Wireless Sensor Networks (WSNs) play an important role in the modern era and security has become an important research area. Intrusion Detection System (IDS) improve network security by monitoring the network state so...
详细信息
Cloud computing (CC) stores and accesses data over the internet, posing security risks from within the cloud service provider (CSP) or outsiders, especially in medical systems. Cryptography is crucial for security, bu...
详细信息
People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces....
详细信息
People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.
With the emergence of smartness in various fields including medical science, forensics and security, remote monitoring of human activities has gained more interests in research. The ambulatory health monitoring servic...
详细信息
CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computationa...
详细信息
CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computational model to predict the associations between circRNAs and diseases. And there is existing more and more evidence indicates that the combination of multi-biomolecular information can improve the prediction accuracy. We propose a novel computational model for CDA prediction named MBCDA, we collect the multi-biomolecular information including circRNA, disease, miRNA and lncRNA based on 6 databases, and construct three heterogeneous network among them, then the multi-heads graph attention networks are applied to these three networks to extract the features of circRNAs and diseases from different views, the obtained features are put into variational graph auto-encoder(VGAE) network to learn the latent distributions of the nodes, a fully connected neural network is adopted to further process the output of VGAE and uses sigmoid function to obtain the predicted probabilities of circRNA-disease *** a result, MBCDA achieved the values of AUC and AUPR under 5-fold cross-validation of 0.893 and 0.887. MBCDA was applied to the analysis of the top-25 predicted associations between circRNAs and diseases, these experimental results show that our proposed MBCDA is a powerful computational model for CDA prediction.
In this paper, we have proposed a multi-task learning model for multi-lingual Optical Character Recognition. Our model does the script identification and text recognition simultaneously of offline machine printed docu...
详细信息
IoT-based healthcare (HC) systems face security and efficiency challenges. Existing solutions, such as secure transmission models, enhanced security protocols, and secure frameworks, neglect patient authentication and...
详细信息
Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical ***,we aim to optimize the ...
详细信息
Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical ***,we aim to optimize the properties of a specific molecule to satisfy the specific properties of the generated *** Matched Molecular Pairs(MMPs),which contain the source and target molecules,are used herein,and logD and solubility are selected as the optimization *** main innovative work lies in the calculation related to a specific transformer from the perspective of a matrix *** intervals and state changes are then used to encode logD and solubility for subsequent *** the experiments,we screen the data based on the proportion of heavy atoms to all atoms in the groups and select 12365,1503,and 1570 MMPs as the training,validation,and test sets,*** models are compared with the baseline models with respect to their abilities to generate molecules with specific *** show that the transformer model can accurately optimize the source molecules to satisfy specific properties.
Deep Learning(DL)is known for its golden standard computing paradigm in the learning ***,it turns out to be an extensively utilized computing approach in the ML ***,attaining superior outcomes over cognitive tasks bas...
详细信息
Deep Learning(DL)is known for its golden standard computing paradigm in the learning ***,it turns out to be an extensively utilized computing approach in the ML ***,attaining superior outcomes over cognitive tasks based on human *** primary benefit of DL is its competency in learning massive *** DL-based technologies have grown faster and are widely adopted to handle the conventional approaches ***,various DL approaches outperform the conventional ML approaches in real-time ***,various research works are reviewed to understand the significance of the individual DL models and some computational complexity is *** may be due to the broader expertise and knowledge required for handling these models during the prediction *** research proposes a holistic approach for pneumonia prediction and offers a more appropriate DL model for classification *** work incorporates a novel fused Squeeze and Excitation(SE)block with the ResNet model for pneumonia prediction and better *** expected model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is *** experimentation is carried out in Keras,and the model’s superiority is compared with various advanced *** proposed model gives 90%prediction accuracy,93%precision,90%recall and 89%*** proposed model shows a better trade-off compared to other *** evaluation is done with the existing standard ResNet model,GoogleNet+ResNet+DenseNet,and different variants of ResNet models.
The prediction of mild cognitive impairment or Alzheimer’s disease(AD)has gained the attention of huge researchers as the disease occurrence is increasing,and there is a need for earlier ***,due to the highdimensiona...
详细信息
The prediction of mild cognitive impairment or Alzheimer’s disease(AD)has gained the attention of huge researchers as the disease occurrence is increasing,and there is a need for earlier ***,due to the highdimensionality nature of neural data and the least available samples,modelling an efficient computer diagnostic system is highly *** approaches,specifically deep learning approaches,are essential in disease *** Learning(DL)approaches are successfully demonstrated for their higher-level performance in various fields like medical imaging.A novel 3D-Convolutional Neural Network(3D-CNN)architecture is proposed to predict AD with Magnetic resonance imaging(MRI)*** proposed model predicts the AD occurrence while the existing approaches lack prediction accuracy and perform binary *** proposed prediction model is validated using the Alzheimer’s disease Neuro-Imaging Initiative(ADNI)*** outcomes demonstrate that the anticipated model attains superior prediction accuracy and works better than the brain-image dataset’s general *** predicted model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is ***’experimentation is carried out,and the model’s superiority is compared with various advanced approaches for multi-level *** proposed model gives better prediction accuracy,precision,recall,and F-measure than other systems like Long Short Term Memory-Recurrent Neural Networks(LSTM-RNN),Stacked Autoencoder with Deep Neural Networks(SAE-DNN),Deep Convolutional Neural Networks(D-CNN),Two Dimensional Convolutional Neural Networks(2D-CNN),Inception-V4,ResNet,and Two Dimensional Convolutional Neural Networks(3D-CNN).
暂无评论