CAAI Transactions on Intelligence Technology最新文献

{"title":"Contrastive learning for nested Chinese Named Entity Recognition via template words","authors":"Yuke Wang,&nbsp;Qiao Liu,&nbsp;Tingting Dai,&nbsp;Junjie Lang,&nbsp;Ling Lu,&nbsp;Yinong Chen","doi":"10.1049/cit2.12403","DOIUrl":"https://doi.org/10.1049/cit2.12403","url":null,"abstract":"<p>Existing Chinese named entity recognition (NER) research utilises 1D lexicon-based sequence labelling frameworks, which can only recognise flat entities. While lexicons serve as prior knowledge and enhance semantic information, they also pose completeness and resource requirements limitations. This paper proposes a template-based classification (TC) model to avoid lexicon issues and to identify nested entities. Template-based classification provides a template word for each entity type, which utilises contrastive learning to integrate the common characteristics among entities with the same category. Contrastive learning makes template words the centre points of their category in the vector space, thus improving generalisation ability. Additionally, TC presents a 2D table-filling label scheme that classifies entities based on the attention distribution of template words. The proposed novel decoder algorithm enables TC recognition of both flat and nested entities simultaneously. Experimental results show that TC achieves the state-of-the-art performance on five Chinese datasets.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 2","pages":"450-459"},"PeriodicalIF":8.4,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12403","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857096","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}

{"title":"Tri-M2MT: Multi-modalities based effective acute bilirubin encephalopathy diagnosis through multi-transformer using neonatal Magnetic Resonance Imaging","authors":"Kumar Perumal,&nbsp;Rakesh Kumar Mahendran,&nbsp;Arfat Ahmad Khan,&nbsp;Seifedine Kadry","doi":"10.1049/cit2.12409","DOIUrl":"https://doi.org/10.1049/cit2.12409","url":null,"abstract":"<p>Acute Bilirubin Encephalopathy (ABE) is a significant threat to neonates and it leads to disability and high mortality rates. Detecting and treating ABE promptly is important to prevent further complications and long-term issues. Recent studies have explored ABE diagnosis. However, they often face limitations in classification due to reliance on a single modality of Magnetic Resonance Imaging (MRI). To tackle this problem, the authors propose a Tri-M2MT model for precise ABE detection by using tri-modality MRI scans. The scans include T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and apparent diffusion coefficient maps to get indepth information. Initially, the tri-modality MRI scans are collected and preprocessesed by using an Advanced Gaussian Filter for noise reduction and <i>Z</i>-score normalisation for data standardisation. An Advanced Capsule Network was utilised to extract relevant features by using Snake Optimization Algorithm to select optimal features based on feature correlation with the aim of minimising complexity and enhancing detection accuracy. Furthermore, a multi-transformer approach was used for feature fusion and identify feature correlations effectively. Finally, accurate ABE diagnosis is achieved through the utilisation of a SoftMax layer. The performance of the proposed Tri-M2MT model is evaluated across various metrics, including accuracy, specificity, sensitivity, F1-score, and ROC curve analysis, and the proposed methodology provides better performance compared to existing methodologies.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 2","pages":"434-449"},"PeriodicalIF":8.4,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12409","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856975","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}

