"The primary goal of this project is to establish a complete hospital-based liver cancer database, profiles for data feature extraction,develop different cancer, prediction models. A Medical AI program to predict the poster treatment (including operationradiofrequency ablation) recurrence of liver cancer will be established. The program system will assist doctors in the medical decision, identify high-risk patients,adjust clinical follow-up programs."

During the pandemic, it is necessary to reduce contactwear masks, but identification is still required. The original technology includes the development of inertial measurement units beneath the walking surface, real-time detection of gait,accurate predictions of gait parameters with machine learning models. Most importantly, our technology can use a small number of samples (only one footstep data) for identifications. Non-contact, non-monitoring,rapid identification are going to be the future applications in smart buildings/homes.

The system, “Occluded Resistant Face DetectionRecognition System”, contains different scale detectors for calculating face locations. Additive angular margin loss is added into the training phase for achieving high efficiencyaccuracy. The system can achieves 80 accuracy under the 50 face occluded.

We constructed a model to differentiate different neurodegenerative diseases by using blood-based biomarkers. Linear Discriminant Analysis (LDA) was used to reduce dimensionsMICE (Multivariate Imputation by Chained Equations) was used for missing value treatmentchoose CART model to predict missing value imputation. This machine learning model would be used for early dectection of neurodegenerative diseases.