劉建良

Controllable Makespan–Tardiness Trade-offs in Job-Shop Scheduling via Soft-Label Behavioral Cloning and Inference-Time Search

工業工程與管理學系

作業排程、多目標、推論時序搜尋、圖像神經網路、行為模仿、Job Shop Scheduling、Multi-Objective Optimization、Inference-Time Search、Graph Neural Networks、Behavioral Cloning

Hybrid Local-Global Representation Learning for Predicting Formation Energy in Defective Black Phosphorus via an ALIGNN-Transformer Architecture

工業工程與管理學系

形成能預測、缺陷黑磷、原子線圖神經網路、Transformer、圖神經網路、自注意力機制、SHAP、Formation energy per site、Defective black phosphorus、ALIGNN、Graph neural network、Self-attention

A Vehicle-Aware Graph Reinforcement Learning Approach for the Min-Max Heterogeneous Capacitated Vehicle Routing Problem

工業工程與管理學系

異質容量車輛路徑規劃、圖注意力網路、神經組合優化、候選節點檢索、策略梯度、Heterogeneous Capacitated Vehicle Routing Problem、Graph Attention Network、Neural Combinatorial Optimization、Candidate Node Retrieval、Policy Gradient

Offline Reinforcement Learning For Job-Shop Scheduling: A Decision Transformer Approach

工業工程與管理學系

零工式排程問題、決策轉換器、離線強化學習、圖神經轉換器網路、智慧製造、Job-Shop Scheduling Problem、Decision Transformer、Offline Reinforcement Learning、Graph Transformer Network、Smart Manufacturing

CLS: A Closed-Loop Skill-Based LLM Framework for Interactive Analog IC Layout Design

管理學院專班-工業工程與管理學程・共同指導：陳宏明

類比積體電路佈局、大型語言模型、閉迴路技能式框架、意圖分派、技能式解析、佈局指標回饋、Analog IC layout、Large Language Models、closed-loop skill-based framework、intent dispatch、skill-based parsing、layout-metric feedback

Unsupervised Anomaly Detection for Industrial Processes via Temporal Modeling with Mahalanobis Distance: A Case Study on the Tennessee Eastman Process

管理學院專班-工業工程與管理學程

異常偵測、無監督學習、自編碼器、長短期記憶網路、馬氏距離、田納西伊士曼製程、Anomaly Detection、Unsupervised Learning、Autoencoder、Long Short-Term Memory、Mahalanobis Distance、Tennessee Eastman Process

A Study of Transfer Learning Strategies for Tea Leaf Disease and Pest Recognition under Different Image Conditions

管理學院專班-工業工程與管理學程

茶葉病蟲害辨識、卷積神經網路、跨資料集評估、遷移學習、分階段微調、資料分布差異、EfficientNet-B0、tea leaf disease and pest recognition、convolutional neural networks、cross-dataset evaluation、transfer learning、staged fine-tuning、dataset shift

A Machine Learning Approach to Forecasting Cryptocurrency Market Capitalization Based on Macroeconomic Factors

管理學院專班-工業工程與管理學程

加密貨幣、機器學習、宏觀經濟變數、Cryptocurrency、Machine Learning、Macroeconomic Features

Predicting Corporate Profitability and EPS with ESG and Financial Indicators

管理學院專班-工業工程與管理學程

ESG評分、ESG預測、財務預測、公司治理、總體經濟變數、ESG scores、ESG-based prediction、financial forecasting、corporate governance、macroeconomic variables

RL-CLIPAD: Reinforced Prompt Learning for Few-Shot Anomaly Detection with Vision-Language Models

工業工程與管理學系

少樣本異常檢測、視覺-語言模型、大型語言模型、強化學習、多模態學習、Few-shot Anomaly detection、Vision-Language Models、Large Language Models、Reinforcement learning、Multimodal learning

Routing-Aware Mixture-of-Experts for Multi-Class Unsupervised Anomaly Detection in Medical Imaging

工業工程與管理學系

非監督式異常檢測、多專家架構、最適傳輸、Vision Transformer、多類別分類、Unsupervised Anomaly Detection、Mixture of Experts、Optimal Transport、Multi-Class Classification

A SoftTime-Enhanced Query-Based Audio-Visual Transformer for Temporal Action Recognition

