Computational Intelligence最新文献_第8页

MultiCogniGraph: A multimodal data fusion and graph convolutional network-based multi-hop reasoning method for large equipment fault diagnosis MultiCogniGraph：基于多模态数据融合和图卷积网络的大型设备故障诊断多跳推理方法

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-06-09 DOI: 10.1111/coin.12646

Sen Chen, Jian Wang

引用次数: 0

BiLSTM-based thunderstorm prediction for IoT applications 基于 BiLSTM 的物联网应用雷暴预测

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-06-09 DOI: 10.1111/coin.12683

Li Zhuang, Lin Zhu

{"title":"BiLSTM-based thunderstorm prediction for IoT applications","authors":"Li Zhuang, Lin Zhu","doi":"10.1111/coin.12683","DOIUrl":"https://doi.org/10.1111/coin.12683","url":null,"abstract":"Although the market demand for smart devices (SDs) in the Internet of Things (IoT) era is surging, the corresponding thunderstorm protection measures have rarely attracted attention. This paper presents a thunderstorm prediction method with elevation correction, to reduce the thunderstorm damage to SDs by visually tracking thunderstorm activities. First, a self-made three-dimensional atmospheric electric field apparatus (3DAEFA) deployed in IoT is developed to collect real-time AEF data. A 3DAEFA-based localization model is established, and the localization formula after correction is derived. AEF data predicted by the bi-directional long short-term memory (BiLSTM) model are input to this formula to obtain thunderstorm point charge localization results. Then, the localization skill is evaluated. Finally, the proposed method is assessed in experiments, under single and multiple point charge conditions. There are significant reductions of at least 33.1% and 8.8% in ranging and elevation angle errors, respectively. Particularly, this post-prediction correction reduces the deviation of fitted point charge moving paths by at most 0.189 km, demonstrating excellent application effects. Comparisons with radar charts and existing methods testify that this method can effectively predict thunderstorms.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141298524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GMINN: Gate-enhanced multi-space interaction neural networks for click-through rate prediction GMINN：用于点击率预测的门增强多空间交互神经网络

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-06-09 DOI: 10.1111/coin.12645

Xingyu Feng, Xuekang Yang, Boyun Zhou

{"title":"GMINN: Gate-enhanced multi-space interaction neural networks for click-through rate prediction","authors":"Xingyu Feng, Xuekang Yang, Boyun Zhou","doi":"10.1111/coin.12645","DOIUrl":"https://doi.org/10.1111/coin.12645","url":null,"abstract":"Click-through rate (CTR) prediction is a pivotal challenge in recommendation systems. Existing models are prone to disturbances from noise and redundant features, hindering their ability to fully capture implicit and higher-order feature interactions present in sparse feature data. Moreover, conventional dual-tower models overlook the significance of layer-level feature interactions. To address these limitations, this article introduces Gate-enhanced Multi-space Interactive Neural Networks (GMINN), a novel model for CTR prediction. GMINN adopts a dual-tower architecture in which a multi-space interaction layer is introduced after each layer in the dual-tower deep neural network. This layer allocates features into multiple subspaces and employs matrix multiplication to establish layer-level interactions between the dual towers. Simultaneously, a field-aware gate mechanism is proposed to extract crucial latent information from the original features. Experimental validation on publicly available datasets, Criteo and Avazu, demonstrates the superiority of the proposed GMINN model. Comparative analyses against baseline models reveal that GMINN substantially improves up to 4.09% in AUC and a maximum reduction of 7.21% in Logloss. Additionally, ablation experiments provide further validation of the effectiveness of GMINN.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141298526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cost-sensitive tree SHAP for explaining cost-sensitive tree-based models 用于解释基于成本敏感树模型的成本敏感树 SHAP

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-06-09 DOI: 10.1111/coin.12651

