MicroRNAs(miRNAs)are closely related to numerous complex human diseases,therefore,exploring miRNA-disease associations(MDAs)can help people gain a better understanding of complex disease *** increasing number of compu...
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MicroRNAs(miRNAs)are closely related to numerous complex human diseases,therefore,exploring miRNA-disease associations(MDAs)can help people gain a better understanding of complex disease *** increasing number of computational methods have been developed to predict ***,the sparsity of the MDAs may hinder the performance of many *** addition,many methods fail to capture the nonlinear relationships of miRNA-disease network and inadequately leverage the features of network and neighbor *** this study,we propose a deep matrix factorization model with variational autoencoder(DMFVAE)to predict potential *** first decomposes the original association matrix and the enhanced association matrix,in which the enhanced association matrix is enhanced by self-adjusting the nearest neighbor method,to obtain sparse vectors and dense vectors,***,the variational encoder is employed to obtain the nonlinear latent vectors of miRNA and disease for the sparse vectors,and meanwhile,node2vec is used to obtain the network structure embedding vectors of miRNA and disease for the dense ***,sample features are acquired by combining the latent vectors and network structure embedding vectors,and the final prediction is implemented by convolutional neural network with channel *** evaluate the performance of DMFVAE,we conduct five-fold cross validation on the HMDD v2.0 and HMDD v3.2 datasets and the results show that DMFVAE performs ***,case studies on lung neoplasms,colon neoplasms,and esophageal neoplasms confirm the ability of DMFVAE in identifying potential miRNAs for human diseases.
CNNs (Convolutional Neural Networks) have a good performance on most classification tasks, but they are vulnerable when meeting adversarial examples. Research and design of highly aggressive adversarial examples can h...
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The transformational and spatial proximities are important cues for identifying inliers from an appearance based match set because correct matches generally stay close in input images and share similar local transform...
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Mass spectrometry plays a crucial role in biomedicine by detecting isotopes,contributing significantly to research,diagnostics,and therapy *** study introduces IsoFusion,a deep learning model designed to address isoto...
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Mass spectrometry plays a crucial role in biomedicine by detecting isotopes,contributing significantly to research,diagnostics,and therapy *** study introduces IsoFusion,a deep learning model designed to address isotope detection in raw mass *** than directly applying convolutional layers to all signal and noise peaks,IsoFusion employs a trial-and-error ***,it investigates all potential charge states(trials)and collects signal peaks around expected m/z values for each ***,convolutional layers extract features from each trial,which are fused to identify the correct ***,the reparameterization trick predicts isotope features based on this correct trial.A key advantage of IsoFusion is shared model parameters across all trials,enhancing feature learning for less common charge states using data from prevalent *** results show a significant accuracy improvement for charge state 5,reaching 99.42%,compared to DeepIso’s 43.36%.Moreover,IsoFusion achieves a 97.33%detection accuracy for isotopes,with 2.4%of detected isotopes previously unidentified by four commonly used methods.
Reversible data hiding in encrypted domain(RDH-ED) fortifies data security and privacy safeguards while upholding the original data's integrity and *** research on RDH-ED focuses on 2D images,while research on 3D ...
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Reversible data hiding in encrypted domain(RDH-ED) fortifies data security and privacy safeguards while upholding the original data's integrity and *** research on RDH-ED focuses on 2D images,while research on 3D mesh models is still *** paper introduces an RDH-ED method using block modulus encryption and multi-MSB ***,the original mesh model is partitioned into non-overlapping subblocks of equal size,and then the vertices in each subblock are encrypted with the same key for additive modulus encryption,ensuring that the spatial correlation present in the original mesh subblocks remains preserved within the encrypted ***,the subblocks are disrupted one by one using the 3D Arnold Transform to enhance *** vertices in each embeddable subblock are divided into a reference vertex and several embeddable vertices,where the multi-MSB prediction strategy is employed to allocate embedding room for each embeddable ***,the secret information is embedded into the allocated *** the proposed method almost completely preserves the spatial correlation within each subblock,the achieved embedding rate surpasses that of all previous outstanding methods that rely on vacating room after encryption(VRAE).The experimental findings demonstrate that the proposed approach achieves an average embedding rate of 45.55 bits per vertex(bpv),surpassing the state-of-the-art method that achieves 25.63 bpv.
