EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition

Wang, Chengyu; Cheng, Mengli; Huang, Jun

doi:10.1609/aaai.v35i18.18028

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…The larger the architecture, the more data is needed to produce viable results. The emergence on deep learning which is a subset of machine learning solved some problems in these fields that can be seen in many application such as image classification [5]- [7], speech recognition [8], object detection [9] and segmentation [10]. The most established algorithm among various deep learning models is CNN.…”

Section: Introductionmentioning

confidence: 99%

Covid-19 detection from chest x-ray images: comparison of well-established convolutional neural networks models

As’ari

Manap

2022

Int. J. Adv. Intell. Informatics

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Coronavirus disease 19 (Covid-19) is a pandemic disease that has already killed hundred thousands of people and infected millions more. At the climax disease Covid-19, this virus will lead to pneumonia and result in a fatality in extreme cases. COVID-19 provides radiological cues that can be easily detected using chest X-rays, which distinguishes it from other types of pneumonic disease. Recently, there are several studies using the CNN model only focused on developing binary classifier that classify between Covid-19 and normal chest X-ray. However, no previous studies have ever made a comparison between the performances of some of the established pre-trained CNN models that involving multi-classes including Covid-19, Pneumonia and Normal chest X-ray. Therefore, this study focused on formulating an automated system to detect Covid-19 from chest X-Ray images by four established and powerful CNN models AlexNet, GoogleNet, ResNet-18 and SqueezeNet and the performance of each of the models were compared. A total of 21,252 chest X-ray images from various sources were pre-processed and trained for the transfer learning-based classification task, which included Covid-19, bacterial pneumonia, viral pneumonia, and normal chest x-ray images. In conclusion, this study revealed that all models successfully classify Covid-19 and other pneumonia at an accuracy of more than 78.5%, and the test results revealed that GoogleNet outperforms other models for achieved accuracy of 91.0%, precision of 85.6%, sensitivity of 85.3%, and F1 score of 85.4%.

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Section: Introductionmentioning

confidence: 99%

Covid-19 detection from chest x-ray images: comparison of well-established convolutional neural networks models

As’ari

Manap

2022

Int. J. Adv. Intell. Informatics

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Non-IID Distributed Learning with Optimal Mixture Weights

Wei

Liu

et al. 2023

Lecture Notes in Computer Science

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A composition–decomposition based federated learning

Sun,

Wang,

et al. 2023

Complex Intell. Syst.

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Federated learning has been shown to be efficient for training a global model without needing to collect all data from multiple entities to the centralized server. However, the model performance, communication traffic, and data privacy and security are still the focus of federated learning after it has been developed. In this paper, a composition–decomposition based federated learning, denoted as CD-FL, is proposed. In the CD-FL approach, the global model, composed of K sub-models with the same framework, will be decomposed and broadcast to all clients. Each client will randomly choose a sub-model, update its parameters using its own dataset, and upload this sub-model to the server. All sub-models, including the sub-models before and after updating, will be clustered into K clusters to form the global model of the next round. Experimental results on Fashion-MNIST, CIFAR-10, EMNIST, and Tiny-IMAGENET datasets show the efficiency of the model performance and communication traffic of the proposed method.

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EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition

Cited by 3 publications

References 4 publications

Covid-19 detection from chest x-ray images: comparison of well-established convolutional neural networks models

Covid-19 detection from chest x-ray images: comparison of well-established convolutional neural networks models

Non-IID Distributed Learning with Optimal Mixture Weights

A composition–decomposition based federated learning

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