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Titlebook: Congress on Smart Computing Technologies; Proceedings of CSCT Jagdish Chand Bansal,Harish Sharma,Antorweep Chakr Conference proceedings 20

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樓主: 拖累
41#
發(fā)表于 2025-3-28 15:15:25 | 只看該作者
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發(fā)表于 2025-3-28 20:17:50 | 只看該作者
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發(fā)表于 2025-3-28 23:55:34 | 只看該作者
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發(fā)表于 2025-3-29 06:22:44 | 只看該作者
45#
發(fā)表于 2025-3-29 08:08:55 | 只看該作者
,Case Study: ANPR Utilized for?Smart Parking, processing. The model is trained on photos of car license plates of different countries, with a detection accuracy rate of 96.75%. This model can be utilized for any task which requires ANPR, e.g. smart parking [.].
46#
發(fā)表于 2025-3-29 12:08:28 | 只看該作者
A Convolution Neural Network Model to Classify Handwritten Digits from Skeletons,with convolution neural network mechanism. Experimental results were conducted on handwritten digit database of MNIST and error rate; classification accuracy was computed. A classification rate of 98.1% and 1.9% of error rate are observed on the subsamples of handwritten digit data of MNIST.
47#
發(fā)表于 2025-3-29 15:33:30 | 只看該作者
Behavior Intrusion Detection System Using SVM and CNN,trusions. To assess our approach, we used an auto-encoder for feature reduction and two models for CNN and SVM evaluation. The experimental study on the dataset demonstrates that the suggested model can produce reliable results. On the UNSW-NB15 dataset, our model gets 99.58% and 99.66% accuracy for SVM and CNN, respectively.
48#
發(fā)表于 2025-3-29 21:28:14 | 只看該作者
49#
發(fā)表于 2025-3-30 03:28:59 | 只看該作者
50#
發(fā)表于 2025-3-30 04:48:19 | 只看該作者
Stephen Glaister,June Burnham,Tony Traversthe same tasks. The paper made use of three classification algorithms which were random forest, decision tree and K-nearest neighbors (KNN). Data cleaning, preprocessing and feature extraction techniques were employed to enhance the overall performance of the system. Random forest gave a testing acc
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