{"title":"Clustering-based recommendation method with enhanced grasshopper optimisation algorithm","authors":"Zihao Zhao,&nbsp;Yingchun Xia,&nbsp;Wenjun Xu,&nbsp;Hui Yu,&nbsp;Shuai Yang,&nbsp;Cheng Chen,&nbsp;Xiaohui Yuan,&nbsp;Xiaobo Zhou,&nbsp;Qingyong Wang,&nbsp;Lichuan Gu","doi":"10.1049/cit2.12408","DOIUrl":"https://doi.org/10.1049/cit2.12408","url":null,"abstract":"<p>In the era of big data, personalised recommendation systems are essential for enhancing user engagement and driving business growth. However, traditional recommendation algorithms, such as collaborative filtering, face significant challenges due to data sparsity, algorithm scalability, and the difficulty of adapting to dynamic user preferences. These limitations hinder the ability of systems to provide highly accurate and personalised recommendations. To address these challenges, this paper proposes a clustering-based recommendation method that integrates an enhanced Grasshopper Optimisation Algorithm (GOA), termed LCGOA, to improve the accuracy and efficiency of recommendation systems by optimising cluster centroids in a dynamic environment. By combining the K-means algorithm with the enhanced GOA, which incorporates a Lévy flight mechanism and multi-strategy co-evolution, our method overcomes the centroid sensitivity issue, a key limitation in traditional clustering techniques. Experimental results across multiple datasets show that the proposed LCGOA-based method significantly outperforms conventional recommendation algorithms in terms of recommendation accuracy, offering more relevant content to users and driving greater customer satisfaction and business growth.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 2","pages":"494-509"},"PeriodicalIF":8.4,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12408","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856976","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}

{"title":"Advancing skin cancer detection integrating a novel unsupervised classification and enhanced imaging techniques","authors":"Md. Abdur Rahman,&nbsp;Nur Mohammad Fahad,&nbsp;Mohaimenul Azam Khan Raiaan,&nbsp;Mirjam Jonkman,&nbsp;Friso De Boer,&nbsp;Sami Azam","doi":"10.1049/cit2.12410","DOIUrl":"https://doi.org/10.1049/cit2.12410","url":null,"abstract":"<p>Skin cancer, a severe health threat, can spread rapidly if undetected. Therefore, early detection can lead to an advanced and efficient diagnosis, thus reducing mortality. Unsupervised classification techniques analyse extensive skin image datasets, identifying patterns and anomalies without prior labelling, facilitating early detection and effective diagnosis and potentially saving lives. In this study, the authors aim to explore the potential of unsupervised learning methods in classifying different types of skin lesions in dermatoscopic images. The authors aim to bridge the gap in dermatological research by introducing innovative techniques that enhance image quality and improve feature extraction. To achieve this, enhanced super-resolution generative adversarial networks (ESRGAN) was fine-tuned to strengthen the resolution of skin lesion images, making critical features more visible. The authors extracted histogram features to capture essential colour characteristics and used the Davies–Bouldin index and silhouette score to determine optimal clusters. Fine-tuned k-means clustering with Euclidean distance in the histogram feature space achieved 87.77% and 90.5% test accuracies on the ISIC2019 and HAM10000 datasets, respectively. The unsupervised approach effectively categorises skin lesions, indicating that unsupervised learning can significantly advance dermatology by enabling early detection and classification without extensive manual annotation.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 2","pages":"474-493"},"PeriodicalIF":8.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12410","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856760","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}

{"title":"3D medical image segmentation using the serial–parallel convolutional neural network and transformer based on cross-window self-attention","authors":"Bin Yu,&nbsp;Quan Zhou,&nbsp;Li Yuan,&nbsp;Huageng Liang,&nbsp;Pavel Shcherbakov,&nbsp;Xuming Zhang","doi":"10.1049/cit2.12411","DOIUrl":"https://doi.org/10.1049/cit2.12411","url":null,"abstract":"<p>Convolutional neural network (CNN) with the encoder–decoder structure is popular in medical image segmentation due to its excellent local feature extraction ability but it faces limitations in capturing the global feature. The transformer can extract the global information well but adapting it to small medical datasets is challenging and its computational complexity can be heavy. In this work, a serial and parallel network is proposed for the accurate 3D medical image segmentation by combining CNN and transformer and promoting feature interactions across various semantic levels. The core components of the proposed method include the cross window self-attention based transformer (CWST) and multi-scale local enhanced (MLE) modules. The CWST module enhances the global context understanding by partitioning 3D images into non-overlapping windows and calculating sparse global attention between windows. The MLE module selectively fuses features by computing the voxel attention between different branch features, and uses convolution to strengthen the dense local information. The experiments on the prostate, atrium, and pancreas MR/CT image datasets consistently demonstrate the advantage of the proposed method over six popular segmentation models in both qualitative evaluation and quantitative indexes such as dice similarity coefficient, Intersection over Union, 95% Hausdorff distance and average symmetric surface distance.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 2","pages":"337-348"},"PeriodicalIF":8.4,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12411","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856822","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}

