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Titlebook: Advanced Network Technologies and Intelligent Computing; Third International Anshul Verma,Pradeepika Verma,Isaac Woungang Conference proce

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發(fā)表于 2025-3-23 10:21:48 | 只看該作者
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發(fā)表于 2025-3-23 17:20:37 | 只看該作者
Strukturpr?gende Gestaltungsprinzipien on improving the traffic sign areas in tough photos. Our technique is tested using the GTSRB dataset, which features traffic recordings collected under various CCs. Using the German Traffic Sign Recognition Benchmark (GTSRB) dataset, the reported technique attained an accuracy of 98.62 In addition,
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發(fā)表于 2025-3-23 22:00:34 | 只看該作者
https://doi.org/10.1007/978-3-662-07594-4thm inspired by the method of chromatographic separation of chemical substances. This method is widely and successfully used in analytical chemistry. The article presents the results of calculations for sample data sets and discusses issues related to the properties of the defined algorithm, which c
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發(fā)表于 2025-3-24 01:55:17 | 只看該作者
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發(fā)表于 2025-3-24 06:22:52 | 只看該作者
Enhanced Residual Network Framework for Robust Classification of Noisy Lung Cancer CT Images this effort will methodically assess different filtering techniques throughout a range of noise densities, from 5% to 50%. The goal of this effort is to identify lung cancer by using machine learning techniques on CT scan pictures, which will enable early and accurate cancer detection. The suggeste
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發(fā)表于 2025-3-24 10:20:20 | 只看該作者
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發(fā)表于 2025-3-24 12:17:23 | 只看該作者
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發(fā)表于 2025-3-24 17:48:23 | 只看該作者
Detection of?Lung Diseases Using Deep Transfer Learning-Based Convolution Neural Networksur models, the Detection performance of MobileNet and ResNet18 is quite encouraging compared to DenseNet121 and GoogLeNet. This approach could revolutionize the early detection and treatment of lung diseases, thereby enhancing patient outcomes and healthcare efficiency by providing insights into the
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發(fā)表于 2025-3-24 22:58:19 | 只看該作者
DG-GAN: A Deep Neural Network for?Real-World Anomaly Detection in?Surveillance Videosective functions. To address these challenges, we present a novel approach called the Dual Generator-based Generative Adversarial Network (DG-GAN). This network comprises two distinct components: a temporal generator and an image generator. The former accepts a single input in the form of a latent v
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發(fā)表于 2025-3-25 01:51:31 | 只看該作者
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