Enhancing multimodal depression diagnosis through representation learning and knowledge transfer.

Depression is a complex mental health disorder that presents significant challenges in diagnosis and treatment. This study proposes an innovative approach, leveraging artificial intelligence advancements, to enhance multimodal depression diagnosis. The diagnosis of depression often relies on subjective assessments and clinical interviews, leading to potential biases and inaccuracies.

Probabilistic Bayesian Deep Learning Approach for Online Forecasting of Fed-Batch Fermentation.

The microbial fermentation process often involves various biological metabolic reactions and chemical processes. The mixed bacterial culture process of 2-keto-l-gulonic acid has strong nonlinear and time-varying characteristics. In this study, a probabilistic Bayesian deep learning approach is proposed to obtain a highly accurate and robust prediction of product formation.

Attention-Based Sentiment Region Importance and Relationship Analysis for Image Sentiment Recognition.

Image sentiment recognition has attracted considerable attention from academia and industry due to the increasing tendency of expressing opinions via images and videos online. Previous studies focus on multilevel representation from global and local views to improve recognition performance. However, it is insufficient to research the importance and relationship of visual regions for image sentiment recognition.