{"title":"A fast surface-defect detection method based on Dense-YOLO network","authors":"Fengqiang Gao,&nbsp;Qingyuan Zhu,&nbsp;Guifang Shao,&nbsp;Yukang Su,&nbsp;Jianbo Yang,&nbsp;Xinyue Yu","doi":"10.1049/cit2.12407","DOIUrl":"https://doi.org/10.1049/cit2.12407","url":null,"abstract":"<p>Efficient detection of surface defects is primary for ensuring product quality during manufacturing processes. To enhance the performance of deep learning-based methods in practical applications, the authors propose Dense-YOLO, a fast surface defect detection network that combines the strengths of DenseNet and you only look once version 3 (YOLOv3). The authors design a lightweight backbone network with improved densely connected blocks, optimising the utilisation of shallow features while maintaining high detection speeds. Additionally, the authors refine the feature pyramid network of YOLOv3 to increase the recall of tiny defects and overall positioning accuracy. Furthermore, an online multi-angle template matching technique is introduced based on normalised cross-correlation to precisely locate the detection area. This refined template matching method not only accelerates detection speed but also mitigates the influence of the background. To validate the effectiveness of our enhancements, the authors conduct comparative experiments across two private datasets and one public dataset. Results show that Dense-YOLO outperforms existing methods, such as faster R-CNN, YOLOv3, YOLOv5s, YOLOv7, and SSD, in terms of mean average precision (mAP) and detection speed. Moreover, Dense-YOLO outperforms networks inherited from VGG and ResNet, including improved faster R-CNN, FCOS, M2Det-320 and FRCN, in mAP.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 2","pages":"415-433"},"PeriodicalIF":8.4,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12407","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856809","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}

{"title":"Robust style injection for person image synthesis","authors":"Yan Huang,&nbsp;Jianjun Qian,&nbsp;Shumin Zhu,&nbsp;Jun Li,&nbsp;Jian Yang","doi":"10.1049/cit2.12361","DOIUrl":"https://doi.org/10.1049/cit2.12361","url":null,"abstract":"<p>Person Image Synthesis has been widely used in fashion with extensive application scenarios. The point of this task is how to synthesise person image from a single source image under arbitrary poses. Prior methods generate the person image with target pose well; however, they fail to preserve the fine style details of the source image. To address this problem, a robust style injection (RSI) model is proposed, which is a coarse-to-fine framework to synthesise target the person image. RSI develops a simple and efficient cross-attention based module to fuse the features of both source semantic styles and target pose for achieving the coarse aligned features. The adaptive instance normalisation is employed to enhance the aligned features in conjunction with source semantic styles. Subsequently, source semantic styles are further injected into the positional normalisation scheme to avoid the fine style details erosion caused by massive convolution. In training losses, optimal transport theory in the form of energy distance is introduced to constrain data distribution to refine the texture style details. Additionally, the authors’ model is capable of editing the shape and texture of garments to the target style separately. The experiments demonstrate that the authors’ RSI achieves better performance over the state-of-art methods.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 2","pages":"402-414"},"PeriodicalIF":8.4,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12361","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856811","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}

