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Many link prediction methods based on non-negative matrix factorization (NMF) have been proposed to solve prediction problem. However, due to the sparsity of real networks, the observed topology information is probably very limited, which affects the performance of existing link prediction methods. In this paper, we utilize Deep Non-negative Matrix Factorization (DNMF) models with Edge Generator to address the network sparsity problem and propose link prediction methods EG-DNMF and EG-FDNMF. Under the framework of DNMF, several representative potential edges are incorporated so as to reconstruct the original network for link prediction. Specifically, in order to explore the potential structural features of the network in a more fine-grained manner, we first divide the original network into three sub-networks. Then, the DNMF models are employed to mine complex and nonlinear interaction relationships in sub-networks, thereby guiding the network reconstruction process. Finally, the NMF algorithm is applied on the reconstructed original network for link prediction. Experiment results on 12 different networks show that our methods have comparable performance with respect to 13 representative link prediction methods which include 6 NMF\u002FDNMF-based approaches and 7 heuristic-based approaches. In addition, experiments also show that the sub-networks after partitioning are beneficial for capturing the underlying features of the network. Codes are available at \n                  https:\u002F\u002Fgithub.com\u002Fyabingyao\u002FEGDNMF4LinkPrediction\n                  \n                 \n                  \n                    \n                  \n                ",{"EN":196,"VI":197},"Deep non-negative matrix factorization with edge generator for link prediction in complex networks","Phân rã ma trận không âm sâu với bộ tạo cạnh để dự đoán liên kết trong các mạng phức tạp",{"VOID":199},"Wahid-Ul-Ashraf A, Budka M, Musial K (2019) How to predict social relationships-physics-inspired approach to link prediction. Physica A 523:1110–1129\nYao Y, Cheng T, Li X, He Y, Yang F, Li T, Liu Z, Xu Z (2023) Link prediction based on the mutual information with high-order clustering structure of nodes in complex networks. Physica A 610:128428\nZhou T (2021) Progresses and challenges in link prediction. Iscience 24(11):103217\nLü L, Zhou T (2011) Link prediction in complex networks: A survey. Physica A 390(6):1150–1170\nLi S, Song X, Lu H, Zeng L, Shi M, Liu F (2020) Friend recommendation for cross marketing in online brand community based on intelligent attention allocation link prediction algorithm. Expert Syst Appl 139:112839\nSu Z, Zheng X, Ai J, Shen Y, Zhang X (2020) Link prediction in recommender systems based on vector similarity. Physica A 560:125154\nLiu G (2022) An ecommerce recommendation algorithm based on link prediction. Alex Eng J 61(1):905–910\nNasiri E, Berahmand K, Rostami M, Dabiri M (2021) A novel link prediction algorithm for protein-protein interaction networks by attributed graph embedding. Comput Biol Med 137:104772\nLi Z, Zhu S, Shao B, Zeng X, Wang T, Liu T-Y (2023) Dsn-ddi: an accurate and generalized framework for drug–drug interaction prediction by dual-view representation learning. Briefings in Bioinformatics 24(1)\nKumar A, Singh SS, Singh K, Biswas B (2020) Link prediction techniques, applications, and performance: A survey. 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The high incidence of respiratory diseases has dramatically increased the medical burden under the COVID-19 pandemic in the year 2020. It is of considerable significance to utilize a new generation of information technology to improve the artificial intelligence level of respiratory disease diagnosis. Methods: Based on the semi-structured data of Chinese Electronic Medical Records (CEMRs) from the China Hospital Pharmacovigilance System, this paper proposed a bi-level artificial intelligence model for the risk classification of acute respiratory diseases. It includes two levels. The first level is a dedicated design of the “BiLSTM+Dilated Convolution+3D Attention+CRF” deep learning model that is used for Chinese Clinical Named Entity Recognition (CCNER) to extract valuable information from the unstructured data in the CEMRs. Incorporating the transfer learning and semi-supervised learning technique into the proposed deep learning model achieves higher accuracy and efficiency in the CCNER task than the popular “Bert+BiLSTM+CRF” approach. Combining the extracted entity data with other structured data in the CEMRs, the second level is a customized XGBoost to realize the risk classification of acute respiratory diseases. Results: The empirical study shows that the proposed model could provide practical technical support for improving diagnostic accuracy. Conclusion: Our study provides a proof-of-concept for implementing a hybrid artificial intelligence-based system as a tool to aid clinicians in tackling CEMR data and enhancing the diagnostic evaluation under diagnostic uncertainty.",{"EN":373,"VI":374},"Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data","Mô hình trí tuệ nhân tạo hai cấp để phân loại nguy cơ mắc các bệnh hô hấp cấp tính dựa trên dữ liệu lâm sàng Trung Quốc",{"VOID":376},"Perrotta DM, Decker M, Glezen WP (1985) Acute respiratory disease hospitalizations as a measure of impact of epidemic influenza. Am J Epidemiol 122:468\nMansmann S, Ur Rehman N, Weiler A, Scholl MH (2014) Discovering OLAP dimensions in semi-structured data. Inf Syst 44:120\nWong ZSY, Zhou J, Zhang Q (2019) Artificial intelligence for infectious disease big data analytics. Infect Dis Health 24:44\nHillestad R, Bigelow J, Bower A, Girosi F, Meili R, Scoville R, Taylor R (2005) Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Aff (Millwood) 24:1103\nSweeney L (1996) Replacing personally-identifying information in medical records, the scrub system. Proc AMIA Annu Fall Symp 333\nSarker A, Mollá D, Paris C (2016) Query-oriented evidence extraction to support evidence-based medicine practice. J Biomed Inform 59:169\nMohamadou Y, Halidou A, Kapen PT (2020) A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19. Appl Intell 50:3913\nZhang H, Zhang H, Pirbhulal S, Wu W (2020) Albuquerque V.H.C.D.: Active Balancing Mechanism for Imbalanced Medical Data in Deep Learning–Based Classification Models. ACM Trans Multimed Comput Commun Appl 16:1\nChiriac AM, Wang Y, Schrijvers R, Bousquet PJ, Mura T, Molinari N, Demoly P (2018) Designing predictive models for Beta-lactam allergy using the drug allergy and hypersensitivity database. The journal of allergy and clinical immunology. In Practice 6:139\nMaxwell A, Li R, Yang B, Weng H, Ou A, Hong H, Zhou Z, Gong P, Zhang C (2017) Deep learning architectures for multi-label classification of intelligent health risk prediction. BMC Bioinformatics 18:523\nSumathi S, Beaulah HL, Vanithamani R (2014) A wavelet transform based feature extraction and classification of cardiac disorder. J Med Syst 38:98\nHira S, Bai A, Hira S (2021) An automatic approach based on CNN architecture to detect Covid-19 disease from chest X-ray images. Appl Intell 51:2864\nPham T, Tran T, Phung D, Venkatesh S (2017) Predicting healthcare trajectories from medical records: a deep learning approach. J Biomed Inform 69:218\nLiu M, Zhang J, Lian C, Shen D (2020) Weakly supervised deep learning for brain disease prognosis using MRI and incomplete clinical scores. IEEE Trans Cybern 50:3381\nZheng N, Du S, Wang J, Zhang H, Cui W, Kang Z, Yang T, Lou B, Chi Y, Long H, Ma M, Yuan Q, Zhang S, Zhang D, Ye F, Xin J (2020) Predicting COVID-19 in China using hybrid AI model. IEEE Trans Cybern 50:2891\nPanwar M., Biswas D., Bajaj H., Jobges M., Turk R., Maharatna K., Acharyya A.: Rehab-Net: Deep learning framework for arm movement classification using wearable sensors for stroke rehabilitation. IEEE Trans Biomed Eng 66. 