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Titlebook: Data Management, Analytics and Innovation; Proceedings of ICDMA Neha Sharma,Amlan Chakrabarti,Alfred M. Bruckstein Conference proceedings 2

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發(fā)表于 2025-3-30 08:27:21 | 只看該作者
Real-Time Soybean Crop Insect Classification Using Customized Deep Learning Models. The VGG16 and GoogLeNet deep learning models have been developed with the dataset and found decent performance as with VGG16 (validation accuracy: 97.78% and validation loss: 0.0852) and GoogLeNet (validation accuracy: 99.03% and validation loss: 69.5098). Finally, an Android-based app has been developed to facilitate real-time working.
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發(fā)表于 2025-3-30 16:09:00 | 只看該作者
Writer-Independent Offline Signature Verification Using Deep Siamese Neural Networkes higher classification accuracy compared to state-of-the-art methods. The results are validated on our created dataset and CEDAR, UTSig, BHSig260, GDPS300, GDPS synthetic signature benchmark datasets.
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發(fā)表于 2025-3-30 19:51:32 | 只看該作者
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發(fā)表于 2025-3-31 00:06:56 | 只看該作者
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發(fā)表于 2025-3-31 02:19:00 | 只看該作者
Cognitive Theory and Documentary Filmin prediction of cancer, identification of cancer stages and classification of tumors as malignant or benign. Broadly the methodologies can be classified into the following categories: (i) Feature extraction from CT/PET(Positron Emission Tomography)/HSI(Hyper spectral Images)/MRI/US (Ultrasound)/CLE
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發(fā)表于 2025-3-31 07:20:26 | 只看該作者
https://doi.org/10.1007/978-3-319-90332-3uths data. In this work, we present three different validation methods which can be used to test the classification accuracy. The geographic area of study, the area surrounding the Kabini reservoir, has a large water body, forest and moderate built-up area. It is a part of the Nilgiri Biosphere Rese
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發(fā)表于 2025-3-31 12:06:50 | 只看該作者
Cognitive Theory and Documentary Filme hitherto unknown way of estimating the reliability of a bank. Subsequently, in the 2nd stage, in order to maximize the reliability of the bank, we formulate an unconstrained optimization problem in a single-objective environment and solve it using the well-known particle swarm optimization (PSO) a
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