Computational Intelligence最新文献_第4页

CCNet: A Cross-Channel Enhanced CNN for Blind Image Denoising CCNet：一种用于盲图像去噪的跨通道增强CNN

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-05-28 DOI: 10.1111/coin.70063

Minling Zhu, Zhixin Xu

{"title":"CCNet: A Cross-Channel Enhanced CNN for Blind Image Denoising","authors":"Minling Zhu, Zhixin Xu","doi":"10.1111/coin.70063","DOIUrl":"https://doi.org/10.1111/coin.70063","url":null,"abstract":"<div>\u0000 \u0000 <p>Nowadays, blind image denoising with deep convolutional neural network (CNN) is one of the research hotspots in the field of image denoising. Relying on the convolutional operation and respective field, CNN is excellent in processing local information. However, this also brings the problems of lack of cross-domain interaction and process of global feature information. We incorporate it with transformer's self-attention mechanism and propose a cross-channel enhanced CNN, namely CCNet, for image denoising. CCNet consists of three parts: the backbone encoder (BE), the cross-channel enhancer (CCE), and the backbone decoder (BD). The BE and BD are constructed using the multiscale symmetric network U-Net and use the residual connection block (RCB) as the basic block for image feature extraction and reconstruction. CCE introduces transposed attention, serving as a complement of BE for cross-channel modeling. Meanwhile, we propose a unique gated fusion block (GFB) to fuse the information of these two modules and further feature learning. To improve training, we use random cropping, shuffling, and mixed noise strategies to expand the noise distribution learned by the model, increasing its noise adaptability. Extensive experiments on grayscale images, color images, and real noisy images demonstrate CCNet's strong performance in these tasks.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171713","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

FNN-ONTOCOM: A Hybrid Cost Estimation Approach Using Fuzzy and Neural Network for Ontology Engineering 基于模糊和神经网络的本体工程混合成本估算方法

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-05-27 DOI: 10.1111/coin.70061

Sonika Malik, Sarika Jain, Geetanjali Sharma

{"title":"FNN-ONTOCOM: A Hybrid Cost Estimation Approach Using Fuzzy and Neural Network for Ontology Engineering","authors":"Sonika Malik, Sarika Jain, Geetanjali Sharma","doi":"10.1111/coin.70061","DOIUrl":"https://doi.org/10.1111/coin.70061","url":null,"abstract":"<div>\u0000 <p>Ontology engineering is crucial for many areas such as information retrieval systems, data integration facilities, and basic decision support systems. Nevertheless, estimating the cost of ontology engineering projects is notoriously difficult to achieve. This challenge stems from the complexity and evolving nature of such projects. To solve this difficulty, we propose to improve the accuracy of cost estimation through a hybrid methodology that combines Fuzzy Ontology Cost Estimation Model (F-ONTOCOM) and Artificial Neural Networks (ANN). Fuzzy logic is used in our model to capture linguistic variables and other complex relationships within the scope of cost estimation. At the same time, ANN allows for the recognition of complex nonlinear interactions, enhancing the overall accuracy of prediction. This integration of fuzzy logic and neural networks leads to enhancements in the model's robustness, adaptability, and precision. Our approach features a methodology for 148 ontology engineering projects that include, but are not limited to, data scraping and preprocessing, fuzzy inference system design, neural network training, and validation processes. The results showed that the hybrid approach was champion over the traditional estimation approach in terms of effort estimation, Mean Relative Error (MRE), Mean Magnitude of Relative Error (MMRE), and the predictive accuracy over 21 randomly selected ontology projects.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144140790","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

Two-Tier Enhanced Hybrid Deep Learning-Based Collaborative Filtering Recommendation System for Online Reviews 基于两层增强混合深度学习的在线评论协同过滤推荐系统

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-05-27 DOI: 10.1111/coin.70062

Harsh Khatter, Pooja Singh, Anil Ahlawat, Ajay Kumar Shrivastava

{"title":"Two-Tier Enhanced Hybrid Deep Learning-Based Collaborative Filtering Recommendation System for Online Reviews","authors":"Harsh Khatter, Pooja Singh, Anil Ahlawat, Ajay Kumar Shrivastava","doi":"10.1111/coin.70062","DOIUrl":"https://doi.org/10.1111/coin.70062","url":null,"abstract":"<div>\u0000 \u0000 <p>Collaborative filtering-based recommender systems have recently attracted audiences due to the precise prediction of user interests and provide recommendations accordingly. The user-specific interests are the main requirement to build any recommendation model that produces the desired recommendation list. But the users' interests are sometimes unpredictable due to the fluctuating nature of the arrival of newer products. To resolve this problem and achieve better recommendation outcomes, a two-tier enhanced hybrid collaborative filtering based recommendation system (EHCFR) is constructed in this work based on deep learning. Initially, users in the dataset are segmented based on their age stratification to obtain users' interests based on age. Then, the major features are extracted from the dataset using the word learning enhanced variational auto-encoder (EVAE). These features are provided along with the rating matrix as the input to the deep belief network (DBN) for rating prediction. Based on the predicted ratings, the top N1 recommendation list is generated. Then, a time window strategy is adapted in the model to determine the dynamic fluctuations of user interests. Another list called the top N2 recommendation list is generated based on these fluctuations. Finally, both these lists are concatenated to provide accurate and favorable recommendations to the users. The proposed model is tested on the user dataset and provides competitive performance against the existing state-of-the-art techniques. Also, a reliable comparison is made with the existing popular datasets, such as Movielens 100k and Jester, and the results prove the efficacy of the proposed method.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148626","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

