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Titlebook: Advances on Intelligent Computing and Data Science; Big Data Analytics, Faisal Saeed,Fathey Mohammed,Mohammed Al-Sarem Conference proceedi

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41#
發(fā)表于 2025-3-28 16:17:21 | 只看該作者
42#
發(fā)表于 2025-3-28 19:11:03 | 只看該作者
Przemys?aw Garsztka,Pawe? Kliberases in tomato crops using leaf images. The process involves building a convolutional neural network using a pre-trained VGG16 model that pre-processes the images according to its requirements and performs segmentation on images before training and testing the data. The model obtained an accuracy of
43#
發(fā)表于 2025-3-29 00:36:25 | 只看該作者
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發(fā)表于 2025-3-29 05:23:50 | 只看該作者
Katarzyna Kuziak,Krzysztof Pionteknd moth-flame optimization (MFO) are effective at optimising functions. This work introduces a novel hybrid sentiment-based SVM optimised by particle swarm and moth-flame algorithms (SVMPSOMFO) to improve predicting accuracy. SVMPSOMFO optimises the model’s parameter values by combining PSO and MFO,
45#
發(fā)表于 2025-3-29 08:03:39 | 只看該作者
Krzysztof Piasecki,Joanna Siwekich are used to determine if transactions are good or bad. The findings of data analysis using Logistic Regression, Linear Discriminant Analysis, Gaussian Naive Bayes, K-Nearest Neighbors Classifier, Decision Tree Classifier, Support Vector Machines, and Random Forest are compared and contrasted in
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發(fā)表于 2025-3-29 13:48:56 | 只看該作者
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發(fā)表于 2025-3-29 16:42:46 | 只看該作者
48#
發(fā)表于 2025-3-29 22:29:40 | 只看該作者
Contemporary Trends in Local Governanceactory, as it was capable of predicting evidence of having a heart condition in a specific patient utilizing DL and the ML Model (Random-Forest-Classifier) that had high accuracies when compared to other employed classifiers. The proposed DL methodology for predicting heart disease is going to impro
49#
發(fā)表于 2025-3-30 01:53:49 | 只看該作者
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發(fā)表于 2025-3-30 06:21:01 | 只看該作者
Advances on Intelligent Computing and Data ScienceBig Data Analytics,
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