Recent years, multi-label feature selection has gradually attracted significant attentions from machine learning, statistical computing and related communities and has been widely applied to diverse problems from music recognition to text mining, image annotation, etc. However, traditional multi-label feature selection methods employ cumulative summation strategy to design methods, which suffers from the problem of overestimation the redundancy of some candidate features. Additionally, the cumulative summation strategy will lead to the high value of the goal function when one candidate feature is completed related with one or few already-selected features, but it is almost independent from the majority of alreadyselected features. To address these issues, we propose a new multi-label feature selection method named Feature Redundancy Maximization (FRM), which combines the cumulative summation of conditional mutual information with the ‘maximum of the minimum’ criterion. Additionally, FRM can be rewritten as another form of multi-label feature selection method that employs interaction information as a measure of feature redundancy that obtains an accurate score of feature redundancy as the number of already-selected features increases. Finally, extensive experiments are implemented on fourteen benchmark multi-label data sets in comparison to six state-of-the-art methods. The experimental results demonstrate the superiority of the proposed method.

Accurate and robust wind power forecasting plays a crucial role in ensuring the safety and stability of the power system. Hybrid spatiotemporal forecasting models based on graph convolutional networks have received widespread attention due to their advantages in spatial feature extraction. However, these methods are susceptible to the quality of the generated graph due to data noise and missing issues, resulting in suboptimal performance. In this paper, we propose a hybrid deep learning model based on spatiotemporal graph contrastive learning to address the above issues. Specifically, the model's encoder combines an adaptive graph convolutional network with LSTM to capture fine-grained spatiotemporal dependencies. To enhance the robustness of the encoder against data noise, we apply feature-level and topology-level data augmentation techniques to the model's input and design two contrastive learning auxiliary tasks from the temporal and spatial dimensions, respectively. Furthermore, to capture more comprehensive spatial correlations, we construct an adaptive graph by fusing the static graph with a learnable parameter matrix. Extensive experimental results on two real-world datasets demonstrate that our proposed model significantly outperforms other state-of-the-art methods.

Building extraction from remote sensing images (RSIs) requires exploring multiscale boundary detailed information and extracting it completely, which is challenging but indispensable. However, existing solutions tend to augment feature information solely through multiscale fusion and apply attention mechanisms to focus on feature relationships within a single layer while ignoring the multiscale information, which affects segmentation results. Therefore, enhancing the capability of the network to adaptively capture multiscale information and capture the global relationship of features remains a pivotal challenge in overcoming the aforementioned hurdles. To address the preceding challenge, we propose a Multiscale Direction Context-aware network with Global Attention (MDCGA-Net), employing a classic encoder–decoder architecture enhanced with direction information and global attention flow. Specifically, in the encoder part, the multiscale layer is used to extract contextual information from the interlayer. In addition, the multiscale direction context-aware module is adopted to adaptively acquire multiscale information. In the decoder part, we propose a global attention gate module to capture discriminative features. Furthermore, we construct an operation of attention feature flow to obtain the global relationship among the different features with long-range dependencies, which guarantees the integrity of results. Finally, we have performed comprehensive experiments on three public datasets to showcase the efficacy and efficiency of MDCGA-Net in building extraction.

Accurate traffic prediction is vital for traffic management, control, and urbanization construction. Extensive research efforts have diligently focused on capturing the intricate spatio-temporal relationships that are inherent in traffic data. However, a limited number of studies have fully exploited the potential of periodicity, a distinctive and valuable characteristic of transportation systems. In this paper, we propose a novel GRU- and Transformer-Based Periodicity Fusion Network (GTPFN) to distinguish the effects of different types of periodic data and integrate them seamlessly and effectively. Initially, the proposed model captures dynamic spatio-temporal correlations and obtains the candidate prediction result by employing a GRU encoder–decoder with spatial attention, focusing on the hourly data. Subsequently, we design the Pattern Induction Block based on GRU layers to extract regular traffic patterns from daily and weekly data. Finally, the Pattern Fusion Transformer integrates these patterns, followed by a Feedforward layer, to yield the final prediction output. Experiments on the Caltrans Performance Measurement System (PEMS) datasets illustrate that the proposed model outperforms state-of-art baseline models on most predicted horizons.

Issues such as data scarcity and data imbalance have long posed significant difficulties in the field of intelligent fault diagnosis. They lead to reduced diagnostic accuracy and endanger the safety and reliability of industrial equipment. To address these challenges, this study introduces a novel channel attention-based conditional diffusion model (CAC-DM) that recalibrates features through a squeeze-and-excitation process. This enhancement boosts the model’s ability to focus on critical features while suppressing irrelevant information, thereby improving the UNet network’s discrimination capability in handling small-sample faults that are highly similar in nature. Experimental validation demonstrates that CAC-DM performs exceptionally well in scenarios with high class similarity, effectively distinguishing among categories with similar distributions in limited data and generating high-quality samples. Compared to existing generative methods, the CAC-DM exhibits significant advantages in producing distinguishable fault samples, particularly in cases of sample imbalance. This approach offers an effective new solution for fault diagnosis.

Road extraction from remote sensing images (RSIs) is a key research area in smart city development. While deep learning techniques have demonstrated remarkable effectiveness in this domain, existing approaches exhibit limitations: convolutional neural network (CNN)-based methods struggle to capture global contextual information for long-range road networks, vision transformer (ViT)-based methods fail to adequately extract multi-scale local features, and hybrid CNN-ViT architectures overlook the synergistic guidance between local and global features. To address these challenges, we propose LG-Umer, a UNet-like network that integrates Local-Global features with a novel attention mechanism, combining the complementary strengths of CNNs and ViTs within an encoder-decoder framework. Specifically, the encoder employs a Multi-scale Strip Deformational (MSD) module, which utilizes deformable convolutions to adaptively extract topological structures and variable-shaped local road features. In the decoder, a Multi-stage Gate Unit (MGU) module is introduced, incorporating a novel attention mechanism to model long-range dependencies by leveraging local features as attention operators for global feature refinement. Extensive experiments on three public benchmarks demonstrate the superiority of LG-Umer. It achieves IoU scores of 70.4%, 71.2% and 68.7% on the Massachusetts Road, DeepGlobe Road, and CHN6-CUG datasets, respectively, surpassing recent state-ofthe-art (SOTA) methods by 1.2%, 0.9%, and 1.1%. These results validate the effectiveness of our approach in balancing local detail preservation and global contextual modeling for road extraction tasks.