Face recognition is a widely utilized biometric method due to its natural and non-intrusive approach. Recently, deep learning networks using Triplet Loss have become a common framework for person identification and ve...
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Face recognition is a widely utilized biometric method due to its natural and non-intrusive approach. Recently, deep learning networks using Triplet Loss have become a common framework for person identification and verification. In this paper, we present a new method on how to select appropriate hard-negatives for training using Triplet Loss. We show that, by incorporating pairs which would otherwise have been discarded yields better accuracy and performance. We also applied Adaptive Moment Estimation algorithm to mitigate the risk of early convergence due to the additional hard-negative pairs. In LFW verification benchmark, we managed to achieve an accuracy of 0.955 and AUC of 0.989 as opposed to 0.929 and 0.973 in the original OpenFace.
Objective A classification and diagnosis model for psoriasis based on deep residual network is proposed in this *** using deep learning technology to classify and diagnose psoriasis can help reduce the burden of docto...
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Objective A classification and diagnosis model for psoriasis based on deep residual network is proposed in this *** using deep learning technology to classify and diagnose psoriasis can help reduce the burden of doctors,simplify the diagnosis and treatment process,and improve the quality of *** Firstly,data enhancement,image resizings,and TFRecord coding are used to preprocess the input of the model,and then a 34-layer deep residual network(ResNet-34)is constructed to extract the characteristics of ***,we used the adam algorithm as the optimizer to train ResNet-34,used cross-entropy as the loss function of ResNet-34 in this study to measure the accuracy of the model,and obtained an optimized ResNet-34 model for psoriasis *** The experimental results based on k-fold cross validation show that the proposed model is superior to other diagnostic methods in terms of recall rate,F1-score and ROC *** The ResNet-34 model can achieve accurate diagnosis of psoriasis,and provide technical support for data analysis and intelligent diagnosis and treatment of psoriasis.
BP neural network and rough set theory play an important role in the field of *** view of the present situation of customer churn in logistics industry,this paper combines rough set and BP neural network to forecast c...
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ISBN:
(纸本)9781510876996
BP neural network and rough set theory play an important role in the field of *** view of the present situation of customer churn in logistics industry,this paper combines rough set and BP neural network to forecast customer attrition behavior in logistics ***,using rough sets to extract rules from normal and abnormal customers to distinguish customer classes in logistics *** processing of information entropy of extracted logistics customer attributes based on rough sets being good at handling discrete ***,according to the strong mobility of logistics customers,adam algorithm is introduced to build an adaptive BP neural network training *** model proposed in this paper is more suitable for real-time data *** experiment proves that the method is feasible and efficient.
ABSTRACTIn this paper, a clear underwater image is attained by a fusion process using Transfer Learning (TL). Two images are selected from the underwater colour image dataset and those images are allowed to Discrete W...
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ABSTRACTIn this paper, a clear underwater image is attained by a fusion process using Transfer Learning (TL). Two images are selected from the underwater colour image dataset and those images are allowed to Discrete Wavelet Transform (DWT), Tetrolet transform and Saliency maps. Here, the outputs gained from images by the Tetrolet transform are fused and allowed for inverse Tetrolet transform. Moreover, the DWT process done with two images is fused and the output gained is allowed for inverse DWT. Similarly, the same fusion process is carried out with image outputs from Saliency maps. Finally, three image outputs that are considered as input to TL with newly devised optimization. Here, Convolutional Neural Network (CNN) is used with hyperparameters from trained models, such as SqueezeNet and AlexNet, where weights are updated using adam Based Bald Eagle algorithm (ABBEA). This ABBEA is obtained by combining the Bald Eagle Search (BES) algorithm and adam algorithm. Further, the ABBEA has Peak Signal-to-Noise Ratio (PSNR) with maximal of 38.95, Mean Squared Error (MSE) with lesser value of 20.14, Structural Similarity Index Measure (SSIM) with maximal value of 0.92, Mutual Information (MI) with maximal value of 0.86, Signal-to-Noise Ratio (SNR) with lesser value of 0.38.
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