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. Additionally, integrating diverse data modalities, such as textual, imaging, and audio information, poses technical challenges due to data heterogeneity and high dimensionality. To address these challenges, this paper proposes the RLKT-MDD (Representation Learning and Knowledge Transfer for Multimodal Depression Diagnosis) model framework. Representation learning enables the model to autonomously discover meaningful patterns and features from diverse data sources, surpassing traditional feature engineering methods. Knowledge transfer facilitates the effective transfer of knowledge from related domains, improving the model's performance in depression diagnosis. Furthermore, we analyzed the interpretability of the representation learning process, enhancing the transparency and trustworthiness of the diagnostic process. We extensively experimented with the DAIC-WOZ dataset, a diverse collection of multimodal data from clinical settings, to evaluate our proposed approach. The results demonstrate promising outcomes, indicating significant improvements over conventional diagnostic methods. Our study provides valuable insights into cutting-edge techniques for depression diagnosis, enabling more effective and personalized mental health interventions.

Sentiment analysis is one of the crucial tasks in the field of natural language processing. Implicit sentiment suffers a significant challenge because the sentence does not include explicit emotional words and emotional expression is vague. This paper proposed a novel implicit sentiment analysis model based on graph attention convolutional neural network. A graph convolutional neural network is used to propagate semantic information. The attention mechanism is employed to compute the contribution to the emotional expression of words. In order to solve the problem of multiple attention preserving repeated information, orthogonal attention constraint was used to make different attention store different emotional information; given the uneven distribution of emotional information, score attention constraint was proposed to make the model focus on a limited number of essential words. The performance of the proposed model was verified on implicit sentiment datasets. The F value reached 88.16%, which is higher than the benchmark model in the literature. The attention mechanism is analyzed to verify the effectiveness of orthogonal constraint and score constraint.

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. This paper proposes an attention-based sentiment region importance and relationship (ASRIR) analysis method, including important attention and relation attention for image sentiment recognition. First, we extract spatial region features using a multilevel pyramid network from the image. Second, we design important attention to exploring the sentiment semantic-related regions and relation attention to investigating the relationship between regions. In order to release the excessive concentration of attention, we employ a unimodal function as the objective function for regularization. Finally, the region features weighted by the attention mechanism are fused and input into a fully connected layer for classification. Extensive experiments on various commonly used image sentiment datasets demonstrate that our proposed method outperforms the state-of-the-art approaches.

An optimized feeding strategy for the fed-batch fermentation of 2-keto-L-gulonic acid (2-KGA) is proposed to enhance the total profit of a workshop. Instead of assigning the same quantity of substrate to each batch in the empirical scheduling, the online feeding strategy offers a practical approach that allows for the precise management of substrate feeding to maximize economic performance. The forecasting of the profit function has been achieved through the proposed similarity measurement and multidimensional time series aggregation method. This method solves the problem of predicting the total economic benefit at the end of fermentation, in the absence of fermentation substrate measurement data. Pseudo-online simulation has been conducted, utilizing data derived from ninety industrial batches. A 6% increase in total profit is observed, which indicates that the optimized strategy utilizes the substrate and other related resources more efficiently and leads to a higher overall profit.