Applied Intelligence最新文献

{"title":"Micro-dynamics prediction of well water level based on GRU and attention mechanism","authors":"Xiaoyu Fang,&nbsp;Lili Zhang,&nbsp;Haoran Li,&nbsp;Yaowen Zhang,&nbsp;Yunsheng Yao","doi":"10.1007/s10489-025-06855-x","DOIUrl":"10.1007/s10489-025-06855-x","url":null,"abstract":"<div><p>Well water level is an important precursor observation, which is expected to be used to extract information on subsurface stress and media changes. Real-time prediction of well water level can help prevent geological disasters, but there are few related experimental studies. This study aims to explore a short-term prediction model of well water level that is more pervasive than the GRU model, explore new methods to enhance the model’s capability, and provide scientific references for the application of deep learning models in the field of well water level prediction. Taking the measured data of the Three Gorges well network from 2012 to 2014 as an example, the performance of the GRU and its variant models on the RMSE, MAE and R² evaluation criteria are compared, and the results show that only the BiGRU-Attention model shows excellent performance at all well points, with better pervasiveness and stability; performing a single-step prediction and adding a 1% standard deviation noise to the training set can improve the robustness and generalisation of the model.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 14","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive deep shared latent representation enables novel multi-omics cancer subtype classification","authors":"Min Li,&nbsp;Zhifang Qi,&nbsp;Shaobo Deng,&nbsp;Lei Wang,&nbsp;Xiang Yu","doi":"10.1007/s10489-025-06848-w","DOIUrl":"10.1007/s10489-025-06848-w","url":null,"abstract":"<div><p>Variations in outcomes among cancer patients are significant even when they have the same type of tumor. Identifying and classifying molecular subtypes of cancer offers a valuable opportunity to enhance prognosis and tailor treatment plans for individuals. Recent efforts have been made to generate extensive multidimensional genomic data to achieve this potential. However, existing algorithms still face challenges in integrating and analyzing such intricate datasets. In this study, we present Adaptive Deep Shared Latent Representation (ADSLR), a novel approach for cancer subtyping that utilizes shared latent representation to reveal distinct molecular subtypes in cancer. It incorporates a cycle autoencoder with a nonnegative matrix factorization layer, capturing consistent signals of nonlinear features at various omics levels. This enables the generation of adaptable representations for shared latent representation across multiple omics levels. We apply ADSLR to multi-omics data obtained from eight different cancer types in the “The Cancer Genome Atlas” dataset, demonstrating significant improvements in the identification of biologically meaningful cancer subtypes. These identified subtypes exhibit noteworthy variations in patient survival rates across seven out of the eight cancer types. Our analysis uncovers integrated patterns involving mRNA expression, miRNA expression, DNA methylation, and protein across multiple cancers while showcasing ADSLR’s versatility for integrating various other omics types.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 14","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing multi-level cross-modal interaction with false negative-aware contrastive learning for text-video retrieval","authors":"Eungyeop Kim,&nbsp;Changhee Lee","doi":"10.1007/s10489-025-06821-7","DOIUrl":"10.1007/s10489-025-06821-7","url":null,"abstract":"<div><p>Text-video retrieval (TVR) has become a crucial branch in multi-modal understanding tasks. Enhanced by CLIP, a well-known contrastive learning framework that connects text and image, TVR has made substantial progress, particularly in developing cross-grained methods that account for both coarse and fine granularity in text and video. Nonetheless, previous cross-grained approaches have overlooked two crucial aspects. First, they utilize text-agnostic video summaries by simply averaging frame-level embeddings, potentially failing to capture crucial frame-level information that is semantically relevant to the corresponding text. Second, these approaches employ contrastive learning that neglects the impact of false negatives containing semantically relevant information. To address the aforementioned aspects, we introduce a novel framework for TVR, referred to as <i>X-MLNet</i>, focusing on capturing multi-level cross-modal interactions across video and text. This is done by first incorporating cross-attention modules at various levels of granularity, ranging from fine-grained (i.e., frame/word-level) representations to coarse-grained (i.e., video/sentence-level) representations. Then, we apply a contrastive learning framework that utilizes a similarity score computed based on the multi-level cross-modal interactions, excluding potential false negatives based on intra-modal connectivity among samples. Our experiments on five real-world benchmark datasets, including MSRVTT, MSVD, LSMDC, ActivityNet, and DiDeMo, demonstrate state-of-the-art performance in both text-to-video and video-to-text retrieval tasks. Our code is available at https://github.com/celestialxevermore/X-VLNet.