龔千芬 副教授
- 系所：智慧商務系
- 研發領域：軟體及應用的開發與分析
- 標籤： 資訊,決策方法,多媒體應用
- 專長： 電子商務,管理決策分析,企業電子化,統計分析與應用,決策數值分析、機器學習、人工智慧、智慧醫療
- 學歷：
國立交通大學管理科學博士
- 經歷：
- 電話： 燕巢 17557
- 傳真：NULL
1
文字與圖片共同學習的探討- 以ImageNet圖像分類任務為例Exploring the Joint Learning of Text and Images: A Case Study on ImageNet Image Classification Task
2
利用機器學習對足底壓力搖晃路徑預測帕金森病人跌倒預警模型之建立Establishing A Prediction Model for Fall Warning for Parkinson's Disease Patients Using Machine Learning with Plantar Pressure Swaying Data
3
利用機器學習對動態足底壓力進行帕金森症患者跌倒嚴重程度預測Prediction of Fall Severity in Parkinson’s Patients with Dynamic Plantar Pressure Using Machine Learning
4
運用機器學習使用步態特徵建立帕金森氏症患者跌倒預測之模型Using machine learning to develop fall prediction models with gait features in Parkinson's disease patients
5
ERP導入及異質系統整合之研究~以A公司為例ERP System implementation and integration ~A case study of the A Corporation, Ltd.
6
使用機器學習對帕金森患者初始步態 進行冰凍步態預測Prediction Freezing of Gait of Parkinson with Gait Initation Using Machine Learning
7
運用機器學習於帕金森症Kinect步態分析之研究Kinect Gait Analysis for Parkinson's Disease Using Machine Learning
8
利用機器學習對動態足底壓力預測帕金森病人冰凍步態之異常Using Machine Learning to Predict Freezing of Gait in Parkinson's Patients with Dynamic Plantar pressure
9
探討神經網路yolo 辨識麵包種類與應用The applications on the breads identification using YOLO
10
應用深度學習於銅片折彎試驗品質判定之研究The Application on the Quality Judgment in the Bend Test of Copper Sheet Using Deep Learning Algorithm
11
使用機器學習針對急診疑似感染症患者進行登革熱預測Using Machine Learning to Predict Dengue Fever for Patients with Suspected Infection in Emergency Department
12
使用機器學習72小時內重複測量Procalcitonin與CRP對嚴重敗血症預後之預測Prediction The Prognosis of Severe Sepsis Using Repeated Measurement of Procalcitonin or CRP Data Within 72 Hours by Machine Learning Algorithm
13
使用機器學習依據患有第二型糖尿病之敗血症患者近期用藥紀錄預測其敗血症之預後Using Machine Learning to Predict the Prognosis of Sepsis Patients with Type 2 Diabetes Mellitus based on Recent Medication Records
14
利用機器學習針對敗血症患者血液中的相關生物標記預測結果Series of biomarker determine the severity of sepsis by machine learning
15
透過AI自動量測並診斷巴金森氏症與進行性核上眼神經麻痺症之研究Automatic measurement and diagnosis of Parkinson’s disease and progressive supranuclear palsy by AI
16
運用機器學習結合整合性資料預測敗血症病患之不良結果Use machine learning with comprehensive data to predict bad outcome of septic patients