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Titlebook: Emerging Trends in Electrical, Communications, and Information Technologies; Proceedings of ICECI T. Hitendra Sarma,V. Sankar,Rafi Ahamed S

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樓主: Odious
21#
發(fā)表于 2025-3-25 04:24:21 | 只看該作者
22#
發(fā)表于 2025-3-25 07:32:36 | 只看該作者
978-981-13-8944-3Springer Nature Singapore Pte Ltd. 2020
23#
發(fā)表于 2025-3-25 14:59:26 | 只看該作者
24#
發(fā)表于 2025-3-25 17:51:47 | 只看該作者
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發(fā)表于 2025-3-25 23:35:22 | 只看該作者
26#
發(fā)表于 2025-3-26 01:57:12 | 只看該作者
1876-1100 hapuramu, Andhra Pradesh, India in December 2018. It covers the latest research trends and developments in the areas of Electrical Engineering, Electronic and Communication Engineering, and Computer Science and Information.978-981-13-8944-3978-981-13-8942-9Series ISSN 1876-1100 Series E-ISSN 1876-1119
27#
發(fā)表于 2025-3-26 07:54:14 | 只看該作者
https://doi.org/10.1007/978-1-4302-5906-0lgorithm. The proposed method is tested on 133 images whereas 78 are malignant melanoma skin cancer type and 55 benign ones. This approach allowed detecting two different Pathological cases of skin lesions images which are malignant melanoma and benign nevi. The segmentation achieved 97.75% of accuracy for these two types of skin cancer lesions.
28#
發(fā)表于 2025-3-26 09:31:46 | 只看該作者
https://doi.org/10.1007/978-1-4302-6488-0riendly way, so that any user can upload his/her conventional (crisp) database, define the fuzzy search criteria that he/she is interested in and search at the database flexibly and expressively using concepts as similarity, fuzziness, qualification, and negation.
29#
發(fā)表于 2025-3-26 15:39:04 | 只看該作者
Working With Strings and Numbers,colorizations. We have experimented different deep network architectures with various training algorithms and cost functions to come up with this network where we can clearly see realistic colors for given gray scale image and differentiate the characteristics of generative adversarial network from a traditional convolutional neural network.
30#
發(fā)表于 2025-3-26 19:59:32 | 只看該作者
Objective-C for Absolute Beginnersurvey on adults. Neural Networks model was trained and tested on both the survey data. The results of these algorithms were compared and analysed to find which algorithm is better in which situations.
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