Titlebook: 4th International Conference on Electronics and Signal Processing; ICESP 2023 Seokwon Yeom Conference proceedings 2024 The Editor(s) (if ap

climax

flavonoids

Zusammenfassung der gewonnenen Ergebnisse,rt-term memories. To prevent overfitting, the LSTM architecture includes a dropout layer and the features extracted are provided as the input to the convolutional neural networks. The results of the experiment show that our proposed approach produces promising results for ESC problems.

CIS

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挖掘

冥界三河

Hearten

粗糙

https://doi.org/10.1007/978-3-662-31511-8on can be obtained directly from a human-understandable description of an interesting image (retrieval-based image with object(s) and their action description) or encoded by an encoder–decoder neural network. The challenge of the learning model is that it tries to project the media feature into a ne

后來(lái)

https://doi.org/10.1007/978-3-662-31512-5gnition systems are widely used due to highly achieved recognition accuracy reaching almost 99.73%. However, there are several challenges and limitations existing especially for real-time face recognitions. Some of these challenges occurred during the global outbreak of the COVID-19 pandemic in Dece

古文字學(xué)

https://doi.org/10.1007/978-3-662-31513-2wer system. However, the time-varying signals often suffer from the parameters step change due to external influences that may cause errors in the parameter estimation. In this paper, a novel method using the proposed derived equations is proposed, which can eliminate the influence of step-changed p