Feature Selection Based Twin-Support Vector Machine for the Diagnosis of Parkinson’s Disease-Reference-Cited by-同舟云学术

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Feature Selection Based Twin-Support Vector Machine for the Diagnosis of Parkinson’s Disease

Published:2020-12-01 Issue: Volume: Page:
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Container-title:2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC)
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Author:

Thapa Surendrabikram,Adhikari Surabhi,Ghimire Awishkar,Aditya Anshuman

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9356869/9356950/09356984.pdf?arnumber=9356984

Cited by 23 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Correlation-Based Weight Algorithm for Diagnosing Parkinson’s Induced Voice Disorder;SN Computer Science;2024-08-29

2. Twin support vector machines based on chaotic mapping dung beetle optimization algorithm;Journal of Computational Design and Engineering;2024-04-22

3. Exploring Computational Models for Parkinson’s Disease Diagnosis: Unveiling Insights with LIME and SHAP Explainability Techniques;2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS);2024-04-18

4. DFS-WR: A novel dual feature selection and weighting representation framework for classification;Information Fusion;2024-04

5. Parkinson classification neural network with mass algorithm for processing speech signals;Neural Computing and Applications;2024-03-05

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