Insul岛
发表于 2025-3-25 07:08:19
Bilingual Word-Level Language Identification for Omotic Languagesrious experiments on various approaches. Then, the combination of the Bert-based pretrained language model and LSTM approach performed better, with an F1-score of 0.72 on the test set. As a result, the work will be effective in tackling unwanted social media issues and providing a foundation for further research in this area.
FAST
发表于 2025-3-25 07:44:21
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LAVA
发表于 2025-3-25 15:04:23
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暂停,间歇
发表于 2025-3-25 15:55:40
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Scintillations
发表于 2025-3-25 21:16:55
Deep Learning-Based Emotion Classification for Amharic Textstperforms better with 77.0% accuracy and F1-score. For the future, we recommend increasing the data and adapting different contextualized pre-trained embeddings for the effectiveness of the proposed deep learning approaches. The dataset is available publicly for further investigation.
神圣在玷污
发表于 2025-3-26 01:46:00
Music Genre Classification Using Deep Neural Network with Feature Selection and Optimization via EvoAlgorithm in Python (DEAP). The result shows that the proposed deep neural network by selecting features using GA and tuning hyper-parameter using DEAP provides better performance with an AUC of 1.00 and an accuracy of 98.61%.
龙卷风
发表于 2025-3-26 07:10:24
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Inferior
发表于 2025-3-26 10:14:17
Breast Cancer Stage Identification Using Machine Learning CBIS-DDSM database. Experiments were conducted using full mammogram images and segmented ROI images, resulting in accuracies of 39%, 85%, 86%, and 91%, respectively. The CNN-SVM model achieved a 6% increase in classification accuracy compared to the state-of-the-art VGGNet-16 model.
Debility
发表于 2025-3-26 16:12:57
2522-8595 ments in modern societies related to important issues such digitization, energy transformation, impact on national economy, and its recent advancements. The papers are relevant to researchers, academics, and professionals.978-3-031-64153-4978-3-031-64151-0Series ISSN 2522-8595 Series E-ISSN 2522-8609
CYN
发表于 2025-3-26 17:28:41
Grundlagen zur Strahlenbiologie der Zellerovements in deep learning-based early cervical cancer detection techniques can potentially lower these challenges while increasing diagnosis accuracy. In this study, experiments were conducted on 465 histopathological images collected from patients with adenocarcinoma, squamous cell carcinoma, and