The proliferation of fake reviews, often produced by organized groups, undermines consumer trust and fair competition on online platforms. These groups employ sophisticated strategies that evade traditional detection methods, particularly in cold-start scenarios involving newly launched products with sparse data. To address this, we propose theDiversity- andSimilarity-awareDynamicGraphAttention-enhancedGraphConvolutionalNetwork (DS-DGA-GCN), a new graph learning model for detecting fake reviewer groups. DS-DGA-GCN achieves robust detection since it focuses on the joint relationships among products, reviews, and reviewers by modeling product-review-reviewer networks. DS-DGA-GCN also achieves adaptive detection by integrating a Network Feature Scoring (NFS) system and a new dynamic graph attention mechanism. The NFS system quantifies network attributes, including neighbor diversity, network self-similarity, as a unified feature score. The dynamic graph attention mechanism improves the adaptability and computational efficiency by captures features related to temporal information, node importance, and global network structure. Extensive experiments conducted on two real-world datasets derived from Amazon and Xiaohongshu demonstrate that DS-DGA-GCN significantly outperforms state-of-the-art baselines, achieving accuracies of up to89.8% and 88.3%, respectively.

With the development of intelligent technology in coal mine industrial Internet of Things (IoT), the demand for salient object detection (SOD) in underground coal mine space has been increasing. The complex scenes and variable backgrounds in coal mine bring challenges for SOD, such as blurred edges, high computational complexity, and long processing times, making it difficult to meet the accuracy and real-time requirements of coal mine industrial IoT applications. To address these issues, we propose the dynamic weighting hybrid attention (DWHA)-partial convolution multiscale strip pooling (PCMSP) network for SOD in the coal mine industrial IoT. First, we introduce the DWHA module, which dynamically fuses self-attention for global context and SBAM for refining channel and spatial information, improving saliency detection accuracy. Second, we propose the PCMSP lightweight module, which the multiscale strip pooling introduces multiscale dilations, enhancing the ability to capture multiscale information and improve feature representation. By using partial convolution, which reduces the consumption of computing resources and running time while ensuring boundary quality. The experimental results indicate that, using self-built dataset for underground coal mine SOD, the DWHA-PCMSP network outperforms the four SOTA: BASNet, U2Net, SUCA, and EDN by achieving an increase of 2.82% in F1-score, a decrease of 23.70% in MAE, a reduction of 72.3G in FLOPs, and an improvement of 6.6 FPS in speed, compared to the worst-performing model.

Gas disaster is one of the major disasters faced by coal mines, and more than 50% of gas disasters are concentrated in the working face. However, due to the small number of gas monitoring sensors and the heavy workload of artificial inspection of the working face, it is very difficult to monitor the high coverage of the working face. Aiming at the problems of low coverage of monitoring data and high acquisition cost, this article uses sparse mobile crowdsensing (MCS) to monitor the gas concentration, which specifically involves the cell selection of sensing area and the data inference of gas concentration in unaware area. First, this article combines gas source distribution and working face air flow characteristics to efficiently divide the gas concentration sensing cells. We propose cell selection based on distributed weighted self-attention mechanism deep reinforcement learning (DWS-DQN). The cell selection algorithm utilizes an attention mechanism to capture the key states of reinforcement learning to assist in optimization and decision making. Second, we propose gas concentration inference based on diffusion coefficient weighting (DCW). Based on the gas concentration diffusion coefficient of coal mine working face, we weighted the quantitative results of different characteristics to construct the gas concentration inference model. Finally, experiments on two real coal mines sensing data sets verify the effectiveness of our proposed algorithms. Compared to the baseline method, the DWS-DQN model and DCW model both exhibit good performance. The method based on the combination of DWS-DQN and DCW reduces the average MAPE result by 6.87%.

With the rapid expansion of e-commerce and social media platforms, the prevalence of fake reviews has become increasingly problematic, misleading consumers and harming both the reputation of businesses and fair market competition. This article aims to develop a more effective technological solution to accurately identify and filter deceptive reviews, ensuring a truthful shopping and communication environment for consumers. Initially, a multifeature fusion strategy is introduced, integrating text characteristics of reviews, reviewer behavior, and product information. Through a parameterized attention mechanism, the model meticulously assigns weights to various influential features, thereby enhancing the detection of deceptive reviews. Furthermore, a composite architecture, DynDistilBERT-BiLSTM-CNN, is proposed. DynDistilBERT employs a control gate to assess the complexity of the input text and the required processing power in real-time during model forward propagation, dynamically selecting active layers within DistilBERT. This selection process is optimized with a hierarchical training strategy to minimize additional computational overhead. BiLSTM excels in processing sequential data, capturing temporal text features, while convolutional neural network focuses on identifying local text features. This approach reduces computational resource consumption for simpler tasks while maintaining high accuracy for complex tasks, recognizing both local features and contextual relationships in text. Extensive testing on the Amazon data set, compared to models like ALBERT, SpanBERT, DistilBERT, and RoBERTa, demonstrates that our model achieves accuracy improvements of approximately 5.7%, 5.2%, 5.0%, and 3.9%, with a peak accuracy of 92.6%. These findings underscore the effectiveness of the multifeature fusion strategy and the superior performance of the DynDistilBERT-BiLSTM-CNN architecture in handling complex textual data.

Image super-resolution reconstruction is one of the methods to improve resolution by learning the inherent features and attributes of images. However, the existing super-resolution models have some problems, such as missing details, distorted natural texture, blurred details and too smooth after image reconstruction. To solve the above problems, this paper proposes a Multi-scale Dual-Attention based Residual Dense Generative Adversarial Network (MARDGAN), which uses multi-branch paths to extract image features and obtain multi-scale feature information. This paper also designs the channel and spatial attention block (CSAB), which is combined with the enhanced residual dense block (ERDB) to extract multi-level depth feature information and enhance feature reuse. In addition, the multi-scale feature information extracted under the three-branch path is fused with global features, and sub-pixel convolution is used to restore the high-resolution image. The experimental results show that the objective evaluation index of MARDGAN on multiple benchmark datasets is higher than other methods, and the subjective visual effect is better. This model can effectively use the original image information to restore the super-resolution image with clearer details and stronger authenticity.