The Internet of Things (IoT), which enables seamless connectivity and effective data exchange between physical items and digital systems, has completely changed the way we interact with our surroundings. This study ev...
详细信息
The Metaverse can leverage intelligent traffic management technology to simulate the Cellular Vehicle-to-Everything (C-V2X) environment, integrating closely with the Internet of Vehicles due to its advanced connectivi...
详细信息
The novel coronavirus disease,or COVID-19,is a hazardous *** is endangering the lives of many people living in more than two hundred *** directly affects the *** general,two main imaging modalities,i.e.,computed tomog...
详细信息
The novel coronavirus disease,or COVID-19,is a hazardous *** is endangering the lives of many people living in more than two hundred *** directly affects the *** general,two main imaging modalities,i.e.,computed tomography(CT)and chest x-ray(CXR)are used to achieve a speedy and reliable medical *** the coronavirus in medical images is exceedingly difficult for diagnosis,assessment,and *** is demanding,time-consuming,and subject to human *** biological disciplines,excellent performance can be achieved by employing artificial intelligence(AI)*** a subfield of AI,deep learning(DL)networks have drawn considerable attention than standard machine learning(ML)*** models automatically carry out all the steps of feature extraction,feature selection,and *** study has performed comprehensive analysis of coronavirus classification using CXR and CT imaging modalities using DL ***,we have discussed how transfer learning is helpful in this ***,the problem of designing and implementing a system using computer-aided diagnostic(CAD)to find COVID-19 using DL approaches highlighted a future research possibility.
Medical image classification is crucial in disease diagnosis,treatment planning,and clinical *** introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation(BSDA...
详细信息
Medical image classification is crucial in disease diagnosis,treatment planning,and clinical *** introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation(BSDA)with a Vision Mamba-based model for medical image classification(MedMamba),enhanced by residual connection blocks,we named the model *** augments medical image data semantically,enhancing the model’s generalization ability and classification ***,a deep learning-based state space model,excels in capturing long-range dependencies in medical *** incorporating residual connections,BSDA-Mamba further improves feature extraction *** comprehensive experiments on eight medical image datasets,we demonstrate that BSDA-Mamba outperforms existing models in accuracy,area under the curve,and *** results highlight BSDA-Mamba’s potential as a reliable tool for medical image analysis,particularly in handling diverse imaging modalities from X-rays to *** open-sourcing of our model’s code and datasets,will facilitate the reproduction and extension of our work.
With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engi...
详细信息
With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.
The aim of this research is to design a facial emotion recognition system based on Raspberry Pi and Convolutional Neural Network (CNN) for analyzing customers' facial expressions in academic customer service. The ...
详细信息
Currently, electricity demand is constantly increasing all over the world, and the demand for this electricity is much higher than the production. As a result, the whole world is facing a global problem. In this decad...
详细信息
Recent advances in data-driven imitation learning and offline reinforcement learning have highlighted the use of expert data for skill acquisition and the development of hierarchical policies based on these skills. Ho...
This research provides through method for building a recommendation system for health concerns for women the significant number of individuals' day-to-day activities are affected by menstruation symptoms including...
详细信息
Machine learning combined with geometric reasoning is a promising approach for generating new perspectives of a scene using limited image captures, known as neural rendering techniques. Neural radiance fields (NeRF) r...
详细信息
暂无评论