RF-NFN: Residual Neuro-Fuzzy Network-Based Multi-Modal Facial Expression Recognition 基于残差神经模糊网络的多模态面部表情识别

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-05-26 DOI: 10.1111/coin.70059

D. Vishnu Sakthi, Ezhumalai

{"title":"RF-NFN: Residual Neuro-Fuzzy Network-Based Multi-Modal Facial Expression Recognition","authors":"D. Vishnu Sakthi,  Ezhumalai","doi":"10.1111/coin.70059","DOIUrl":"https://doi.org/10.1111/coin.70059","url":null,"abstract":"<div>\u0000 \u0000 <p>Human beings show multiple responses to various emotional states, like anger, disgust, surprise, happiness, sadness, and fear. Among various emotions, facial expressions are widely informative as they exhibit a person's intentions and character. Facial expression recognition is used in many applications, such as marketing, research, customer service, neuroscience, and psychology. Traditional unimodal methods for facial expression recognition are ineffective due to the scarcity of data. In this paper, the Residual Fused Neuro-Fuzzy Network (RF-NFN) is used for the recognition of facial expressions and detection of emotion type using video and Electroencephalogram (EEG) signals. Here, the video frame is allowed for pre-processing done by Non-Local Means (NLM) filtering. Then, pre-processed video frames and input EEG signals are fed toward feature extraction, which is then followed by feature selection. Finally, facial expressions are recognized and the type of emotion is detected by RF-NFN, which is designed by incorporation of Hybrid Cascade Neuro-Fuzzy Network (Hybrid Cascade NFN) and Deep Residual Network (DRN). Moreover, the performance of the RF-NFN model is validated by three performance measures that exhibited a maximum accuracy of 90.88%, precision of 91.77%, and recall of 94.57%.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135629","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

Determining Treatment Dosage for Hypothyroidism Using Machine Learning 使用机器学习确定甲状腺功能减退的治疗剂量

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-05-15 DOI: 10.1111/coin.70060

Christina Zammit, Edward R. Sykes

引用次数: 0

A Deformable Convolutional Neural Network for Video Super-Resolution 用于视频超分辨率的可变形卷积神经网络

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-05-03 DOI: 10.1111/coin.70052

Xi Chen, Qi Zhang, Kai Liu, Yong Zhang

{"title":"A Deformable Convolutional Neural Network for Video Super-Resolution","authors":"Xi Chen, Qi Zhang, Kai Liu, Yong Zhang","doi":"10.1111/coin.70052","DOIUrl":"https://doi.org/10.1111/coin.70052","url":null,"abstract":"<div>\u0000 \u0000 <p>Convolutional Neural Networks used deep architectures to achieve deep feature extraction in video super-resolution. However, they suffered from challenges of rapid motion and complex scenes in video super-resolution. In this paper, we present a deformable convolutional neural network for video super-resolution (DVSRNet). DVSRNet mainly contains forward and backward feature propagation blocks (FPBs), feature enhancement blocks (FEBs), a feature fusion block (FFB), and a reconstruction block (RB). FPBs can leverage temporal sequence information to capture rich temporal dimensional information in video super-resolution. To restore detailed information, an optical flow technique guided a CNN to align the obtained structural information of different frames to reduce motion-induced blur and artifacts. To address deformable videos from motioned objects, two FEBs utilized deformable convolutions to adaptively correct misaligned objects to improve spatial continuity of videos. To improve reliability of obtained videos, an FFB is used to integrate relations of different video frames from forward and backward propagations. Finally, an RB via upsampling operations and a residual learning technique is used to construct high-quality videos. Experimental results demonstrate that our DVSRNet exhibits superior performance on multiple public datasets for video super-resolution. Its codes can be available at https://github.com/leyoukai/DVSRNet.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901086","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 收缩

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-05-02 DOI: 10.1111/coin.70054

引用次数: 0

RETRACTION 收缩

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-05-02 DOI: 10.1111/coin.70056

引用次数: 0

RETRACTION 收缩

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-05-02 DOI: 10.1111/coin.70055