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Before classification, we perform a simple image pre-processing for feature extraction. This pre-processing separates every walking doppler radar image into four parts on the vertical and horizontal axes and the number of feature points is then counted in every separated part after binarization to create eight features. In this binarization, the optimized threshold is obtained by experimentally sliding the threshold. We found that our proposed neural network achieved an accuracy of more than 75% in apathy classification. This accuracy is

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The findings are useful in guiding assessments and planning interventions. Further research should address the effectiveness of specific support strategies and the development of social policies and indicators for inclusion that involve assessing support needs.The age- and sex-adjusted incidence rate of Gram-negative bloodstream infection (GN-BSI) is 84 [...].Several antimicrobial peptides (AMPs) have been discovered, developed, and purified from natural sources and peptide engineering; however, the clinical applications of these AMPs a