作者:
Kumar, G. MuthuHemanand, D.
Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India
Department of Computer Science and Engineering Tamil Nadu Chennai India
The field of artificial intelligence (AI) has seen significant advancements in recent years. These days, artificial intelligence (AI) tools are being utilized by organizations in both the public and commercial sectors...
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ISBN:
(纸本)9798350375237
The field of artificial intelligence (AI) has seen significant advancements in recent years. These days, artificial intelligence (AI) tools are being utilized by organizations in both the public and commercial sectors all over the world. Individuals, organizations, and society as a whole will reap broad and significant advantages as a result of the capabilities of artificial intelligence (AI) both today and in the near future. Nevertheless, these very same technical advancements give rise to significant concerns, such as the question of how to ensure that artificial intelligence technology is built and implemented in a manner that is in accordance with the applicable data privacy laws and standards. The fast development of artificial intelligence presents substantial hurdles in terms of protecting customers' privacy and the confidentiality of their data. The purpose of this essay is to suggest an all-encompassing strategy for the development of a framework to solve these concerns. First, an overview of prior research on security and privacy in artificial intelligence is presented, with an emphasis on both the progress that has been made and the limits that still remain. In the same vein, open research topics and gaps that need to be addressed in order to improve existing frameworks are recognized. Regarding the development of the framework, the topic of data protection in artificial intelligence is discussed. This includes elaborating on the significance of protecting the data that is utilized in artificial intelligence models, as well as elaborating on the policies and practices that are in place to ensure the data's safety and the methods that are utilized to maintain the data's integrity. Additionally, the security of artificial intelligence is investigated, which includes an analysis of the vulnerabilities and dangers that are present in artificial intelligence systems, as well as the presentation of instances of potential assaults and malevolent manipulations,
Tuberculosis (TB) has been a great challenge in the health world, and proper treatment requires proper diagnosis at the right time. This paper has classified the bacilli in sputum samples into single/simple and clump ...
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This research addresses the pressing issue of breast cancer detection, emphasizing the development and evaluation of deep learning models using two distinct datasets. The first dataset involves histology images, where...
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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.
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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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.
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.
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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