Time Series Classification for Portable Medical Devices

Zhong, Zhaoyi; Sun, Le; Subramani, Sudha; Peng, Dandan; Wang, Yilin

doi:10.4108/eetsis.v10i3.3219

Cited by 2 publications

(2 citation statements)

References 44 publications

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“…Mental health was analyzed based on emotion recognition from facial expressions and psychometric evaluations [17]. A personalized arrhythmia detection system based on attention mechanism called personAD, was proposed in [18]. On MIT-BIH Arrhythmia Database, the arrhythmia detection system achieved 98.03%.…”

Section: Introductionmentioning

confidence: 99%

Fast Lung Image Segmentation Using Lightweight VAEL-Unet

Hao,

Zhang,

2024

ICST Transactions on Scalable Information Systems

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INTRODUCTION: A lightweght lung image segmentation model was explored. It was with fast speed and low resouces consumed while the accuracy was comparable to those SOAT models. OBJECTIVES: To improve the segmentation accuracy and computational efficiency of the model in extracting lung regions from chest X-ray images, a lightweight segmentation model enhanced with a visual attention mechanism called VAEL-Unet, was proposed. METHODS: Firstly, the bneck module from the MobileNetV3 network was employed to replace the convolutional and pooling operations at different positions in the U-Net encoder, enabling the model to extract deeper-level features while reducing complexity and parameters. Secondly, an attention module was introduced during feature fusion, where the processed feature maps were sequentially fused with the corresponding positions in the decoder to obtain the segmented image. RESULTS: On ChestXray, the accuracy of VAEL-Unet improves from 97.37% in the traditional U-Net network to 97.69%, while the F1-score increases by 0.67%, 0.77%, 0.61%, and 1.03% compared to U-Net, SegNet, ResUnet and DeepLabV3+ networks. respectively. On LUNA dataset. the F1-score demonstrates improvements of 0.51%, 0.48%, 0.22% and 0.46%, respectively, while the accuracy has increased from 97.78% in the traditional U-Net model to 98.08% in the VAEL-Unet model. The training time of the VAEL-Unet is much less compared to other models. The number of parameters of VAEL-Unet is only 1.1M, significantly less than 32M of U-Net, 29M of SegNet, 48M of Res-Unet, 5.8M of DeeplabV3+ and 41M of DeepLabV3Plus_ResNet50. CONCLUSION: These results indicate that VAEL-Unet’s segmentation performance is slightly better than other referenced models while its training time and parameters are much less.

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Section: Introductionmentioning

confidence: 99%

Fast Lung Image Segmentation Using Lightweight VAEL-Unet

Hao,

Zhang,

2024

ICST Transactions on Scalable Information Systems

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“…Additionally, in terms of input coding, certain studies focus exclusively on categorized fields. They neglect numeric fields [15] or resort to simplistic encoding techniques like one-hot coding for the categorized fields [16]. Consequently, crucial feature information is lost during the data processing stage, potentially compromising the effectiveness of IDS.…”

Section: Introductionmentioning

confidence: 99%

DTT: A Dual-domain Transformer model for Network Intrusion Detection

Xu,

Sun,

2024

ICST Transactions on Scalable Information Systems

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With the rapid evolution of network technologies, network attacks have become increasingly intricate and threatening. The escalating frequency of network intrusions has exerted a profound influence on both industrial settings and everyday activities. This underscores the urgent necessity for robust methods to detect malicious network traffic. While intrusion detection techniques employing Temporal Convolutional Networks (TCN) and Transformer architectures have exhibited commendable classification efficacy, most are confined to the temporal domain. These methods frequently fall short of encompassing the entirety of the frequency spectrum inherent in network data, thereby resulting in information loss. To mitigate this constraint, we present DTT, a novel dual-domain intrusion detection model that amalgamates TCN and Transformer architectures. DTT adeptly captures both high-frequency and low-frequency information, thereby facilitating the simultaneous extraction of local and global features. Specifically, we introduce a dual-domain feature extraction (DFE) block within the model. This block effectively extracts global frequency information and local temporal features through distinct branches, ensuring a comprehensive representation of the data. Moreover, we introduce an input encoding mechanism to transform the input into a format suitable for model training. Experiments conducted on two distinct datasets address concerns regarding data duplication and diverse attack types, respectively. Comparative experiments with recent intrusion detection models unequivocally demonstrate the superior performance of the proposed DTT model.

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Time Series Classification for Portable Medical Devices

Cited by 2 publications

References 44 publications

Fast Lung Image Segmentation Using Lightweight VAEL-Unet

Fast Lung Image Segmentation Using Lightweight VAEL-Unet

DTT: A Dual-domain Transformer model for Network Intrusion Detection

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