Autonomous mobile robots (AMRs) must operate in dynamic, unstructured environments where traditional control and reinforcement learning (RL) face adaptability and reward design limitations. This study proposes a hybrid framework combining proximal policy optimization (PPO) with a large language model (LLM) as an adaptive reward designer. Using GPT-4o-mini, the LLM dynamically shapes rewards based on performance logs, improving exploration and stability. Experiments in complex indoor navigation show the LLM-PPO model reduces collisions by 38%, shortens completion time by 21%, and increases rewards by 8% over PPO. Results demonstrate LLM-RL integration enhances safety, efficiency, and consistency, offering a promising paradigm for AMR control.
In education reform, a growing number of courses are being taught in conjunction with online platforms, posing new challenges for predicting students' performance. This study focuses on ideological and political education courses delivered through a massive open online courses platform. This paper gathered student behavior data, screened relevant features, and proposed an improved beetle antennae search-backpropagation neural network (IBAS-BPNN) method to forecast students' grades after learning the course. Moreover, experiments were conducted using the collected dataset. The findings indicated that the features selected based on information gain rate exhibited superior predictive capabilities compared to those selected using the Pearson correlation coefficient. By utilizing the top ten features as inputs to the IBAS-BPNN model, the model achieved a macroP value of 88.86%, a macroR value of 87.52%, and a macroF1-score of 88.18%. These results outperformed other optimized BPNN algorithms and surpassed machine learning approaches such as k-nearest neighbor and decision tree. The outcomes validate the reliability of the proposed method for grade prediction and its potential applicability in practice.
This paper mainly studied the loneliness of international students in China under the influence of social support and mobile network media. Data were collected through a questionnaire survey utilizing the perceived social support scale (PSSS), mobile network media use scale, and University of California, Los Angeles (UCLA) loneliness scale. The data were analyzed using SPSS version 22.0. It was found that the total score of social support was 5.15 +/- 1.12 points, and the school support score was low. The use intensity of mobile network media was 4.06 +/- 0.55 points, and the use behavior was 4.08 +/- 0.56 points, which was above the medium level. The total score of loneliness was 45.84 +/- 7.45 points. No remarkable differences were observed in gender, length of stay in China, and Chinese proficiency (p>0.05). Correlation and regression analyses revealed that social support and mobile network media were significantly correlated with loneliness of international students. The findings confirm that social support and mobile network media have an impact on loneliness, suggesting that loneliness can be alleviated through improving social support and other means.
Early detection of Parkinson's disease (PD) remains a critical challenge in healthcare, as current diagnostic methods often rely on subjective clinical assessments when motor symptoms have already progressed. This paper presents a novel deep learning approach for automated PD detection using digital handwriting analysis. We propose a dual-stream attention-enhanced bidirectional long short-term memory (BiLSTM) network that separately processes spatial trajectory features and dynamic behavioral features through dedicated pathways. Our architecture employs stream-specific attention mechanisms to identify discriminative temporal segments within each feature modality, followed by an attention-based fusion layer that integrates complementary representations from both streams. Extensive experiments on a comprehensive dataset of handwriting samples from both PD patients and healthy controls demonstrate that our approach achieves state-of-the-art performance with 94.7% accuracy and 0.9872 AUC. The attention mechanism provides interpretable insights into which temporal phases of handwriting are most indicative of PD symptoms, offering potential clinical value for understanding disease progression. Our findings suggest that automated analysis of digital handwriting can serve as an effective, non-invasive screening tool for early PD detection.
Plant leaf automatic recognition is a challenging issue in both computer vision and plant taxonomy areas, and its keys are extracting discriminative features effectively from leaf images and matching them fast. To deal with the scale parameter selection problem in multi-scale feature description methods, an effective rule is proposed to generate constant scale parameters for leaf image recognition. Given the current scale, a new scale can be determined through the trisection of the leaf contour. And for each point on the leaf contour, their paired neighbor points under each scale are located using these scale parameters. Angle values calculated by these neighbor points can describe a leaf in a multi-scale manner. The final multi-scale leaf contour descriptor is made compact using fast Fourier transform. In the matching stage, support vector machine and Manhattan distance are used for recognition and retrieval tasks. Leaf recognition and retrieval experiments were conducted with standard evaluation metrics on three well-known datasets, called Swedish, Flavia, and ImageCLEF2012 leaf datasets. The method achieves 97.27% accuracy on Swedish, 93.78% on Flavia, and 0.597 S-score on ImageCLEF2012 dataset. Experimental comparisons with notable methods show that the proposed method achieved higher recognition accuracy and computation speed.
