With the maturity of deep learning technology, reading comprehension model (given an article and a question, the AI model automatically finds the answer to the question from the article) has become a key element in natural language applications. Such as knowledge extraction and knowledge graph construction can be solved through reading comprehension model. In this project, we investigate the employment of the reading comprehension model to build an agricultural knowledge graph from Taiwan agricultural literatures. One challenge, however, is that existing reading comprehension models are not tailored for agricultural literature and therefore cannot be used directly. In this project, we leverage the question generation technology as a mechanism for agricultural data augmentation, and then train the literature reading comprehension model in the agricultural field.

This research project aims to establish an intelligent agricultural cultivation support system, by integrating unmanned aerial vehicle (UAV) surveillanceartificial intelligent (AI) analytical techniques.

This research project aims to establish an intelligent agricultural cultivation support system, by integrating unmanned aerial vehicle (UAV) surveillanceartificial intelligent (AI) analytical techniques.

This research project aims to establish an intelligent agricultural cultivation support system, by integrating unmanned aerial vehicle (UAV) surveillanceartificial intelligent (AI) analytical techniques.

This research project aims to establish an intelligent agricultural cultivation support system, by integrating unmanned aerial vehicle (UAV) surveillanceartificial intelligent (AI) analytical techniques.

Multidisciplinary rounds (MDRs) are scheduled, patient-focused communication mechanisms among multidisciplinary providers in the intensive care unit (ICU). The surgical ICU team of National Cheng Kung University Hospital has developed and integrated i-Dashboard as a platform to facilitate MDRs. i-Dashboard is a custom-developed visualization dashboard that supports (1) key information retrieval and reorganization, (2) time-series data, and (3) display on large touchscreens during MDRs. The i-Dashboard increases the efficiency in data gathering and enhances communication accuracy and information exchange in MDRs.

The NeRF-based technique describes a super-fast convergence approach to reconstructing the per-scene radiance field from a set of images that capture the scene with known poses.

By using the machine learning algorithms, this study developed a risk stratification model for predicting the occurrence of in-hospital cardiac arrest (IHCA) events in patients admitted from the emergency department with COVID-19 and pneumonia. The results showed that the model's performance is better than by using the National Early Warning Score (NEWS).

Our team develops an accurate PWV estimation algorithm that uses wrist PPG and ECG signals from wearable devices. A missing-feature imputation and ambiguous-feature resolution technique is developed and the availability of wrist PPG morphological features is raised from 60% to 99.1%. A weighted pulse decomposition approach is adopted and 5 component waves can be acquired to examine more detailed properties. The PWV is then estimated by XGBoost algorithm with the hierarchical regression model.

The Pericardium/Aorta Segmentation and Cardiovascular Risk Prediction AI Total Solution Model, HeaortaNet, is a deep learning model based on UNet and attention gate, and had been trained by >70,000 axial images with verified annotations of the pericardium and aorta. It shortens the time for data processing from 60 minutes, by manual segmentation of both pericardium and aorta, to 0.4 seconds. The segmentation accuracy is 94.8% for the pericardium, and 91.6% for the aorta. The applicability of HeaortaNet had been demonstrated by analyzing the non-contrast chest CT scans (>5,000 cases) deposited in the mega-image bank of National Health Insurance Databank.