In the existing false information detection methods, the quality of the extracted single-modality features is low, the information between different modalities cannot be fully fused, and the original information will ...
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In the existing false information detection methods, the quality of the extracted single-modality features is low, the information between different modalities cannot be fully fused, and the original information will be lost when the information of different modalities is fused. This paper proposes a false information detection via multimodal feature fusion and multi-classifier hybrid prediction. In this method, first, bidirectional encoder representations for transformers are used to extract the text features, and S win-transformer is used to extract the picture features, and then, the trained deep autoencoder is used as an early fusion method of multimodal features to fuse text features and visual features, and the low-dimensional features are taken as the joint features of the multimodalities. The original features of each modality are concatenated into the joint features to reduce the loss of original information. Finally, the text features, image features and joint features are processed by three classifiers to obtain three probability distributions, and the three probability distributions are added proportionally to obtain the final prediction result. Compared with the attention-based multimodal factorized bilinear pooling, the model achieves 4.3% and 1.2% improvement in accuracy on Weibo dataset and Twitter dataset. The experimental results show that the proposed model can effectively integrate multimodal information and improve the accuracy of false information detection.
BackgroundFraud, Waste, and Abuse (FWA) in medical claims have a negative impact on the quality and cost of healthcare. A major component of FWA in claims is procedure code overutilization, where one or more prescribe...
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BackgroundFraud, Waste, and Abuse (FWA) in medical claims have a negative impact on the quality and cost of healthcare. A major component of FWA in claims is procedure code overutilization, where one or more prescribed procedures may not be relevant to a given diagnosis and patient profile, resulting in unnecessary and unwarranted treatments and medical payments. This study aims to identify such unwarranted procedures from millions of healthcare claims. In the absence of labeled examples of unwarranted procedures, the study focused on the application of unsupervised machine learning *** were conducted with deep autoencoders to find claims containing anomalous procedure codes indicative of FWA, and were compared against a baseline density-based clustering model. Diagnoses, procedures, and demographic data associated with healthcare claims were used as features for the models. A dataset of one hundred thousand claims sampled from a larger claims database is used to initially train and tune the models, followed by experimentations on a dataset with thirty-three million claims. Experimental results show that the autoencoder model, when trained with a novel feature-weighted loss function, outperforms the density-based clustering approach in finding potential outlier procedure *** the unsupervised nature of our experiments, model performance was evaluated using a synthetic outlier test dataset, and a manually annotated outlier test dataset. Precision, recall and F1-scores on the synthetic outlier test dataset for the autoencoder model trained on one hundred thousand claims were 0.87, 1.0 and 0.93, respectively, while the results for these metrics on the manually annotated outlier test dataset were 0.36, 0.86 and 0.51, respectively. The model performance on the manually annotated outlier test dataset improved further when trained on the larger thirty-three million claims dataset with precision, recall and F1-scores of 0.48, 0.90 and
Precise prognostic classification of patients and identifying survival subgroups and their associated genes can be important clinical references when designing treatment strategies for cancer patients. Multi-omics and...
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Precise prognostic classification of patients and identifying survival subgroups and their associated genes can be important clinical references when designing treatment strategies for cancer patients. Multi-omics and data integration techniques are powerful tools to achieve this goal. This study aimed to introduce a machine learning method to integrate three types of biological data, and investigate the performance of two other methods, in identifying the survival dependency of patients. The data included TCGA RNA-seq gene expression, DNA methylation, and clinical data from 368 patients with colon cancer also we use an independent external validation data set, containing 232 samples. Three methods including, hyper-parameter optimized autoencoders (HPOAE), normal autoencoder, and penalized principal component analysis (PPCA) were used for simultaneous data integration and estimation under a COX hazards model. The HPOAE was thought to outperform other methods. The HPOAE had the Log Rank Mantel-Cox value of 14.27 & PLUSMN;2, and a Breslow-Generalized Wilcoxon value of 13.13 & PLUSMN;1. Ten miRNA, 11 methylated genes, and 28 mRNA all by (importance of marginal cutoff > 0.95) were identified. The study demonstrated that hsa-miR-485-5p targets both ZMYM1 and tp53, the latter of which has been previously associated with cancer in numerous studies. Furthermore, compared to other methods, the HPOAE exhibited a greater capacity for identifying survival subgroups and the genes associated with them in patients with colon cancer. However, all of the results were obtained by computational methods, and clinical and experimental studies are needed to validate these results.
