Computational Intelligence最新文献_第9页

Video text rediscovery: Predicting and tracking text across complex scenes 视频文本再发现：预测和跟踪复杂场景中的文字

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-06-18 DOI: 10.1111/coin.12686

Veronica Naosekpam, Nilkanta Sahu

引用次数: 0

Retraction: Sunita Satish Patil, Thangamuthu Senthil Kumaran. Fuzzy based rendezvous points selection for mobile data gathering in wireless sensor network. Comput Intell 40: e12486, 2024 (10.1111/coin.12486) 撤回： Sunita Satish Patil、Thangamuthu Senthil Kumaran. 基于模糊的交会点选择，用于无线传感器网络中的移动数据采集。 Comput Intell 40: e12486, 2024 (10.1111/coin.12486)

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-06-17 DOI: 10.1111/coin.12668

引用次数: 0

Breast tumor detection using multi-feature block based neural network by fusion of CT and MRI images 通过融合 CT 和 MRI 图像，使用基于多特征块的神经网络检测乳腺肿瘤

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-06-17 DOI: 10.1111/coin.12652

Bersha Kumari, Amita Nandal, Arvind Dhaka

{"title":"Breast tumor detection using multi-feature block based neural network by fusion of CT and MRI images","authors":"Bersha Kumari, Amita Nandal, Arvind Dhaka","doi":"10.1111/coin.12652","DOIUrl":"https://doi.org/10.1111/coin.12652","url":null,"abstract":"Radiologists and clinicians must automatically examine breast and tumor locations and sizes accurately. In recent years, several neural network-based feature fusion versions have been created to improve medical image segmentation. Multi-modal image fusion photos may efficiently identify tumors. This work uses image fusion to identify computed tomography and magnetic resonance imaging alterations. A Gauss-log ratio operator is recommended for difference image production. The Gauss-log ratio and log ratio difference image complement the objective of improving the difference map through image fusion. The feature change matrix extracts edge, texture, and intensity from each picture pixel. The final change detection map classifies feature vectors as “changed” or “unchanged” which has been mapped for high-resolution or low-resolution pixels. This paper proposes a multi-feature blocks (MFB) based neural network for multi-feature fusion. This neural network modeling approach globalizes pixel spatial relationships. MFB-based feature fusion also aims to capture channel interactions between feature maps. The proposed technique outperforms state-of-the-art approaches which have been discussed in detail in experimental results section.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424942","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

UReslham: Radar reflectivity inversion for smart agriculture with spatial federated learning over geostationary satellite observations UReslham：利用对地静止卫星观测数据的空间联合学习，雷达反射率反演用于智能农业

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-06-17 DOI: 10.1111/coin.12684

Zhengyong Jin, Xiaolong Xu, Muhammad Bilal, Songyu Wu, Huichao Lin

{"title":"UReslham: Radar reflectivity inversion for smart agriculture with spatial federated learning over geostationary satellite observations","authors":"Zhengyong Jin, Xiaolong Xu, Muhammad Bilal, Songyu Wu, Huichao Lin","doi":"10.1111/coin.12684","DOIUrl":"https://doi.org/10.1111/coin.12684","url":null,"abstract":"The frequent occurrence of severe convective weather has certain adverse effects on the smart agriculture industry. To enhance the prediction of severe convective weather, the inversion model effectively fills radar reflectivity data gaps by leveraging geostationary satellite data, offering more comprehensive and accurate support for meteorological information in smart agriculture systems. Nevertheless, collaborative cross-regional inversion driven by dispersed radar data faces challenges in efficiency, privacy, and model accuracy. To this end, we employ an U-shaped residual network with an embedded light hybrid attention mechanism and utilize a federated averaging algorithm for efficient distributed training across multiple devices which could preserve the privacy of data from different locations, thereby improving inversion performance. In addition, to address the unbalanced nature of radar data, a weighted loss function is designed to enhance the model's sensitivity to high radar reflectivity. Experimental results demonstrate that the proposed model exhibits a certain level of improvement in evaluating radar reflectivity inversion performance across different thresholds compared to other models, thus substantiating the superiority of the proposed approach.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.12684","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424989","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

Retraction: Vinod Kumar, R, Kavithaa, G, Jayanthi, D. Lifetime maximization energy-aware routing protocol for route optimization to improve quality of service in wireless sensor networks. Comput Intell 40: e12485, 2024 (10.1111/coin.12485) 撤回： Vinod Kumar, R, Kavithaa, G, Jayanthi, D. 用于路由优化以提高无线传感器网络服务质量的寿命最大化能量感知路由协议。 Comput Intell 40: e12485, 2024 (10.1111/coin.12485)

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-06-17 DOI: 10.1111/coin.12667

引用次数: 0

Continuous identity authentication protocol against quantum attacks in satellite integrated smart grid 针对卫星集成智能电网中量子攻击的连续身份验证协议

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-06-17 DOI: 10.1111/coin.12647