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 14","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A transfer learning-based fault diagnosis method for rolling bearings with variable operating conditions","authors":"Cunli Song,&nbsp;Xiaomeng Yuan","doi":"10.1007/s10489-025-06811-9","DOIUrl":"10.1007/s10489-025-06811-9","url":null,"abstract":"<div><p>Aiming at the problem that fault feature information cannot be completely extracted and it is difficult to obtain a large amount of sample data for fault labeling in real production life, we propose a transfer learning-based fault diagnosis method for rolling bearings with variable operating conditions. First, in order to make up for the single limitation of the feature extraction of the original vibration signal, a new feature signal is formed by fusing the time domain features on the basis of the original vibration signal, which is used as the input of the model, and a lightweight one-dimensional convolutional neural network(1d-CNN) is constructed, and an efficient channel attention mechanism is introduced to extract the fault features, so as to get the source domain diagnostic model. Then, according to the idea of transfer learning, the vibration signals under different working conditions are input into the fine-tuned model to realize the rolling bearing fault diagnosis under multiple working conditions. The results show that the method can realize migration under different working conditions and accurately and efficiently realize rolling bearing fault diagnosis.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 14","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based multi-element identification system for percutaneous endoscopic spine surgery: development and comparative evaluation of neural network models","authors":"Jinhui Bu,&nbsp;Yari Wang,&nbsp;Jiaqi Zhao,&nbsp;Sen Huang,&nbsp;Jun Liang,&nbsp;Zhenfei Wang,&nbsp;Long Xu,&nbsp;Yan Lei,&nbsp;Bo He,&nbsp;Minghui Dong,&nbsp;Guangpu Liu,&nbsp;Ru Niu,&nbsp;Chao Ma,&nbsp;Guangwang Liu","doi":"10.1007/s10489-025-06641-9","DOIUrl":"10.1007/s10489-025-06641-9","url":null,"abstract":"<div><p>As the update of medical equipment and technology accelerates, the surgical options of spinal disease have gradually developed from the traditional open surgery to the present various minimally invasive endoscopic surgery. Among these, percutaneous endoscopic discectomy stands out as one of the main procedures for treating lumbar disc herniation and lumbar spinal stenosis. Currently, the application of computer deep learning technology has demonstrated promising results in clinical diagnosis and treatment. The report aims to first describe a deep learning-based multi-element identification system for the visual field in percutaneous endoscopic spine surgery and to evaluate its feasibility. We established an image database by collecting surgical videos from 80 patients diagnosed with lumbar disc herniation and lumbar spinal stenosis, which were labeled by two spinal surgeons. We selected 10000 images of the visual field of percutaneous endoscopic spine surgery (including various tissue structures and surgical instruments), divided into the training data, validation data, and test data according to 3:1:1. We developed neural network models based on instance segmentation - VMamba, Mask RCNN, HIRI-ViT-B. Mean average precision (mAP) and frames per second (FPS) were used to measure the performance of each model for classification ,localization and recognition in real-time, and AP (average) is used to evaluate how easily an element is detected by neural networks based on computer deep learning. Combining the structural characteristics and performance comparison of the various types of models, the results from the test dataset show that VMamba (SSM) performs best in image boundary box detection (mAP = 79.1%) and contour segmentation (mAP = 81.6%), while HIRI-ViT-B is faster in real-time image processing (FPS = 42.7). Combining the average precision of the elements in the bounding box test and segmentation tasks in each network, the AP(average) was highest for tool 3 (bbox-0.91,segm-0.89) and lowest for tool 5 (bbox-0.80,segm-0.74) in the instrumentation. Among the tissue elements, the accuracy of bounding box detection and contour segmentation was highest for the ligamentum flavum (bbox-0.80,segm-0.75), and lowest for extra-dural fat (bbox-0.57,segm-0.54). This study creates the first instance segmentation-based dataset focusing on multiple elements (anatomical tissue, surgical instruments)within the field of view of spinal endoscopic surgery, and integrate computer vision intelligence with spinal endoscopic surgery by developing various neural networks to recognize, classify, and segment the target elements in the dataset, tracking the whole operation. By comparing three models - VMamba, Mask RCNN, HIRI-ViT-B, we recommended VMamba (SSM) model for the intraoperative real-time assistance system for spinal endoscopic operation.