工業工程與管理學系

未剪裁影片、影音分析、深度學習、多模態學習、Transformer、Untrimmed Video、Audio-Visual Learning、Deep Learning、Multimodal Learning

A Hybrid Approach to Automated Object Placement Sequence Optimization in Retail Stores Using Deep Reinforcement Learning and Constraint Programming

工業工程與管理學系

設施規劃、限制式規劃、深度強化學習、多模態模型、Facility Layout Problem、Constraint Programming、Deep Reinforcement Learning、Multimodal Models

Synergizing Deep Reinforcement Learning and Graph Neural Networks For Optical Network Routing Optimization

工業工程與管理學系

光傳輸網路、軟體定義網路、深度 Q 網路、圖神經網路、約束規劃、變壓器模型、Software-defined networking、Graph neural network、Optical transport network、Constraint Programming、Transformer、Deep Q-network

Artificial Intelligence for Solving Dynamic Scheduling Problems

工業工程與管理學系

平行機台排程問題、零工式排程問題、人工智慧、機器學習、深度學習、深度強化學習、圖注意力網路、Parallel machine scheduling problem、Job shop scheduling problem、Artificial intelligence、Machine learning、Deep learning、Deep reinforcement learning、Graph attention network

Applying Machine Learning Modeling to Predict the Impact of Early Life Risk Factors on Pediatric Chronic Kidney Disease

工業工程與管理學系

兒童慢性腎臟病、生命早期風險因子、類別不平衡、機器學習、可解釋模型(SHAP)、Pediatric chronic kidney disease、Early-life risk factors、Imbalance dataset、Machine learning、Explainable models (SHAP)

Enhancing Motor Imagery BCI: Integrating Dual-Domain Analysis with Attention Mechanisms

工業工程與管理學系

腦電圖、運動想像、腦機介面、深度學習、注意力機制、Electroencephalography、Attention Mechanisms、Deep Learning、Motor Imagery、Brain-Computer Interfaces

Applying Machine Learning to Predict the Impact of Maternal Medication Before and During Pregnancy on Preterm Birth and Low Birth Weight

工業工程與管理學系

機器學習、電子健康紀錄、低出生體重、小於妊娠年齡、早產、SHAP、Machine Learning、Electronic health records、Low Birth Weight、Small for Gestational Age (SGA)、Preterm Birth

STAR-net : A Spatial Temporal Attention Residual Networks For Automatic 12-Lead ECG Signals Diagnosis

工業工程與管理學系

深度學習、心電圖辨識、神經網路、殘差網路、Deep Learning、ECG Classification、Neural Network、Residual Network

Dynamic Job-Shop Scheduling Problem via Heterogeneous Graph Neural Networks and Deep Reinforcement Learning

工業工程與管理學系

動態零工式排程、異構圖神經網路、深度強化學習、智慧生產、Dynamic Job-Shop Scheduling、Heterogeneous Graph Neural Network、Deep Reinforcement Learning、Smart Manufacturing

Building a Predictive Model for Kidney Replacement Therapy Occurrence Using Machine Learning and Transfer Learning

工業工程與管理系所

腎臟病、機器學習、腎臟替代治療、遷移學習、kidney disease、machine learning、Kidney Replacement Therapy、transfer learning

Using Machine Learning to Develop a Risk Score in Predicting Long-Term Clinical Outcome for Stroke Patients

工業工程與管理系所

中風、機器學習、LightGBM、預後結果、LIME、部分依賴圖、SHAP、Stroke、Machine Learning、clinical outcome、Partial Dependence Plots

Dynamic Job Shop Scheduling Problems using Graph Neural Network and Deep Reinforcement Learning

工業工程與管理系所

零工式排程問題、動態排程、即時排程、圖神經網路、深度強化學習、Job Shop Scheduling Problem、Dynamic Scheduling、Real-time Scheduling、Graph Neural Networks、Deep Reinforcement Learning

Comparison of Deep Learning Models for COVID-19 Pneumonia Detection and Classification From Chest X-Rays

管理學院工業工程與管理學程

新冠肺炎、深度學習、卷積神經網路、COVID-19、Deep learning、Convolutional neural network

Using Supervised Machine Learning and Multivariate Regression in Glucose Monitoring

