Stragglers' effects are known to degrade FL performance. In this paper, we investigate federated learning (FL) over wireless networks in the presence of communication stragglers, where the power-constrained client...
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The escalating prevalence of violent threats necessitates proactive security measures to safeguard human life. Within the realm of artificial intelligence (AI), computer vision has emerged as a pivotal tool, leveragin...
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Document Classification is a Natural Language Processing task, which generally uses deep neural networks to extract features from full textual information. The extracted features may or may not be relevant for classif...
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Anomaly detection systems are vital for identifying malicious activities within network traffic, but they often face challenges such as class imbalance, high dimensionality, and feature correlation in datasets like NS...
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Classification of multiclass breast cancer through histopathological images is indispensable and poses daunting challenges due to color inconsistencies, high appearance variations, and large inter-class similarities. ...
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In this paper, the cooperative output regulation(COR) problem of a class of unknown heterogeneous multi-agent systems(MASs) with directed graphs is studied via a model-free reinforcement learning(RL) based fully distr...
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In this paper, the cooperative output regulation(COR) problem of a class of unknown heterogeneous multi-agent systems(MASs) with directed graphs is studied via a model-free reinforcement learning(RL) based fully distributed eventtriggered control(ETC) strategy. First, we consider the scenario that the exosystem is accessible globally to all agents, an internal model-based augmented algebraic Riccati equation(AARE) is constructed, and its solution is learned by the proposed model-free RL algorithm via online input-output data. Further, for the scenario that the exosystem is accessible only to its adjacent followers, the distributed observers are designed for each agent to get the state of the exosystem, and an internal modelbased fully distributed adaptive ETC protocol is then synthesized to construct the corresponding AARE, and the feedback gain matrix is learned in a model-free fashion. The model-free RL-based control protocol proposed in this paper can not only remove the prior knowledge of agents' dynamics, but also release the dependence on global information by the adaptive event-triggered mechanism(ETM) and the new graph-based Lyapunov function. Finally, simulation results are illustrated to show the feasibility and effectiveness of the proposed control scheme.
Potato, a crucial global food crop, faces persistent threats from diseases like early blight and late blight, jeopardizing both yields and economic stability. In response, we present an innovative approach using CNN f...
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This study explores the application of deep learning models for the detection of lung cancer subtypes utilizing histopathological images. Leveraging a diverse dataset containing images of adenocarcinoma, squamous cell...
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In recent years, there has been a significant increase in the use of neural networks for Abstractive Text Summarization (ATS). Notably, Generative Adversarial Networks (GANs) have demonstrated remarkable efficacy in d...
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Ensuring the authenticity and integrity of digital images is a major concern in multimedia forensics, driving research on universal schemes for detecting diverse image manipulations (or processing operations). Althoug...
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