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Titlebook: Emerging Technologies in Computing; Third EAI Internatio Mahdi H. Miraz,Peter S. Excell,Maaruf Ali Conference proceedings 2020 ICST Institu

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樓主: monster
31#
發(fā)表于 2025-3-26 22:56:34 | 只看該作者
A Review of Underwater Acoustic, Electromagnetic and Optical Communicationsns, for example between Scuba divers and the ship, optical frequencies are feasible. For long distance undersea communications, very low electromagnetic frequencies are still the most established way to maintain contact, whilst the vessel remain submerged.
32#
發(fā)表于 2025-3-27 03:20:40 | 只看該作者
https://doi.org/10.1007/978-1-4020-5018-3.12% accuracy for 1D CNN and LSTM, respectively. In addition, it has been observed that dimension reduction techniques have no positive impact on 1D-CNN and LSTM. Without any dimension reduction technique, MFCC with 1D-CNN has demonstrated better accuracy compared to MFCC with LSTM by showing 97.26% and 93.83% of accuracy, respectively.
33#
發(fā)表于 2025-3-27 08:01:04 | 只看該作者
34#
發(fā)表于 2025-3-27 12:12:21 | 只看該作者
35#
發(fā)表于 2025-3-27 15:20:00 | 只看該作者
Bangla Speech Recognition Using 1D-CNN and LSTM with Different Dimension Reduction Techniques.12% accuracy for 1D CNN and LSTM, respectively. In addition, it has been observed that dimension reduction techniques have no positive impact on 1D-CNN and LSTM. Without any dimension reduction technique, MFCC with 1D-CNN has demonstrated better accuracy compared to MFCC with LSTM by showing 97.26% and 93.83% of accuracy, respectively.
36#
發(fā)表于 2025-3-27 19:25:56 | 只看該作者
Comparative Analysis of Dimension Reduction Techniques Over Classification Algorithms for Speech Emo dimension reduction techniques namely Recursive Feature Elimination, Principal Component Analysis and P-value Calculation had been applied to the dataset. Then classifier algorithms were used for accuracy again. Later this study showed that a progress in terms of accuracy (63.12%) had resulted from Gradient Boosting.
37#
發(fā)表于 2025-3-28 01:50:56 | 只看該作者
Investigations on Performances of Pre-trained U-Net Models for 2D Ultrasound Kidney Image Segmentatifor segmentation of kidneys from 2D ultrasound images. Experimentation results obtained shows that U-Net model with VGG-16 backbone outperformed with a promising accuracy of 0.89, thus demonstrating that segmentation can be done even with limited count of images within the dataset.
38#
發(fā)表于 2025-3-28 05:10:06 | 只看該作者
39#
發(fā)表于 2025-3-28 08:37:35 | 只看該作者
978-3-030-60035-8ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020
40#
發(fā)表于 2025-3-28 11:35:44 | 只看該作者
Emerging Technologies in Computing978-3-030-60036-5Series ISSN 1867-8211 Series E-ISSN 1867-822X
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