管理學院工業工程與管理學程

血糖、ICU、機器學習、mid IR、SHapley Additive exPlanations(SHAP)、Blood glucose、Machine Learning、SHapley Additive exPlanations (SHAP)

WResHDual-ViT: A Multimodality Model Combining Spatial-Temporal and Frequency Representations for Improved ECG Classification

工業工程與管理系所

心電圖分類、多模態模型、時空表示、頻率表示、離散小波包變換、階層雙重注意力Transformer、ECG classification、Multimodality model、Spatial-temporal representation、Frequency representation、Discrete wavelet packet transformation、Hierarchical dual attention Transformer

Solar Photovoltaic Energy Prediction from All-Sky Images with Factorize Transformer

工業工程與管理系所

再生能源、深度學習、太陽能光伏發電預測、Transformer、Renewable energy、Solar Power Prediction、Deep Learning

Improving Anomaly Detection and Localization with Reverse Distillation

工業工程與管理系所

深度學習、計算機視覺、異常偵測、異常定位、逆向蒸餾、Deep Learning、Computer Vision、Anomaly Detection、Anomaly Localization、Reverse Distillation

Forecasting and Explaining the Impact of Air Quality on Solar Panel Power Generation using Machine Learning Techniques

工業工程與管理系所

再生能源、太陽能、機器學習、SHAP 分析、無母數分析、renewable energy、solar energy、SHAP、non-parametric analysis

Enhancing Predictive Maintenance through Wavelet Packet Transformation and Self-Supervised Deep Learning

工業工程與管理系所

剩餘使用壽命、對比學習、自注意力機制、小波轉換、自監督式學習、remaining useful life、contrastive learning、self-attention、wavelet transform、self-supervised learning

Identification of Patient Impact of Pringle Maneuver during Laparoscopic Surgery Using Trajectory Analysis

工業工程與管理系所

原發性肝癌、長期追蹤資料、潛在類分析、增長混和模型、潛在類增長分析、軌跡分析、Primary liver cancer、Longitudinal Data、Latent Class Model、Growth Mixture Model、Latent Class Growth Analyze

Imbalanced Machine Learning Techniques for Prediction of hypotension on hemodialysis

工業工程與管理系所

類別不平衡、時間序列、機器學習、透析中低血壓、集成學習、極限梯度提升、資料抽樣、Imbalanced data、time-series data、machine learning、Intradialytic hypotension、class imbalance、ensemble learning、XGBoost、subsampling

Machine Learning Algorithms for Bleeding Risk Prediction Model After Oral Anticoagulant Therapy

工業工程與管理系所

心房顫動、口服抗凝血劑、出血、機器學習、生存模型、出血風險評分、Atrial Fibrillation、Oral anticoagulant、bleeding、Machine Learning、Survival Model、HASBLED

Machine Learning for Prediction and Risk Stratification of Adverse Kidney Outcome Post-acute Kidney Injury

工業工程與管理系所

急性腎損傷、長期追蹤資料、潛在類分析、增長混合模型、聯合潛在類模型、軌跡分析、Acute Kidney Injury、Longitudinal Data、Growth Mixture Model、Joint Latent Class Model、Trajectory Analyze、Latent Class Model

Long-term Building Load Forecasting using Spatial-Temporal Attention Model

工業工程與管理系所

長時間序列預測、建築物用電、空間及時間注意力、LSTM、週期性萃取、稀疏注意力、Long-term Forecasting、Building Load、Spatio-Temporal Attention、Periodic extraction、ProbSparse Attention

Noncontact Estimation of Remote Photoplethysmography Based on Facial Videos Using Deep Learning

工業工程與管理系所

遠程光電容積描記術、端到端估計、脈衝波、卷積神經網絡、Remote Photoplethysmography、End-to-end estimation、pulse wave、Convolutional neaural network

Feature Selection Methods Using SHAP Importance for Predicting Fever of ICU Patients in Machine Learning

工業工程與管理系所

機器學習、特徵選擇方法、可解释性、SHAP、Gradient Boosting、Machine learning、Feature selection、Explainabilityv

Using Machine Learning Approach in Prediction of Recurrent Stroke

工業工程與管理系所

中風、中風復發、機器學習、危險因子、SHapley Additive exPlanations(SHAP)、Stroke、Recurrent Stroke Prediction、Machine Learning、Risk Factors

