CAAI Transactions on Intelligence Technology最新文献

{"title":"Tibetan Medical Named Entity Recognition Based on Syllable-Word-Sentence Embedding Transformer","authors":"Jin Zhang,&nbsp;Ziyue Zhang,&nbsp;Lobsang Yeshi,&nbsp;Dorje Tashi,&nbsp;Xiangshi Wang,&nbsp;Yuqing Cai,&nbsp;Yongbin Yu,&nbsp;Xiangxiang Wang,&nbsp;Nyima Tashi,&nbsp;Gadeng Luosang","doi":"10.1049/cit2.70029","DOIUrl":"10.1049/cit2.70029","url":null,"abstract":"<p>Tibetan medical named entity recognition (Tibetan MNER) involves extracting specific types of medical entities from unstructured Tibetan medical texts. Tibetan MNER provide important data support for the work related to Tibetan medicine. However, existing Tibetan MNER methods often struggle to comprehensively capture multi-level semantic information, failing to sufficiently extract multi-granularity features and effectively filter out irrelevant information, which ultimately impacts the accuracy of entity recognition. This paper proposes an improved embedding representation method called syllable–word–sentence embedding. By leveraging features at different granularities and using un-scaled dot-product attention to focus on key features for feature fusion, the syllable–word–sentence embedding is integrated into the transformer, enhancing the specificity and diversity of feature representations. The model leverages multi-level and multi-granularity semantic information, thereby improving the performance of Tibetan MNER. We evaluate our proposed model on datasets from various domains. The results indicate that the model effectively identified three types of entities in the Tibetan news dataset we constructed, achieving an F1 score of 93.59%, which represents an improvement of 1.24% compared to the vanilla FLAT. Additionally, results from the Tibetan medical dataset we developed show that it is effective in identifying five kinds of medical entities, with an F1 score of 71.39%, which is a 1.34% improvement over the vanilla FLAT.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"1148-1158"},"PeriodicalIF":7.3,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910125","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":"Robot Manipulation Based on Embodied Visual Perception: A Survey","authors":"Sicheng Wang,&nbsp;Milutin N. Nikolić,&nbsp;Tin Lun Lam,&nbsp;Qing Gao,&nbsp;Runwei Ding,&nbsp;Tianwei Zhang","doi":"10.1049/cit2.70022","DOIUrl":"10.1049/cit2.70022","url":null,"abstract":"<p>Visual perception is critical in robotic operations, particularly in collaborative and autonomous robot systems. Through efficient visual systems, robots can acquire and process environmental information in real-time, recognise objects, assess spatial relationships, and make adaptive decisions. This review aims to provide a comprehensive overview of the latest advancements in the field of vision as applied to robotic perception, focusing primarily on visual applications in the areas of object perception, self-perception, human–robot collaboration, and multi-robot collaboration. By summarising the current state of development and analysing the challenges and opportunities that remain in these areas, this paper offers a thorough examination of the integration of visual perception with operational robotics. It further inspires future research and drives the application and development of visual perception across various robotic domains, enabling operational robots to better adapt to complex environments and reliably accomplish tasks.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"945-958"},"PeriodicalIF":7.3,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910063","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":"RNN-Based Sequence-Aware Recommenders for Tourist Attractions","authors":"Hee Jun Lee,&nbsp;Yang Sok Kim,&nbsp;Won Seok Lee,&nbsp;In Hyeok Choi,&nbsp;Choong Kwon Lee","doi":"10.1049/cit2.70027","DOIUrl":"10.1049/cit2.70027","url":null,"abstract":"<p>Selecting appropriate tourist attractions to visit in real time is an important problem for travellers. Since recommenders proactively suggest items based on user preference, they are a promising solution for this problem. Travellers visit tourist attractions sequentially by considering multiple attributes at the same time. Therefore, it is desirable to consider this when developing recommenders for tourist attractions. Using GRU4REC, we proposed RNN-based sequence-aware recommenders (RNN-SARs) that use multiple sequence datasets for training the recommended model, named multi-RNN-SARs. We proposed two types of multi-RNN-SARs—concatenate-RNN-SARs and parallel-RNN-SARs. In order to evaluate multi-RNN-SARs, we compared hit rate (HR) and mean reciprocal rank (MRR) of the item-based collaborative filtering recommender (item-CFR), RNN-SAR with the single-sequence dataset (basic-RNN-SAR), multi-RNN-SARs and the state-of-the-art SARs using a real-world travel dataset. Our research shows that multi-RNN-SARs have significantly higher performances compared to item-CFR. Not all multi-RNN-SARs outperform basic-RNN-SAR but the best multi-RNN-SAR achieves comparable performance to that of the state-of-the-art algorithms. These results highlight the importance of using multiple sequence datasets in RNN-SARs and the importance of choosing appropriate sequence datasets and learning methods for implementing multi-RNN-SARs in practice.