Cross-Entropy Learning for Aortic Pathology Classification of Artificial Multi-Sensor Impedance Cardiography Signals

Spindelböck, Tobias; Ranftl, Sascha; Linden, Wolfgang von der

doi:10.3390/e23121661

Cited by 4 publications

(1 citation statement)

References 31 publications

(63 reference statements)

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“…The loss function is defined simply as follows:

where y is the sample label, which takes the value of 1 if the sample is a positive case and 0 otherwise, and

is the probability that the model predicts that the sample is a positive case. In general, the lower the value of the cross-entropy loss function, the higher the classification effect [ 52 , 53 , 54 , 55 ].…”

Section: Methodsmentioning

confidence: 99%

A Machine Learning Method to Identify Umami Peptide Sequences by Using Multiplicative LSTM Embedded Features

Jiang

et al. 2023

Foods

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Umami peptides enhance the umami taste of food and have good food processing properties, nutritional value, and numerous potential applications. Wet testing for the identification of umami peptides is a time-consuming and expensive process. Here, we report the iUmami-DRLF that uses a logistic regression (LR) method solely based on the deep learning pre-trained neural network feature extraction method, unified representation (UniRep based on multiplicative LSTM), for feature extraction from the peptide sequences. The findings demonstrate that deep learning representation learning significantly enhanced the capability of models in identifying umami peptides and predictive precision solely based on peptide sequence information. The newly validated taste sequences were also used to test the iUmami-DRLF and other predictors, and the result indicates that the iUmami-DRLF has better robustness and accuracy and remains valid at higher probability thresholds. The iUmami-DRLF method can aid further studies on enhancing the umami flavor of food for satisfying the need for an umami-flavored diet.

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“…The loss function is defined simply as follows:

where y is the sample label, which takes the value of 1 if the sample is a positive case and 0 otherwise, and

Section: Methodsmentioning

confidence: 99%

A Machine Learning Method to Identify Umami Peptide Sequences by Using Multiplicative LSTM Embedded Features

Jiang

et al. 2023

Foods

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Intelligent Assessment Method of Enterprise Tax Risk Based on Deep Learning

Guo¹

2022

Wireless Communications and Mobile Computing

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Enterprise tax risk involves many aspects. In order to accurately realize enterprise tax risk assessment, an intelligent assessment method of enterprise tax risk based on deep learning is proposed. Collect enterprise tax risk information; reasonably distinguish the same type of risk information according to category, type, and appearance; extract the correlation characteristics of enterprise tax risk assessment; use the correlation characteristics to build a deep learning enterprise tax risk assessment model; search the best parameters of the automatic encoder; build a stacked automatic encoder based on the automatic encoder; combine it with the classifier; and complete the design of enterprise tax risk intelligent evaluation model. The experimental results show that the deviation ratio of Receiver Operating Characteristic (ROC), the risk assessment index of the proposed method, is less than 0.38 as a whole. Therefore, the enterprise tax risk intelligent assessment method based on deep learning is better.

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PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images

Zhang,

Qu,

Tian

et al. 2023

BMC Cancer

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Objective To investigate the correlation between CT imaging features and pathological subtypes of pulmonary nodules and construct a prediction model using deep learning. Methods We collected information of patients with pulmonary nodules treated by surgery and the reference standard for diagnosis was post-operative pathology. After using elastic distortion for data augmentation, the CT images were divided into a training set, a validation set and a test set in a ratio of 6:2:2. We used PB-LNet to analyze the nodules in pre-operative CT and predict their pathological subtypes. Accuracy was used as the model evaluation index and Class Activation Map was applied to interpreting the results. Comparative experiments with other models were carried out to achieve the best results. Finally, images from the test set without data augmentation were analyzed to judge the clinical utility. Results Four hundred seventy-seven patients were included and the nodules were divided into six groups: benign lesions, precursor glandular lesions, minimally invasive adenocarcinoma, invasive adenocarcinoma Grade 1, Grade 2 and Grade 3. The accuracy of the test set was 0.84. Class Activation Map confirmed that PB-LNet classified the nodules mainly based on the lungs in CT images, which is in line with the actual situation in clinical practice. In comparative experiments, PB-LNet obtained the highest accuracy. Finally, 96 images from the test set without data augmentation were analyzed and the accuracy was 0.89. Conclusions In classifying CT images of lung nodules into six categories based on pathological subtypes, PB-LNet demonstrates satisfactory accuracy without the need of delineating nodules, while the results are interpretable. A high level of accuracy was also obtained when validating on real data, therefore demonstrates its usefulness in clinical practice.

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Cross-Entropy Learning for Aortic Pathology Classification of Artificial Multi-Sensor Impedance Cardiography Signals

Cited by 4 publications

References 31 publications

A Machine Learning Method to Identify Umami Peptide Sequences by Using Multiplicative LSTM Embedded Features

A Machine Learning Method to Identify Umami Peptide Sequences by Using Multiplicative LSTM Embedded Features

Intelligent Assessment Method of Enterprise Tax Risk Based on Deep Learning

PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images

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