Convolution-augmented Attention Mechanism for Wind Power Prediction

工業工程與管理系所

注意力、遞迴神經網路、再生能源、風力預測、卷積網路、Attention、Recurrent neural networks、Renewable energy、Wind power forecast、Convolutional neural networks

Training Framework of Deep Learning Model For Abnormal User Detection

工業工程與管理系所

深度學習、異常偵測、資料分析、不平衡資料、使用者分類、Deep Learning、Fraud Detection、Data Analysis、Imbalance Data、User Classification

A Density-based Sampling Method for Data Imbalance Problems

工業工程與管理系所

資料不平衡、資料合成、監督式自動編碼器、Imbalanced Data、Synthetic Data、Supervised Autoencoder

Identifying Premature Heart Aging with Deep Neural Network Based on Electrocardiograms

工業工程與管理系所

心臟年齡、心電圖、深度學習、殘差網路、擠壓-激勵式模塊、多任務學習、Biological Heart Age、Electrocardiogram、Deep Learning、Residual Network、Squeeze-and-Excitation Block、Multitask Learning

Job Shop Scheduling Problems with different arrival time using Deep Reinforcement Learning based on Actor-Critic

工業工程與管理系所

零工式排程問題、深度強化學習、深度學習、強化學習、job shop scheduling problem、deep reinforcement learning、deep learning、reinforcement learning、actor-critic、or-library

Deep Metric Learning for Unsupervised Electrocardiogram Representation and Phenotyping

數據科學與工程研究所・共同指導：曾新穆

心電圖分析、深度學習、無監督式學習、特徵學習、度量學習、Electrocardiogram analysis、deep neural network、unsupervised learning、representation learning、metric learning

Parallel Machine Scheduling with Deep Q-network Algorithm

工業工程與管理系所

平行機台調度問題、機台負荷平衡、強化學習、DQN 演算法、parallel machine scheduling problems、machine load balancing、reinforcement learning、DQN algorithm

Classification of Arrhythmias with 12-lead ECG using Deep Learning

工業工程與管理系所

12 導程⼼電圖、分類模型、深度學習、卷積神經網路、⻑期短期記憶、特徵標準化、12-lead ECG、Classification Model、Deep Learning、CNN、LSTM、Normalization

Multi-label Classification with Multi-task learning - A case study of Natural Language Processing

工業工程與管理系所

深度學習、機器學習、多任務學習、⾃然語⾔處理、Deep learning、Machine learning、Multi-task learning、Natural language processing

Job Shop Scheduling with A Generative Adversarial Imitation Learning

工業工程與管理系所

零工式排程、模仿學習、強化學習、生成對抗網路、Job Shop Scheduling、Imitation Learning、Reinforcement Learning、Generative Adversarial Nets

Novel Metric-based Meta Learning Algorithms for Few-shot Learning

工業工程與管理系所

少樣本學習、元學習、度量學習、原型網絡、few-shot learning、meta learning、metric learning、prototypical networks

Semi-supervised Regression with Metric Learning

工業工程與管理系所

半監督式學習、深度學習、度量學習、迴歸問題、Semi-Supervised Learning、Deep Learning、Metric Learning、Regression

Classification of Atrial Flutter and Atrial Fibrillation with 12-lead ECG

管理學院工業工程與管理學程

心電圖分類、十二導程心電圖、深度學習、卷積神經網路、ECG classification、12-lead ECG、deep learning、convolutional neural network

Semi-supervised learning with triplet network

工業工程與管理系所

卷積神經網絡、機器學習、圖像分類、半監督、convolutional neural network、machine learning、image classification、semi-supervised

Predicting the Renal Recovery of Hospital Discharged Patients with Acute Kidney Injury Using Machine Learning

工業工程與管理系所

急性腎損傷、腎功能復原、機器學習、預測模型、可解釋性、Acute Kidney Injury (AKI)、Renal Function Recovery、Machine Learning、Prediction Model、Explainable Artificial Intelligence

Classification of Rare Cardiovascular Diseases with Multi-Task Learning

工業工程與管理系所

心電圖、深度學習、多任務學習、遷移式學習、ECG、Deep Learning、Multi-Task Learning、Transfer Learning

The estimation of production capacity using machine learning and dispatching rules

