Multi-component attention-based convolution network for color difference recognition with wavelet entropy strategy

Zhuang, Jichao; Peng, Qingjin; Wu, Fenghe; Guo, Bingxuan

doi:10.1016/j.aei.2022.101603

Cited by 6 publications

(3 citation statements)

References 37 publications

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“…By calculating the wavelet entropy of the image, the local feature information [19] of the image can be extracted, which can be used for edge detection, texture analysis, and other tasks in image processing [20].…”

Section: A Feature Extraction Technology-wavelet Entropymentioning

confidence: 99%

Multiple Sclerosis Recognition by Wavelet Entropy and Self-adaptive PSO

Hou

2023

Preprint

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Multiple sclerosis is a chronic, autoimmune disease that mainly affects the central nervous system, including the brain, spinal cord, and optic nerve. This disease can cause clinical symptoms such as cognitive decline, muscle weakness, spasms, and fatigue in patients, and the onset tends to be younger. Current medication can only prevent or alleviate symptoms, so early diagnosis of this disease can increase patients' chances of treatment. Although the use of nuclear magnetic resonance detection can improve the efficiency of early auxiliary diagnosis, it still requires experienced doctors to spend too much time and energy on comprehensive judgment. To reduce the time cost and improve the efficiency of multiple sclerosis diagnosis, this article proposes a detection and recognition algorithm for multiple sclerosis based on wavelet entropy and self-adaptive particle swarm optimization. Firstly, a triple discrete wavelet transform is performed on the brain image of multiple sclerosis, and then 10 wavelet entropies are extracted from the decomposed wavelet subbands, which can reduce the feature dimensions of the image; Then, the algorithm of self-adaptive particle swarm optimization is used to optimize the feedforward neural network, in order to obtain the optimal connection weights and thresholds during the training process. The result of the model is with an average sensitivity of 92.29±1.89, specificity of 92.54±0.67, precision of 92.48±0.59, accuracy of 92.42±0.88, and F1 score of 84.85±1.74, Matthews correlation coefficient of 92.37±0.96, and Fowlkes223 Mallows Index of 92.38±0.96. The experimental results indicate that this algorithm has a very important data support role in detecting multiple sclerosis.

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Section: A Feature Extraction Technology-wavelet Entropymentioning

confidence: 99%

Multiple Sclerosis Recognition by Wavelet Entropy and Self-adaptive PSO

Hou

2023

Preprint

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“…Attention mechanism is a resource allocation scheme, which can strengthen the ability of the model to focus on more critical information [35][36][37][38][39][40][41][42]. The principle of attention mechanism used in the paper is shown in Fig.…”

Section: Attention Mechanism In the Clstm Modelmentioning

confidence: 99%

A multi-task prediction method for acid concentration based on attention-CLSTM

Zhu

Liu

Cao

et al. 2022

Preprint

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The accurate measurement of acid concentration, including hydrogen chloride (HCl) and ferrous chloride concentrations (FeCl2), is a critical part of ensuring the quality of strip steel pickling. In this study, a multi-task attention convolutional long short-term memory (MACL) neural network model was proposed to predict hydrogen ion and ferrous ion concentrations simultaneously. Firstly, in order to extract significant information from the input sequence, an attention mechanism was added to the model to calculate the correlation between the input features and the acid concentration at each time step. Next, multi-task learning explores the connections between the two tasks and exploits hidden constraints to improve prediction accuracy. Finally, deep features were extracted through convolutional long short-term memory (CLSTM) neural network, and the acid concentration was predicted. The proposed MACL model was compared with other popular prediction models. The experimental results show that proposed MACL model generally outperforms other models, indicating that proposed model has excellent predictive performance and effectiveness.

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“…A reliable fault detection methodology must include the extraction of defect features, which can be effectively performed through signal decomposition and filtering approach. To tackle with the signals having many frequency components, the eminent researchers have successfully developed adaptive methodologies [11,12] including empirical mode decomposition (EMD) and non-adaptive decomposition techniques based on wavelet transform [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%