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    政大機構典藏 > 商學院 > 資訊管理學系 > 會議論文 >  Item 140.119/137211
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/137211


    Title: Comparison of Fully Connected Net with Particle Swarm Optimization Neural Network and PSO in the Diagnosis of Heart
    Authors: 姜國輝
    Chiang, Johannes K.
    Contributors: 資管系
    Keywords: Artificial Neural Networks (ANN);Particle Swarm Optimization;PSO-ANN;Fully Connected;Heart Disease
    Date: 2021-08
    Issue Date: 2021-09-22 10:20:13 (UTC+8)
    Abstract: This paper proposes an Enhanced Hybrid Particle Swarm Optimization (PSO) with Artificial Neural Network (ANN), which is applied in the diagnosis of heart disease of the common features in University of California, Irvine (UCI) dataset. This UCI data includes 303 test results and consist of 13 features with two classes. One class is with health people and the other class of people are with heart disease. PSO-ANN combined Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN), using ANN`s escaping mechanism to enhance the deficiency of PSO slow convergence and easy to fall into the local optimal solution. The overall search ability is increased and the tracking time is reduced. This paper uses fully connected net with PSO-ANN with Python environment compares with PSO in R, the result demonstrates that the proposed model is better than PSO around 12%.
    Relation: ICIM2021, 中華民國資訊管理學會
    Data Type: conference
    Appears in Collections:[資訊管理學系] 會議論文

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