Research on Speech Emotion Recognition Method Based A-CapsNet

Qi, Yingmei; Huang, Heming; Zhang, Huiyun

doi:10.3390/app122412983

Cited by 2 publications

(1 citation statement)

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“…In this study, the structure and behavior of Caps Nets are examined and evaluated, and some of these networks' possible explainability characteristics are also highlighted . Using transformation matrices [20], the instantiation parameters of capsules at higher levels may be predicted from active capsules at lower levels. A higher-level capsule starts to work when numerous forecasts agree.…”

Section: Related Workmentioning

confidence: 99%

Survey On Medical Image Classification Using CAPSGNN

Shanmugam¹,

P²,

Ma³

2023

RRRJ

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The general Convolutional Neural Networks (CNNs) have been in practice, being the most conventional algorithm for image-based detection and classification. But over the years, after extensive use of CNN algorithms with different architectures, it has been shown that CNN tends to lose details and features of the image. This led to the use of Capsule-based neural networks for image detection and classification. On the other side, CNN has evolved and integrated with another type of neural network called the Graph Neural Network (GNN). Many existing systems have drawbacks such as feature loss and computation efficiency. Several transfer learning models have been introduced to solve these problems by modifying the existing models and adding different combinations of layers and hyper parameters. However, they still don't provide a clear solution as they are just derived algorithms. Therefore, there is a need to design an algorithm and technique that approaches the image classification process in a unique and different way. This is where the CAPSGNN algorithm comes into use. This proposed model uses the best features of all the other algorithms and fuses them into one algorithm. This reduces the computation time and solves the feature loss problems. Now, reports can be generated faster and more accurately for assisting the process of disease diagnosis in hospitals and saving doctors' time spent on reviewing every report. These speeds up the cycle of the medical field, as the identification of diseases takes more time than the actual treatment and needs to be processed faster for faster treatment and recovery.

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Section: Related Workmentioning

confidence: 99%