Marija Kopanja, Stefan Hačko, Sanja Brdar, Miloš Savić

{"title":"Cost-sensitive tree SHAP for explaining cost-sensitive tree-based models","authors":"Marija Kopanja, Stefan Hačko, Sanja Brdar, Miloš Savić","doi":"10.1111/coin.12651","DOIUrl":"https://doi.org/10.1111/coin.12651","url":null,"abstract":"Cost-sensitive ensemble learning as a combination of two approaches, ensemble learning and cost-sensitive learning, enables generation of cost-sensitive tree-based ensemble models using the cost-sensitive decision tree (CSDT) learning algorithm. In general, tree-based models characterize nice graphical representation that can explain a model's decision-making process. However, the depth of the tree and the number of base models in the ensemble can be a limiting factor in comprehending the model's decision for each sample. The CSDT models are widely used in finance (e.g., credit scoring and fraud detection) but lack effective explanation methods. We previously addressed this gap with cost-sensitive tree Shapley Additive Explanation Method (CSTreeSHAP), a cost-sensitive tree explanation method for the single-tree CSDT model. Here, we extend the introduced methodology to cost-sensitive ensemble models, particularly cost-sensitive random forest models. The paper details the theoretical foundation and implementation details of CSTreeSHAP for both single CSDT and ensemble models. The usefulness of the proposed method is demonstrated by providing explanations for single and ensemble CSDT models trained on well-known benchmark credit scoring datasets. Finally, we apply our methodology and analyze the stability of explanations for those models compared to the cost-insensitive tree-based models. Our analysis reveals statistically significant differences between SHAP values despite seemingly similar global feature importance plots of the models. This highlights the value of our methodology as a comprehensive tool for explaining CSDT models.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141298523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Utilizing passage-level relevance and kernel pooling for enhancing BERT-based document reranking 利用段落级相关性和内核池增强基于 BERT 的文档重排能力

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-06-07 DOI: 10.1111/coin.12656

Min Pan, Shuting Zhou, Teng Li, Yu Liu, Quanli Pei, Angela J. Huang, Jimmy X. Huang

{"title":"Utilizing passage-level relevance and kernel pooling for enhancing BERT-based document reranking","authors":"Min Pan, Shuting Zhou, Teng Li, Yu Liu, Quanli Pei, Angela J. Huang, Jimmy X. Huang","doi":"10.1111/coin.12656","DOIUrl":"https://doi.org/10.1111/coin.12656","url":null,"abstract":"The pre-trained language model (PLM) based on the Transformer encoder, namely BERT, has achieved state-of-the-art results in the field of Information Retrieval. Existing BERT-based ranking models divide documents into passages and aggregate passage-level relevance to rank the document list. However, these common score aggregation strategies cannot capture important semantic information such as document structure and have not been extensively studied. In this article, we propose a novel kernel-based score pooling system to capture document-level relevance by aggregating passage-level relevance. In particular, we propose and study several representative kernel pooling functions and several different document ranking strategies based on passage-level relevance. Our proposed framework KnBERT naturally incorporates kernel functions from the passage level into the BERT-based re-ranking method, which provides a promising avenue for building universal retrieval-then-rerank information retrieval systems. Experiments conducted on two widely used TREC Robust04 and GOV2 test datasets show that the KnBERT has made significant improvements over other BERT-based ranking approaches in terms of MAP, P@20, and NDCG@20 indicators with no extra or even less computations.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.12656","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

引用次数: 0

Low overhead vector codes with combination property and zigzag decoding for edge-aided computing in UAV network 用于无人机网络边缘辅助计算的具有组合特性和之字形解码的低开销矢量编码

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-06-06 DOI: 10.1111/coin.12642

Mingjun Dai, Ronghao Huang, Jinjin Wang, Bingchun Li

引用次数: 0

Detection of multi-class lung diseases based on customized neural network 基于定制神经网络的多类肺部疾病检测

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-04-23 DOI: 10.1111/coin.12649

Azmat Ali, Yulin Wang, Xiaochuan Shi

{"title":"Detection of multi-class lung diseases based on customized neural network","authors":"Azmat Ali, Yulin Wang, Xiaochuan Shi","doi":"10.1111/coin.12649","DOIUrl":"https://doi.org/10.1111/coin.12649","url":null,"abstract":"In the medical image processing domain, deep learning methodologies have outstanding performance for disease classification using digital images such as X-rays, magnetic resonance imaging (MRI), and computerized tomography (CT). However, accurate diagnosis of disease by medical personnel can be challenging in certain cases, such as the complexity of interpretation and non-availability of expert personnel, difficulty at pixel-level analysis, etc. Computer-aided diagnostic (CAD) systems with proper training have shown the potential to enhance diagnostic accuracy and efficiency. With the exponential growth of medical data, CAD systems can analyze and extract valuable information by assisting medical personnel during the disease diagnostic process. To overcome these challenges, this research introduces CX-RaysNet, a novel deep-learning framework designed for the automatic identification of various lung disease classes in digital chest X-ray images. The core novelty of the CX-RaysNet framework lies in the integration of both convolutional and group convolutional layers, along with the usage of small filter sizes and the incorporation of dropout regularization. This phenomenon helps us optimize the model's ability to distinguish minute features that reveal different lung diseases. Additionally, data augmentation techniques are implemented to augment the training and testing datasets, which enhances the model's robustness and generalizability. The performance evaluation of CX-RaysNet reveals promising results, with the proposed model achieving a multi-class classification accuracy of 97.25%. Particularly, this study represents the first attempt to optimize a model specifically for low-power embedded devices, aiming to improve the accuracy of disease detection while minimizing computational resources.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140633671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contour wavelet diffusion: A fast and high-quality image generation model 轮廓小波扩散：快速、高质量的图像生成模型