工業工程與管理系所

調度問題、基因演算法、微影 (黃光) 製程、機器學習、Scheduling Problem、Lithography、Genetic Algorithm、Machine Learning

Deep Reinforcement Learning for Solving Job Shop Scheduling Problems

工業工程與管理系所・共同指導：林春成

零工式排程問題、深度學習、強化學習、job shop scheduling problem、deep learning、reinforcement learning

ECG Classification with Siamese Network

工業工程與管理系所

十二導程心電圖、深度學習、卷積神經網路、殘差網路、轉移學習、孿生網路、注意力機制、12-lead ECG signal、deep learning、convolution neural network、residual network、transfer learning、Siamese Network、attention based

Predicting Wafer Material Removal Rate for Semiconductor Chemical Mechanical Polishing using Machine Learning

工業工程與管理系所

化學機械平坦化、機器學習、預測模型、評分模型、Chemical mechanical polishing (CMP)、Machine learning (ML)、Model prediction、Scoring model

Deep Discriminative Features Learning and Sampling for Imbalanced Data Problem

資訊科學與工程研究所・共同指導：曾新穆

資料不平衡問題、人造資料樣本、特徵嵌入空間、中心損失函數、三重損失函數、Imbalanced Data、Synthetic Sampling、Feature Embedding、Center Loss、Triplet Loss

Multivariate Time Series Early Classification with Interpretability using Attention Mechanism

資訊科學與工程研究所・共同指導：曾新穆

時間序列之早期預測、類神經網路、注意力機制、early classification on time-series、neural network、attention

The prediction of Chronic Kidney Disease stages and mining risk factors using machine learning

工業工程與管理系所

慢性腎臟病、機器學習、資料探勘、預測模型、風險因素、Chronic Kidney Disease (CKD)、Machine learning、Data mining、Model prediction、Risk factors

Predicting short-term survival after liver transplantation using machine learning

工業工程與管理系所

風險分數、機器學習、隨機森林、肝臟移植、生存率、特徵選取、缺失值、Risk Score、Machine Learning、Random Forest、Liver Transplantation、Survival Rate、Feature Selection、Missing Value

Deep 3D Convolutional Neural Network Architecture for Alzheimer's Disease Diagnosis

工業工程與管理系所

診斷阿茲海默、深層 3D 捲積神經網路、深層殘留網絡、神經影像、機器學習、Alzheimer’s Disease Diagnosis、3D Convolutional Neural Network、Deep Residual Network、Neuroimaging、Machine Learning

Hypothesis Selection for Ensemble Learning

資訊科學與工程研究所・共同指導：莊仁輝、李嘉晃

整體式學習、假設選擇、假設發散、階層式假設結構、Ensemble Learning、Hypothesis Selection、Hypothesis Divergence、Hypothesis Hierarchical Structure

Click Through Rate Prediction Using Character-level Wide and Deep Convolutional Neural Network

多媒體工程研究所・共同指導：莊仁輝、李嘉晃

類神經網路、卷積類神經網路、超出詞彙問題、neural network、convolutional neural network、out of vocabulary

Unpaired Translation between Images and Texts Using Generative Adversarial Networks

資訊科學與工程研究所・共同指導：莊仁輝、李嘉晃

類神經網路、深度學習、產生性對抗式網路、Neural networks、Deep learning、Generative adversarial networks

Multivariate Convolutional Neural network for time series classification

資訊科學與工程研究所・共同指導：莊仁輝、李嘉晃

時間序列、機器學習、深度學習、類神經網路、卷積類神經網路、多變量、time series、machine learning、deep learning、neural network、convolutional neural network、multivariate

Developing a practical warning model for postoperative acute rejection of liver transplant patient

工業工程與管理系所

肝臟移植、急性排斥、資料探勘、整體式學習於選取特徵、連續時間決策樹、Liver transplantation、Acute rejection、Data mining、Feature selection for Ensemble learning、Continuous time decision tree

Model-Based Synthetic Sampling for Imbalanced Data

工業工程與管理系所

以模型為基礎之資料生成法、分類問題、不平衡資料、不平衡問題、Model-based synthetic sampling (MBS)、Classification、Imbalanced data、Imbalance problem

Developing a Scoring System for Acute Allograft Rejection of Liver Transplantation.

