Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
本研究旨在提升湖南省盛夏(7、8月)高温过程的延伸期预报技巧。本文利用1999—2022年湖南省97个站点逐日最高气温资料以及次季节-季节(sub-seasonal to seasonal prediction,S2S)模式数据中欧洲中期天气预报中心(ECMWF)和美国国家环境...本研究旨在提升湖南省盛夏(7、8月)高温过程的延伸期预报技巧。本文利用1999—2022年湖南省97个站点逐日最高气温资料以及次季节-季节(sub-seasonal to seasonal prediction,S2S)模式数据中欧洲中期天气预报中心(ECMWF)和美国国家环境预报中心(NCEP)两种模式预报产品,并基于模式温度与环流预报产品提取物理因子,结合卷积神经网络(convolutional neural network,CNN)构建了湖南省盛夏高温过程的预报模型(high temperature prediction model,HTPM);对订正后的S2S模式和构建的预报模型结果进行集成,以实现对区域高温过程较为稳定的相对高技巧预报。结果表明:S2S模式的原始预报技巧较低,偏差订正能显著提高预报效果,但存在较高的空报率;基于ECMWF的S2S数据训练的高温预报模型(HTPM-ECS2S)和基于NCEP的S2S数据训练的高温预报模型(HTPM-NCEPS2S)能有效捕捉高温事件,在高温预报中具有较高的预报技巧;集成方案有效整合了多模型优点,可提升预报的准确性和可靠性。展开更多
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
文摘本研究旨在提升湖南省盛夏(7、8月)高温过程的延伸期预报技巧。本文利用1999—2022年湖南省97个站点逐日最高气温资料以及次季节-季节(sub-seasonal to seasonal prediction,S2S)模式数据中欧洲中期天气预报中心(ECMWF)和美国国家环境预报中心(NCEP)两种模式预报产品,并基于模式温度与环流预报产品提取物理因子,结合卷积神经网络(convolutional neural network,CNN)构建了湖南省盛夏高温过程的预报模型(high temperature prediction model,HTPM);对订正后的S2S模式和构建的预报模型结果进行集成,以实现对区域高温过程较为稳定的相对高技巧预报。结果表明:S2S模式的原始预报技巧较低,偏差订正能显著提高预报效果,但存在较高的空报率;基于ECMWF的S2S数据训练的高温预报模型(HTPM-ECS2S)和基于NCEP的S2S数据训练的高温预报模型(HTPM-NCEPS2S)能有效捕捉高温事件,在高温预报中具有较高的预报技巧;集成方案有效整合了多模型优点,可提升预报的准确性和可靠性。
