Titlebook: Computational Data and Social Networks; 12th International C Minh Hoàng Hà,Xingquan Zhu,My T. Thai Conference proceedings 2024 The Editor(s

Systemic

TextFocus: Efficient Multi-scale Detection for Arbitrary Scene Textngle GPU when scaled normally. When the larger the training size, the better the result is basic tactic, our method demonstrates that training on high resolution scale might not be ideal. Our implementation using ResNet-18 backbone with segment-like head achieves . F1 score on the SCUT-CTW1500 [.] d

遭受

Fake Face Recognition on Images Generated by Various Deepfakes Toolsin images. Experimental results show that the Neural Textures tool is the most sophisticated in creating fake faces, which is the most challenging for the considered fake image detection algorithms. In addition, we propose an architecture that can obtain better performance in fake/real face detectio

外形

Multi-scale Aggregation Network for Speech Emotion Recognitionolutional neural network of the feature pyramid network (FPN) family for SER. This network aggregates multi-scale features from different layers of the feature extractor via a top-down pathway and lateral connections. This methodology empowers our proposed network to encompass a more comprehensive a

AVID

VN-Legal-KG: Vietnam Legal Knowledge Graph for Legal Statute Identification on Land Law MattersSI task as a multi-label classification problem, better aligning with real-world legal practices. Through experimentation with real-world data, our approach demonstrates favorable performance when compared to previous models reported in the literature.

完成

hypotension

先兆

blister

An Approach for Web Content Classification with FastTexting machine learning models such as SVM, Naive Bayes, Neural Network, and Random Forest to find the most effective method. The Random Forest combined with the FastText method was highly evaluated, achieving a success rate of 82% when measured against essential evaluation criteria of accuracy, precis

清楚

FLIT

Computational Data and Social Networks978-981-97-0669-3Series ISSN 0302-9743 Series E-ISSN 1611-3349