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A study conducted by researchers at Henan Normal University in China has proposed a new method for monitoring the training load of athletes using sensor technology and motion image analysis. The researchers combined the advantages of vision-based motion monitoring and wearable sensor-based motion monitoring to create a method that can be used for motion monitoring anytime and anywhere. They also developed a two-stage neural network motion monitoring algorithm to identify different types of motions separately, which improves the accuracy of recognition. Additionally, the study introduced a data enhancement algorithm based on acceleration data to increase the number of samples without the need for re-collecting data. The results of simulation experiments showed that this method can be applied to real-life situations. [Extracted from the article]

A study conducted by researchers at the Nanyang Institute of Technology in Henan, China, proposes a new methodology for the early diagnosis of melanoma skin cancer using dermoscopic images. The method involves scaling and enhancing the quality of the images, extracting features based on the Gray-Level Co-occurrence Matrix (GLCM), and using a newly developed version of the Archimedes Optimization Algorithm (DAOA) to select the most relevant features. The selected features are then classified using a Support Vector Machine (SVM) to distinguish between benign and malignant lesions. The proposed method achieved high values for performance indicators and demonstrated an accuracy of 88%, sensitivity of 96%, specificity of 81%, precision of 97%, and F-measure of 97% for the diagnosis of melanoma. [Extracted from the article]