Uncertainty Modeling for Multicenter Autism Spectrum Disorder Classification Using Takagi–Sugeno–Kang Fuzzy Systems

Hu, Zhongyi; Jun, Wang; Zhang, Chunxiang; Luo, Zhenzhen; Luo, Xiangfeng; Xiao, Lei

doi:10.1109/tcds.2021.3073368

Cited by 41 publications

(12 citation statements)

References 59 publications

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“…Owing to its strong optimization capability, the proposed GOMGBO can also be applied to other optimization problems, such as regression tasks, 141 medical diagnosis, [142][143][144][145] covert communication systems, [146][147][148] service ecosystem, 149,150 image editing, [151][152][153] energy storage planning and scheduling, 154 social recommendation and quality-of-service (QoS)-aware service composition, [155][156][157] active surveillance, 158 pedestrian dead reckoning, 159 evaluation of human lower limb motions, 160 image super resolution, [161][162][163] sentiment classification, 164 data-to-text generation, 165 crowd sensing, 166 and feature selection. [167][168][169]…”

Section: Experimental Results Of the Mammographic Data Setmentioning

confidence: 99%

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Qiao

Shan

Jiang

et al. 2021

Int J of Intelligent Sys

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Gradient‐based optimizer (GBO) is a metaphor‐free mathematic‐based algorithm proposed in recent years. Encouraged by the gradient‐based Newton's method, this algorithm combines with population‐based evolutionary methods. The disadvantage of the traditional GBO algorithm is that the global search ability of the algorithm is too strong, and the local search ability is too weak; accordingly, it is difficult to obtain the global optimal solution efficiently. Therefore, a new improved GBO algorithm (GOMGBO) is developed to mitigate such performance concerns by introducing a Gaussian bare‐bones mechanism, an opposition‐based learning mechanism, and a moth spiral mechanism enhanced GBO algorithm. The proposed GOMGBO has been compared against many famous methods and improved variants on 30 benchmark functions. The experimental results show that GOMGBO has apparent advantages in convergence speed and precision. In addition, this paper analyzes the balance and diversity of the GOMGBO algorithm and compares GOMGBO with other algorithms on several engineering problems. The experimental results show that the GOMGBO algorithm is also better than the competitive algorithm in engineering problems. This study uses the GOMGBO algorithm to optimize kernel extreme learning machine (KELM), and a new GOMGBO‐KELM model is proposed. The model is used to deal with four clinical disease diagnosis problems. Compared with GBO‐KELM, back propagation neural network algorithm, and other models, comparative experiments show that GOMGBO‐KELM has high performance in dealing with practical cases. We invite the community to investigate further our method for solving problems more efficiently with reasonable speed and efficiency. Readers of this study can refer to https://aliasgharheidari.com for any guidance about the proposed GOMGBO method.

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Section: Experimental Results Of the Mammographic Data Setmentioning

confidence: 99%

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Qiao

Shan

Jiang

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

“…Due to its strong optimization capability, the developed MSMA can also be applied to other optimization problems, such as multi-objective or many optimization problems [75][76][77], big data optimization problems [78], and combination optimization problems [79]. Moreover, it can be applied to tackle the practical problems such as medical diagnosis [80][81][82][83], location-based service [84,85], service ecosystem [86], communication system conversion [87][88][89], kayak cycle phase segmentation [90], image dehazing and retrieval [91,92], information retrieval service [93][94][95], multi-view learning [96], human motion capture [97], green supplier selection [98], scheduling [99][100][101], and microgrid planning [102] problems.…”

Section: Discussionmentioning

confidence: 99%

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

2022

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In this study, the authors aimed to study an effective intelligent method for employment stability prediction in order to provide a reasonable reference for postgraduate employment decision and for policy formulation in related departments. First, this paper introduces an enhanced slime mould algorithm (MSMA) with a multi-population strategy. Moreover, this paper proposes a prediction model based on the modified algorithm and the support vector machine (SVM) algorithm called MSMA-SVM. Among them, the multi-population strategy balances the exploitation and exploration ability of the algorithm and improves the solution accuracy of the algorithm. Additionally, the proposed model enhances the ability to optimize the support vector machine for parameter tuning and for identifying compact feature subsets to obtain more appropriate parameters and feature subsets. Then, the proposed modified slime mould algorithm is compared against various other famous algorithms in experiments on the 30 IEEE CEC2017 benchmark functions. The experimental results indicate that the established modified slime mould algorithm has an observably better performance compared to the algorithms on most functions. Meanwhile, a comparison between the optimal support vector machine model and other several machine learning methods on their ability to predict employment stability was conducted, and the results showed that the suggested the optimal support vector machine model has better classification ability and more stable performance. Therefore, it is possible to infer that the optimal support vector machine model is likely to be an effective tool that can be used to predict employment stability.

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“…Our approach not only improves the classification performance, but also provides new analytical ideas for other researchers. In the future, a combination of fuzzy systems [ 30 ] and the method proposed in this paper will be applied to more brain disease classifications.…”

Section: Discussionmentioning

confidence: 99%

Multi-Perspective Feature Extraction and Fusion Based on Deep Latent Space for Diagnosis of Alzheimer’s Diseases

Gao

et al. 2022

Brain Sciences

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Resting-state functional magnetic resonance imaging (rs-fMRI) has been used to construct functional connectivity (FC) in the brain for the diagnosis and analysis of brain disease. Current studies typically use the Pearson correlation coefficient to construct dynamic FC (dFC) networks, and then use this as a network metric to obtain the necessary features for brain disease diagnosis and analysis. This simple observational approach makes it difficult to extract potential high-level FC features from the representations, and also ignores the rich information on spatial and temporal variability in FC. In this paper, we construct the Latent Space Representation Network (LSRNet) and use two stages to train the network. In the first stage, an autoencoder is used to extract potential high-level features and inner connections in the dFC representations. In the second stage, high-level features are extracted using two perspective feature parses. Long Short-Term Memory (LSTM) networks are used to extract spatial and temporal features from the local perspective. Convolutional neural networks extract global high-level features from the global perspective. Finally, the fusion of spatial and temporal features with global high-level features is used to diagnose brain disease. In this paper, the proposed method is applied to the ANDI rs-fMRI dataset, and the classification accuracy reaches 84.6% for NC/eMCI, 95.1% for NC/AD, 80.6% for eMCI/lMCI, 84.2% for lMCI/AD and 57.3% for NC/eMCI/lMCI/AD. The experimental results show that the method has a good classification performance and provides a new approach to the diagnosis of other brain diseases.

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Uncertainty Modeling for Multicenter Autism Spectrum Disorder Classification Using Takagi–Sugeno–Kang Fuzzy Systems

Cited by 41 publications

References 59 publications

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

Multi-Perspective Feature Extraction and Fusion Based on Deep Latent Space for Diagnosis of Alzheimer’s Diseases

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