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 11","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144927120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GAN-CLC-DGSR: Generative adversarial network framework with contrastive learning classifier for simultaneous time series data generation and state recognition","authors":"Weidong Wang,&nbsp;Yuxin Wu,&nbsp;Yang Song,&nbsp;Xuan Zhao,&nbsp;Yao Cui,&nbsp;Yuhan Fan,&nbsp;Yanbo Liu,&nbsp;Ziqi Lv","doi":"10.1007/s10489-025-06856-w","DOIUrl":"10.1007/s10489-025-06856-w","url":null,"abstract":"<div><p>Accurate identification of abnormal states is crucial for the continuous stable operation of equipment and timely intervention. However, the scarcity of abnormal data leads to low recognition accuracy in traditional methods when handling the data imbalance problem. To address this issue, we propose a novel Generative Adversarial Network Framework with Contrastive Learning Classifier for Simultaneous Time Series Data Generation and State Recognition (GAN-CLC-DGSR). In this framework, the generator not only synthesizes realistic signals but also enables the conversion between different signal categories. In addition to the conventional discriminator used to distinguish real from fake data, we design a contrastive learning-based classification discriminator. This discriminator maps the time-domain and frequency-domain features of the signal to a unified space, capturing invariant characteristics of the signal. This aids the generator in producing samples with higher category distinguishability. The classification discriminator is also trained as a state recognizer. We conduct extensive experiments on the vibration screen dataset from a coal preparation plant, a bearing dataset, and an epilepsy dataset. The results demonstrate that the proposed method outperforms other comparative methods in both data generation and state recognition, and it exhibits strong generalization capability.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 14","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crack segmentation network based on hybrid-window transformer and dual-branch fusion","authors":"Jianxin Wang,&nbsp;Jin Wang,&nbsp;Yi Li,&nbsp;Dongdong Ge,&nbsp;Siyuan Zhou","doi":"10.1007/s10489-025-06822-6","DOIUrl":"10.1007/s10489-025-06822-6","url":null,"abstract":"<div><p>Cracks are external manifestations of infrastructure damage, and routine inspections are crucial for assessing their structural safety. However, due to factors such as crack diversity, background noise interference, and information loss, high-precision crack segmentation still faces numerous challenges. To alleviate the influence of these factors, a crack segmentation network based on hybrid-window Transformer and dual-branch fusion (CSHD) is proposed. The CSHD network can effectively capture local texture details and global context modeling to achieve high-precision crack segmentation. First, a hybrid-window attention mechanism(HWA) is designed as the core component, which employs a dual-branch parallel architecture to integrate channel attention and multi-scale depth-wise convolution modules on the value path of window attention, achieving spatial receptive field expansion and cross-window feature interaction. Second, to enhance feature processing capabilities, a locally enhanced gated FeedForward network (LeGN) is proposed, which achieves adaptive feature aggregation through overlapping multi-scale deformable convolution, and a gated unit is designed to optimize the information flow. Thirdly, a dual-branch fusion module (DBF) is introduced in skip-connections of encoder-decoder layers to enhance cross-level feature interaction while effectively mitigating information loss during the downsampling process. Finally, comparative experimental results on three benchmark datasets (CrackLS315, DeepCrack537, and YCD776) with seven advanced networks demonstrate that the proposed network achieves excellent performance, obtaining mean intersection over union (mIoU) scores of 71.26%, 86.58%, and 83.93%, respectively. Code is available at: https://github.com/wjxcsust2024/CSHD.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 13","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VAHMSE: an efficient anomaly detection model based on variational autoencoder and heterogeneous multi-stacking ensemble learning","authors":"Rui Wang,&nbsp;Jiayao Li","doi":"10.1007/s10489-025-06845-z","DOIUrl":"10.1007/s10489-025-06845-z","url":null,"abstract":"<div><p>With the advent of the information age, data has become an important resource and production factor. However, the existence of abnormal data causes the lose of personal privacy, business operations and national security, therefore, anomaly detection has received increasing attention in recent years. Most existing anomaly detection models are based on machine learning or deep learning models, but the use of a single model leads to the problems such as overfitting, weak generalization and poor stability. Meanwhile, there is a serious data imbalance problem due to the significantly few number of abnormal data compared to normal data, which reduces the detection performance. To address these issues, this paper proposes an anomaly detection model called VAHMSE based on <i>v</i>ariational <i>a</i>utoencoder and <i>h</i>eterogeneous <i>m</i>ulti-<i>s</i>tacking <i>e</i>nsemble learning to improve the detection performance. In the data augmentation phase, the <i>v</i>ariational <i>a</i>uto<i>e</i>ncoder (VAE) is used to replace traditional oversampling and other class balancing techniques to solve the data imbalance problem, and the mutual information is added to the loss function of traditional VAE to solve the problem of posterior distribution collapsing to prior distribution, thereby improving the quality of data generation. In the anomaly detection phase, the heterogeneous multi-stacking ensemble learning-based anomaly detection method is proposed, where five machine learning models with good performance are selected as the base learners in the first layer stacking process, and the TCN is selected as the meta learner in the second layer stacking process; In addition, the Squeeze and Excitation module is integrated into the traditional TCN model to explicitly model the interdependence between convolutional feature channels and improve the representation ability of network. Extensive experiments on six widely used datasets show that compared with five state-of-the-art models, the proposed VAHMSE achieves better performance in accuracy, recall, precision and F1-score, and it also achieves better stability.