A thermal face recognition under disguised conditions using model fusion is proposed. The proposed model fusion has three main approaches: Linear support vector machine classifier (Linear SVC), Convolutional Neural Ne...
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ISBN:
(纸本)9781728128177
A thermal face recognition under disguised conditions using model fusion is proposed. The proposed model fusion has three main approaches: Linear support vector machine classifier (Linear SVC), Convolutional Neural Network (CNN) and Ordinary Least Square (OLS). A grid of 22 thermal face points based on physiological information is extracted for training Linear SVC. The supportvectors of testing images using Linear SVC are calculated to find the hyperplane for classification. The novelty is that we firstly apply temperature information in face recognition. In CNN model, the multiple layers of convolutional layer are utilized to extract more effective features. The fully connected layer (FC layer) is trained using the feature matrix of the last convolutional layer. This FC layer is then a classifier to identify the category of the test image. In the training phase, the predicted values from above two approaches are provided to the OLS for linear regression. The OLS assigns weighting values to these two approaches. This can effectively compensate for the advantages and disadvantages of two approaches. In addition to the comparison with the traditional thermal face recognition, an experiment under disguised conditions was conducted. Experimental results of the proposed method outperform the existing methods.
Background: RNA-protein interaction plays an essential role in several biological processes, such as protein synthesis, gene expression, posttranscriptional regulation and viral infectivity. Identification of RNA-bind...
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Background: RNA-protein interaction plays an essential role in several biological processes, such as protein synthesis, gene expression, posttranscriptional regulation and viral infectivity. Identification of RNA-binding sites in proteins provides valuable insights for biologists. However, experimental determination of RNA-protein interaction remains time-consuming and labor-intensive. Thus, computational approaches for prediction of RNA-binding sites in proteins have become highly desirable. Extensive studies of RNA-binding site prediction have led to the development of several methods. However, they could yield low sensitivities in trade-off for high specificities. Results: We propose a method, RNAProB, which incorporates a new smoothed position-specific scoring matrix (PSSM) encoding scheme with a supportvectormachine model to predict RNA-binding sites in proteins. Besides the incorporation of evolutionary information from standard PSSM profiles, the proposed smoothed PSSM encoding scheme also considers the correlation and dependency from the neighboring residues for each amino acid in a protein. Experimental results show that smoothed PSSM encoding significantly enhances the prediction performance, especially for sensitivity. Using five-fold cross-validation, our method performs better than the state-of-the-art systems by 4.90%similar to 6.83%, 0.88%similar to 5.33%, and 0.10 similar to 0.23 in terms of overall accuracy, specificity, and Matthew's correlation coefficient, respectively. Most notably, compared to other approaches, RNAProB significantly improves sensitivity by 7.0%similar to 26.9% over the benchmark data sets. To prevent data over fitting, a three-way data split procedure is incorporated to estimate the prediction performance. Moreover, physicochemical properties and amino acid preferences of RNA-binding proteins are examined and analyzed. Conclusion: Our results demonstrate that smoothed PSSM encoding scheme significantly enhances the performanc
As a non-invasive angle closure glaucoma diagnosis procedure, Van Herick's slit-lamp Limbal Anterior Chamber Depth Estimation (LACDE) technique is preferably the current standard screening method preliminarily per...
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ISBN:
(纸本)9781509022502
As a non-invasive angle closure glaucoma diagnosis procedure, Van Herick's slit-lamp Limbal Anterior Chamber Depth Estimation (LACDE) technique is preferably the current standard screening method preliminarily performed at hospitals and eye clinics. However, accuracy of depth estimation is very much dependent on the slit projection distance from the limbus (LD). This study investigated the relation and designed a parametric model to reduce the inconsistencies between results and improving the Van Herick's method accuracy. Additionally, two different types of classifiers such as supportvectormachine (SVM) and Decision Tree with addition of LD as a predictor were trained and tested for comparison with parametric model. In the end acquired results clearly showed the effectiveness of LD consideration in LACDE, which lead to improvement in the experimental results.
Diabetic Retinopathy (DR), the most common eye disease of the diabetic patients, occurs when small blood vessels gets damaged in the retina, due to high glucose level. It affects 80% of all patients who have had diabe...
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ISBN:
(纸本)9781467348652
Diabetic Retinopathy (DR), the most common eye disease of the diabetic patients, occurs when small blood vessels gets damaged in the retina, due to high glucose level. It affects 80% of all patients who have had diabetes for 10 years or more, which can also lead to vision loss. Detection of diabetic retinopathy in advance, protects patients from vision loss. The major symptom of diabetic retinopathy is the exudates. Exudate is a fluid that filters from the circulatory system into lesions or area of inflammation. Detecting retinal fundus diseases in an early stage, helps the ophthalmologists apply proper treatments that might eliminate the disease or decrease the severity of it. This paper focuses on automatic detection of diabetic retinopathy through detecting exudates in colour fundus retinal images and also classifies the rigorousness of the lesions. Decision making of the severity level of the disease was performed by SVM classifier.
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