(1) In this work, a set of deep autoencoders with different numbers of layers and layer sizes were trained using a set of open access hand tracking datasets. Examining the results allowed to determine optimal number o...
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ISBN:
(纸本)9798400708688
(1) In this work, a set of deep autoencoders with different numbers of layers and layer sizes were trained using a set of open access hand tracking datasets. Examining the results allowed to determine optimal number of layers for deep autoencoders and augmentation parameters for denoising autoencoders. Additionally, variations of autoencoders reconstruction loss depending on hidden unit size and latent space size were observed. (2) Results. Trained autoencoders showed better results in representation extraction than PCA method. A positive relationship between autoencoder depth and model performance was also proven. Future work will be aimed at applying obtained results to transfer learning problems.
In the field of an unmanned aerial vehicle (UAV), the navigation algorithm with high precision and easy implementation is a hot topic of research, and the key of UAV control is to obtain accurate and real-time attitud...
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ISBN:
(纸本)9781728103785;9781728103778
In the field of an unmanned aerial vehicle (UAV), the navigation algorithm with high precision and easy implementation is a hot topic of research, and the key of UAV control is to obtain accurate and real-time attitude information. In this paper, a feature fusion algorithm based on unsupervised deep autoencoder (DAE) is proposed. It is used for data fusion of multiple sensors. The experimental results show that the unsupervised feature fusion algorithm can effectively improve the accuracy and has the potential to be applied to the data fusion of UAV sensors.
Multi-view subspace clustering aims to find the inherent structure of data as much as possible by fusing complementary information of multiple views to achieve better clustering ***,most of the traditional multiview s...
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Multi-view subspace clustering aims to find the inherent structure of data as much as possible by fusing complementary information of multiple views to achieve better clustering ***,most of the traditional multiview subspace clustering algorithms are only shallow clustering algorithms,which does not capture the deep information of the data well,and does not conduct in-depth research at the self-representation level of the *** this end,this paper proposes a novel deep multi-view subspace clustering model that introduces exclusive constraints.A deep autoencoder is used to perform nonlinear low-dimensional subspace mapping for each view to learn the deep structure of the original *** better retain multiple views’ local structure and better reflect the complementarity,the exclusive constraints are introduced into the self-representation matrix which located in the middle layer of the deep *** multi-view consensus self-representation matrix is used to capture the consistency information between the multi-view *** update of autoencoder parameters and clustering parameters are iteratively optimized under the same learning framework to improve the clustering *** on multi-view data sets prove that this method can better dig out the inherent complementary structure of multi-view data,which reflects the superiority of this method.
The rapid advance of multimedia devices, including sensors, cameras and mobile phones, has given rise to the prevalence of Internet of Multimedia Things (IoMT), generating huge volumes of application-oriented multimed...
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The rapid advance of multimedia devices, including sensors, cameras and mobile phones, has given rise to the prevalence of Internet of Multimedia Things (IoMT), generating huge volumes of application-oriented multimedia data. At the same time, network security issues in the multimedia big data environment also increases. Network intrusion detection (NID) system demonstrates its power in preventing cyber-attacks against multimedia platforms. However, the existing NID methods which are based on machine learning or deep learning classifiers may fail when there is a lack of abnormal traffic samples for training in the real-world scenario. We propose a novel approach for intrusion detection based on deep autoencoder and Differential comparison named AED, which only requires the normal traffic samples in the training phase. We conduct extensive experiments on two real-world datasets to evaluate the effectiveness of the proposed AED. The experimental results show that AED can outperform the baseline methods of three categories in terms of accuracy, precision, recall and F1-score.
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