3026 (2019)\nUpadhyay J, Tiwari N, Rana M, Rana A, Durgapal S, Bisht SS (2019) Pathophysiology, etiology, and recent advancement in the treatment of congenital heart disease. J Indian Coll Cardiol 9:67\nZhou J, Theesfeld CL, Yao K, Chen KM, Wong AK, Troyanskaya OG (2018) Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat Genet 50:1171\nCeli L.A., Davidzon G., Johnson A.E., Komorowski M., Marshall D.C., Nair S.S., Phillips C.T., Pollard T.J., Raffa J.D., Salciccioli J.D., Salgueiro F.M., Stone D.J.: Bridging the Health Data Divide. J Med Internet Res 18. e325 (2016)\nLian C, Liu M, Pan Y, Shen D (2020) Attention-guided hybrid network for dementia diagnosis with structural MR images. IEEE Trans Cybern. PP\nHsieh N, Hsieh N, Hung L, Hung L, Shih C, Shih C, Keh H, Keh H, Chan C, Chan C (2012) Intelligent postoperative morbidity prediction of heart disease using artificial intelligence techniques. J Med Syst 36:1809\nPhegley JW, Perkins K, Gupta L, Hughes LF (2005) Multicategory prediction of multifactorial diseases through risk factor fusion and rank-sum selection. IEEE Trans Syst Man Cybern Syst Hum 35:718\nHewson PJ, Bailey TC (2010) Modelling multivariate disease rates with a latent structure mixture model. Stat Model 10:241\nHuang Z, Dong W, Duan H, Liu J (2018) A regularized deep learning approach for clinical risk prediction of acute coronary syndrome using electronic health records. IEEE Trans Biomed Eng 65:956\nHao Y, Usama M, Yang J, Hossain MS, Ghoneim A (2019) Recurrent convolutional neural network based multimodal disease risk prediction. Futur Gener Comput Syst 92:76\nWang T, Qiu RG, Yu M, Zhang R (2020) Directed disease networks to facilitate multiple-disease risk assessment modeling. Decis Support Syst 129:113171\nLiang H, Tsui BY, Ni H, Valentim CCS, Baxter SL, Liu G, Cai W, Kermany DS, Sun X, Chen J, He L, Zhu J, Tian P, Shao H, Zheng L, Hou R, Hewett S, Li G, Liang P et al (2019) Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med 25:433\nOhsaki M, Abe H, Tsumoto S, Yokoi H, Yamaguchi T (2007) Evaluation of rule interestingness measures in medical knowledge discovery in databases. Artif Intell Med 41:177\nChen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 785. Association for Computing Machinery,San Francisco California USA\nChang C, Hsu C, Lui S (2003) Automatic information extraction from semi-structured web pages by pattern discovery. Decis Support Syst 35:129\nLeng J, Jiang P (2016) A deep learning approach for relationship extraction from interaction context in social manufacturing paradigm. Knowl-Based Syst 100:188\nLiu Z, Tang B, Wang X, Chen Q (2017) De-identification of clinical notes via recurrent neural network and conditional random field. J Biomed Inform 75:S34\nVaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention Is All You Need. In: Advances in Neural Information Processing Systems 30, vol 5998\nDai Z, Yang Z, Yang Y, Carbonell J, Le QV, Salakhutdinov R (2019) Transformer-xl: attentive language models beyond a fixed-length context\nWei Y, Xiao H, Shi H, Jie Z, Feng J, Huang TS (2018) Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 7268. Salt Lake City, USA. June 18–22, 2018\nOktay O, Schlemper J, Le Folgoc L, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, Glocker B, Rueckert D (2018) Attention u-net: learning where to look for the pancreas\nYang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov R, Le QV (2019) XLNet: Generalized Autoregressive Pretraining for Language Understanding\nSun Q, Liu Y, Chua T, Schiele B (2019) Meta-Transfer Learning for Few-Shot Learning. In: Proceedings of the IEEE\u002FCVF Conference on Computer Vision and Pattern Recognition (CVPR), vol. 403. California, USA. June 16–20, 2019\nQiu J, Zhou Y, Wang Q, Ruan T, Gao J (2019) Chinese clinical named entity recognition using residual dilated convolutional neural network with conditional random field. IEEE Trans Nanobiosci 18:306\nLi X, Zhang H, Zhou X (2020) Chinese clinical named entity recognition with variant neural structures based on BERT methods. J Biomed Inform 107:103422\nLeng J, Jiang P (2017) Mining and matching relationships from interaction contexts in a social manufacturing paradigm. IEEE Trans Syst Man Cybern Syst 47:1\nMuhammad K, Khan S, Ser JD, de Albuquerque V (2020) Deep learning for multigrade brain tumor classification in smart healthcare systems: a prospective survey. IEEE Trans Neural Netw Learn Syst. PP\nKarnofsky. (2008) Karnofsky performance score. Encyclopedia of Cancer. Springer, Berlin\nKe G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T (2017) Lightgbm: A highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, vol. Long Beach, CA, USA. Dec 4–9, 2017\nYang L, Qian Y (2016) A sparse logistic regression framework by difference of convex functions programming. Appl Intell 45:241\nMehmood Z, Mahmood T, Javid MA (2018) Content-based image retrieval and semantic automatic image annotation based on the weighted average of triangular histograms using support vector machine. Appl Intell 48:166\nKim S, Jeong M, Ko BC (2021) Lightweight surrogate random forest support for model simplification and feature relevance. Appl Intell 10\nLeng J, Chen Q, Mao N, Jiang P (2018) Combining granular computing technique with deep learning for service planning under social manufacturing contexts. 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We determine the spatial information that should be analysed in this kind of interface, as well as indicate which information can be omitted. This study is a contribution to our long-term research program on man-machine communication. Implementation of a virtual agent (ACALA) equipped with a natural language interface is important from a methodological point of view because it permits us to observe natural language interaction between the human user and the computer-controlled system.",{"EN":534,"VI":535},"Ontology of Spatial Concepts in a Natural Language Interface for a Mobile Robot","Ontology các khái niệm không gian trong giao diện ngôn ngữ tự nhiên cho robot di động",{"EN":537},"",{"VOID":539},"10.1023\u002FA:1020039416483",[541],"EN",[207],"https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1023\u002FA:1020039416483",[545,560],{"id":546,"sortIndex":21,"researcher":20,"roles":547,"affiliations":548,"properties":557,"displayName":559,"givenName":20,"familyName":20},"bff2147b-f128-425e-b77f-8ede0a92d237",[],[549],{"id":550,"sortIndex":21,"affiliation":551,"properties":20},"db15a1b7-40c4-4b8c-b3e1-49595c1d722e",{"id":550,"createTime":20,"updateTime":20,"relativeEntities":552,"slug":20,"properties":553,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":556,"statistic":20},[],{"title":554},{"EN":555},"Department of Computer Linguistics and Artificial Intelligence, Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poznan, Poland",[],{"title":558},{"EN":559},"Jacek Marciniak",{"id":561,"sortIndex":228,"researcher":20,"roles":562,"affiliations":563,"properties":570,"displayName":572,"givenName":20,"familyName":20},"e22c1d68-2bbe-4f3a-98ad-7f16beaf4ec4",[],[564],{"id":550,"sortIndex":21,"affiliation":565,"properties":20},{"id":550,"createTime":20,"updateTime":20,"relativeEntities":566,"slug":20,"properties":567,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":569,"statistic":20},[],{"title":568},{"EN":555},[],{"title":571},{"EN":572},"Zygmunt 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Vetulani and J. Marciniak, “Corpus based methodology in the study and design of systems with emulated linguistic competence,” in Natural Language Processing-NLP 2000, Second International Conference, Patras, Greece, June 2000, edited by D.N. Christodoulakis, Lecture