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"1077-1088"},"PeriodicalIF":7.3,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910064","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":"Diverse Models, United Goal: A Comprehensive Survey of Ensemble Learning","authors":"Ziwei Fan,&nbsp;Zhiwen Yu,&nbsp;Kaixiang Yang,&nbsp;Wuxing Chen,&nbsp;Xiaoqing Liu,&nbsp;Guojie Li,&nbsp;Xianling Yang,&nbsp;C. L. Philip Chen","doi":"10.1049/cit2.70030","DOIUrl":"10.1049/cit2.70030","url":null,"abstract":"<p>Ensemble learning, a pivotal branch of machine learning, amalgamates multiple base models to enhance the overarching performance of predictive models, capitalising on the diversity and collective wisdom of the ensemble to surpass individual models and mitigate overfitting. In this review, a four-layer research framework is established for the research of ensemble learning, which can offer a comprehensive and structured review of ensemble learning from bottom to top. Firstly, this survey commences by introducing fundamental ensemble learning techniques, including bagging, boosting, and stacking, while also exploring the ensemble's diversity. Then, deep ensemble learning and semi-supervised ensemble learning are studied in detail. Furthermore, the utilisation of ensemble learning techniques to navigate challenging datasets, such as imbalanced and high-dimensional data, is discussed. The application of ensemble learning techniques across various research domains, including healthcare, transportation, finance, manufacturing, and the Internet, is also examined. The survey concludes by discussing challenges intrinsic to ensemble learning.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"959-982"},"PeriodicalIF":7.3,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909914","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":"Co-DeepNet: A Cooperative Convolutional Neural Network for DNA Methylation-Based Age Prediction","authors":"Najmeh Sadat Jaddi,&nbsp;Mohammad Saniee Abadeh,&nbsp;Niousha Bagheri Khoulenjani,&nbsp;Salwani Abdullah,&nbsp;MohammadMahdi Ariannejad,&nbsp;Mohd Zakree Ahmad Nazri,&nbsp;Fatemeh Alvankarian","doi":"10.1049/cit2.70026","DOIUrl":"10.1049/cit2.70026","url":null,"abstract":"<p>Prediction of the age of each individual is possible using the changing pattern of DNA methylation with age. In this paper an age prediction approach to work out multivariate regression problems using DNA methylation data is developed. In this research study a convolutional neural network (CNN)-based model optimised by the genetic algorithm (GA) is addressed. This paper contributes to enhancing age prediction as a regression problem using a union of two CNNs and exchanging knowledge between them. This specifically re-starts the training process from a possibly higher-quality point in different iterations and, consequently, causes potentially yeilds better results at each iteration. The method proposed, which is called cooperative deep neural network (Co-DeepNet), is tested on two types of age prediction problems. Sixteen datasets containing 1899 healthy blood samples and nine datasets containing 2395 diseased blood samples are employed to examine the method's efficiency. As a result, the mean absolute deviation (MAD) is 1.49 and 3.61 years for training and testing data, respectively, when the healthy data is tested. The diseased blood data show MAD results of 3.81 and 5.43 years for training and testing data, respectively. The results of the Co-DeepNet are compared with six other methods proposed in previous studies and a single CNN using four prediction accuracy measurements (<i>R</i>², MAD, MSE and RMSE). The effectiveness of the Co-DeepNet and superiority of its results is proved through the statistical analysis.