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 13","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid CNN-RWKV with high-frequency enhancement for real-world chinese-english scene text image super-resolution","authors":"Yanbin Liu,&nbsp;Yu Zhu,&nbsp;Hangyu Li,&nbsp;Xiaofeng Ling","doi":"10.1007/s10489-025-06785-8","DOIUrl":"10.1007/s10489-025-06785-8","url":null,"abstract":"<div><p>Existing scene text image super-resolution (STISR) methods primarily focus on the restoration of fixed-size English text images. Compared to English characters, Chinese characters present a greater variety of categories and more intricate stroke structures. In recent years, Transformer-based methods have achieved significant progress in image super-resolution task, but face the dilemma between global modeling and efficient computation. The emerging Receptance Weighted Key Value (RWKV) model can serve as a promising alternative to Transformer, enabling long-distance modeling with linear computational complexity. In this paper, we propose a Hybrid CNN-RWKV with High-Frequency Enhancement (HCR-HFE) model for STISR task. First, we design a recurrent bidirectional WKV (Re-Bi-WKV) attention which integrates bidirectional WKV (Bi-WKV) attention with a recurrent mechanism. Bi-WKV achieves global receptive field with linear complexity, while the recurrent mechanism establishes 2D image dependencies from different scanning directions. Additionally, a computationally efficient high-frequency enhancement module (HFEM) is incorporated to enhance high-frequency details, such as character edge information. Furthermore, we design a multi-scale large kernel convolutional (MLKC) block which integrates large kernel decomposition, gated aggregation and multi-scale mechanism to capture various-range dependencies with reduced computational cost. Finally, we introduce a multi-frequency channel attention (MFCA) which extends channel attention to the frequency domain, enabling the model to focus on critical features. Extensive experiments on real-world Chinese-English (Real-CE) dataset demonstrate that HCR-HFE outperforms previous methods in both quantitative metrics and visual results. Furthermore, HCR-HFE achieves excellent performance on natural image datasets, demonstrating its broad applicability.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 13","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural-FCM: a deep learning approach for weight matrix optimization in Fuzzy Cognitive Map classifiers","authors":"Theodoros Tziolas,&nbsp;Konstantinos Papageorgiou,&nbsp;Ioannis Apostolopoulos,&nbsp;Elpiniki Papageorgiou","doi":"10.1007/s10489-025-06795-6","DOIUrl":"10.1007/s10489-025-06795-6","url":null,"abstract":"<div><p>The demand for interpretable and accurate machine learning models continues to grow, especially in critical domains. The data-driven Fuzzy Cognitive Map (FCM) classifier is an interpretable and transparent decision-making method. Its core element, the weight matrix, is derived using predominantly population-based supervised learning methods which often suffer from degraded performance. Recent research has adopted gradient-based learning techniques to compete with the predictive performance of black-box models. Nonetheless, such methods modify foundational principles and compromise interpretability, highlighting the necessity to improve existing approaches. In this work, we introduce a novel learning and structural modeling method, termed Neural-FCM, which leverages deep neural networks and gradient descent to enhance the accuracy and robustness of FCM learning. Neural-FCM employs a hybrid network comprising both dense and convolutional layers and is trained using a categorical cross-entropy loss function specifically aligned with FCM reasoning. This hybrid model is trained to output instance-specific weight matrices for effective and targeted FCM inference, introducing structural adaptability, a feature not supported by previous static or globally optimized approaches. Focusing on generalization across domains, the Neural-FCM approach is evaluated on different classification tasks across six widely used public datasets and one proprietary medical dataset, consistently showing improved predictive performance. Notably, the comparative analysis against standard population-based FCM learning methods reveals consistent accuracy improvements, with gains of up to 34%. While less transparent gradient-based methods also yield improved accuracy, Neural-FCM demonstrates competitive or superior performance in most cases, with accuracy improvements ranging from 1 to 6% across different domains, while preserving the underlying interpretability. The performance enhancement and the use of instance-specific matrices contribute to the broader goal of developing gradient-based models that balance computational efficiency with the intrinsic FCM interpretability.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 13","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-025-06795-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}