Reversible data hiding in encrypted domain (RDH-ED) fortifies data security and privacy safeguards while upholding the original data’s integrity and accessibility. Current research on RDH-ED focuses on 2D images, whi...
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CNNs(Convolutional Neural Networks) have a good performance on most classification tasks,but they are vulnerable when meeting adversarial *** and design of highly aggressive adversarial examples can help enhance the s...
CNNs(Convolutional Neural Networks) have a good performance on most classification tasks,but they are vulnerable when meeting adversarial *** and design of highly aggressive adversarial examples can help enhance the security and robustness of *** transferability of adversarial examples is still low in black-box ***,an adversarial example method based on probability histogram equalization,namely HE-MI-FGSM(Histogram Equalization Momentum Iterative Fast Gradient Sign Method) is *** each iteration of the adversarial example generation process,the original input image is randomly histogram equalized,and then the gradient is calculated to generate adversarial perturbations to mitigate overfitting in the adversarial *** effectiveness of the method is verified on the ImageNet *** with the advanced method I-FGSM(Iterative Fast Gradient Sign Method) and MI-FGSM(Momentum I-FGSM),the attack success rate in the adversarial training network increased by 27.9% and 7.7% on average,respectively.
Adding subtle perturbations to an image can cause the classification model to misclassify, and such images are called adversarial examples. Adversarial examples threaten the safe use of deep neural networks, but when ...
Adding subtle perturbations to an image can cause the classification model to misclassify, and such images are called adversarial examples. Adversarial examples threaten the safe use of deep neural networks, but when combined with reversible data hiding(RDH) technology, they can protect images from being correctly identified by unauthorized models and recover the image lossless under authorized models. Based on this, the reversible adversarial example(RAE) is rising. However, existing RAE technology focuses on feasibility, attack success rate and image quality, but ignores transferability and time complexity. In this paper,we optimize the data hiding structure and combine data augmentation technology,which flips the input image in probability to avoid overfitting phenomenon on the dataset. On the premise of maintaining a high success rate of white-box attacks and the image's visual quality, the proposed method improves the transferability of reversible adversarial examples by approximately 16% and reduces the computational cost by approximately 43% compared to the state-of-the-art method. In addition, the appropriate flip probability can be selected for different application scenarios.
CNNs (Convolutional Neural Networks) have a good performance on most classification tasks, but they are vulnerable when meeting adversarial examples. Research and design of highly aggressive adversarial examples can h...
CNNs (Convolutional Neural Networks) have a good performance on most classification tasks, but they are vulnerable when meeting adversarial examples. Research and design of highly aggressive adversarial examples can help enhance the security and robustness of CNNs. The transferability of adversarial examples is still low in black-box settings. Therefore, an adversarial example method based on probability histogram equalization, namely HE-MI-FGSM (Histogram Equalization Momentum Iterative Fast Gradient Sign Method) is proposed. In each iteration of the adversarial example generation process, the original input image is randomly histogram equalized, and then the gradient is calculated to generate adversarial perturbations to mitigate overfitting in the adversarial example. The effectiveness of the method is verified on the ImageNet dataset. Compared with the advanced method I-FGSM (Iterative Fast Gradient Sign Method) and MI-FGSM (Momentum I-FGSM), the attack success rate in the adversarial training network increased by 27.9% and 7.7% on average, respectively.
Adding subtle perturbations to an image can cause the classification model to misclassify, and such images are called adversarial examples. Adversarial examples threaten the safe use of deep neural networks, but when ...
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