This research introduces an original approach to time series forecasting through the use of multi-scale convolutional neural networks with Transformer modules. The objective is to focus on the limitations of short-term load forecasting in terms of complex spatio-temporal dependencies. The model begins with the convolutional layers, which perform feature extraction from the time series data to look for features with different temporal resolutions. The last step involves making use of the self-attention component of the Transformer block, which tries to find the long-range dependencies within the series. Also, a spatial attention layer is included to handle the interactions among the different samples. Equipped with these features, the model is able to make predictions. Experimental results show that this model performs better compared to the time series forecasting models in the literature. It is worth mentioning that the MSE score or mean square error of the model was 0.62, while the measure of fit R² was 0.91 in predicting the individual household electric power consumption dataset. The baseline models for this dataset such as the LSTM model had an MSE of 2.324 and R² value of 0.79, showing that the proposed model was significantly improved by a margin.

With the advances of technology, the banking industry faces the challenge of processing large-scale, heterogeneous human resource data. Traditional methods have difficulties in providing efficient and accurate analysis and prediction. This paper proposes an optimized human resource allocation algorithm based on deep latent Semantic Model (DLSM), which improves the accuracy and efficiency of data analysis by integrating deep neural network (DNN) and transfer learning technology. The experimental results show that the DLSM model performs well in text classification and information retrieval tasks, with the accuracy, accuracy, recall and F1 scores improving by 7.5%, 5.5%, 6.0% and 5.8%, respectively. This study confirms the excellent performance of DLSM model in HR data processing, provides an efficient and scientifically based solution for recruitment optimization in the banking industry, and enhances the competitiveness of enterprises.

This paper focuses on the efficient fine-tuning of large language models within the federated learning framework. To address the performance bottlenecks caused by multi-source heterogeneity and structural inconsistency, a structure-aware federated fine-tuning method is proposed. The method incorporates a graph representation module (GRM) to model internal structural relationships within text and employs a segmentation mechanism (SM) to reconstruct and align semantic structures across inputs, thereby enhancing structural robustness and generalization under non-IID (non-Independent and Identically Distributed) settings. During training, the method ensures data locality and integrates structural pruning with gradient encryption (SPGE) strategies to balance privacy preservation and communication efficiency. Compared with representative federated fine-tuning baselines such as FedNLP and FedPrompt, the proposed method achieves consistent accuracy and F1-score improvements across multiple tasks. To evaluate the effectiveness of the proposed method, extensive comparative experiments are conducted across tasks of text classification, named entity recognition, and question answering, using multiple datasets with diverse structures and heterogeneity levels. Experimental results show that the proposed approach significantly outperforms existing federated fine-tuning strategies on most tasks, achieving higher performance while preserving privacy, and demonstrating strong practical applicability and generalization potential.

This paper provides a comprehensive review of mechanical equipment fault diagnosis methods, focusing on the advancements brought by Transformer-based models. It details the structure, working principles, and benefits of Transformers, particularly their self-attention mechanism and parallel computation capabilities, which have propelled their widespread application in natural language processing and computer vision. The discussion highlights key Transformer model variants, such as Vision Transformers (ViT) and their extensions, which leverage self-attention to improve accuracy and efficiency in visual tasks. Furthermore, the paper examines the application of Transformer-based approaches in intelligent fault diagnosis for mechanical systems, showcasing their superior ability to extract and recognize patterns from complex sensor data for precise fault identification. Despite these advancements, challenges remain, including the reliance on extensive labeled datasets, significant computational demands, and difficulties in deploying models on resource-limited devices. To address these limitations, the paper proposes future research directions, such as developing lightweight Transformer architectures, integrating multimodal data sources, and enhancing adaptability to diverse operational conditions. These efforts aim to further expand the application of Transformer-based methods in mechanical fault diagnosis, making them more robust, efficient, and suitable for real-world industrial environments.