Complex & Intelligent Systems最新文献

{"title":"Dual stream spatiotemporal feature learning framework for neurological disease severity level classification using fMRI data","authors":"C. S. Smitha,S Sathiya,T Soumya","doi":"10.1007/s40747-026-02527-6","DOIUrl":"https://doi.org/10.1007/s40747-026-02527-6","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"23 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-modal parameter-efficient fine-tuning via hypergraph adapters","authors":"Dongmei Wang,Wenjie Pan,Junyan Lv,Jiaxuan Lu","doi":"10.1007/s40747-026-02534-7","DOIUrl":"https://doi.org/10.1007/s40747-026-02534-7","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"9 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial neural networks as discrete complex systems: native vector-ternary training via the leech lattice $$Lambda _{24}$$","authors":"Alexander Lavicka","doi":"10.1007/s40747-026-02517-8","DOIUrl":"https://doi.org/10.1007/s40747-026-02517-8","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"3 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An active recommendation algorithm for trade union rights and interests services in power grid enterprises based on knowledge graphs and dynamic user personas","authors":"Xuyu Chen,Caihua Song,Hao Lin,Kehuan Li","doi":"10.1007/s40747-026-02486-y","DOIUrl":"https://doi.org/10.1007/s40747-026-02486-y","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"219 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RMTA-Net: recurrent conditioned memory-guided temporal attention with spatial feature enhancement for video anomaly detection","authors":"Santosh Prakash Chouhan,Narinder Singh Punn,Mahua Bhattacharya","doi":"10.1007/s40747-026-02506-x","DOIUrl":"https://doi.org/10.1007/s40747-026-02506-x","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"25 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CoR-Hate: counterfactual retrieval and fairness-regularized hate-speech detection","authors":"Ehtesham Hashmi,John McCrae,Sule Yildirim Yayilgan,Rajendra Akerkar","doi":"10.1007/s40747-026-02528-5","DOIUrl":"https://doi.org/10.1007/s40747-026-02528-5","url":null,"abstract":"Abstract The growing presence of hate speech on social media, targeting groups based on race, religion, gender, and sexual orientation, raises critical concerns regarding fairness and reliability in automated moderation systems. This study introduces CoR-Hate, a fairness-regularized framework that integrates Sentence-Transformer-based semantic retrieval with pretrained transformer models to mitigate algorithmic bias while maintaining strong classification performance. Unlike prior approaches that construct synthetic counterfactuals through identity-term substitutions, manually defined templates, or large-language-model generation, CoR-Hate retrieves corpus-grounded counterfactual proxies from naturally occurring samples using FAISS-based similarity search. A consistency-driven regularization objective is then applied to improve prediction stability across identity-sensitive variations and reduce reliance on identity-specific cues. Comprehensive bias analysis at both entity and pairwise levels is conducted to evaluate fairness robustness across demographic attributes. Experiments on the HateXplain dataset using two Sentence-Transformer variants, all-mpnet-base-v2 and all-MiniLM-L6-v2 , demonstrate consistent improvements over baseline models. Notably, the CoR-Hate BERT model with MPNet embeddings achieves the strongest overall fairness-performance trade-off, attaining an F1-score of 0.79 and an accuracy of 0.80 while reducing the flip rate to 0.10, the lowest among all evaluated models. Furthermore, Grad-CAM-based interpretability provides transparent insights into model behaviour by visualizing token-level importance across identity substitutions. The results indicate reduced sensitivity to identity-specific terms for few evaluated substitutions, although residual bias remains for certain terms and model architectures. Importantly, a lower flip rate reflects prediction consistency only for the predefined identity substitutions and should not be interpreted as evidence that all forms of algorithmic bias have been eliminated. Overall, CoR-Hate provides a unified, corpus-grounded, and explainable framework for mitigating identity-related bias in hate speech detection.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"21 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A holistic trustworthy AI pipeline for building trusted AI-enabled applications","authors":"Bilal Sardar,Shareeful Islam,Dmitry Amelin,Spyridon Papastergiou","doi":"10.1007/s40747-026-02508-9","DOIUrl":"https://doi.org/10.1007/s40747-026-02508-9","url":null,"abstract":"Abstract AI-enabled applications have achieved widespread adoption for complex problem-solving and informed decision-making. However, growing concerns regarding AI system failures that lead to bias, inequalities, and untrustworthy outcomes necessitate a move beyond performance evaluation to ensure trustworthy, equitable decisions. Although existing practices identify key trustworthiness characteristics, they lack a unified pipeline for systematically aligning these traits across the development lifecycle. To address this gap, this work presents a novel holistic Trustworthy AI (T-AI) pipeline that integrates six key characteristics—privacy, fairness, security, robustness, safety, and explainability—into specific development phases. To formally govern this integration, we introduce a novel Adaptive Trustworthiness Integration (ATI) algorithm, which enforces an irreversibility-based ordering of characteristics and applies conditional gating at each phase boundary to prevent propagation of unresolved violations, producing an empirically measured inter-characteristic trade-off matrix that makes intervention costs and benefits transparent and reproducible. The pipeline is validated through two experiments: an industrial pilot at Fraunhofer IBMT, extended with multi-seed validation across three seeds and a large-scale public microscopy benchmark (PanNuke; 7,904 images), achieving 99.5% mAP@50; and a healthcare experiment using the Synthea dataset, maintaining 89.5% predictive accuracy. These results demonstrate that the pipeline reduces demographic fairness gaps from 46% to under 5%, achieves a 0% rate of clinically dangerous hallucinations via a four-layer safety architecture, and confirms consistent trustworthiness guarantees across datasets and random initialisations. Ultimately, this work advances United Nations Sustainable Development Goal 9 by ensuring fair and privacy-preserving data collection, preventing system failures through adversarial hardening, and enabling transparent decision-making.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"67 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149090548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive graph structure learning for skeleton-based gait recognition","authors":"Zheze Liu, Lichang Guan, Lei Feng, Song Zhang, Hongyun Bao, Xuemei Jiang, Xingchun Zhao, Nan Zheng","doi":"10.1007/s40747-026-02524-9","DOIUrl":"https://doi.org/10.1007/s40747-026-02524-9","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"9 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149067367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual stream knowledge distillation for 3D LiDAR segmentation model with large sparse multi-directional convolution","authors":"Ye Gu, Huancheng Xiao, Gengliang Chen, Lixin Liang","doi":"10.1007/s40747-026-02523-w","DOIUrl":"https://doi.org/10.1007/s40747-026-02523-w","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"15 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149067368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond stationarity: dynamic spectral–structural modeling for remote sensing object detection","authors":"Haiyan Yang, Peng Wang, Jia Sun, Yuanling Zhang","doi":"10.1007/s40747-026-02504-z","DOIUrl":"https://doi.org/10.1007/s40747-026-02504-z","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"16 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149067369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}