Accurate prediction of shoulder joint angles based on surface electromyography (sEMG) signals is critical in human–machine interaction and rehabilitation engineering. However, due to the shoulder joint’s complex degrees of freedom, dynamically varying muscle coordination patterns, and the susceptibility of sEMG signals to cross-talk and noise interference, achieving high-precision prediction remains challenging. In this study, LASENet (BiLSTM–Attention–SE Network) is proposed as an end-to-end deep learning framework that integrates a bidirectional long short-term memory network (BiLSTM), a multi-head self-attention (MHSA) mechanism, and a squeeze-and-excitation (SE) block to predict shoulder joint angles across three degrees of freedom directly from raw sEMG signals. By jointly modeling temporal dependencies, long-range feature interactions, and channel-wise importance, LASENet effectively captures motion-related patterns while suppressing redundant noise. Experimental results demonstrate that LASENet demonstrates outperforms baseline models in terms of root mean square error (RMSE) and correlation coefficient (CC), achieving superior prediction accuracy and stability. These findings demonstrate that LASENet is an effective solution for accurate shoulder joint angle prediction from sEMG signals.

Cross-lingual image description, the task of generating image captions in a target language from images and descriptions in a source language, is addressed in this study through a novel approach that combines neural network models and semantic matching techniques. Experiments conducted on the Flickr8k and AraImg2k benchmark datasets, featuring images and descriptions in English and Arabic, showcase remarkable performance improvements over state-of-the-art methods. Our model, equipped with the Image & Cross-Language Semantic Matching module and the Target Language Domain Evaluation module, significantly enhances the semantic relevance of generated image descriptions. For English-to-Arabic and Arabic-to-English cross-language image descriptions, our approach achieves a CIDEr score for English and Arabic of 87.9% and 81.7%, respectively, emphasizing the substantial contributions of our methodology. Comparative analyses with previous works further affirm the superior performance of our approach, and visual results underscore that our model generates image captions that are both semantically accurate and stylistically consistent with the target language. In summary, this study advances the field of cross-lingual image description, offering an effective solution for generating image captions across languages, with the potential to impact multilingual communication and accessibility. Future research directions include expanding to more languages and incorporating diverse visual and textual data sources.