Animal Fur Recognition Algorithm Based on Feature Fusion
                    Network

Liu, Peng; Lei, Tao; Xiang, Qian; Wang, Zexuan; Wang, Jiwei

doi:10.33851/jmis.2022.9.1.1

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…As a result, a different solution method is needed, as there may be passwords that have been leaked but are not yet known. Therefore, a new solution is proposed using deep learning technology [10][11][12]. This solution utilizes the characteristic of deep learning technology that allows for the learning of the relationship between two values if the input and output data are determined.…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Classification Prediction Model for Password Strength Based on Deep Learning

Kim¹,

Lee²

2023

J Multimed Inf Syst

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Various indexes are being used today to evaluate the strength of passwords. In these indexes, the strength of a password is evaluated to be high if it takes longer for an attacker to predict it. Therefore, using such an evaluation, there is a problem that a leaked password may reduce the reliability of the index by increasing vulnerability if an attacker attempts to attack using a leaked password. Hence, estimating the leaked frequency when considering strength is important for reducing vulnerability. This paper proposes a password strength evaluation model using deep learning-based multi-class classification, which solves the existing problem of leaked frequency not being considered during evaluation. Data preprocessing modeling is critical to improve the performance of this model. Additionally, since selecting and extracting feature values of preprocessing data is also important, a model that accurately estimates the degree of leakage through an evaluation method of existing indexes is proposed. To evaluate the performance of the proposed model, an experiment that compares the password leaked frequency stored in a database using a password list was conducted. As a result, the proposed model correctly evaluated 99% of the 345 leaked passwords. Therefore, the effectiveness of the proposed model was verified.

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Section: Introductionmentioning

confidence: 99%

Multi-Class Classification Prediction Model for Password Strength Based on Deep Learning

Kim¹,

Lee²

2023

J Multimed Inf Syst

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“…A discrete cosine residual network algorithm was introduced for recognizing myocardial infarctions, optimizing time-frequency characteristics through a discrete cosine transformation method [8]. A variable scale fusion network model was employed with residual blocks and an attention mechanism to convert signals into a spectrogram [9]. Zhai [10] converted the signals into double-coupled matrices as two-dimensional feature inputs for a CNN model.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Modeling of Cardiac Arrhythmia Classification on Information Feature Fusion Image with Attention Mechanism

Zhang,

Jin,

Zheng

et al. 2023

Entropy

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The electrocardiogram (ECG) is a crucial tool for assessing cardiac health in humans. Aiming to enhance the accuracy of ECG signal classification, a novel approach is proposed based on relative position matrix and deep learning network information features for the classification task in this paper. The approach improves the feature extraction capability and classification accuracy via techniques of image conversion and attention mechanism. In terms of the recognition strategy, this paper presents an image conversion using relative position matrix information. This information is utilized to describe the relative spatial relationships between different waveforms, and the image identification is successfully applied to the Gam-Resnet18 deep learning network model with a transfer learning concept for classification. Ultimately, this model achieved a total accuracy of 99.30%, an average positive prediction rate of 98.76%, a sensitivity of 98.90%, and a specificity of 99.84% with the relative position matrix approach. To evaluate the effectiveness of the proposed method, different image conversion techniques are compared on the test set. The experimental results demonstrate that the relative position matrix information can better reflect the differences between various types of arrhythmias, thereby improving the accuracy and stability of classification.

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Image recognition of traditional Chinese medicine based on deep learning

Miao

Huang

Wang

et al. 2023

Front. Bioeng. Biotechnol.

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Chinese herbal medicine is an essential part of traditional Chinese medicine and herbalism, and has important significance in the treatment combined with modern medicine. The correct use of Chinese herbal medicine, including identification and classification, is crucial to the life safety of patients. Recently, deep learning has achieved advanced performance in image classification, and researchers have applied this technology to carry out classification work on traditional Chinese medicine and its products. Therefore, this paper uses the improved ConvNeXt network to extract features and classify traditional Chinese medicine. Its structure is to fuse ConvNeXt with ACMix network to improve the performance of ConvNeXt feature extraction. Through using data processing and data augmentation techniques, the sample size is indirectly expanded, the generalization ability is enhanced, and the feature extraction ability is improved. A traditional Chinese medicine classification model is established, and the good recognition results are achieved. Finally, the effectiveness of traditional Chinese medicine identification is verified through the established classification model, and different depth of network models are compared to improve the efficiency and accuracy of the model.

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Animal Fur Recognition Algorithm Based on Feature Fusion Network

Cited by 3 publications

References 19 publications

Multi-Class Classification Prediction Model for Password Strength Based on Deep Learning

Multi-Class Classification Prediction Model for Password Strength Based on Deep Learning

Deep Learning Modeling of Cardiac Arrhythmia Classification on Information Feature Fusion Image with Attention Mechanism

Image recognition of traditional Chinese medicine based on deep learning

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