Chao Huang, Min Yang, Bo Li, Lin Yu

{"title":"Continuous identity authentication protocol against quantum attacks in satellite integrated smart grid","authors":"Chao Huang, Min Yang, Bo Li, Lin Yu","doi":"10.1111/coin.12647","DOIUrl":"https://doi.org/10.1111/coin.12647","url":null,"abstract":"To address the issue of low efficiency caused by the repeated use of quantum attack resistant static identity authentication methods in a satellite integrated smart grid, this paper proposes a quantum attack resistant continuous identity authentication protocol. First, in the initial authentication stage, in order to reduce computational complexity, the key encryption mechanism in the CRYSTALS-Kyber algorithm was improved and combined with the NTRU message recovery digital signature scheme to construct a lattice based explicit AKE (Kyber NTRU. AKE), which achieved mutual authentication and negotiated shared tokens. Second, in the continuous authentication stage, incorporating quantum attack resistant tokens into the current algorithm to improve authentication efficiency. The formal analysis results indicate that compared to the weakly forward secure Kyber.AKE in the CRYSTALS-Kyber algorithm, Kyber-NTRU.AKE achieves complete forward secrecy, while the non-formal analysis results demonstrate the security of the continuous authentication phase. Through theoretical analysis and efficiency comparison with Cyber.AKE, the analysis shows that the Cyber-NTRU.AKE has higher computational and communication efficiency than Cyber.AKE.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424999","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

Retraction: Muthuramalingam Sivakumar, Perumal Renuka, Pandian Chitra, Sundararajan Karthikeyan. IoT incorporated deep learning model combined with SmartBin technology for real-time solid waste management. Comput Intell 38: 323–344, 2022 (10.1111/coin.12495) 撤回： Muthuramalingam Sivakumar, Perumal Renuka, Pandian Chitra, Sundararajan Karthikeyan. 物联网深度学习模型与智能垃圾桶技术相结合，用于实时固体废物管理。 Comput Intell 38: 323-344, 2022 (10.1111/coin.12495)

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-06-17 DOI: 10.1111/coin.12669

引用次数: 0

MRD-GAN: Multi-representation discrimination GAN for enhancing the diversity of the generated data MRD-GAN：用于提高生成数据多样性的多表征判别 GAN

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-06-17 DOI: 10.1111/coin.12685

Mohammed Megahed, Ammar Mohammed

{"title":"MRD-GAN: Multi-representation discrimination GAN for enhancing the diversity of the generated data","authors":"Mohammed Megahed, Ammar Mohammed","doi":"10.1111/coin.12685","DOIUrl":"https://doi.org/10.1111/coin.12685","url":null,"abstract":"The generative adversarial network (GAN) is a highly effective member of the generative models category and is extensively employed for generating realistic samples across various domains. The fundamental concept behind GAN involves two networks, a generator and a discriminator, competing against each other. During the training process, generator and discriminator networks encounter several issues that can potentially affect the quality and diversity of the generated samples. One such critical issue is mode collapse, where the generator fails to create varied samples. To tackle this issue, this article introduces a GAN approach called the multi-representation discrimination GAN (MRD-GAN). In this approach, the discriminator supports concurrent network discrimination flows to manage different representations of the data through various transformation functions, such as dimension rescaling, brightness adjustment, and gamma correction applied to the input data of the discriminator. We use a fusion function to aggregate the output of all flows and return a consolidated loss value to update the generator's weights. Hence, the discriminator conveys diverse feedback to the generator. The proposed approach has been evaluated on four distinct benchmarks, namely CelebA, Cifar-10, Fashion-Mnist, and Mnist. The experimental results demonstrate that the proposed approach surpasses the existing state-of-the-art GAN models in terms of FID metric that measures the diversity of the generated samples. Significantly, the proposed approach demonstrates remarkable FID scores of 14.02, 30.19, 9.42, and 3.14 on the CelebA, Cifar-10, Fashion-Mnist, and Mnist datasets, respectively.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425025","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

Robust colored point cloud alignment based on L*a*b* guided and Cauchy kernel 基于 L*a*b* 导向和 Cauchy 核的鲁棒彩色点云配准

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-06-17 DOI: 10.1111/coin.12657

Teng Wan, Shaoyi Du, Qiang Zhang, Ying Qi, Chunyao Huang, Wei Zeng

{"title":"Robust colored point cloud alignment based on L*a*b* guided and Cauchy kernel","authors":"Teng Wan, Shaoyi Du, Qiang Zhang, Ying Qi, Chunyao Huang, Wei Zeng","doi":"10.1111/coin.12657","DOIUrl":"https://doi.org/10.1111/coin.12657","url":null,"abstract":"Precision agriculture benefits from point set registration, which can monitor plant health and growth in real time, promote the precise application of fertilizers and pesticides, and provide technical support for achieving sustainable development of agriculture. In this work, we propose a robust point set registration method for precision agriculture based on L*a*b* color guidance, bidirectional search and Cauchy distribution. First, the L*a*b* color guidance is applied to establish accurate correspondences between agricultural RGB-D data. Second, the bidirectional nearest neighbor search strategy between point sets improves the reliability of establishing correspondences and broadens the convergence domain of the algorithm. Third, Cauchy distribution is utilized as an energy function for noise suppression, which further improves the robustness of the algorithm in dealing with complex vegetation scenes. Finally, results of ablation and simulation experiments indicate that the proposed registration algorithm can achieve more accurate and robust alignment results than other classic and state-of-the-art point cloud registration algorithms to achieve monitoring and comparison of plant growth.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424981","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

Retraction: Neeraj Kumar, Upendra Kumar. Artificial intelligence for classification and regression tree based feature selection method for network intrusion detection system in various telecommunication technologies. Comput Intell 40: e12500, 2024 (10.1111/coin.12500) 撤回： Neeraj Kumar, Upendra Kumar. 基于人工智能的分类和回归树特征选择方法，用于各种电信技术中的网络入侵检测系统。 Comput Intell 40: e12500, 2024 (10.1111/coin.12500)

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-06-12 DOI: 10.1111/coin.12672

引用次数: 0