{"title":"Longitudinal velocity control of autonomous driving based on extended state observer","authors":"Hongbo Gao,&nbsp;Hanqing Yang,&nbsp;Xiaoyu Zhang,&nbsp;Xiangyun Ren,&nbsp;Fenghua Liang,&nbsp;Ruidong Yan,&nbsp;Qingchao Liu,&nbsp;Mingmao Hu,&nbsp;Fang Zhang,&nbsp;Jiabing Gao,&nbsp;Siyu Bao,&nbsp;Keqiang Li,&nbsp;Deyi Li,&nbsp;Danwei Wang","doi":"10.1049/cit2.12397","DOIUrl":"https://doi.org/10.1049/cit2.12397","url":null,"abstract":"<p>Active Disturbance Rejection Control (ADRC) possesses robust disturbance rejection capabilities, making it well-suited for longitudinal velocity control. However, the conventional Extended State Observer (ESO) in ADRC fails to fully exploit feedback from first-order and higher-order estimation errors and tracking error simultaneously, thereby diminishing the control performance of ADRC. To address this limitation, an enhanced car-following algorithm utilising ADRC is proposed, which integrates the improved ESO with a feedback controller. In comparison to the conventional ESO, the enhanced version effectively utilises multi-order estimation and tracking errors. Specifically, it enhances convergence rates by incorporating feedback from higher-order estimation errors and ensures the estimated value converges to the reference value by utilising tracking error feedback. The improved ESO significantly enhances the disturbance rejection performance of ADRC. Finally, the effectiveness of the proposed algorithm is validated through the Lyapunov approach and experiments.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 1","pages":"36-46"},"PeriodicalIF":8.4,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12397","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143533419","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}

{"title":"Random Strip Peeling: A novel lightweight image encryption for IoT devices based on colour planes permutation","authors":"Kenan İnce,&nbsp;Cemile İnce,&nbsp;Davut Hanbay","doi":"10.1049/cit2.12401","DOIUrl":"https://doi.org/10.1049/cit2.12401","url":null,"abstract":"<p>This paper introduces a novel lightweight colour image encryption algorithm, specifically designed for resource-constrained environments such as Internet of Things (IoT) devices. As IoT systems become increasingly prevalent, secure and efficient data transmission becomes crucial. The proposed algorithm addresses this need by offering a robust yet resource-efficient solution for image encryption. Traditional image encryption relies on confusion and diffusion steps. These stages are generally implemented linearly, but this work introduces a new RSP (Random Strip Peeling) algorithm for the confusion step, which disrupts linearity in the lightweight category by using two different sequences generated by the 1D Tent Map with varying initial conditions. The diffusion stage then employs an XOR matrix generated by the Logistic Map. Different evaluation metrics, such as entropy analysis, key sensitivity, statistical and differential attacks resistance, and robustness analysis demonstrate the proposed algorithm's lightweight, robust, and efficient. The proposed encryption scheme achieved average metric values of 99.6056 for NPCR, 33.4397 for UACI, and 7.9914 for information entropy in the SIPI image dataset. It also exhibits a time complexity of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>O</mi>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 <mo>×</mo>\u0000 <mi>M</mi>\u0000 <mo>×</mo>\u0000 <mi>N</mi>\u0000 </mrow>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 </mrow>\u0000 <annotation> $O(2times Mtimes N)$</annotation>\u0000 </semantics></math> for an image of size <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>M</mi>\u0000 <mo>×</mo>\u0000 <mi>N</mi>\u0000 </mrow>\u0000 <annotation> $Mtimes N$</annotation>\u0000 </semantics></math>.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 2","pages":"529-544"},"PeriodicalIF":8.4,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12401","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856788","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}

{"title":"Which is more faithful, seeing or saying? Multimodal sarcasm detection exploiting contrasting sentiment knowledge","authors":"Yutao Chen,&nbsp;Shumin Shi,&nbsp;Heyan Huang","doi":"10.1049/cit2.12400","DOIUrl":"https://doi.org/10.1049/cit2.12400","url":null,"abstract":"<p>Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly common. However, detecting sarcasm in various forms of communication can be difficult due to conflicting sentiments. In this paper, we introduce a contrasting sentiment-based model for multimodal sarcasm detection (CS4MSD), which identifies inconsistent emotions by leveraging the CLIP knowledge module to produce sentiment features in both text and image. Then, five external sentiments are introduced to prompt the model learning sentimental preferences among modalities. Furthermore, we highlight the importance of verbal descriptions embedded in illustrations and incorporate additional knowledge-sharing modules to fuse such image-like features. Experimental results demonstrate that our model achieves state-of-the-art performance on the public multimodal sarcasm dataset.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 2","pages":"375-386"},"PeriodicalIF":8.4,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12400","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857074","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}