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1 result for “Hybrid 1D and 3D Convolution-based Adaptive Residual DenseNet”

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Research ArticleOpen accessEvery word in the titleNovel Depression Classification Framework Using Optimal Feature Integration with Hybrid Convolution (1D/3D) Based Adaptive Residual DenseNetB. Manjulatha, Suresh PabbojuThis study introduces a novel depression classification method by incorporating multimodal data to reduce this limitation and improve the accuracy of depression diagnosis. In the beginning, the multimodal data such as speech signal, Electroencephalographic (EEG), and text data is obtained from the benchmark datasets. These acquired text data are subjected to the text pre-processing phase, where the stemming, character removal, … …Journal of Computer Science, Vol. 21, Issue 3 · 25 Feb 2025 · 5,860 accessesView PDFCite