Notes in Artificial Intelligence, vol. 1835, Springer Verlag: Berlin, pp. 346–357, 2000.",{"id":20,"text":623,"url":20,"identifiers":20},"G. Ligozat, J. Marciniak, J. Martinek, and Z. Vetulani, “Modeling linguistic competence for guiding a robot: A corpus-based approach,” in Spatial and Temporal Reasoning, IJCAI'97, Nagoya, Japan, August 23-29, 1997, edited by H. Guesgen, pp. 19–23, 1997.",{"id":20,"text":625,"url":20,"identifiers":20},"J. Marciniak, “Langage, perception, action: Raisonnement spatiotemporel dans le guidage d'un agent virtuel,” Ph.D. Dissertation, Paris XI University, Notes et Documents LIMSI, no. 99-09, Orsay, France, 1999.",{"id":20,"text":627,"url":20,"identifiers":20},"B. Landau and R. Jackendoff, “'What’ and ‘Where’ in spatial language and spatial cognition,” Behavioral and Brain Sciences vol. 16, pp. 217–265, 1993.",{"id":20,"text":629,"url":20,"identifiers":20},"Z. Vetulani, “A system for computer understanding of texts,” in Euphony and Logos (Poznan Studies in the Philosophy of the Sciences and the Humanities, vol. 57), edited by R. Murawski and J. Pogonowski, Rodopi: Amsterdam, pp. 387–416, 1997.",{"id":631,"createTime":632,"updateTime":633,"relativeEntities":634,"slug":635,"properties":636,"entityType":202,"verifyStatus":203,"verifyTime":646,"verifyNote":205,"languages":20,"translateLanguages":647,"viewCount":21,"primaryUrl":648,"fullTextUrl":20,"authors":649,"publicationType":310,"publisherRelationship":713,"citationCount":20,"citationInfo":20,"publishDate":761,"publishYear":762,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":763,"openAccess":20,"references":20,"isForceReanalyzing":362},"77484d12-7e1c-4c58-9c55-a080da83dd1b","2024-02-16T11:49:17.280+00:00","2026-09-06T08:12:51.773+00:00",[],"Maximum-likelihood-based-influence-maximization-in-social-networks",{"abstract":637,"title":639,"references":642,"doi":644},{"EN":638},"Influence Maximization (IM) is an important issue in network analyzing which widely occurs in social networks. The IM problem aims to detect the top-k influential seed nodes that can maximize the influence spread. Although a lot of studies have been performed, a novel algorithm with a better balance between time-consumption and guaranteed performance is still needed. In this work, we present a novel algorithm called MLIM for the IM problem, which adopts maximum likelihood-based scheme under the Independent Cascade(IC) model. We construct thumbnails of the social network and calculate the L-value for each vertex using the maximum likelihood criterion. A greedy algorithm is proposed to sequentially choose the seeds with the smallest L-value. Empirical results on real-world networks have proved that the proposed method can provide a wider influence spreading while obtaining lower time consumption.",{"EN":640,"VI":641},"Maximum likelihood-based influence maximization in social networks","Tối đa hóa mức độ ảnh hưởng dựa trên ước lượng hợp lý cực đại trong các mạng xã hội",{"VOID":643},"Ko Y-Y, Cho K-J, Kim S-W (2018) Efficient and effective influence maximization in social networks: a hybrid-approach. Inf Sci 465:144–161\nQiang J-P, Li Y, Yuan Y-H, Wu X-D (2018) Short text clustering based on pitman-yor process mixture model. Appl. Intell. 48:1802–1812\nQiang J-P, Li Y, Yuan Y-H, Liu W (2018b) SnapshotEnsembles of Non-negative Matrix Factorization for Stability of Topic Modeling. Appl.Intell 48(11):3963–3975\nBrown JJ, Reingen PH (1987) Social ties and word-of-mouth referral behavior. J Consum Res 14(3):350–362\nDomingos P, Richardson M (2001) Mining the Network Value of Customers, in: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 57–66\nGoldenberg J, Libai B (2001) Using complex systems analysis to advance marketing theory development: modeling heterogeneity effects on new product growth through stochastic cellular automata. J Acad Market Sci 9(3):1–18\nGoldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Market Lett 12(3):211–223\nRichardson M, Domingos P (2002) Mining Knowledge-Sharing Sites for Viral Marketing, in: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 61–70\nGong MG, Yan JN, Shen B, Ma LJ, Cai Q (2016) Influence maximization in social networks based on discrete particle swarm optimization. Inf Sci 367-368:600–614\nZia MA, Zhang ZB, Che LT, Ahmad H, Su S (2017) Identifying influential people based on interaction strength. J Inf Proc Syst 13(4):987–999\nKempe D, Kleinberg J, Tardos E (2003) Maximizing the Spread of Influence Through a Social Network, in: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 137–146\nAbbassi Z, Bhaskara A, Misra V (2015) Optimizing Display Advertising in Online Social Networks, in: Proceedings of the Twenty-Fourth International Conference on World Wide Web, ACM, pp. 1–11\nChen W, Wang YJ, Yang SY (2009) Efficient Influence Maximization in Social Networks, in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 199–208\nChen W, Yuan YF, Zhang L (2010) Scalable Influence Maximization in Social Networks under the Linear Threshold Model, in: Proceedings of the Tenth IEEE International Conference on Data Mining, IEEE, pp. 88–97\nGoyal A, Lu W, Lakshmanan LVS (2011) Celf++: Optimizing the Greedy Algorithm for Influence Maximization in Social Networks, in: Proceedings of the Twentieth International Conference Companion on World Wide Web, ACM, pp. 47–48\nGoyal A, Lu W, Lakshmanan LVS (2011) SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model, in: Proceedings of the Eleventh IEEE International Conference on Data Mining, IEEE pp. 211–220\nKim J, Kim SK, Yu H (2013) Scalable and Parallelizable Processing of Influence Maximization for Large-Scale Social Networks, in: Proceedings of the Twenty-Ninth International Conference on Data Engineering, IEEE, pp. 266–277\nLeskovec J, Krause A, Guestrin C, et al (2007) Cost-Effective Outbreak Detection in Networks, in: Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 420–429\nLiu Q , Xiang B, Chen E, Xiong H, Tang F, Yu JX (2014) Influence Maximization over Large-Scale Social Networks: a Bounded Linear Approach, in: Proceedings of the ACM International Conference on Conference on Information and Knowledge Management, ACM, pp. 171–180\nZhou C, Zhang P, Guo J, Zhu XQ, Guo L (2013) UBLF: An Upper Bound Based Approach to Discover Influential Nodes in Social Networks, in: Proceedings of the Thirteenth IEEE International Conference on Data Mining, IEEE, pp. 907–916\nTirkolaee EB, Hosseinabadi AAR, Soltani M, Sangaiah AK, Wang J (2018) A hybrid genetic algorithm for multi-trip green capacitated arc outing problem in the scope of urban services. Sustainability 10(5):1–21\nTu Y, Lin Y, Wang J, Kim JU (2018) Semi-supervised learning with generative adversarial networks on digital signal modulation classification. CMC-Comput Mater Con 55(2):243–254\nWang J, Cao YQ, Li B, Kim HJ, Lee SY (2017) Particle swarm optimization based clustering algorithm with Mobile sink for WSNs. Future Gener Comp Sy 76:452–457\nZeng DJ, Dai Y, Li F, Sherratt RS, Wang J (2018) Adversarial learning for distant supervised relation extraction. CMC-Comput Mater Con 55(1):121–136\nWang Y, Cong G, Song GJ, et al (2010) Community-based Greedy Algorithm for Mining Top-K Influential Nodes in Mobile Social Networks, in: Proceedings of the Sixteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 1039–1048\nYang JW, Leskovec J (2015) Defining and evaluating network communities based on ground-truth. Knowl Inf Syst 42(1):181–213\nKempe D, Kleinberg J, Tardos E (2005) Influential Nodes in A Diffusion Model for Social Networks, in: Proceedings of the Thirty-Second International Conference on Automata, Languages and Programming, Springer, pp. 1127–1138\nKimura M, Saito K (2006) Tractable Models for Information Diffusion in Social Networks, in: Proceedings of the European Conference on Principles of