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"1118-1134"},"PeriodicalIF":7.3,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909915","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":"Exploring High Dimensional Feature Space With Channel-Spatial Nonlinear Transforms for Learned Image Compression","authors":"Wen Tan,&nbsp;Fanyang Meng,&nbsp;Chao Li,&nbsp;Youneng Bao,&nbsp;Yongsheng Liang","doi":"10.1049/cit2.70025","DOIUrl":"10.1049/cit2.70025","url":null,"abstract":"<p>Nonlinear transforms have significantly advanced learned image compression (LIC), particularly using residual blocks. This transform enhances the nonlinear expression ability and obtain compact feature representation by enlarging the receptive field, which indicates how the convolution process extracts features in a high dimensional feature space. However, its functionality is restricted to the spatial dimension and network depth, limiting further improvements in network performance due to insufficient information interaction and representation. Crucially, the potential of high dimensional feature space in the channel dimension and the exploration of network width/resolution remain largely untapped. In this paper, we consider nonlinear transforms from the perspective of feature space, defining high-dimensional feature spaces in different dimensions and investigating the specific effects. Firstly, we introduce the dimension increasing and decreasing transforms in both channel and spatial dimensions to obtain high dimensional feature space and achieve better feature extraction. Secondly, we design a channel-spatial fusion residual transform (CSR), which incorporates multi-dimensional transforms for a more effective representation. Furthermore, we simplify the proposed fusion transform to obtain a slim architecture (CSR-sm), balancing network complexity and compression performance. Finally, we build the overall network with stacked CSR transforms to achieve better compression and reconstruction. Experimental results demonstrate that the proposed method can achieve superior rate-distortion performance compared to the existing LIC methods and traditional codecs. Specifically, our proposed method achieves 9.38% BD-rate reduction over VVC on Kodak dataset.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"1235-1253"},"PeriodicalIF":7.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910121","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":"Intelligent Medical Diagnosis Model Based on Graph Neural Networks for Medical Images","authors":"Ashutosh Sharma,&nbsp;Amit Sharma,&nbsp;Kai Guo","doi":"10.1049/cit2.70020","DOIUrl":"10.1049/cit2.70020","url":null,"abstract":"<p>Recently, numerous estimation issues have been solved due to the developments in data-driven artificial neural networks (ANN) and graph neural networks (GNN). The primary limitation of previous methodologies has been the dependence on data that can be structured in a grid format. However, physiological recordings often exhibit irregular and unordered patterns, posing a significant challenge in conceptualising them as matrices. As a result, GNNs which comprise interactive nodes connected by edges whose weights are defined by anatomical junctions or temporal relationships have received a lot of consideration by leveraging implicit data that exists in a biological system. Additionally, our study incorporates a structural GNN to effectively differentiate between different degrees of infection in both the left and right hemispheres of the brain. Subsequently, demographic data are included, and a multi-task learning architecture is devised, integrating classification and regression tasks. The trials used an authentic dataset, including 800 brain x-ray pictures, consisting of 560 instances classified as moderate cases and 240 instances classified as severe cases. Based on empirical evidence, our methodology demonstrates superior performance in classification, surpassing other comparison methods with a notable achievement of 92.27% in terms of area under the curve as well as a correlation coefficient of 0.62.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"1201-1216"},"PeriodicalIF":7.3,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909957","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":"Optimising PID Controllers for Multi-Area Automatic Generation Control With Improved NSGA-II","authors":"Yang Yang,&nbsp;Yuchao Gao,&nbsp;Shangce Gao,&nbsp;Jinran Wu","doi":"10.1049/cit2.70024","DOIUrl":"10.1049/cit2.70024","url":null,"abstract":"<p>Modern automated generation control (AGC) is increasingly complex, requiring precise frequency control for stability and operational accuracy. Traditional PID controller optimisation methods often struggle to handle nonlinearities and meet robustness requirements across diverse operational scenarios. This paper introduces an enhanced strategy using a multi-objective optimisation framework and a modified non-dominated sorting genetic algorithm II (SNSGA). The proposed model optimises the PID controller by minimising key performance metrics: integration time squared error (ITSE), integration time absolute error (ITAE), and rate of change of deviation (J). This approach balances convergence rate, overshoot, and oscillation dynamics effectively. A fuzzy-based method is employed to select the most suitable solution from the Pareto set. The comparative analysis demonstrates that the SNSGA-based approach offers superior tuning capabilities over traditional NSGA-II and other advanced control methods. In a two-area thermal power system without reheat, the SNSGA significantly reduces settling times for frequency deviations: 2.94s for <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Δ</mi>\u0000 <msub>\u0000 <mi>f</mi>\u0000 <mn>1</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${Delta }{f}_{1}$</annotation>\u0000 </semantics></math> and 4.98s for <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Δ</mi>\u0000 <msub>\u0000 <mi>f</mi>\u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${Delta }{f}_{2}$</annotation>\u0000 </semantics></math>, marking improvements of 31.6% and 13.4% over NSGA-II, respectively.