首页|Research on Machine Learning Detailed by a Researcher at PLA Strategic Support F orce Information Engineering University (Prediction of image interpretation cogn itive ability under different mental workloads: a task-state fMRI study)
Research on Machine Learning Detailed by a Researcher at PLA Strategic Support F orce Information Engineering University (Prediction of image interpretation cogn itive ability under different mental workloads: a task-state fMRI study)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news originating from Zhengzhou, People's Republic of China, by NewsRx correspondents, research stated, "Visual imaging ex perts play an important role in multiple fields, and studies have shown that the combination of functional magnetic resonance imaging and machine learning techn iques can predict cognitive abilities, which provides a possible method for sele cting individuals with excellent image interpretation skills." Financial supporters for this research include Sti2030-major Projects; National Natural Science Foundation of China. The news reporters obtained a quote from the research from PLA Strategic Support Force Information Engineering University: "We recorded behavioral data and neur al activity of 64 participants during image interpretation tasks under different workloads. Based on the comprehensive image interpretation ability, participant s were divided into two groups. general linear model analysis showed that during image interpretation tasks, the high-ability group exhibited higher activation in middle frontal gyrus (MFG), fusiform gyrus, inferior occipital gyrus, superio r parietal gyrus, inferior parietal gyrus, and insula compared to the low-abilit y group. The radial basis function Support Vector Machine (SVM) algorithm shows the most excellent performance in predicting participants' image interpretation abilities (Pearson correlation coefficient = 0.54, R2 = 0.31, MSE = 0.039, RMSE = 0.002). Variable importance analysis indicated that the activation features of the fusiform gyrus and MFG played an important role in predicting this ability. "
PLA Strategic Support Force Information Engineering UniversityZhengzhouPeople's Republic of ChinaAsiaCyborgsEm erging TechnologiesMachine Learning
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