Titlebook: Intelligent Systems; 12th Brazilian Confe Murilo C. Naldi,Reinaldo A. C. Bianchi Conference proceedings 2023 The Editor(s) (if applicable)

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978-3-031-45388-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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nonradioactive

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啪心兒跳動(dòng)

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Hierarchical Graph Convolutional Networks for?Image Classificationor converting images to graphs often fail to preserve the hierarchical information of the image elements and produce sub-optimal or poor regions. To address these limitations, we propose a novel approach that uses a hierarchical image segmentation technique to generate graphs at multiple segmentatio

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Interpreting Convolutional Neural Networks for?Brain Tumor Classification: An Explainable Artificialficantly improve outcomes and quality of life for patients with brain tumors. Magnetic resonance (MRI) is a powerful diagnostic tool, and convolutional neural networks (CNNs) are efficient deep learning algorithms for image analysis. In this study, we explored using two CNN models for brain tumor cl

Noctambulant

Enhancing Stock Market Predictions Through the?Integration of?Convolutional and?Recursive LSTM Blockgrates convolutional networks, which learn to process signals through filters, with recursive LSTM blocks to account for critical temporal information often overlooked in convolutional approaches. Our investigation primarily revolves around two research questions: (1) Can integrating convolutional a

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