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"1135-1147"},"PeriodicalIF":7.3,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909973","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":"Graph Neural Networks Empowered Origin-Destination Learning for Urban Traffic Prediction","authors":"Chuanting Zhang,&nbsp;Guoqing Ma,&nbsp;Liang Zhang,&nbsp;Basem Shihada","doi":"10.1049/cit2.70021","DOIUrl":"10.1049/cit2.70021","url":null,"abstract":"<p>Urban traffic prediction with high precision is always the unremitting pursuit of intelligent transportation systems and is instrumental in bringing smart cities into reality. The fundamental challenges for traffic prediction lie in the accurate modelling of spatial and temporal traffic dynamics. Existing approaches mainly focus on modelling the traffic data itself, but do not explore the traffic correlations implicit in origin-destination (OD) data. In this paper, we propose STOD-Net, a dynamic spatial-temporal OD feature-enhanced deep network, to simultaneously predict the in-traffic and out-traffic for each and every region of a city. We model the OD data as dynamic graphs and adopt graph neural networks in STOD-Net to learn a low-dimensional representation for each region. As per the region feature, we design a gating mechanism and operate it on the traffic feature learning to explicitly capture spatial correlations. To further capture the complicated spatial and temporal dependencies among different regions, we propose a novel joint feature, learning block in STOD-Net and transfer the hybrid OD features to each block to make the learning process spatiotemporal-aware. We evaluate the effectiveness of STOD-Net on two benchmark datasets, and experimental results demonstrate that it outperforms the state-of-the-art by approximately 5% in terms of prediction accuracy and considerably improves prediction stability up to 80% in terms of standard deviation.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"1062-1076"},"PeriodicalIF":7.3,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910461","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":"Cross-Domain Graph Anomaly Detection via Graph Transfer and Graph Decouple","authors":"Changqin Huang,&nbsp;Xinxing Shi,&nbsp;Chengling Gao,&nbsp;Qintai Hu,&nbsp;Xiaodi Huang,&nbsp;Qionghao Huang,&nbsp;Ali Anaissi","doi":"10.1049/cit2.70014","DOIUrl":"10.1049/cit2.70014","url":null,"abstract":"<p>Cross-domain graph anomaly detection (CD-GAD) is a promising task that leverages knowledge from a labelled source graph to guide anomaly detection on an unlabelled target graph. CD-GAD classifies anomalies as unique or common based on their presence in both the source and target graphs. However, existing models often fail to fully explore domain-unique knowledge of the target graph for detecting unique anomalies. Additionally, they tend to focus solely on node-level differences, overlooking structural-level differences that provide complementary information for common anomaly detection. To address these issues, we propose a novel method, Synthetic Graph Anomaly Detection via Graph Transfer and Graph Decouple (GTGD), which effectively detects common and unique anomalies in the target graph. Specifically, our approach ensures deeper learning of domain-unique knowledge by decoupling the reconstruction graphs of common and unique features. Moreover, we simultaneously consider node-level and structural-level differences by transferring node and edge information from the source graph to the target graph, enabling comprehensive domain-common knowledge representation. Anomalies are detected using both common and unique features, with their synthetic score serving as the final result. Extensive experiments demonstrate the effectiveness of our approach, improving an average performance by 12.6<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>%</mi>\u0000 </mrow>\u0000 <annotation> $%$</annotation>\u0000 </semantics></math> on the AUC-PR compared to state-of-the-art methods.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"1089-1103"},"PeriodicalIF":7.3,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910129","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}