Data Mining and Knowledge Discovery, Springer, pp. 259–271\nEstevez PA, Vera P, Saito K (2007) Selecting the Most Influential Nodes in Social Networks, in: Proceedings of the International Joint Conference on Neural Networks, IEEE, pp. 2397–2402\nBharathi S, Kempe D, Salek M (2007) Competitive Influence Maximization in Social Networks, in: Proceedings of the International Workshop on Web and Internet Economics, IEEE, pp. 306–311\nLi Y-C, Fan J, Wang Y-H, Tan K-L (2018) Influence maximization on social graphs: a survey. IEEE T Knowl Data En 30(10):1852–1872\nLi JS, Yu YY (2012) Scalable Influence Maximization in Social Networks Using the Community Discovery Algorithm, in: Proceedings of the 2012 Sixth International Conference on Genetic and Evolutionary Computing, IEEE, pp. 284–287\nZhu YQ, Wu WL, Bi YJ et al (2015) Better approximation algorithms for influence maximization in online social networks. J Comb Optim 30(1):97–108\nLu F, Zhang WK, Shao LW, Jiang XF, Xu P, Jin H (2017) Scalable influence maximization under independent Cascade model. J Netw Comput Appl 86:15–23\nSingh SS, Singh K, Kumar A, Biswas B (2019) ACO-IM:maximizing influence in social networks using ant Colony optimization, Soft Comput 1–23\nChen H, Wang YT (2012) Threshold-based heuristic algorithm for influence maximization. J Com Res Dev 49(10):2181–2188\nTian JT, Wang YT, Feng XJ (2011) A new hybrid algorithm for influence maximization in social networks. Chinese J Comp 34(10):1956–1965\nGomez-Rodriguez M, Leskovec J, Krause A (2012) Inferring networks of diffusion and influence. ACM T Knowl Discov D 5(4):1–37\nMathioudakis M, Bonchi F, Castillo C, Gionis A, Ukkonen A (2011) Sparsification of Influence Networks, in: Proceedings of the Seventeenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 529–537\nGalstyan A, Musoyan V, Cohen P (2009) Maximizing influence propagation in networks with community structure. Phys Rev E 79(5):56–102\nLappas T, Terzi E, Dimitrios G, et al (2010) Finding Effectors in Social Networks, in: Proceedings of the Sixteenth ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining, ACM, pp. 1059–1068\nLi CT, Lin SD, Shan MW (2011) Finding Influential Mediators in Social Networks, in: Proceedings of the Twentieth International Conference Companion on World Wide Web, ACM, pp. 75–76\nYang W-J, Leonardo B, Alessandro G (2019) Influence Maximization in Independent Cascade Networks Based on Activation Probability Computation. IEEE ACCESS 7:13745–13757\nSrivastava A, Chelmis C, Prasanna VK (2015) Social Influence Computation and Maximization in Signed Networks with Competing Cascades, in: Proceedings of the IEEE\u002FACM International Conference on Advances in Social Networks Analysis and Mining, IEEE, pp. 41–48\nChen W, Collins A, Cummings Rachel R, et al (2011) Influence Maximization in Social Networks when Negative Opinions May Emerge and Propagate, in: Proceedings of the Eleventh SIAM International Conference on Data Mining, SIAM, pp. 379–390\nChen SB, He KJ (2015) Influence Maximization on Signed Social Networks with Integrated PageRank, in: Proceedings of the IEEE International Conference on Smart City\u002FSocialCom\u002FSustainCom, IEEE, pp. 289–292\nJendoubi S, Martin A, Liétard L, et al (2016) Maximizing Positive Opinion Influence Using An Evidential Approach, ArXiv preprint arXiv:1610.06340v1 [cs.SI]\nLeskovec J, Kleinberg J, Faloutsos C (2007) Graph evolution: densification and shrinking diameters. ACM Trans Knowl Discov D 1(1):2\nRui X-B, Meng F-R, Wang Z-X, Yuan G (2019) A Reversed Node Ranking Approach for Influence Maximization in Social Networks. Appl. Intell 49:2684–2698",{"VOID":645},"10.1007\u002Fs10489-020-01747-8","2025-01-16T04:40:40.979+00:00",[207],"https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10489-020-01747-8",[650,674,687,700],{"id":651,"sortIndex":21,"researcher":20,"roles":652,"affiliations":653,"properties":671,"displayName":673,"givenName":20,"familyName":20},"e7bfe629-0c89-4788-b871-c58cf4966a43",[213],[654,662],{"id":655,"sortIndex":21,"affiliation":656,"properties":20},"0afaba17-1f1c-42a3-9d73-b8ce2f472bdf",{"id":655,"createTime":20,"updateTime":20,"relativeEntities":657,"slug":20,"properties":658,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":661,"statistic":20},[],{"title":659},{"VI":660},"College of Information Engineering of Yangzhou University, Yangzhou, China",[],{"id":663,"sortIndex":228,"affiliation":664,"properties":670},"a4b308a2-5bdf-4e1a-9f0c-2ae35160be66",{"id":663,"createTime":20,"updateTime":20,"relativeEntities":665,"slug":20,"properties":666,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":669,"statistic":20},[],{"title":667},{"VI":668},"The Laboratory for Internet of Things and Mobile Internet Technology of Jiangsu Province, Huaiyin Institute of Technology, Huaiyin, China",[],{},{"title":672},{"VI":673},"Wei 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paper is a proposal of a modified internal model control based on an intelligent technique. The indirect field oriented control strategy (IFOC) is used as a permanent magnet synchronous motor (PMSM) drive platform. Neural network controller and estimator are respectively added to replace the conventional speed regulator and the speed encoder in the global drive scheme. A wide speed working range is considered and high speed mode is incorporated in the study testes. In the IFOC inner control loops, the commonly used synchronous frame conventional proportional plus integral (PI) controllers are replaced by two modified internal model control (IMC) regulators. Therefore, a method based on the bacterial foraging optimization (BFO) algorithm is performed to optimize and adjust the IMC low pass filter parameters. The robustness of the proposed PMSM sensorless drive scheme is confirmed by simulation tests in the MATLAB\u002FSIMULINK. Moreover, a comparative evaluation results are illustrated to prove the effectiveness of the proposed control algorithm according to different controllers combinations.",{"EN":774},"A novel IMC controller based on bacterial foraging optimization algorithm applied to a high speed range PMSM drive",{"VOID":776},"[\"11367383333966243458\"]",{"VOID":778},"Zhu ZQ, Howe D (2007) Electrical machines and drives for electric vehicle, hybrid and fuel cell vehicles. Proc IEEE Trans 95(4):746–765\nSharma RK, Sanadhya V, Behera L, Bhattacharya S (2008) Vector control of a permanent magnet synchronous motor. In: India conference INDICON 2008. Annual IEEE, vol 1, pp 81–86\nHe Y, Hu W, Wang Y, Wu J, Wang Z (2009) Speed and position sensorless control for dual-three-phase PMSM drives. In: 24th annual IEEE applied power electronics conference and exposition (APEC-2009), pp 945–950\nKraiem H, Messaoudi M, Sbita L, Abdelkrim MN (2010) EKF-based sensorless direct torque control of permanent magnet synchronous motor: comparison between two different selection tables. Glob J Technol Optim 1:159–165\nJurkovic S, Stranges EG (2011) Design and analysis of a high gain observer for the operation of SPM machine under saturation. IEEE Trans Energy Convers 26(2):417–427\nShan-Mao G, Feng-you H (2009) Study on extended Kalman filter at low speed in sensorless PMSM drives. In: International IEEE conference on electronic computer technology, pp 311–316\nFlah A, Kraiem H, Dhaoi M, Sbita L (2011) A recurrent neural network speed and position controller of an induction motor drive. Int J Res Rev Comput Sci 2(4):1075–1081\nBen Hamed M, Sbita L (2008) Neural networks for controlled speed sensorless direct field oriented induction motor drives. J Electr Eng 8(2):81–88\nVieira RP, Azzolin RZ (2009) A sensorless single-phase induction motor drive with a MRAC controller. In: 35th IEEE annual conference on industrial electronics, pp 1003–1008\nXudong W, Risha N (2009) Simulation of PMSM filed oriented