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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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51#
發(fā)表于 2025-3-30 11:23:28 | 只看該作者
52#
發(fā)表于 2025-3-30 13:42:29 | 只看該作者
https://doi.org/10.1007/b138878paces, critical infrastructure, and commercial facilities. Automated anomaly detection plays an important role in surveillance systems as it reduces the need for human involvement and the associated costs. Autoencoders in recent years have proven to be effective as anomaly detectors learn only from
53#
發(fā)表于 2025-3-30 17:03:02 | 只看該作者
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發(fā)表于 2025-3-30 23:42:11 | 只看該作者
https://doi.org/10.1007/b138878 logins which are said to be protected. In response to counter this security threat an internally curated dataset is generated to facilitate the detection of BitM within the context of phishing. As the existing approaches emphasize the mitigation of BitM phishing instead of its detection, this study
55#
發(fā)表于 2025-3-31 04:06:53 | 只看該作者
56#
發(fā)表于 2025-3-31 07:00:47 | 只看該作者
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發(fā)表于 2025-3-31 09:50:16 | 只看該作者
https://doi.org/10.1007/b138878egularities with regard to dataset. On the other hand, CIL method is used to reduce the irregularities present. Again, fuzzy SVM also plays great role in manipulating the irregularities. In this paper, we imposed the fuzzy weighted kernel in evaluating the membership values and these memberships val
58#
發(fā)表于 2025-3-31 15:55:55 | 只看該作者
https://doi.org/10.1007/978-3-322-94502-0ractical factors, which are frequently overlooked, like machine orientation and inputs/outputs, when modeling and resolving 2D layout issues. The recommended resolution technique makes it possible to calculate transportation distances that are more in line with practical needs. An improved and fine-
59#
發(fā)表于 2025-3-31 19:58:39 | 只看該作者
60#
發(fā)表于 2025-3-31 22:28:55 | 只看該作者
Strukturpr?gende Gestaltungsprinzipienearning and machine learning models for the classification of skin lesions. However, the training dataset consists of unbalanced and rare skin disease entities, which poses challenges in automatically classifying skin cancer. Additionally, the model’s cross-domain adaptability and robustness are imp
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