Understanding how people use the spaces they inhabit—where they live, work, and gather—is key to effective urban planning ...
The study, titled “GenAI-Powered Framework for Reliable Sentiment Labeling in Drug Safety Monitoring,” published in Applied ...
Abstract: Text classification (TC) has gained substantial importance across diverse domains due to the increasing proliferation of internet-based and digital technologies. Organizations worldwide ...
This study aims to establish an interpretable disease classification model via machine learning and identify key features related to the disease to assist clinical disease diagnosis based on a ...
Abstract: Multi-label image classification in privacy-sensitive domains faces several challenges, particularly in multi-label electricity scene classification tasks where equipment wear and similar ...
Individual sensor systems have limitations in the complex task of classifying shredded tobacco. This study aims to overcome these limitations by developing a novel evolutionary algorithm-based feature ...
State Key Laboratory of Soil Pollution Control and Safety, and Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China ...
Deep learning model for multi-label thoracic disease detection from chest X-ray images using ResNet-50 and Grad-CAM visualization on the NIH ChestXray14 dataset.
I have read the paper and it seemed to be a single-label multi-classification problem. But the code use BCE and sigmoid instead of crossEntropy and softmax. So does it mean that the patient may have ...
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