Electroencephalography (EEG) has been widely used in the research of stress detection in recent years; yet, how to analyze an EEG is an important issue for upgrading the accuracy of stress detection. This study aims to collect the EEG of table tennis players by a stress test and analyze it with machine learning to identify the models with optimal accuracy. The research methods are collecting the EEG of table tennis players using the Stroop color and word test and mental arithmetic, extracting features by data preprocessing and then making comparisons using the algorithms of logistic regression, support vector machine, decision tree C4.5, classification and regression tree, random forest, and extreme gradient boosting (XGBoost). The research findings indicated that, in three-level stress classification, XGBoost had an 86.49% accuracy in the case of the generalized model. This study outperformed other studies by up to 11.27% in three-level classification. The conclusion of this study is that a stress detection model that was built with the data on the brain waves of table tennis players could distinguish high stress, medium stress, and low stress, as this study provided the best classifying results based on the past research in three-level stress classification with an EEG.

The exploration of the performance of elite athletes by cognitive neuroscience as a research method has become an emerging field of study in recent years. In the research of cognitive abilities and athletic performance of elite athletes, the tasks of an experiment are usually performed by athletics task of closed skills rather than open skills. Thus, little has been conducted to explore the cognitive abilities and athletic performance of elite athletes with open skills. This study is novel as it attempts at predicting how table tennis athletes perform by collecting their dynamic brain waves when executing specific plays of table tennis, and then putting the data of dynamic brain waves to deep neural network algorithms. The method of this study begins with the collection of data on the dynamic brain waves of table tennis athletes and then converts the time domain data into frequency domain data before improving the accuracy of categorization using a hybrid convolutional neural networks (CNN) framework of deep learning. The findings hereof were that the algorithm of hybrid deep neural networks proposed herein was able to predict the sports performance of athletes from their dynamic brain waves with an accuracy up to 96.70%. This study contributes to the literature in cognitive neuroscience on dynamic brain waves in open skills and creates a novel hybrid deep CNN classification model for identifying dynamic brain waves associated with good elite sports performance.

Glioblastoma multiforme (GBM) is the most aggressive and common primary brain tumor. Magnetic resonance imaging (MRI) provides detailed visualization of tumor morphology, edema, and necrosis. However, manually segmenting GBM from MRI scans is time-consuming, subjective, and prone to inter-observer variability. Therefore, automated and reliable segmentation methods are crucial for improving diagnostic accuracy. This study employs an image semantic segmentation model to segment brain tumors in MRI scans of GBM patients. The MRI recall images include T1-weighted imaging (T1WI) and fluid-attenuated inversion recovery (FLAIR) sequences. To enhance the performance of the semantic segmentation model, image preprocessing techniques were applied before analyzing and comparing commonly used segmentation models. Additionally, a survival model was constructed using discrete genotype attributes of GBM patients. The results indicate that the DeepLabV3+ model achieved the highest accuracy for semantic segmentation, with an accuracy of 77.9% on T1WI image sequences, while the U-Net model achieved 80.1% accuracy on FLAIR image sequences. Furthermore, in constructing the survival model using a discrete attribute dataset, the dataset was divided into three subsets based on different missing value handling strategies. This study found that replacing missing values with 1 resulted in the highest accuracy, with the Bernoulli Bayesian model and the multinomial Bayesian model achieving an accuracy of 94.74%. This study integrates image preprocessing techniques and semantic segmentation models to improve the accuracy and efficiency of brain tumor segmentation while also developing a highly accurate survival model. The findings aim to assist physicians in saving time and facilitating preliminary diagnosis and analysis.

This study aims to evaluate whether safety nets on construction sites are correctly installed using an image processing and deep learning technique. The developed method performs data preprocessing, including horizontal flipping, rotation, and contrast-limited adaptive histogram equalization, and then applies the YOLO model to estimate the accuracy of safety net installation. The developed method significantly improved the accuracy of the YOLO model detection and mitigated errors associated with large safety net surfaces and slanted steel beams using the novel linear intersection over union as a metric. The proposed method effectively improved the assessment of safety net installation.