位
出版者
著錄名稱、卷期、頁數 Concurrency and Computation: practice and experience 37(27-28) p. e70442
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.
關鍵字
語言 zh_TW
ISSN
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 否
國別 TWN
公開徵稿
出版型式 ,電子版
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