IECE - Institute of Emerging and Computer Engineers

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    Author 2
    Peiyuan Chen
    Oregon State University
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    Free Access | Research Article | 22 December 2024
    Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis
    by
    Huadong Pang
    ,
    Li Zhou
    ,
    Yiping Dong
    ,
    Peiyuan Chen
    ,
    Dian Gu
    ,
    Tianyi Lyu
    ,
    Hansong Zhang
    IECE Transactions on Intelligent Systematics | Volume 2, Issue 1: 1-13, 2024 | DOI:10.62762/TIS.2025.367320
    Abstract
    In the healthcare sector, the application of deep learning technologies has revolutionized data analysis and disease forecasting. This is particularly evident in the field of diabetes, where the deep analysis of Electronic Health Records (EHR) has unlocked new opportunities for early detection and effective intervention strategies. Our research presents an innovative model that synergizes the capabilities of Bidirectional Long Short-Term Memory Networks-Conditional Random Field (BiLSTM-CRF) with a fusion of XGBoost and Logistic Regression. This model is designed to enhance the accuracy of diabetes risk prediction by conducting an in-depth analysis of electronic medical records data. The fir... More >
    Graphical Abstract
    Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis
    Free Access | Research Article | 08 December 2024
    Optimized CNNs for Rapid 3D Point Cloud Object Recognition
    by
    Tianyi Lyu
    ,
    Dian Gu
    ,
    Peiyuan Chen
    ,
    Yaoting Jiang
    ,
    Zhenhong Zhang
    ,
    Huadong Pang
    ,
    Li Zhou
    ,
    Yiping Dong
    IECE Transactions on Internet of Things | Volume 2, Issue 4: 83-94, 2024 | DOI:10.62762/TIOT.2024.758153
    Abstract
    This study introduces a method for efficiently detecting objects within 3D point clouds using convolutional neural networks (CNNs). Our approach adopts a unique feature-centric voting mechanism to construct convolutional layers that capitalize on the typical sparsity observed in input data. We explore the trade-off between accuracy and speed across diverse network architectures and advocate for integrating an L1 penalty on filter activations to augment sparsity within intermediate layers. This research pioneers the proposal of sparse convolutional layers combined with L1 regularization to effectively handle large-scale 3D data processing. Our method’s efficacy is demonstrated on the MVTec... More >
    Graphical Abstract
    Optimized CNNs for Rapid 3D Point Cloud Object Recognition