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-04-23 DOI: 10.1111/coin.12644

Yaoyao Ding, Xiaoxi Zhu, Yuntao Zou

{"title":"Contour wavelet diffusion: A fast and high-quality image generation model","authors":"Yaoyao Ding, Xiaoxi Zhu, Yuntao Zou","doi":"10.1111/coin.12644","DOIUrl":"https://doi.org/10.1111/coin.12644","url":null,"abstract":"Diffusion models can generate high-quality images and have attracted increasing attention. However, diffusion models adopt a progressive optimization process and often have long training and inference time, which limits their application in realistic scenarios. Recently, some latent space diffusion models have partially accelerated training speed by using parameters in the feature space, but additional network structures still require a large amount of unnecessary computation. Therefore, we propose the Contour Wavelet Diffusion method to accelerate the training and inference speed. First, we introduce the contour wavelet transform to extract anisotropic low-frequency and high-frequency components from the input image, and achieve acceleration by processing these down-sampling components. Meanwhile, due to the good reconstructive properties of wavelet transforms, the quality of generated images can be maintained. Second, we propose a Batch-normalized stochastic attention module that enables the model to effectively focus on important high-frequency information, further improving the quality of image generation. Finally, we propose a balanced loss function to further improve the convergence speed of the model. Experimental results on several public datasets show that our method can significantly accelerate the training and inference speed of the diffusion model while ensuring the quality of generated images.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140633669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Novel mixture allocation models for topic learning 用于主题学习的新型混合分配模型

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-04-11 DOI: 10.1111/coin.12641

Kamal Maanicshah, Manar Amayri, Nizar Bouguila

{"title":"Novel mixture allocation models for topic learning","authors":"Kamal Maanicshah, Manar Amayri, Nizar Bouguila","doi":"10.1111/coin.12641","DOIUrl":"https://doi.org/10.1111/coin.12641","url":null,"abstract":"Latent Dirichlet allocation (LDA) is one of the major models used for topic modelling. A number of models have been proposed extending the basic LDA model. There has also been interesting research to replace the Dirichlet prior of LDA with other pliable distributions like generalized Dirichlet, Beta-Liouville and so forth. Owing to the proven efficiency of using generalized Dirichlet (GD) and Beta-Liouville (BL) priors in topic models, we use these versions of topic models in our paper. Furthermore, to enhance the support of respective topics, we integrate mixture components which gives rise to generalized Dirichlet mixture allocation and Beta-Liouville mixture allocation models respectively. In order to improve the modelling capabilities, we use variational inference method for estimating the parameters. Additionally, we also introduce an online variational approach to cater to specific applications involving streaming data. We evaluate our models based on its performance on applications related to text classification, image categorization and genome sequence classification using a supervised approach where the labels are used as an observed variable within the model.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.12641","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140546858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

引用次数: 0

Graph embedded low-light image enhancement transformer based on federated learning for Internet of Vehicle under tunnel environment 基于联合学习的图嵌入式低照度图像增强变换器，用于隧道环境下的车联网

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-04-11 DOI: 10.1111/coin.12648

Yuan Shu, Fuxi Zhu, Zhongqiu Zhang, Min Zhang, Jie Yang, Yi Wang, Jun Wang

{"title":"Graph embedded low-light image enhancement transformer based on federated learning for Internet of Vehicle under tunnel environment","authors":"Yuan Shu, Fuxi Zhu, Zhongqiu Zhang, Min Zhang, Jie Yang, Yi Wang, Jun Wang","doi":"10.1111/coin.12648","DOIUrl":"https://doi.org/10.1111/coin.12648","url":null,"abstract":"The Internet of Vehicles (IoV) autonomous driving technology based on deep learning has achieved great success. However, under the tunnel environment, the computer vision-based IoV may fail due to low illumination. In order to handle this issue, this paper deploys an image enhancement module at the terminal of the IoV to alleviate the low illumination influence. The enhanced images can be submitted through IoT to the cloud server for further processing. The core algorithm of image enhancement is implemented by a dynamic graph embedded transformer network based on federated learning which can fully utilize the data resources of multiple devices in IoV and improve the generalization. Extensive comparative experiments are conducted on the publicly available dataset and the self-built dataset which is collected under the tunnel environment. Compared with other deep models, all results confirm that the proposed graph embedded Transformer model can effectively enhance the detail information of the low-light image, which can greatly improve the following tasks in IoV.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140546874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0