工業工程與管理系所

評分系統、肝臟移植、急性排斥、特徵選取、隨機森林、羅吉斯迴歸、Scoring System、Liver Transplantation、Acute Rejection、Feature Selection、Random Forest、Logistic Regression

Optimizing the Latency and Cost of Software-Defined Vehicular Networks

工業工程與管理系所・共同指導：林春成

車載隨意網路、軟體定義車載網路、延遲時間、改良型基因演算法、基因演算法、VANET、SDVN、latency、IGA、GA

Estimating the Effect of Spatially Clustered Shopping on Purchase of Convenience Goods

管理科學系所・共同指導：包曉天

地理行銷、空間群聚購物行為、傾向評分匹配、個人化購買熱區、空間便利、線性機率模型、Geomarketing、spatially clustered shopping behavior、Propensity Score Matching、personalized shopping hotspot、spatial convenience、Linear Probability Model

Semantics-based Image Retrieval

資訊科學與工程研究所・共同指導：李嘉晃、莊仁輝

基於內容的圖像檢索、類神經網路、深度學習、卷積類神經網路、基於區域的卷積類神經網路、潛在語意空間、Content-based Image Retrieval、Neural Network、Deep Learning、Convolution Neural Network、Region-based Convolutional Network、Latent Semantic Space

Predict the Winning Price of Real-Time Bidding Based on Locality

多媒體工程研究所・共同指導：李嘉晃、莊仁輝

即時競價、得標價、需求方平台、Real-Time Bidding、Winning Price、Demand Side Platform

Mood Based Video Background Music Recommendation via Latent Factor

多媒體工程研究所・共同指導：李嘉晃

背景音樂推薦系統、基於情緒的、潛在因素、Background music recommendation system、mood based、latent factor model

Feature-Based Ensemble Clustering with Indian Buffet Process

資訊科學與工程研究所・共同指導：李嘉晃、莊仁輝

印度餐廳過程、整合分群、特征抽取、Indian buffet process、ensemble clustering、feature subsets selection

Epilepsy prediction with convolutional neural network

資訊科學與工程研究所・共同指導：莊仁輝

癫痫病、卷积神经网络、深度学习、epilepsy prediction、deep learning、transfer learning

Max-Margin Sparse Coding with Universum

資訊科學與工程研究所・共同指導：李嘉晃

分類、支持向量機器、稀疏編碼、Classi cation、SVM、U-SVM、Universum Learning、Sparse Coding、Block Coordinate Descent

User Behavior Analysis via Clustering with Automatic Tagging

多媒體工程研究所・共同指導：李嘉晃

分群、標籤、無母數、clustering、taging、nonparametric、labeling

Bayesian Exploratory Clustering Based on Chinese Restaurant Process

資訊科學與工程研究所・共同指導：李嘉晃

中國餐廳過程、無母數模型、分群法、non-parametric、clustering、Chinese Restaurant Process

Text Mining with Semi-Supervised Learning

資訊科學與工程研究所・共同指導：李嘉晃

機器學習、監督式學習、非監督式學習、半監督式學習、Universum學習、稀疏編碼、Machine Learning、Supervised Learning、Unsupervised Learning、Semi-supervised Learning、Universum Learning、Sparse Coding

Maximum Margin Sparse Coding

資訊科學與工程研究所・共同指導：李嘉晃

最大化邊界、稀疏編碼、坐標下降法、Max-margin、Sparse Coding、coordinate descent

Fast Recommendation on Latent Collaborative Relations

資訊科學與工程研究所・共同指導：李嘉晃

協同過濾、潛在因素、快速推薦、Locality sensitive hashing、Collaborative filtering、Latent Factor、Fast recommendation

Bayesian Nonparametric Multi-Label Clustering

資訊科學與工程研究所・共同指導：李嘉晃

貝氏無母數、多重標記、Bayesian Nonparametric、Multi-Label

A Group-Based Trajectory Analysis of the Association between Preventive Health Care Awareness and Mammography Screening

醫務管理研究所・共同指導：錢慶文

預防保健意識、預防保健、乳房攝影、乳癌、群組軌跡模型、健康狀態、preventive health care awareness、preventive health care、mammography、breast cancer、group-based trajectory model、health status