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Titlebook: Intelligent Systems and Networks; Selected Articles fr Thi Dieu Linh Nguyen,Elena Verdú,Maria Ganzha Conference proceedings 2023 The Editor

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31#
發(fā)表于 2025-3-27 21:42:44 | 只看該作者
32#
發(fā)表于 2025-3-27 23:19:25 | 只看該作者
,Design of?Measuring and?Warning System Based on?Sound Intensity in?High-Traffic Areas,intensity is too great (the threshold), the waiting time for the red light will increase. It will directly affect the perception of people and thus reduce the noise. The results show that the system has recovered noise with an accuracy of up to 96%.
33#
發(fā)表于 2025-3-28 03:12:54 | 只看該作者
Proposing Lung Abnormality Detection Model Using AI,stions on abnormal areas and creating an AI application to recognize images that can be detected abnormal areas or not. The results show that although the accuracy of the proposed solution only achieves about 30% due to the small number of samples, it promises to be highly feasible when applied in practice.
34#
發(fā)表于 2025-3-28 09:56:58 | 只看該作者
35#
發(fā)表于 2025-3-28 11:42:36 | 只看該作者
Optimized PID Controller for Two-Wheeled Self-balancing Robot Based on Genetic Algorithm, is easy to implement in practice. Based on the simulation result, it can be seen clearly that the TWSB robot control system’s output values closely match the target values and that the error is roughly zero, which demonstrates the proposed controller’s effectiveness and performance.
36#
發(fā)表于 2025-3-28 17:29:18 | 只看該作者
37#
發(fā)表于 2025-3-28 20:20:49 | 只看該作者
2367-3370 mics/researchers. It covers topics such as Computational Intelligence in Language and Speech Processing; Software development methods; Wireless Communications and Signal Processing; IoT and Sensor Embedded Systems ; etc..978-981-99-4727-0978-981-99-4725-6Series ISSN 2367-3370 Series E-ISSN 2367-3389
38#
發(fā)表于 2025-3-29 02:08:57 | 只看該作者
39#
發(fā)表于 2025-3-29 06:09:02 | 只看該作者
40#
發(fā)表于 2025-3-29 11:08:14 | 只看該作者
Pancreatic Cancer Detection Based on CT Images Using Deep Learning, CNN network with the addition of the Densely Connected Convolutional layers. Our method has shown superior results compared to other research and deep learning method with an accuracy of 97.4%, sensitivity of 98.3%, and specificity of 96.6%.
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