The research of application models based on traditional convolutional neural networks has gradually entered the bottleneck period of performance improvement, and the improvement of chest X-ray image models has gradually become a difficult problem in the study. In this paper, the Swin Transformer is introduced into the application model of pneumonia recognition in chest X-ray images, and it is optimized according to the characteristics of chest X-ray images. The experimental results based on the model in this paper are compared with those of the model built with the traditional convolutional neural network as the backbone network, and the accuracy of the model is proved to be greatly improved. After the comparison experiments on two different datasets, the experimental results show that the accuracy of the model in this paper improves from 76.3% to 87.3% and from 92.8% to 97.2%, respectively. The experiments show that the accuracy of image enhancement based on the features of chest X-ray images in this model will be higher than the accuracy without image enhancement. In the experiments of this paper, the identification decision factors in the chest X-ray images were extracted by grad-cam combined with a transformer to find the corresponding approximate lesion regions.

Background: Deep‐learning object detection has been applied in various industries, including healthcare, to address hair loss.Methods: In this paper, YOLOv5 object detection algorithm was used to detect hair follicles in a small and specific image dataset collected using a specialized camera on the scalp of individuals with different ages, regions, and genders. The performance of YOLOv5 was compared with other popular object detection models.Results: YOLOv5 performed well in the detection of hair follicles, and the follicles were classified into five classes based on the number of hairs and the type of hair contained. In single‐class object detection experiments, a smaller batch size and the smallest YOLOv5s model achieved the best results, with an map of 0.8151. In multiclass object detection experiments, the larger YOLOv5l model was able to achieve the best results, and batch size affected the result of model training.Conclusion: YOLOv5 is a promising algorithm for detecting hair follicles in a small and specific image dataset, and its performance is comparable to other popular object detection models. However, the challenges of small‐scale data and sample imbalance need to be addressed to improve the performance of target detection algorithms.

Temperature climate is an essential component of weather forecasting and is vital in predicting future weather patterns. Accurate temperature predictions can assist individuals and organizations in preparing for potential weather-related events such as heat waves or cold snaps. However, achieving precise temperature predictions necessitates thoroughly comprehending the underlying factors influencing climate patterns. The study utilized two models, LSTM and DLSTM, to forecast daily air temperature using 1097 data points gathered from central and southern regions of Tabriz city of Iran in Asia from 2017 to 2019. The results indicated that the proposed model had a high accuracy rate for predicting daily air temperature for test data, with RMSE_DLSTM = 0.08 °C and R-Square_DLSTM = 0.99. The DLSTM algorithm is known for its high speed, accuracy, time series prediction, noise reduction capabilities for data, the large volume of data processing, and improved performance of predicted data. In summary, while both LSTM and DLSTM are used for predicting data points, DLSTM is a more advanced version that includes multiple layers of memory cells and is better suited for handling complex sequences of events.