Convolutional and Deep Neural Networks based techniques for extracting the age-relevant features of the speaker

Kuppusamy, K.; Chandra, E.

doi:10.1007/s12652-021-03238-1

Cited by 6 publications

(1 citation statement)

References 39 publications

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“…One of the most well-known is the Convolutional Neural Network (CNN), a branch of deep learning [10]. CNN has special network layer characteristics or architectures such as a convolutional layer, pooling layer, and fully connected layer [11], while other terms that are often encountered in CNN sections are input layer, feature learning, classification, and output prediction.…”

Section: Introductionmentioning

confidence: 99%

CNN Ensemble Learning Method for Transfer learning: A Review

Sulistya,

Br Bangun,

Tyas

2023

Ilk. J. Ilm.

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This study provides a review of CNN's ensemble learning method for transfer learning by highlighting sections such as review studies, datasets, pre-trained models, transfer learning, ensemble learning, and performance. The results indicate that the trend of ensemble learning, transfer learning ensemble, and transfer learning is growing every year. In 2022, there will be 35 papers reviewed related to this topic in this study. Some datasets contain apparent information starting from the dataset name, total data points, dataset splitting, target dataset availability, and type classification. ResNet-50, VGG-16, InceptionV3, and VGG-19 are used in most papers as pre-trained models and transfer learning processes. 50 (90.1%) papers use ensemble learning, and 5 (9.1%) do without ensemble learning. The reviewed paper summarizes several performance measurements, including accuracy, precision, recall, f1score, sensitivity, specificity, training accuracy, validation accuracy, test accuracy, training losses, validation losses, test losses, training time, and AUC, DSC. In the last section, 49 papers produce the best model performance using the proposed model, and 6 other papers use DenseNet, DeQueezeNet, Extended Yager Model, InceptionV3,

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Section: Introductionmentioning

confidence: 99%