Huber loss based distributed robust learning algorithm for random vector functional-link network

Xie, Jin; Liu, Sanyang; Chen, Jiaxi; Jia, Jinping

doi:10.1007/s10462-022-10362-7

Cited by 4 publications

(1 citation statement)

References 43 publications

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“…However, when decoding to restore the original data information, the decoder is trained using the noise-free original data, explaining why the dimensionality reduced data after MDSAE exhibit improved robustness. Additionally, the MDSAE module employs the Huber Loss [47] as the loss function. Finally, by extracting the trained encoder part from the MDSAE, high-dimensional features are reduced to 30 dimensions.…”

Section: Feature Dimension Reduction Module Based On Mdsaementioning

confidence: 99%

Machine Learning-Based Intrusion Detection for Rare-Class Network Attacks

Yang,

Gu,

Yan

2023

Electronics

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Due to the severe imbalance in the quantities of normal samples and attack samples, as well as among different types of attack samples, intrusion detection systems suffer from low detection rates for rare-class attack data. In this paper, we propose a geometric synthetic minority oversampling technique based on the optimized kernel density estimation algorithm. This method can generate diverse rare-class attack data by learning the distribution of rare-class attack data while maintaining similarity with the original sample features. Meanwhile, the balanced data is input to a feature extraction module built upon multiple denoising autoencoders, reducing information redundancy in high-dimensional data and improving the detection performance for unknown attacks. Subsequently, a soft-voting ensemble learning technique is utilized for multi-class anomaly detection on the balanced and dimensionally reduced data. Finally, an intrusion detection system is constructed based on data preprocessing, imbalance handling, feature extraction, and anomaly detection modules. The performance of the system was evaluated using two datasets, NSL-KDD and N-BaIoT, achieving 86.39% and 99.94% multiclassification accuracy, respectively. Through ablation experiments and comparison with the baseline model, it is found that the inherent limitations of a single machine-learning model directly affect the accuracy of the intrusion detection system, while the superiority of the proposed multi-module model in detecting unknown attacks and rare classes of attack traffic is demonstrated.

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Section: Feature Dimension Reduction Module Based On Mdsaementioning

confidence: 99%

Machine Learning-Based Intrusion Detection for Rare-Class Network Attacks

Yang,

Gu,

Yan

2023

Electronics

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A new representation learning based maximum power operation towards improved energy management integration with DG controllers for photovoltaic generators using online deep exponentially expanded RVFLN algorithm

Satpathy,

Dhar,

Dash

et al. 2024

Applied Soft Computing

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Utilizing ResNet for enhanced quality prediction in PET production: an AI-driven approach

Zheng,

Shi,

Chen

2024

Journal of Polymer Engineering

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To promote theoretical understanding for optimizing the entire process parameters (temperature, pressure, flow rate, etc.) and quality indicators (molar fraction, end-group concentration, and number-average molecular weight) in the industrial production of polyethylene terephthalate (PET), a dataset construction for production parameters and product quality indicators was accomplished in conjunction with industrial process simulation software. A complete deep learning workflow including data collection, dataset construction, model training, and evaluation was established. A prediction method for process-product quality of PET production based on the residual neural network (ResNet) network was proposed to reduce the complexity of quality control in polyester production. The results show that compared to traditional convolutional neural network (CNN), ResNet has higher accuracy (R 2 ≥ 0.9998) in predicting the PET production process and product quality. It can accurately establish the mapping relationship between production parameters and product quality indicators, providing theoretical guidance for intelligent production.

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Huber loss based distributed robust learning algorithm for random vector functional-link network

Cited by 4 publications

References 43 publications

Machine Learning-Based Intrusion Detection for Rare-Class Network Attacks

Machine Learning-Based Intrusion Detection for Rare-Class Network Attacks

A new representation learning based maximum power operation towards improved energy management integration with DG controllers for photovoltaic generators using online deep exponentially expanded RVFLN algorithm

Utilizing ResNet for enhanced quality prediction in PET production: an AI-driven approach

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