Titlebook: Advances in Computational Collective Intelligence; 15th International C Ngoc Thanh Nguyen,János Botzheim,Adrianna Kozierki Conference proce

proptosis

書目名稱Advances in Computational Collective Intelligence影響因子(影響力)




書目名稱Advances in Computational Collective Intelligence影響因子(影響力)學(xué)科排名




書目名稱Advances in Computational Collective Intelligence網(wǎng)絡(luò)公開度




書目名稱Advances in Computational Collective Intelligence網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in Computational Collective Intelligence被引頻次




書目名稱Advances in Computational Collective Intelligence被引頻次學(xué)科排名




書目名稱Advances in Computational Collective Intelligence年度引用




書目名稱Advances in Computational Collective Intelligence年度引用學(xué)科排名




書目名稱Advances in Computational Collective Intelligence讀者反饋




書目名稱Advances in Computational Collective Intelligence讀者反饋學(xué)科排名

尖

https://doi.org/10.1057/9780230250987e method incorporates transfer learning, where a pre-trained CNN model on a large dataset of chest X-ray images is fine-tuned for the specific task of detecting COVID-19. This approach can help to reduce the amount of labeled data required for the specific task and improve the overall performance of

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https://doi.org/10.1057/9780230250987successes of DL models in Arabic SA, there are still areas for improvement in terms of contextual information and implicit mining expressed in different real-world cases. In this paper, the authors introduce a deep Bi-LSTM network to ameliorate Tunisian SA during the spread of the Coronavirus Pandem

figure

Dimos Chatzinikolaou,Charis Vladosxture of recurrent neural network (RNN) and convolutional neural network (CNN). Parkinson’s disease dataset was used in a resting state, which is consists of twenty PD and twenty normal subjects. The main objectives of CRNN use are that can extract automatically multiple characteristics from the inp

向下

Laura Schengel,Véronique Goehlichd regularization. The classification performance of these two networks is analyzed with the help of confusion matrix and its parameters. Simulation results have shown that the VGG16 model gives 97.06% accuracy. It also observed that the proposed system gives more accurate results than any previous s

使人煩燥

colloquial

https://doi.org/10.1057/9780230307728 how novel methods of Machine Learning are able to perform analysis of texts in polish language related to the medical and pharmaceutical industries, and then extract key information on a given topic.

裂縫

https://doi.org/10.1057/9780230307728er based model to disambiguate new words and evaluate the performance of the corpus. The experimental results show that the baseline approach achieves an accuracy of around 90%. The corpus is publically available upon request and is open for extension.

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