Corona disease has caused a variety of problems for people since it was formed and spread around the world. In this study, diagnosis and differentiation of this disease have been investigated in the form of genomic se...
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VGIS (Virtual Geographic Information System) Platform is a unified oilfield operations management platform based on MaaS (Management as a Service) that integrates advanced technologies such as AIoT (Artificial Intelli...
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When the eye uses the brain and heart, the cardiovascular and nervous systems integrate and interact. Because changes in retinal microcirculation are independent predictors of cardiovascular events, the eye serves as ...
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When the eye uses the brain and heart, the cardiovascular and nervous systems integrate and interact. Because changes in retinal microcirculation are independent predictors of cardiovascular events, the eye serves as a "display" to the cardiovascular system and brain. The eye, which has two circulatory systems and a rich vascular supply, is a prime candidate for this study and benefits from early damage to the target organ. Eye movements performed during the visual search pose a challenge in identifying critical points in the eye scene. Because it uses different brain pathways and relates to the cardiac cycle, humans’ ability to spot anomalies under challenging circumstances means they are always needed for visual search. ECG (electrocardiogram), electroencephalogram (EEG), and eye tracking can improve visual search training and attention-tracking performance. EEG data can also be analyzed in real time using eye-tracking technology. Previous work has discussed the EEG or ECG concerning attraction during visual search. The eyeball’s movement combined with the ECG in the previous investigation and introduced large electroencephalographic (EEG) artifacts. This assessment aims to (a) identify brain–heart coherent features influenced by the visual search task and (b) discover the behavior of EEG frequency bands and heart rate variability (HRV) features. EEG and ECG were used to analyze and predict inattention in individuals during a visual search task. The EEG determines human brain function and considers to detect the variability in the EEG frequency band. The work proposed a visual search task with EEG and ECG analysis. Five participants recorded EEG and ECG recordings in three different scenarios: rest, gaze tracking, and normal. Statistical evaluation was used to compare EEG and ECG characteristics and Pearson’s correlation was employed for statistical analysis. Statistical ANOVA analysis revealed statistically significant (p > 0.05) differences between theta (F3) an
Nowadays, the increasing use of internet in vehicular environments leads to the Vehicular Social Network (VSN) concept as an instance of Internet of Things applications in transportation industry. Information sharing ...
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In this paper,an induced current learning method(ICLM)for microwave through wall imaging(TWI),named as TWI-ICLM,is *** the inversion of induced current,the unknown object along with the enclosed walls are treated as a...
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In this paper,an induced current learning method(ICLM)for microwave through wall imaging(TWI),named as TWI-ICLM,is *** the inversion of induced current,the unknown object along with the enclosed walls are treated as a combination of ***,a non-iterative method called distorted-Born backpropagation(DB-BP)is utilized to generate the initial *** the training stage,several convolutional neural networks(CNNs)are cascaded to improve the estimated induced *** addition,a hybrid loss function consisting of the induced current error and the permittivity error is used to optimize the network ***,the relative permittivity images are conducted analytically using the predicted current based on *** the numerical and experimental TWI tests prove that,the proposed method can achieve better imaging accuracy compared to traditional distorted-Born iterative method(DBIM).
The k-nearest neighbour (KNN) is a simple and yet effective classification rule. To achieve robustness against outliers, several local mean-based extensions of the KNN classifier have been proposed which assign the qu...
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This study examines eyeblink synchronization in interactions characterized by mutual gaze without task-related or conversational elements that can trigger similarities in visual, auditory, or cognitive processing. We ...
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As software development models and methods mature, large-scale software systems emerge. However, a critical challenge remains: the lack of a comprehensive software test data management model that integrates basic data...
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Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but th...
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Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.
Multimodal Sentiment Analysis(SA)is gaining popularity due to its broad application *** existing studies have focused on the SA of single modalities,such as texts or photos,posing challenges in effectively handling so...
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Multimodal Sentiment Analysis(SA)is gaining popularity due to its broad application *** existing studies have focused on the SA of single modalities,such as texts or photos,posing challenges in effectively handling social media data with multiple ***,most multimodal research has concentrated on merely combining the two modalities rather than exploring their complex correlations,leading to unsatisfactory sentiment classification *** by this,we propose a new visualtextual sentiment classification model named Multi-Model Fusion(MMF),which uses a mixed fusion framework for SA to effectively capture the essential information and the intrinsic relationship between the visual and textual *** proposed model comprises three deep neural *** different neural networks are proposed to extract the most emotionally relevant aspects of image and text ***,more discriminative features are gathered for accurate sentiment ***,a multichannel joint fusion modelwith a self-attention technique is proposed to exploit the intrinsic correlation between visual and textual characteristics and obtain emotionally rich information for joint sentiment ***,the results of the three classifiers are integrated using a decision fusion scheme to improve the robustness and generalizability of the proposed *** interpretable visual-textual sentiment classification model is further developed using the Local Interpretable Model-agnostic Explanation model(LIME)to ensure the model’s explainability and *** proposed MMF model has been tested on four real-world sentiment datasets,achieving(99.78%)accuracy on Binary_Getty(BG),(99.12%)on Binary_iStock(BIS),(95.70%)on Twitter,and(79.06%)on the Multi-View Sentiment Analysis(MVSA)*** results demonstrate the superior performance of our MMF model compared to single-model approaches and current state-of-the-art techniques based on model evaluation cr
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