Provide the latest information of AI research centers and applied industries
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Snippet Policy Network: Knee-Guided Neuroevolution for Multi-Lead ECG Early Classification
We have proposed in this project the first time series classification technique that considers accuracy, earliness, and varied lengths simultaneously, containing a novel deep reinforcement learning framework and a new multi-objective optimization neural network algorithm. The proposed technique is fit for the problem of early classification of cardiovascular diseases based on ECG signals and shown to deliver the best performance in this area, holding the leading position worldwide.
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Data Representation and Learning for Dialogue System
The application of voice assistants is becoming more and more popular, however, due to the inefficiency of artificial intelligence-based technology, current products are mostly built by using rules-based methods. Therefore, in this project, we would like to propose some corresponding solutions for different components of the dialogue system to improve the data efficiency and work efficiency of each component.
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Super-fast Convergence for Radiance Fields Reconstruction
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.
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Integration of an ICU Visualization Dashboard (i-Dashboard) as a Platform to Facilitate Multidisciplinary Rounds
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.
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Machine Learning to Predict In-Hospital Cardiac Arrest in Patients Admitted from the Emergency Department with COVID-19 and Suspected Pneumonia
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).