Cognitive Computation and Systems最新文献

{"title":"Multi-Task Recurrent Convolutional Network for Rib Fracture Healing Period Prediction","authors":"Hong Shangguan,&nbsp;Hongrui Zhao,&nbsp;Xiong Zhang,&nbsp;Ranran Li,&nbsp;Jie Yang,&nbsp;Yina Guo","doi":"10.1049/ccs2.70005","DOIUrl":"https://doi.org/10.1049/ccs2.70005","url":null,"abstract":"<p>Rib fracture CT image sequences present challenges such as small fracture regions, subtle inter-class differences and complex temporal changes during healing. In this work, we propose a multi-task recurrent convolutional network (MRCN) for simultaneous rib fracture healing period prediction and fracture type classification. The method uses a shared feature encoder to extract semantic features from CT images and two task-specific branches to model healing status and fracture type information. In the prediction branch, a CE-LSTM module with contextual expression is introduced to capture temporal dependencies and contextual information across CT image sequences. In the classification branch, Bi-VA and CAM modules are used for multi-scale feature fusion and fracture-region enhancement. A joint optimisation strategy enables feature sharing and complementary learning between the two tasks. Experiments on the rib fracture dataset from Shanxi Bethune Hospital show that MRCN achieves 93.20% accuracy for healing period prediction and 87.90% accuracy for fracture type classification, showing better performance than the compared methods.</p>","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"8 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148754151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Research on Military-Retrieval-Augmented Generation Question Answering System Based on MiliGNER","authors":"Jiaqi Zhang,&nbsp;Ruicong Zhi","doi":"10.1049/ccs2.70004","DOIUrl":"https://doi.org/10.1049/ccs2.70004","url":null,"abstract":"<p>With the rapid development of large language models, generative named entity recognition and retrieval-augmented generation have opened new methodological pathways for intelligent question answering in specialised domains. To address the characteristics of military texts, including complex entity naming, tightly coupled attribute dependencies and highly implicit semantic expressions, this study proposes a military-RAG framework for military question–answering tasks and designs a generative named entity recognition module, termed MiliGNER, as the core component for structured extraction. The proposed method formulates entity recognition as a sequence generation process from input text to structured output, directly generating entity–attribute–value triplets to enhance the representation of complex military entities and their attribute relationships. To improve the quality of structured extraction and downstream question answering, the proposed framework is optimised from three perspectives: structural consistency, semantic consistency and question–answering correctness. Specifically, joint training with positive and negative samples enhances the model's ability to discriminate invalid inputs and nontriggering cases; an attribute validation module is introduced to reduce attribute mismatches and semantic drift; and entity-aware attention structure-guided decoding, together with a multipath error-correction mechanism, improve the completeness and robustness of structured generation in complex scenarios. On this basis, military-RAG uses validated structured triplets for retrieval prompt construction and answer generation, thereby improving the answer reliability of retrieval inputs and the semantic alignment between the final answer and the original question. Experiments were conducted on both public benchmark datasets and a self-constructed military-domain dataset. The results show that, under the current experimental setting, MiliGNER achieves consistent improvements over the selected comparison methods in structured entity recognition tasks, whereas military-RAG also demonstrates strong overall performance in question–answering tasks relative to representative reference systems. Ablation studies further indicate that each key module makes a practical contribution to overall system performance. Overall, the proposed method provides an effective collaborative framework of structured modelling and generative reasoning for highly specialised question–answering tasks in the military domain.</p>","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"8 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Path Recommender for Mobile Robot in Multi-Robot System","authors":"Bandita Sahu,&nbsp;Pradipta Kumar Das,&nbsp;Sanjay Kumar Kuanar,&nbsp;Indrajeet Kumar","doi":"10.1049/ccs2.70003","DOIUrl":"https://doi.org/10.1049/ccs2.70003","url":null,"abstract":"<p>The paper presents an intelligent approach for computing a path for mobile robot in a multi-robot environment. Based on the personal requirement and user's feedback, the robot transits from predetermined starting position to a defined goal position. The solution is achieved through the effective blending of content-based filtering with K-NN path recommendation. The goal of this merging behaviour is to leverage the combined merits of recommendation and the mechanism for updating the position. Specifically, the proposed paper focus the integration of path recommendation using a filtering approach through past history to accomplish the task of object transportation from source to destinations. The discussed approach is validated through simulation using programming in a C environment. The evaluation demonstrates the ability and efficiency to achieve minimal path, time and collision freeness. Performance of the proposed approach with other existing approaches such as QCOV-R, MCS, PRRS and DRS has been analysed to witness the efficiency.