control based on SVPWM. In: IEEE conference on vehicle power and propulsion, pp 1465–1469\nMazinan AH, Sheikhan M (2010) On the practice of artificial intelligence based predictive control scheme: a case study. Appl Intell 36(1):178–189\nLishan S, Xiao P (2011) IMC-fed PMSM control system based on fuzzy PI. In: IEEE international conference on computer science and automation engineering, pp 489–492\nBiyanto et al (2010) Artificial neural network based modeling and controlling of distillation column system. Int J Eng Sci Technol 2(6):177–188\nAnuradha DB, Prabhaker-Reddy G, Murthy JSN (2009) Direct inverse neural network control of a continuous stirred tank reactor (CSTR). In: Proceedings of the international multi conference of engineers and computer scientists, vol 2\nAlarçïn F (2007) Internal model control using neural network for ship roll stabilization. J Mar Sci Technol 15(2):141–147\nEl-Rabaie NM, Awad HA, Mahmoud TA (2007) A novel neural network-based control scheme for controlling the multivariable anaesthesia. Minufiya J Electron Eng Res 17(1)\nDongcai QU, Guorong Z (2010) On IMC structure scheme based on ANN’s inverse model for nonlinear system and simulation researches. In: Proceedings of the 29th Chinese control conference\nZhang N, Feng ZR, Ke LJ (2010) Guidance-solution based ant colony optimization for satellite control resource scheduling problem. Appl Intell 35(3):436–444\nKhan SA, Engelbrecht AP (2010) A fuzzy particle swarm optimization algorithm for computer communication network topology design. Appl Intell 36(1):161–177\nPassino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67\nJiun TJ, Chen TC (2006) Robust speed controlled induction motor drive based on recurrent neural network. In: Electric power systems research, pp 1064–1074\nFlah A, Kraiem H, Sbita L (2012) Robust high speed control algorithm for PMSM sensorless drives. 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AAAI Press, pp 157–163",{},{"id":20,"text":1083,"url":20,"identifiers":1084},"Hartline J, Yan Q (2011) Envy, truth, and profit. In: Proceedings of the 12th ACM conference on electronic commerce. ACM, pp 243–252",{"doi":1085},"10.1145\u002F1993574.1993612",{"id":20,"text":1087,"url":20,"identifiers":1088},"Kalinowski T, Narodytska N, Walsh T (2013) A social welfare optimal sequential allocation procedure. In: Proceedings of the twenty-third international joint conference on artificial intelligence. AAAI Press, pp 227–233",{},{"id":20,"text":1090,"url":20,"identifiers":1091},"Kalinowski T, Narodytska N, Walsh T, Xia L (2012) Strategic behavior in a decentralized protocol for allocating indivisible goods. In: Proceedings of the 4th international workshop on computational social choice, vol 12. Kraków, pp 251–262",{},{"id":20,"text":1093,"url":20,"identifiers":1094},"Kalinowski T, Narodytska N, Walsh T, Xia L (2013) Strategic behavior when allocating indivisible goods sequentially. In: Proceedings of the 27th national conference on artificial intelligence. Bellevue, pp 452–458",{"doi":1095},"10.1609\u002Faaai.v27i1.8697",{"id":20,"text":1097,"url":20,"identifiers":1098},"Lehmann B, Lehmann D, Nisan N (2001) Combinatorial auctions with decreasing marginal utilities. In: Proceedings of the 3rd ACM conference on electronic commerce. ACM, pp 18–28",{"doi":1099},"10.1145\u002F501158.501161",{"id":20,"text":1101,"url":20,"identifiers":1102},"Likhodedov A, Sandholm T (2005) Approximating revenue-maximizing combinatorial auctions. In: Proceedings of the 20th national conference on artificial intelligence, vol 5. Pittsburgh, pp 267–274",{},{"id":20,"text":1104,"url":20,"identifiers":1105},"Lumet C, Bouveret S, Lemaitre M (2012) Fair division of indivisible goods under risk. In: Proceedings of the twentieth European conference on artificial intelligence. IOS Press, pp 564–569",{},{"id":20,"text":1107,"url":20,"identifiers":1108},"Mirchevska V, Luṡtrek M, BeŻek A, Gams M (2014) Discovering strategic behaviour of multi-agent systems in adversary settings. Comput Inf 33(1):79–108",{},{"id":20,"text":1110,"url":20,"identifiers":1111},"Procaccia AD (2013) Cake cutting: not just child’s play. Commun ACM 56(7):78–87",{"doi":1112},"10.1145\u002F2483852.2483870",{"id":20,"text":1114,"url":20,"identifiers":1115},"Rothkopf MH, Pekeċ A, Harstad RM (1998) Computationally manageable combinational auctions, vol 44",{"doi":1116},"10.1287\u002Fmnsc.44.8.1131",{"id":20,"text":1118,"url":20,"identifiers":1119},"Sandholm T (2002) Algorithm for optimal winner determination in combinatorial auctions, vol 135",{"doi":1120},"10.1016\u002FS0004-3702(01)00159-X",{"id":20,"text":1122,"url":20,"identifiers":1123},"Schrijver A (2003) Combinatorial optimization: polyhedra and efficiency, vol 24. Springer",{},{"id":20,"text":1125,"url":20,"identifiers":1126},"Sun N, Yang Z (2009) A double-track adjustment process for discrete markets with substitutes and complements. Econometrica 77(3):933–952",{"doi":1127},"10.3982\u002FECTA6514",{"id":20,"text":1129,"url":20,"identifiers":1130},"Talman D, Yang Z (2008) A dynamic auction for differentiated items under price rigidities. Econ Lett 99(2):278–281",{"doi":1131},"10.1016\u002Fj.econlet.2007.07.002",{"id":20,"text":1133,"url":20,"identifiers":1134},"Wang JJD, Zender JF (2002) Auctioning divisible goods. Econ Theory 19:673–705",{"doi":1135},"10.1007\u002Fs001990100191",{"id":20,"text":1137,"url":20,"identifiers":1138},"Zhang D, Huang W, Perrussel L (2010) Dynamic auction: a tractable auction procedure. In: Proceedings of the 24th national conference on artificial intelligence. Atlanta, pp 935–940",{"doi":1139},"10.1609\u002Faaai.v24i1.7633",{"id":1141,"createTime":1142,"updateTime":1143,"relativeEntities":1144,"slug":1145,"properties":1146,"entityType":202,"verifyStatus":203,"verifyTime":1158,"verifyNote":205,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1159,"fullTextUrl":20,"authors":1160,"publicationType":310,"publisherRelationship":1178,"citationCount":21,"citationInfo":1221,"publishDate":1224,"publishYear":1222,"citationAnalyzeStatus":1022,"lastCitationAnalyze":1225,"indexDatabases":1226,"openAccess":20,"references":20,"isForceReanalyzing":362},"3e2e3e82-27b1-42f9-8141-2eacbb6a0655","2024-04-08T07:10:27.219+00:00","2026-07-29T05:41:12.390+00:00",[],"Neural-Networks-and-Structured-Knowledge-Rule-Extraction-and-Applications",{"abstract":1147,"title":1149,"gsPaper":1151,"keywords":1153,"references":1154,"doi":1156},{"EN":1148},"As the second part of a special issue on “Neural Networks and Structured Knowledge,” the contributions collected here concentrate on the extraction of knowledge, particularly in the form of rules, from neural networks, and on applications relying on the representation and processing of structured knowledge by neural networks. The transformation of the low-level internal representation in a neural network into higher-level knowledge or information that can be interpreted more easily by humans and integrated with symbol-oriented mechanisms is the subject of the first group of papers. The second group of papers uses specific applications as starting point, and describes approaches based on neural networks for the knowledge representation required to solve crucial tasks in the respective application. The companion first part of the special issue [1] contains papers dealing with representation and reasoning issues on the basis of neural networks.",{"EN":1150},"Neural Networks and Structured Knowledge: Rule Extraction and Applications",{"VOID":1152},"[\"8890008739738161475\"]",{"EN":537},{"VOID":1155},"F.J. Kurfeß, Special issue on “Neural Networks and Structured Knowledge: Representation and Reasoning” (guest editor), Applied Intelligence, vol. 11, no. 1, 1999.\nJ. Denker, D. Schwartz, B. Wittner, S. Solla, R. Howard, L. Jacket, and J. Hopfield, “Automatic learning, rule extraction and generalization,” Complex Systems, vol. 1, no. 5, pp. 877–922, 1987.