Abstract This paper proposes a smart algorithm based teaching model for language education, leveraging a hybrid intelligent algorithm that combines Particle Swarm Optimization (PSO) and Differential Evolution (DE). The model aims to enhance teaching effectiveness by optimizing classroom content and adapting to students’ diverse learning needs. The study identifies 18 effective teaching behaviors through expert consultation and applies the hybrid algorithm to analyze and improve... This paper proposes a smart algorithm based teaching model for language education, leveraging a hybrid intelligent algorithm that combines Particle Swarm Optimization (PSO) and Differential Evolution (DE). The model aims to enhance teaching effectiveness by optimizing classroom content and adapting to students’ diverse learning needs. The study identifies 18 effective teaching behaviors through expert consultation and applies the hybrid algorithm to analyze and improve linguistics instruction. Experimental results show the hybrid PSO-DE algorithm outperforms traditional methods like genetic algorithms and ant colony optimiza- tion in convergence speed and solution accuracy. The research highlights that expert teachers’ strategies follow a pyramid shaped effectiveness structure, emphasizing the importance of tailored, data driven in- struction. Findings suggest that integrating adaptive algorithms can significantly boost learning efficiency, student satisfaction, and overall educational quality in language teaching. The authors advocate for broader adoption of such intelligent systems to support teacher development and modernize linguistics education in the digital era. Read More Read Less

Abstract The paper proposes a practical teaching platform for engineering students based on a tag based talent search algorithm, aiming to bridge the gap between theoretical education and real world engineering demands. Traditional teaching methods often neglect hands on experience, limiting students’ problem solving and teamwork abilities. To address this, the authors design a system that matches students with suitable practical... The paper proposes a practical teaching platform for engineering students based on a tag based talent search algorithm, aiming to bridge the gap between theoretical education and real world engineering demands. Traditional teaching methods often neglect hands on experience, limiting students’ problem solving and teamwork abilities. To address this, the authors design a system that matches students with suitable practical projects by aligning student tags (e.g., skills, interests, experience) with project tags (e.g., domain, complexity, type). The algorithm leverages techniques like TF-IDF, binary encoding, and classification models (e.g., Naive Bayes, neural networks) to enable precise matching. The study outlines steps including tag definition, feature selection, dataset construction, algorithm training, and performance evaluation using metrics like accuracy, recall, and F1 score. Experimental results suggest that increased historical data improves recommendation accuracy, though the tag based approach shows comparable though not always superior performance against other algorithms in large data scenarios. The platform enhances students’ practical competencies and employability while supporting applied talent cultivation models in engineering education. However, the research acknowledges limitations, such as a small dataset and the need for broader algorithm comparisons. Future work includes scaling the dataset and exploring alternative matching algorithms to improve generalization and effectiveness. The paper contributes to the growing field of intelligent educational systems by integrating talent matching algorithms in to engineering pedagogy. Read More Read Less

Abstract The paper presents a neural network based intelligent robot English translation system enhanced by transfer learning and end to end training. Recognizing the limitations of traditional rule or statistical based translation methods particularly their inability to handle contextual nuance and language ambiguity the authors leverage deep learning to improve translation accuracy, fluency, and efficiency. The system architecture integrates data collection,... The paper presents a neural network based intelligent robot English translation system enhanced by transfer learning and end to end training. Recognizing the limitations of traditional rule or statistical based translation methods particularly their inability to handle contextual nuance and language ambiguity the authors leverage deep learning to improve translation accuracy, fluency, and efficiency. The system architecture integrates data collection, preprocessing, neural modeling (including encoder decoder frameworks with attention mechanisms), and user interfaces tailored for human robot interaction. A key innovation is the use of transfer learning, in which a model pre-trained on a high resource English Chinese parallel corpus is adapted to lowresource English contexts, thereby mitigating data scarcity. The study employs Deep Convolutional Neural Networks (DCNNs) with techniques like Batch Normalization and regularization to reduce overfitting and accelerate convergence. Experimental results demonstrate that the model achieves strong performance in translation quality, especially when enhanced with attention mechanisms and domain adaptation strategies. The authors also address challenges such as distribution mismatch between source and target domains and propose solutions like model pruning and dual stream CNNs to improve generalization. Evaluated using standard metrics like BLEU scores and test accuracy, the system shows promise for real world deployment in education, healthcare, and tourism. The research underscores the potential of combining neural machine translation with transfer learning to empower intelligent robots with robust multilingual communication capabilities, while acknowledging the need for larger, more diverse datasets and further architectural refinements. Read More Read Less