Traditional news analysis models struggle to jointly achieve accurate sentiment recognition and reliable popularity prediction because sentiment expression and dissemination behavior are coupled in a complex, cross-modal manner. This paper proposes a multi-task framework jointly modeling news sentiment and dissemination effects, taking text content and dissemination behavior as joint inputs. A domain-pre-trained language model extracts semantic representations, multi-head attention captures sentiment-dissemination correlations, and a fusion space combining text semantics, sentiment intensity, and dissemination features supports collaborative optimization of sentiment classification and popularity prediction on a shared feature layer. Experiments on a public news dataset show the model achieves precision, recall, and F1-score of 0.878, 0.871, and 0.874, with R2 of 0.863 and a mean absolute error of 0.278 for popularity prediction, confirming the cooperative benefit of domain pre-training, attention modeling, and feature fusion for intelligent public opinion analysis and dissemination prediction.
Pavement crack segmentation is challenged by complex road-surface textures, foreground-background imbalance, and texture ambiguity under low brightness. The authors of this study propose a framework combining trainable Log-Gabor kernels and a wavelet branch, fused via a residual module, with a RepViT backbone and a context-aware object contextual representation head. Using a validation-based checkpoint-selection protocol on DeepCrack, Crack500, and crack forest dataset, the authors enabled the method to attain a mean F1 score of 63.04% and an Intersection over Union of 47.24% over 12 repeated runs on DeepCrack. These results are comparable to a same-seed baseline without the texture front end but show a statistically significant gain on Crack500 (+11.6 F1, p = .014) and a similarly-sized, nonsignificant trend on crack forest dataset (+9.7 F1, p = .059). Against 14 single-run literature methods, the method is competitive, but not uniformly best; a thick-versus-thin annotation mismatch between datasets and fragmentation errors contributes most to the residual gap. Repeated-trial statistics, a ConvNeXt V2 comparison, computational-cost analysis, and error categorization are also reported.
Personalized clothing customization often struggles to integrate precise 3D human body data with high-fidelity digital simulation. In this paper, the authors present an end-to-end framework that bridges this gap by merging conditional deep generative adversarial networks with differential geometry-driven finite element simulation. Their generation method extracts 3D principal component analysis features and style preferences into a conditional variational autoencoder, optimized by a composite loss function that combines curvature matching and size constraints to produce tailored 3D meshes and 2D patterns. During virtual display, the authors incorporated curvature matching and geodesic regularization into the finite element model to accurately simulate fabric deformation. Experimental results confirmed the framework’s efficacy, reducing mean fitting error to 2.5 mm and achieving a key size deviation of 1.8%. Additionally, the authors’ simulation model attained a 35.0 frames per s rendering rate with a 90.0% physical consistency score, demonstrating a robust solution for automated digital clothing customization.
Chemical process operations require fault-adaptive and energy-aware automation, yet conventional control methods often struggle to maintain robustness, fault tolerance, and energy efficiency under dynamic operating conditions. This paper presents an artificial intelligence-enhanced process monitoring and control framework for the Tennessee Eastman process that integrates a hybrid temporal convolutional neural network-bidirectional gated recurrent unit encoder for fault detection with a safety-constrained reinforcement learning-based adaptive controller. The proposed framework achieves superior fault detection performance, with an F1 score of 0.957, an area under the receiver operating characteristic curve of 0.981, and a median detection time of 10.2 s, outperforming long short-term memory and autoencoder baseline models. It also delivers improved control performance, achieving an energy index of 0.866 and a control efficiency of 1.113, while reducing energy consumption compared with the long short-term memory, autoencoder, and model predictive control baselines. These findings demonstrate that combining temporal feature learning with safety-constrained policy optimization provides a practical approach for developing more resilient, energy-efficient industrial process automation systems.
Students’ family backgrounds and behaviors can affect their learning difficulties, but teachers managing large classes often cannot monitor each student’s complex situation. Machine learning offers a solution by identifying anomalies in student performance data, enabling early detection of at-risk students. This study proposed a method that preprocessed data and applied smooth target encoding with a global mean prior and smoothing to address high-dimensional sparsity and small-sample category noise. Fitting encoding was performed on the training set to prevent data leakage into features. After training, temperature scaling calibration on the validation set produced probability outputs suitable for threshold-sensitive decision-making. Experimental results showed 92.31% accuracy, 95.24% F1 score, and 97.97% receiver operating characteristic–area under curve, demonstrating strong predictive performance for final student grades. This approach provided robust technical support for teachers to enhance teaching quality.