\nJ.-S. Roger Jang, “Rule extraction using generalized neural networks,” in Proc. of the 4th IFSA World Congress(in the Volume for Artificial Intelligence), July 1991, pp. 82–86.\nC. McMillan, M.C. Mozer, and P. Smolensky, “The connectionist science game: Rule extraction and refinement in a neural network,” in Proceedings of the 13th Annual Conference of the Cognitive Science Society, 1991.\nR. Setiono and H. Liu, “Understanding neural networks via rule extraction,” edited by Chris S. Mellish, in Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, San Mateo, August 20–25, 1995, Morgan Kaufmann, pp. 480–487.\nR. Andrews and J. Diederich (Eds.), Rule Extraction Workshop, Neural Information Processing Systems (NIPS) 9, 1996.\nR. Andrews, J. Diederich, and A.B. Tickle, “Survey and critique of techniques for extracting rules from trained artificial neural networks,” Knowledge Based Systems, vol. 8, no. 6, pp. 373–389, December 1995.\nJ. Köbler, U. Schöning, and J. Toran, The Graph Isomorphism Problem: Its Structural Complexity, Birkhäuser: Boston, 1993.\nA. Waibel, T. Hanazawa, G. Hinton, K. Shikano, and K.J. Lang, “Phoneme recognition using time-delay neural networks,” IEEE Transactions on Acoustics, Speech, & Signal Processing, vol. 37, no. 3, pp. 328–339, 1989.\nK. Schädler and F. Wysotzki, “Comparing structures using a hopfield-style network,” Applied Intelligence, vol. 11, no. 1, pp. 15–30, 1999.\nP. Myllymäki, “Massively parallel probabilistic reasoning with boltzmann machines,” Applied Intelligence, vol. 11, no. 1, pp. 31–44, 1999.\nS. Hölldobler, Y. Kalinke, and H.-P. Störr, “Approximating the semantics of logic programs by recurrent neural networks,” Applied Intelligence, vol. 11, no. 1, pp. 45–58, 1999.\nA.S. d'Avila Garcez and G. Zaverucha, “The connectionist inductive learning and logic programming system,” Applied Intelligence, vol. 11, no. 1, pp. 59–78, 1999.\nL. Shastri, “Advances in SHRUTI—a neurally motivated model of relational knowledge representation and rapid inference using temporal synchrony,” Applied Intelligence, vol. 11, no. 1, pp. 79–108, 1999.\nR. Sun, T. Peterson, and E. Merrill, “Ahybrid architecture for situated learning of reactive sequential decision making,” Applied Intelligence, vol. 11, no. 1, pp. 109–127, 1999.\nF.J. Kurfeß (Ed.), Neural Networks and Structured Knowledge, European Coordinating Committee for Artificial Intelligence (ECCAI), European Conference on Artificial Intelligence (ECAI '96), Workshop Proceedings, Budapest, 1996.\nR. Sun and F. Alexandre (Eds.), Connectionist-Symbolic Integration, Lawrence Erlbaum, 1997.\nG. Paaß and F.J. Kurfeß (Eds.), Wissensverarbeitung mit neuronalen Netzen (Knowledge Processing with Neural Networks), number 221 in GMD-Studien, Schloß Birlinghoven, 53757 Sankt Augustin, Germany, September 1993, Gesellschaft für Mathematik und Datenverarbeitung (GMD), Workshop KI'\nG. Paaß and F.J. Kurfeß, Wissensverarbeitung mit neuronalen Netzen, O. Herzog, T. Christaller, and D. Schütt (Eds.), in Grundlagen und Anwendungen der Künstlichen Intelligenz-17, Fachtagung für Künstliche Intelligenz (KI '93), Informatik aktuell, Subreihe Künstliche Intelligenz, Springer Verlag, Berlin, pp. 217–225, 1993.\nF.J. Kurfeß and G. Paaß (Eds.), Integration Neuronaler und Wissensbasierter Ansätze, number 242 in GMD-Studien, D-53754 Sankt Augustin, September 1994, Gesellschaft für Informatik (GI), Gesellschaft für Mathematik und Datenverarbeitung (GMD), Workshop at the KI '94 Conference, Saarbrücken, Germany.\nI. Duwe, F.J. Kurfeß, G. Paaß, and S. Vogel (Eds.), Konnektionismus und neuronale Netze—Beiträge zur Herbstschule HeKoNN 94, number 242 in GMD-Studien, D-53754 Sankt Augustin, Oktober 1994.\nF.J. Kurfeß,Wissensverarbeitung mit neuronalen Netzen, edited by G. Dorffner, K. Möller, G. Paaß, and S. Vogel, in Konnektionismus und neuronale Netze—Beiträge zur Herbstschule HeKoNN '95, GMD-Studien, D-53754 Sankt Augustin, Oktober 1995, Gesellschaft für Mathematik und Datenverarbeitung (GMD), pp. 211–223.",{"VOID":1157},"10.1023\u002FA:1008344602888","2024-06-26T23:07:12.326+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1023\u002FA:1008344602888",[1161],{"id":1162,"sortIndex":21,"researcher":20,"roles":1163,"affiliations":1164,"properties":1173,"displayName":1175,"givenName":20,"familyName":20},"182c6ee6-16e5-493f-91ec-6eb2e07a0a08",[213],[1165],{"id":1166,"sortIndex":21,"affiliation":1167,"properties":20},"b3ce89c3-a016-47a2-a6b4-35a51555ef52",{"id":1166,"createTime":20,"updateTime":20,"relativeEntities":1168,"slug":20,"properties":1169,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1172,"statistic":20},[],{"title":1170},{"VI":1171},"Department of Computer Science, Concordia University, Montreal, Quebec, Canada",[],{"title":1174,"gsAuthor":1176},{"VI":1175},"Franz J. Kurfess",{"VOID":1177},"[\"6NLfhv0AAAAJ\"]",{"url":20,"publisher":1179,"properties":20},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1180,"slug":10,"properties":1181,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1185,"manageAffiliations":1190,"indexDatabases":1201,"url":20,"thumbnailPath":20,"statistic":1216,"gsStatistic":20,"type":20,"analyzePriority":20},[],{"issn":1182,"title":1183,"eissn":1184},{"VOID":13},{"EN":15},{"VOID":17},[1186],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":1187,"label":1188,"description":1189,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},[1191,1196],{"id":31,"createTime":20,"updateTime":20,"relativeEntities":1192,"slug":20,"properties":1193,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1195,"statistic":20},[],{"title":1194},{"EN":35},[],{"id":38,"createTime":20,"updateTime":20,"relativeEntities":1197,"slug":20,"properties":1198,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1200,"statistic":20},[],{"title":1199},{"EN":42},[44],[1202,1209],{"id":47,"indexDatabase":1203,"url":60,"indexYears":20,"academicFieldIds":1208,"indexDatabaseRanking":20},{"id":49,"createTime":20,"updateTime":20,"relativeEntities":1204,"label":1205,"description":1206,"key":56,"publicationTags":1207,"standard":20},[],{"EN":52,"VI":52},{"EN":54,"VI":55},[58,59],[62],{"id":64,"indexDatabase":1210,"url":75,"indexYears":76,"academicFieldIds":1215,"indexDatabaseRanking":79},{"id":66,"createTime":20,"updateTime":20,"relativeEntities":1211,"label":1212,"description":1213,"key":72,"publicationTags":1214,"standard":20},[],{"EN":69,"VI":69},{"EN":69,"VI":71},[74],[78],{"impactFactor":21,"impactFactorByYear":1217,"i10Index":94,"i10IndexLast5Year":95,"totalPublication":96,"totalPublicationByYear":1218,"totalCitation":128,"totalCitationByYear":1219,"totalCitationPerPublication":155,"totalCitationPerPublicationByYear":1220,"hindexLast5Year":181,"hindex":181},{"2012":82,"2013":83,"2014":84,"2015":85,"2016":86,"2017":87,"2018":88,"2019":89,"2020":90,"2021":91,"2022":92,"2023":93},{"1991":98,"1992":99,"1993":100,"1994":101,"1995":102,"1996":103,"1997":104,"1998":105,"1999":106,"2000":106,"2001":107,"2002":106,"2003":108,"2004":109,"2005":110,"2006":111,"2007":112,"2008":113,"2009":114,"2010":115,"2011":116,"2012":112,"2013":112,"2014":117,"2015":118,"2016":119,"2017":120,"2018":121,"2019":122,"2020":123,"2021":124,"2022":125,"2023":126,"2024":127},{"1991":105,"1992":130,"1993":131,"1994":132,"1995":133,"1996":134,"2004":135,"2005":136,"2006":137,"2007":138,"2008":139,"2009":140,"2010":141,"2011":122,"2012":142,"2013":143,"2014":144,"2015":145,"2016":146,"2017":147,"2018":148,"2019":149,"2020":150,"2021":151,"2022":152,"2023":153,"2024":154},{"1991":132,"1992":157,"1993":158,"1994":159,"1995":160,"1996":161,"2004":132,"2005":162,"2006":163,"2007":164,"2008":165,"2009":166,"2010":167,"2011":168,"2012":169,"2013":170,"2014":171,"2015":172,"2016":173,"2017":174,"2018":175,"2019":176,"2020":177,"2021":178,"2022":179,"2023":85,"2024":180},{"total":21,"publishYear":1222,"statisticByYear":1223},2000,{},"2000-01-01","2026-07-29T05:41:12.389+00:00",[79,58],{"id":1228,"createTime":1229,"updateTime":1230,"relativeEntities":1231,"slug":1232,"properties":1233,"entityType":202,"verifyStatus":203,"verifyTime":1244,"verifyNote":205,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1245,"fullTextUrl":20,"authors":1246,"publicationType":310,"publisherRelationship":1288,"citationCount":21,"citationInfo":1336,"publishDate":1339,"publishYear":1337,"citationAnalyzeStatus":19,"lastCitationAnalyze":1230,"indexDatabases":1340,"openAccess":20,"references":20,"isForceReanalyzing":362},"39fe4b8f-4e5f-40fc-8141-02c0a5a1f691","2024-01-15T04:55:55.728+00:00","2026-07-27T20:00:33.966+00:00",[],"A-link-prediction-algorithm-based-on-ant-colony-optimization",{"abstract":1234,"title":1236,"gsPaper":1238,"references":1240,"doi":1242},{"EN":1235},"The