Background: Current programs to engage marginalized populations such as gay and bisexual individuals and other men who have sex with men (MSM) in HIV prevention interventions do not often reach all MSM who may benefit from them. To reduce the global burden of HIV, far-reaching strategies are needed to engage MSM in HIV prevention and treatment. Globally, including low- and middle-income countries, MSM are now widely using internet-based social and mobile technologies (SMTs; eg, dating apps, social media, and WhatsApp [Meta]), which provides an unprecedented opportunity to engage unreached and underserved groups, such as MSM for HIV prevention and care.
View Article and Find Full Text PDFEmotions play a vital role in recognizing a person's thoughts and vary significantly with stress levels. Emotion and stress classification have gained considerable attention in robotics and artificial intelligence applications. While numerous methods based on machine learning techniques provide average classification performance, recent deep learning approaches offer enhanced results.
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