Computational Intelligence最新文献_第6页

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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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

Privacy preserving support vector machine based on federated learning for distributed IoT-enabled data analysis 基于联合学习的隐私保护支持向量机，用于分布式物联网数据分析

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-04-03 DOI: 10.1111/coin.12636

Yu-Chi Chen, Song-Yi Hsu, Xin Xie, Saru Kumari, Sachin Kumar, Joel Rodrigues, Bander A. Alzahrani

{"title":"Privacy preserving support vector machine based on federated learning for distributed IoT-enabled data analysis","authors":"Yu-Chi Chen, Song-Yi Hsu, Xin Xie, Saru Kumari, Sachin Kumar, Joel Rodrigues, Bander A. Alzahrani","doi":"10.1111/coin.12636","DOIUrl":"https://doi.org/10.1111/coin.12636","url":null,"abstract":"In a smart city, IoT devices are required to support monitoring of normal operations such as traffic, infrastructure, and the crowd of people. IoT-enabled systems offered by many IoT devices are expected to achieve sustainable developments from the information collected by the smart city. Indeed, artificial intelligence (AI) and machine learning (ML) are well-known methods for achieving this goal as long as the system framework and problem statement are well prepared. However, to better use AI/ML, the training data should be as global as possible, which can prevent the model from working only on local data. Such data can be obtained from different sources, but this induces the privacy issue where at least one party collects all data in the plain. The main focus of this article is on support vector machines (SVM). We aim to present a solution to the privacy issue and provide confidentiality to protect the data. We build a privacy-preserving scheme for SVM (SecretSVM) based on the framework of federated learning and distributed consensus. In this scheme, data providers self-organize and obtain training parameters of SVM without revealing their own models. Finally, experiments with real data analysis show the feasibility of potential applications in smart cities. This article is the extended version of that of Hsu et al. (Proceedings of the 15th ACM Asia Conference on Computer and Communications Security. ACM; 2020:904-906).","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140345600","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 algorithm machine translation for language translation tool 语言翻译工具的新算法机器翻译

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-04-03 DOI: 10.1111/coin.12643

K. Jayasakthi Velmurugan, G. Sumathy, K. V. Pradeep

{"title":"Novel algorithm machine translation for language translation tool","authors":"K. Jayasakthi Velmurugan, G. Sumathy, K. V. Pradeep","doi":"10.1111/coin.12643","DOIUrl":"https://doi.org/10.1111/coin.12643","url":null,"abstract":"Fuzzy matching techniques are the presently used methods in translating the words. Neural machine translation and statistical machine translation are the methods used in MT. In machine translator tool, the strategy employed for translation needs to handle large amount of datasets and therefore the performance in retrieving correct matching output can be affected. In order to improve the matching score of MT, the advanced techniques can be presented by modifying the existing fuzzy based translator and neural machine translator. The conventional process of modifying architectures and encoding schemes are tedious process. Similarly, the preprocessing of datasets also involves more time consumption and memory utilization. In this article, a new spider web based searching enhanced translation is presented to be employed with the neural machine translator. The proposed scheme enables deep searching of available dataset to detect the accurate matching result. In addition, the quality of translation is improved by presenting an optimal selection scheme for using the sentence matches in source augmentation. The matches retrieved using various matching scores are applied to an optimization algorithm. The source augmentation using optimal retrieved matches increases the translation quality. Further, the selection of optimal match combination helps to reduce time requirement, since it is not necessary to test all retrieved matches in finding target sentence. The performance of translation is validated by measuring the quality of translation using BLEU and METEOR scores. These two scores can be achieved for the TA-EN language pairs in different configurations of about 92% and 86%, correspondingly. The results are evaluated and compared with other available NMT methods to validate the work.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140345602","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 fine-grained visual recognition with images based on internet of things 基于物联网的稳健细粒度图像视觉识别

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-03-19 DOI: 10.1111/coin.12638

