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Titlebook: Computational Intelligence in Data Science; 5th IFIP TC 12 Inter Lekshmi Kalinathan,Priyadharsini R.,Manisha S. Conference proceedings 2022

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31#
發(fā)表于 2025-3-27 00:58:52 | 只看該作者
32#
發(fā)表于 2025-3-27 03:42:12 | 只看該作者
Logistics — Physical Distributionarning algorithms were employed on this data set and an empirical analysis was presented in this paper. Data augmentation technique was applied to overcome the data imbalance. Among the several variations that are implemented, the model with Word2Vec vectorizer and Random Forest classifier on augmen
33#
發(fā)表于 2025-3-27 07:43:30 | 只看該作者
34#
發(fā)表于 2025-3-27 09:43:09 | 只看該作者
The Internationalisation of Retailingst the attacks are studied. The effect of noise in FGSM is studied in detail. Retinal fundus images for the diabetic retinopathy disease are used in the experimentation. The effect of the attack reveals the vulnerability of the Resnet model for these attacks.
35#
發(fā)表于 2025-3-27 17:34:38 | 只看該作者
The Internationalisation of Retailingapproach combining the ResNet and the InceptionNet architectures. Skip connections and the . convolutions in both the architectures are put to good use here. We achieve an accuracy of 99.08% in the PlantVillage dataset [.] and reasonable accuracy of 66.06% on the PlantDoc dataset [.] which is an inc
36#
發(fā)表于 2025-3-27 18:11:44 | 只看該作者
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發(fā)表于 2025-3-28 01:39:53 | 只看該作者
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發(fā)表于 2025-3-28 03:12:38 | 只看該作者
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發(fā)表于 2025-3-28 08:23:37 | 只看該作者
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發(fā)表于 2025-3-28 14:18:29 | 只看該作者
,Analysis of Age Sage Classification for Students’ Social Engagement Using REPTree and Random Forestg Machine Learning. In this paper, the decision-making classification is compared. The Reduced Error Pruning Tree (REPTree) and Random Forest algorithm is implemented on train dataset with diverse nodes. The attributes are focused as age & time spent on social media as per necessity of study. This p
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