problem of link prediction has attracted considerable recent attention from various domains such as sociology, anthropology, information science, and computer sciences. In this paper, we propose a link prediction algorithm based on ant colony optimization. By exploiting the swarm intelligence, the algorithm employs artificial ants to travel on a logical graph. Pheromone and heuristic information are assigned in the edges of the logical graph. Each ant chooses its path according to the value of the pheromone and heuristic information on the edges. The paths the ants traveled are evaluated, and the pheromone information on each edge is updated according to the quality of the path it located. The pheromone on each edge is used as the final score of the similarity between the nodes. Experimental results on a number of real networks show that the algorithm improves the prediction accuracy while maintaining low time complexity. We also extend the method to solve the link prediction problem in networks with node attributes, and the extended method also can detect the missing or incomplete attributes of data. Our experimental results show that it can obtain higher quality results on the networks with node attributes than other algorithms.",{"EN":1237},"A link prediction algorithm based on ant colony optimization",{"VOID":1239},"[\"15353604698030444148\"]",{"VOID":1241},"Lichtenwalter RN (2010) New precepts and method in link prediction. In: Proceedings of ACM KDD’10, pp 243–252\nLü L, Zhou T (2011) Link prediction in complex networks:A survey. Phys A 390:1150–1170\nAiroldi EM, Blei DM, Fienberg SE (2006) Mixed membership stochastic block models for relational data with application to protein-protein interactions. In: Proceedings of the international biometrics society annual meeting\nGuimera R, Sales-Pardo M (2010) Missing and spurious interactions and the reconstruction of complex networks. Proc. Natl Acad Sci USA 106(52):22073–22078\nPapadimitriou A, Symeonidis P, Yannis M (2012) Fast and accurate link prediction in social networking systems. 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In: 2011 IEEE third international conference on privacy, security, risk and trust (passat), and 2011 IEEE third international conference on social computing (socialcom). IEEE, pp 73–80\nFriedman N, Getoor L, Koller D (1999) Learning probabilistic relational models. IJCAI 99:1300–1309\nHeckerman D, Meek C, Koller D (2007), Probabilistic entity-relationship models, PRMs, and plate models. Introduction to statistical relational learning\nYu K, Chu W, Yu S (2006) Stochastic relational models for discriminative link prediction. NIPS, pp 1553–1560\nSarukkai RR (2000) Link prediction and path analysis using Markov chains. Comput Netw 33(1):377–386\nKashima H, Abe N. (2006) A parameterized probabilistic model of network evolution for supervised link prediction. In: IEEE sixth international conference on Data Mining, 2006. ICDM’06, pp 340–349\nRodrigues H, Prudencio RBC (2011) Supervised link prediction in weighted networks. 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IEEE Comput Intell Mag 11:28–39\nBlum C (2009) Ant Colony Optimization, Proceedings of The 2009 Genetic and Evolutionary Computation Conference, Montreal, pp 2835–2851\nBlum C, Ant colony optimization: Introduction and recent trends (2005). Phys Life Rev 2:353–373\nWu J, Abbas-Turki A, El Moudni A (2012) Cooperative driving: an ant colony system for autonomous intersection management. Appl Intell 37(2):207–222\nLu M, Xu B, Sheng A (2014) Modeling analysis of ant system with multiple tasks and its application to spatially adjacent cell state estimate. Appl Intell:1–17\nKhan SA, Engelbrecht AP (2012) A fuzzy particle swarm optimization algorithm for computer communication network topology design. Appl Intell 36(1):161–177\nRivero J, Cuadra D, Calle J (2012) Using the ACO algorithm for path searches in social networks. Appl Intell 36(4):899–917\nZhang N, Feng ZR, Ke LJ (2011) Guidance-solution based ant colony optimization for satellite control resource scheduling problem. Appl Intell 35(3):436–444\nShuang B, Chen J, Li Z (2011) Study on hybrid PS-ACO algorithm. Appl Intell 34(1):64–73\nMerkle D, Middendorf M (2003) Ant colony optimization with global pheromone evaluation for scheduling a single machine. Appl Intell 18(1):105–111\nFreeman L (1977) A set of measures of centrality based on betweenness. Sociometry 40:35–41\nhttp:\u002F\u002Fwww.linkprediction.org\u002Findex.php\u002Flink\u002Fresource\u002Fdata\nLatora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys Rev Lett 67:198701–198704\nWatts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442\nNewman MEJ (2002) Assortative mixing in networks. Phys Rev Lett 89:208701–208705\nhttp:\u002F\u002Fwww.informatik.uni-trier.de\u002F~ley\u002Fdb\u002F",{"VOID":1243},"10.1007\u002Fs10489-014-0558-5","2024-06-26T23:17:17.637+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10489-014-0558-5",[1247,1264],{"id":1248,"sortIndex":21,"researcher":20,"roles":1249,"affiliations":1250,"properties":1259,"displayName":1261,"givenName":20,"familyName":20},"3ea55f7f-79cf-48fe-84b7-0b9fa1014830",[213],[1251],{"id":1252,"sortIndex":21,"affiliation":1253,"properties":20},"7830c666-3005-4b82-800e-edafebe2518a",{"id":1252,"createTime":20,"updateTime":20,"relativeEntities":1254,"slug":20,"properties":1255,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1258,"statistic":20},[],{"title":1256},{"VI":1257},"Department of Computer Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 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data imputation is an important issue in machine learning and data mining. In this paper, we propose a new and efficient imputation method for a kind of missing data: semi-parametric data. Our imputation method aims at making an optimal evaluation about Root Mean Square Error (RMSE), distribution function and quantile after missing-data are imputed. We evaluate our approaches using both simulated data and real data experimentally, and demonstrate that our stochastic semi-parametric regression imputation is much better than existing deterministic semi-parametric regression imputation in efficiency and effectiveness.",{"EN":1351},"Semi-parametric optimization for missing data imputation",{"VOID":1353},"[\"10939880436710846870\"]",{"VOID":1355},"Allison P (2001) Missing data. Sage Publication, Inc\nCios K, Kurgan L (2002) Trends in data mining and knowledge discovery. In: Pal N, Jain L, Teoderesku N (eds) Knowledge discovery in advanced information systems. Springer\nClifton C (2003) Change detection in overhead imagery using neural networks. Appl Intell 18(2):215–234\nDempster et al (1983) Incomplete data in sample surveys. In: Madow WG, Olkin I, Rubin D (eds) Sample surveys Vol.: Theory and annotated bibliography, New York, NY, Academic Press, pp 3–10\nEngle RF et al (1986) Semiparametric estimates of the relation between weather and electricity sales. J Am Statist Assoc 81(394), Applications.