Zhenhuang Cai, Shuai Yan, Dan Huang

{"title":"Robust fine-grained visual recognition with images based on internet of things","authors":"Zhenhuang Cai, Shuai Yan, Dan Huang","doi":"10.1111/coin.12638","DOIUrl":"https://doi.org/10.1111/coin.12638","url":null,"abstract":"Labeling fine-grained objects manually is extremely challenging, as it is not only label-intensive but also requires professional knowledge. Accordingly, robust learning methods for fine-grained recognition with web images collected from Internet of Things have drawn significant attention. However, training deep fine-grained models directly using untrusted web images is confronted by two primary obstacles: (1) label noise in web images and (2) domain variance between the online sources and test datasets. To this end, in this study, we mainly focus on addressing these two pivotal problems associated with untrusted web images. To be specific, we introduce an end-to-end network that collaboratively addresses these concerns in the process of separating trusted data from untrusted web images. To validate the efficacy of our proposed model, untrusted web images are first collected by utilizing the text category labels found within fine-grained datasets. Subsequently, we employ the designed deep model to eliminate label noise and ameliorate domain mismatch. And the chosen trusted web data are utilized for model training. Comprehensive experiments and ablation studies validate that our method consistently surpasses other state-of-the-art approaches for fine-grained recognition tasks in real-world scenarios, demonstrating a significant improvement margin (2.51% on CUB200-2011 and 2.92% on Stanford Dogs). The source code and models can be accessed at: \u0000https://github.com/Codeczh/FGVC-IoT.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140164392","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

A hybrid data fusion approach with twin CNN architecture for enhancing image source identification in IoT environment 采用双 CNN 架构的混合数据融合方法，增强物联网环境中的图像源识别能力

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-03-18 DOI: 10.1111/coin.12631

Surjeet Singh, Vivek Kumar Sehgal

{"title":"A hybrid data fusion approach with twin CNN architecture for enhancing image source identification in IoT environment","authors":"Surjeet Singh, Vivek Kumar Sehgal","doi":"10.1111/coin.12631","DOIUrl":"https://doi.org/10.1111/coin.12631","url":null,"abstract":"With the proliferation of digital devices in internet of things (IoT) environment featuring advanced visual capabilities, the task of Image Source Identification (ISI) has become increasingly vital for legal purposes, ensuring the verification of image authenticity and integrity, as well as identifying the device responsible for capturing the original scene. Over the past few decades, researchers have employed both traditional and machine-learning methods to classify image sources. In the current landscape, data-driven approaches leveraging deep learning models have emerged as powerful tools for achieving higher accuracy and precision in source prediction. The primary focus of this research is to address the complexities arising from diverse image sources and variable quality in IoT-generated multimedia data. To achieve this, a Hybrid Data Fusion Approach is introduced, leveraging multiple sources of information to bolster the accuracy and robustness of ISI. This fusion methodology integrates diverse data streams from IoT devices, including metadata, sensor information, and contextual data, amalgamating them into a comprehensive data set for analysis. This study introduces an innovative approach to ISI through the implementation of a Twin Convolutional Neural Network Architecture (TCA) aimed at enhancing the efficacy of source classification. In TCA, the first CNN architecture, referred to as DnCNN, is employed to eliminate noise from the original data set, generating 256 × 256 patches for both training and testing. Subsequently, the second CNN architecture is employed to classify images based on features extracted from various convolutional layers using a 3 × 3 filter, thereby enhancing prediction efficiency. The proposed model demonstrates exceptional accuracy in effectively classifying image sources, showcasing its potential as a robust solution in the realm of ISI.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161352","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

Sentiment analysis incorporating convolutional neural network into hidden Markov model 将卷积神经网络纳入隐马尔可夫模型的情感分析

IF 2.8 4区计算机科学

Computational Intelligence Pub Date : 2024-03-18 DOI: 10.1111/coin.12633

Maryam Khanian Najafabadi

{"title":"Sentiment analysis incorporating convolutional neural network into hidden Markov model","authors":"Maryam Khanian Najafabadi","doi":"10.1111/coin.12633","DOIUrl":"https://doi.org/10.1111/coin.12633","url":null,"abstract":"The analysis of sentiments and mining of opinions have become more and more important in years because of the development of social media technologies. The methods that utilize natural language processing and lexicon-based sentiment analysis techniques to analyze people's opinions in texts require the proper extraction of sentiment words to ensure accuracy. The current issue is tackled with a novel perspective in this paper by introducing a hybrid sentiment analysis technique. This technique brings together Convolutional Neural Network (CNN) and Hidden Markov Models (HMMs), to accurately categorize text data and pinpoint feelings. The proposed method involves 1D convolutional-layer CNN to extract hidden features from comments and applying HMMs on a feature-sentence matrix, allowing for the utilization of word sequences in extracting opinions. The method effectively captures diverse text patterns by extracting a range of features from texts using CNN. Text patterns are learned using text HMM by calculating the probabilities between sequences of feature vectors and clustering feature vectors. The paper's experimental evaluation employs benchmark datasets such as CR, MR, Subj, and SST2, demonstrating that the proposed method surpasses existing sentiment analysis techniques and traditional HMMs. One of its strengths is to analyze a range of text patterns and identify crucial features that recognize the emotion of different pieces of a sentence. Additionally, the research findings highlight the improved performance of sentiment analysis tasks through the strategic use of zero padding in conjunction with the masking technique.","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.12633","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161353","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