With the increasing use of electronic commerce, online purchasing users have been rapidly rising. Predicting user behavior has therefore become a vital issue based on the collected data. However, traditional machine learning algorithms for prediction require significant computing time and often produce unsatisfactory results. In this paper, a prediction model based on XGBoost is proposed to predict user purchase behavior. Firstly, a user value model (LDTD) utilizing multi-feature fusion is proposed to differentiate between user types based on the available user account data. The multi-feature behavior fusion is carried out to generate the user tag feature according to user behavior patterns. Next, the XGBoost feature importance model is employed to analyze multi-dimensional features and identify the model with the most significant weight value as the key feature for constructing the model. This feature, together with other user features, is then used for prediction via the XGBoost model. Compared to existing machine learning models such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Back Propagation Neural Network (BPNN), the eXtreme Gradient Boosting (XGBoost) model outperforms with an accuracy of 0.9761, an F1 score of 0.9763, and a ROC value of 0.9768. Thus, the XGBoost model demonstrates superior stability and algorithm efficiency, making it an ideal choice for predicting user purchase behavior with high levels of accuracy.

Deep-learning-based multiple label chest X-ray classification has achieved significant success, but existing models still have three main issues: fixed-scale convolutions fail to capture both large and small lesions, standard pooling is lacking in the lack of attention to important regions, and linear classification lacks the capacity to model complex dependency between features. To circumvent these obstacles, we propose CONVFCMAE, a lightweight yet powerful framework that is built on a backbone that is partially frozen (77.08 % of the initial layers are fixed) in order to preserve complex, multi-scale features while decreasing the number of trainable parameters. Our architecture adds (1) an intelligent global pooling module that is learnable, with $1\times 1$ convolutions that are dynamically weighted by their spatial location, and (2) a multi-head attention block that is dedicated to channel re-calibration, along with (3) a two-layer MLP that has been enhanced with ReLU, batch normalization, and dropout. This module is used to enhance the non-linearity of the feature space. To further reduce the noise associated with labels and the imbalance in class distribution inherent to the NIH ChestXray14 dataset, we utilize a combined loss that combines BCEWithLogits and Focal Loss as well as extensive data augmentation. On ChestXray14, the average ROC–AUC of CONVFCMAE is 0.852, which is 3.97 percent greater than the state of the art. Ablation experiments demonstrate the individual and collective effectiveness of each component. Grad-CAM visualizations have a superior capacity to localize the pathological regions, and this increases the interpretability of the model. Overall, CONVFCMAE provides a practical, generalizable solution to the problem of extracting features from medical images in a practical manner.

Steel surface defect classification is critical for industrial quality control, yet existing methods struggle to balance accuracy and efficiency for real-time deployment in vision-based sensor systems. This paper presents MobileSteelNet, a lightweight deep learning framework that introduces two novel modules: multi-scale feature fusion (MSFF), for integrating multi-stage features; and Cross-Interactive Efficient Multi-Scale Attention (CIEMA), which unifies inter-channel interaction, parallel multi-scale spatial extraction, and grouped efficient computation. Experiments on the NEU-DET dataset demonstrate that MobileSteelNet achieves 91.36% average accuracy, surpassing ResNet-50 (88.01%) and lightweight networks, including MobileNetV2 (86.08%). Notably, it achieves 93.70% accuracy on Scratch-type defects, representing an 82.12 percentage point improvement over baseline MobileNetV1. With a model size of only 8.2 MB, MobileSteelNet maintains superior performance while meeting lightweight deployment requirements, making it suitable for edge deployment in vision sensor systems for steel manufacturing.

Steel surface defect detection is a core component of quality control in steel manufacturing and is crucial for product performance and production efficiency. In recent years, computer-vision-based deep learning methods have decisively surpassed traditional machine-vision approaches built on handcrafted features, propelling steel defect detection into the intelligent era. The review itself is limited to deep learning methods; handcrafted machine vision is background only. This paper provides a comprehensive survey of deep-learning-based steel surface defect detection, citing 184 publications, of which 183 were published between 2015 and 2026. We organize the literature around the three core tasks—classification, detection, and segmentation—and review each task from the perspective of network architecture optimization. We further summarize more than ten public datasets, including NEU and GC10-DET, together with the commonly used evaluation metrics and open-source code resources, and compile literature-reported results for dozens of models, including the YOLO series, DETR, and Swin Transformer. Those figures are copied from the original papers and are not a unified benchmark. Finally, we analyze the core challenges—data scarcity, small-target detection, model generalization, and real-time performance—and discuss future research directions, including high-resolution image processing, few-shot learning, and large-model-assisted quality assessment.

Facial expression recognition plays a pivotal role in various domains, including human-computer interaction and healthcare. In healthcare, recognizing pain and smile expressions aids in assessing patients' pain status and recovery progress. However, challenges such as inaccurate data labeling and complex model training hinder real-world deployment. This study introduces a novel lightweight neural network model (A-C model) incorporating an attention mechanism (D-SE-BAM) and transfer learning to recognize spontaneous expressions with limited datasets. The model demonstrated excellent performance, achieving 93.1% accuracy on the GENKI-4K dataset, 88.8% on the UNBC database, and 89.02% in real scenarios. These findings provide valuable insights for advancing facial expression recognition technologies.