\nFriedman JH, Khavi R, Yun Y (1996) Lazy decision trees. In: Proceedings of the 13th national conference on artificial intelligence, AAAI Pres\u002FMIT Press, pp 717–724\nGhahramani et al (1997) Mixture models for Learning from incomplete data. In: Greiner R, Petsche T, Hanson SJ (eds) Computational learning theory and natural learning systems, Volume IV: Making learning systems practical, Cambridge, MA, The MIT Press, pp 67–85\nHan J, Kamber M (2000) Data mining concepts and techniques. Morgan Kaufmann Publishers\nHand D et al (1994) A handbook of small data sets. London, Chapman & Hall, pp 208–211\nHoti F, Holmstrom L (2004) A semiparametric density estimation approach to pattern classification. Patt Recog 37:409–419\nHu X (2005) A data mining approach for retailing bank customer attrition analysis. Appl Intell 22(1):47–60\nKaya M, Alhajj R (2006) Utilizing genetic algorithms to optimize membership functions for fuzzy weighted association rule mining. Appl Intell 24(1):7–15\nKim Y (2001) The curse of the missing data. In: http:\u002F\u002F209.68.240.11:8080\nLittle R, Rubin D (2002) Statistical analysis with missing data (2nd edn.). John Wiley and Sons, New York\nLiu WZ, White AP, Thompson SG, Bramer MA (1997) Techniques for dealing with missing values in classification. In: IDAL97, vol 1280 of Lecture notes, pp 527–536\nRamoni M (1997) Learning Bayesian networks from incomplete databases. Technical report kmi-97-6, Knowledge Media Institute, The Open University\nMillimet D, List J, Stengos T (2003) The environmental kuznets curve: Real progress or misspecified models? Rev Econ Stat 85(4):1038–1047\nPeixoto J (1990) A property of well-formulated polynomial regression models. Am Stat 44:26–30\nPickle S et al (2005). Robust parameter design: a semi-parametric approach. In: http:\u002F\u002Fwww.stat.vt.edu\u002Ftech_reports\u002FVTTechReport05-7.pdf\nPin T, James L (1999) The elasticity of demand for gasoline: a semi-parametric analysis. In http:\u002F\u002Fuiuc.edu\u002F∼ng\u002Fworking\u002Fgas.ps\nPyle D (1994) Data preparation for data mining. Morgan Kaufmann Publishers, Inc\nQin YS, Rao JNK (2004) Confidence intervals for parameters of the response variable in a linear model with missing data. Technique Report\nQuinlan JR (1989) Unknown attribute values in induction. In: proc. 6th int’ workshop on machine learning, Ithaca, pp 164–168\nQuinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo, USA\nSilverman B (1986) Density estimation for statistics and data analysis. Chapman and Hall, New York\nWang Q, Rao JNK (2002a) Empirical likelihood-based inference in linear models with missing data. Scand J Statist 29:563–576\nWang Q, Rao J (2002b) Empirical likelihood-based inference under imputation with missing response. Ann Statistics 30:563–576\nWang Q, Hardle W (2004) Semiparametric regression analysis with missing response at random. J Am Statistical Assoc 99\nWhite AP (1987) Probabilistic induction by dynamic path generation in virtual trees. In: Bramer MA (ed) Research and development in expert systems III. Cambridge, Cambridge University Press, pp 35–46\nZhang C, Yang Q, Liu B (2005) Intelligent data preparation. IEEE Trans Knowl Data Eng 17(9):1163–1165\nZhang C, Zhang S, Webb G (2003) Identifying approximate itemsets of interest in large databases. Appl Intell 18:91–104\nZhang S, Zhang C, Yang Q (2004) Information enhancement for data mining. IEEE Intell Syst 19(2):12–13\nZhang S, Qin ZX, Ling CX, Sheng SL (2005) Missing is useful: missing values in cost-sensitive decision trees. IEEE Trans Knowl Data Eng 17(12):1689–1693\nZhang S et al (2006) Optimized parameters for missing data imputation. In: Proceedings of PRICAI 2006, Guilin, China, August 7–11, 2006 Proceedings, pp 1010–1016",{"VOID":1357},"10.1007\u002Fs10489-006-0032-0","2024-06-22T23:53:31.009+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10489-006-0032-0",[1361,1376,1393,1408,1423],{"id":1362,"sortIndex":21,"researcher":20,"roles":1363,"affiliations":1364,"properties":1373,"displayName":1375,"givenName":20,"familyName":20},"5faf824e-a325-417d-be15-49d69eb58594",[213],[1365],{"id":1366,"sortIndex":21,"affiliation":1367,"properties":20},"8bb7b9b5-6c9a-4577-adeb-37b01620aec9",{"id":1366,"createTime":20,"updateTime":20,"relativeEntities":1368,"slug":20,"properties":1369,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1372,"statistic":20},[],{"title":1370},{"VI":1371},"Deparment of Computer Science, Guangxi Normal University, Beijing, China",[],{"title":1374},{"VI":1375},"Yongsong 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order to respect the standards of the “semantic web” which allows the data to be shared and reused between several applications, it became necessary to model web text documents with a vision based on the concepts and exploit available linguistic resources. It’s evident that the extraction of semantic tokens ensures semantic modelling of web documents. Unfortunately, terminology extraction techniques from unstructured Web text remain unable to provide powerful results. Indeed, systems developed based on the classical techniques extract massively high amounts of candidate terms and leave the task of separation between relevant and irrelevant candidates for post-processing. In this paper, we introduce HMM-Extract a novel model for terminology retrieval based on Markov model. Our model integrates two modules that work in cascade: a module based on Hidden Markov Model (HMM) for complex term extraction and a module based on Markov Chain for filtering terms provided by the HMM. Thus, we try to focus on three main contributions: firstly, we provide a linguistic and statistical specification of relevant terms. Secondly, we show the possibility of using a HMM to extract relevant terms from unstructured textual documents. Finally, we prove the importance of integrating statistical knowledge in a Markov Chain and we show, experimentally, its contribution to the field of terminology extraction.",{"EN":1502},"Hidden data states-based complex terminology extraction from textual web data model",{"VOID":1504},"[\"9631056063840652889\"]",{"VOID":1506},"Aggarwal CC (2018) Information extraction. Springer International Publishing, Cham, pp 381–411\nAnthony L (2013) Developing antconc for a new generation of corpus linguists. In: Proceedings of the corpus linguistics conference (CL 2013), pp 14–16\nAubin S, Hamon T (2006) Improving term extraction with terminological resources. In: Proceedings of the 5th international conference on advances in natural language processing, FinTAL’06. Springer, Berlin, pp 380–387\nAvinash M, Sivasankar E (2019) A study of feature extraction techniques for sentiment analysis. In: Abraham A, Dutta P, Mandal JK, Bhattacharya A, Dutta S. (eds) Emerging technologies in data mining and information security. Springer, Singapore, pp 475–486\nBarkman J (1958) Phytosociology and ecology of cryptogamic epiphytes: including a taxonomic survey and description of their vegetation units in Europe. Van Gorcum\nBoukhari K, Omri MN (2015) SAID: a new stemmer algorithm to indexing unstructured document. In: 15th International conference on intelligent systems design and applications, ISDA 2015, Marrakech, Morocco, December 14-16, 2015, pp 59–63\nBourigault D (1993) Analyse syntaxique locale pour le repérage de termes complexes dans un texte. T.A.L. Traitement automatique des langues 34(2):105–117\nBourigault D (1995) Lexter: a terminology extraction software for knowledge acquisition from texts. In: KAW’95\nBourigault D, Jacquemin C (2000) Construction de ressources terminologiques. In: Ingénierie des langues. 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