IECE - Institute of Emerging and Computer Engineers

  • Sign Up Log in
    Log in
    • [email protected]
    Author
    Contributions by role
    Author 2
    ORCID
    0009-0003-0852-6023
    Weicheng Sun
    Huazhong Agricultural University
    Summary
    Edited Journals
    IECE Contributions
    Articles
    2
    Edited
    0
    Free Access | Research Article | 23 May 2024
    Machine Learning-Based Prediction of Cardiovascular Diseases
    by
    Weicheng Sun
    ,
    Ping Zhang
    ,
    Zilin Wang
    ,
    Dongxu Li
    IECE Transactions on Internet of Things | Volume 2, Issue 2: 50-54, 2024 | DOI:10.62762/TIOT.2024.128976
    Abstract
    With the rapid development of artificial intelligence, extracting latent information from medical data has become increasingly critical. Cardiovascular disease is now a major threat to human health, being one of the leading causes of mortality. Therefore, developing effective prediction methods for cardiovascular diseases is urgently needed. Current medical approaches primarily focus on disease detection rather than prediction, which limits early intervention. By leveraging computational methods, it is possible to predict cardiovascular disease in advance, enabling timely treatment and potentially reducing the disease’s impact. This study employs machine learning techniques, including Supp... More >
    Graphical Abstract
    Machine Learning-Based Prediction of Cardiovascular Diseases
    Free Access | Research Article | 12 February 2024
    Application of Dimension Reduction Methods to High-Dimensional Single-Cell 3D Genomic Contact Data
    by
    Zilin Wang
    ,
    Ping Zhang
    ,
    Weicheng Sun
    ,
    Dongxu Li
    IECE Transactions on Internet of Things | Volume 2, Issue 1: 20-25, 2024 | DOI:10.62762/TIOT.2024.186430
    Abstract
    The volume and complexity of data in various fields, particularly in biology, are increasing exponentially, posing a challenge to existing analytical methods, which often struggle with high-dimensional data such as single-cell Hi-C data. To address this issue, we employ unsupervised methods, specifically Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), to reduce data dimensions for visualization. Furthermore, we assess the information retention of the decomposed components using a Linear Discriminant Analysis (LDA) classifier model. Our findings indicate that these dimensionality reduction techniques effectively capture and present information not r... More >
    Graphical Abstract
    Application of Dimension Reduction Methods to High-Dimensional Single-Cell 3D Genomic Contact Data