</p>","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"8 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148533496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved UNet-based magnetic resonance imaging segmentation of demyelinating diseases with small lesion regions","authors":"Minhui Liu,&nbsp;Tianlei Wang,&nbsp;Dekang Liu,&nbsp;Feng Gao,&nbsp;Jiuwen Cao","doi":"10.1049/ccs2.12099","DOIUrl":"10.1049/ccs2.12099","url":null,"abstract":"<p>Accurate magnetic resonance imaging (MRI) segmentation plays a critical role in the diagnosis and treatment of demyelinating diseases. But the existing automatic segmentation methods are not suitable for the segmentation of demyelinating lesions with small lesion size, highly diffuse edges and complex boundary shapes. An improved model is proposed for demyelinating diseases MRI segmentation based on the U-shaped structure convolution neural networks (UNet). A context information weighting fusion (CIWF) module and a modified channel attention (MCA) module are developed and embedded in UNet to address the small lesion region and diffuse edge issues. The CIWF module can dynamically screen and fuse shallow and deep features at different stages, making the model pay more attention to small lesions. The MCA module enables the model to learn diverse features by adding weights to the channel, which helps in diffuse edge segmentation. Comparisons with many existing methods on real-world demyelinating disease MRI segmentation dataset show that our method achieve the highest Dice metric.</p>","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"7 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.12099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140081213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Improved UNet-Based Magnetic Resonance Imaging Segmentation of Demyelinating Diseases With Small Lesion Regions”","authors":"","doi":"10.1049/ccs2.70002","DOIUrl":"10.1049/ccs2.70002","url":null,"abstract":"<p>M. Liu, T. Wang, D. Liu, F. Gao and J. Cao: Improved UNet-Based Magnetic Resonance Imaging Segmentation of Demyelinating Diseases With Small Lesion Regions. <i>Cogn. Comput. Syst.</i> 1–8 (2024). https://doi.org/10.1049/ccs2.12099.</p><p>The details of the ethical approval and consent for the study were not stated in the article. The details are listed below as follows:</p><p>“This study has been approved by the Ethic Committee of the Children's Hospital, Zhejiang University School of Medicine (2020-IRB-124), and registered in Chinese Clinical Trial Registry (ChiCTR2000028804). All patients provided informed consent before inclusion in the study.”</p><p>The authors apologize for this error.</p>","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"7 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145101734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"7 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148082901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Brain Network Analysis of Benign Childhood Epilepsy With Centrotemporal Spikes: With Versus Without Interictal Spikes”","authors":"","doi":"10.1049/ccs2.70001","DOIUrl":"10.1049/ccs2.70001","url":null,"abstract":"<p>Z. Hong, D. Hu, R. Zheng, T. Jiang, F. Gao, J. Fang, and J. Cao: Brain Network Analysis of Benign Childhood Epilepsy With Centrotemporal Spikes: With Versus Without Interictal Spikes. <i>Cogn. Comput. Syst</i>. 6(4), 135–147 (2024). https://doi.org/10.1049/ccs2.12115.</p><p>The details of the ethical approval and consent for the study were not stated in the article. The details are listed below as follows:</p><p>“This study has been approved by the Children's Hospital, Zhejiang University School of Medicine and registered in Chinese Clinical Trial Registry (ChiCTR2000028804) and by the Ethic Committee of the fourth Affiliated Hospital of Anhui Medical University (PJ-YX2021-019). All patients provided informed consent before inclusion in the study.”</p><p>The authors apologise for this error.</p>","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"7 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145101309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"7 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148061543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"7 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148072418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Efficient Ensemble Learning Model Integrating Multi-Branch Sub-Networks for Facial Expression Recognition","authors":"Golam Jilani,&nbsp;Samara Paul,&nbsp;Sadia Sultana","doi":"10.1049/ccs2.70000","DOIUrl":"10.1049/ccs2.70000","url":null,"abstract":"<p>Accurate facial expression recognition is still challenging due to occlusion and location variability. Reducing computing overhead is also important because facial expression detection systems may be used in real-time applications. This research provides an effective ensemble learning architecture for facial emotion identification using advanced data augmentation and transfer learning techniques. The architecture uses a multi-branch sub-network framework. We chose the EfficientNet-B0, RegNet_Y_800MF and MobileNetV2 for ensembling because they are significantly smaller in terms of FLOPs and number of parameters than other variations, such as the EfficientNet-B7 and RegNet_Y_800MF. We included data augmentation methods such as Mixup and CutMix to make our system more resilient to overfitting. As demonstrated by our proposed approach, combining smaller models is more efficient than using a single large model. The proposed architecture achieves state-of-the-art results with an accuracy of 96.42% and 97.55% on the SUFEDB and KDEF datasets, respectively.</p>","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"7 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144256450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}