Globally, lung and colon cancer are two of the most prevalent and deadly diseases which require efficient and accurate detection for proper treatment in early stages. Histopatho-logical images analysis is important fo...
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作者:
Wanjari, KetanVerma, Prateek
Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India
Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India
Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image...
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Distributed stochastic gradient descent and its variants have been widely adopted in the training of machine learning models,which apply multiple workers in *** them,local-based algorithms,including Local SGD and FedA...
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Distributed stochastic gradient descent and its variants have been widely adopted in the training of machine learning models,which apply multiple workers in *** them,local-based algorithms,including Local SGD and FedAvg,have gained much attention due to their superior properties,such as low communication cost and ***,when the data distribution on workers is non-identical,local-based algorithms would encounter a significant degradation in the convergence *** this paper,we propose Variance Reduced Local SGD(VRL-SGD)to deal with the heterogeneous *** extra communication cost,VRL-SGD can reduce the gradient variance among workers caused by the heterogeneous data,and thus it prevents local-based algorithms from slow convergence ***,we present VRL-SGD-W with an effectivewarm-up mechanism for the scenarios,where the data among workers are quite *** from eliminating the impact of such heterogeneous data,we theoretically prove that VRL-SGD achieves a linear iteration speedup with lower communication complexity even if workers access non-identical *** conduct experiments on three machine learning *** experimental results demonstrate that VRL-SGD performs impressively better than Local SGD for the heterogeneous data and VRL-SGD-W is much robust under high data variance among workers.
This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden *** MVS under consideration consists of a leader vehicle with a...
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This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden *** MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles,connected via a directed interaction topology,subject to simultaneous unknown heterogeneous nonlinearities and external *** central aim is to achieve effective and collisionfree formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering,while not demanding global information of the interaction *** this goal,a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle ***,a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is *** is proved that,with the proposed protocol,the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is *** simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.
Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentati...
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Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.
The unprecedented availability of new types of data coupled with the invention of new technologies combine to enable entirely new or higher-resolution services that in turn enable more rational and data-driven process...
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Reconstructing perceived images from human brain activity forms a crucial link between human and machine learning through Brain-computer Interfaces. Early methods primarily focused on training separate models for each...
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With the advances in microfluidics, electrowetting-on-dielectric (EWOD) chips have widely been applied to various biological and chemical laboratory protocols. Glass-based EWOD chips with nonregular electrodes are pro...
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The fact that deaths from bore wells persist in India is highly alarming, particularly when young people are involved. Since 2009, there have been more than 40 documented child deaths, and the National Disaster Respon...
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Machine learning (ML) models have been used in functional neuroimaging for wide-ranging tasks, ranging from disease diagnosis to disease prognosis. There have been successive functional connectivity-based ML studies f...
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