{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T21:40:11Z","timestamp":1723412411081},"reference-count":40,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T00:00:00Z","timestamp":1672358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Tianjin Natural Science Foundation of China","award":["20JCYBJC01060"]},{"name":"National Natural Science Foundation of China","award":["62103203","61973175"]},{"name":"Fundamental Research Funds for the Central Universities, Nankai University","award":["63221218"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"Dempster\u2013Shafer evidence theory is an effective way to solve multi-sensor data fusion problems. After developing many improved combination rules, Dempster\u2013Shafer evidence theory can also yield excellent results when fusing highly conflicting evidence. However, these approaches still have deficiencies if the conflicting evidence is due to sensor malfunction. This work presents a combination method by integrating information interaction graph and Dempster\u2013Shafer evidence theory; thus, the multiple evidence fusion process is expressed as a network. In particular, the credibility of each piece of evidence is obtained by measuring the distance between the evidence first. After that, the credibility of the evidence is evaluated, keeping the unreliable evidence out of the information interaction network. With the fusion of connected evidence, the accuracy of the fusion result is improved. Finally, application results show that the presented method is effective.<\/jats:p>","DOI":"10.3390\/e25010069","type":"journal-article","created":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T08:31:17Z","timestamp":1672389077000},"page":"69","source":"Crossref","is-referenced-by-count":5,"title":["A Networked Method for Multi-Evidence-Based Information Fusion"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-8326-4284","authenticated-orcid":false,"given":"Qian","family":"Liang","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, No. 38 Tongyan Road, Jinnan District, Tianjin 300350, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3565-4800","authenticated-orcid":false,"given":"Zhongxin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, No. 38 Tongyan Road, Jinnan District, Tianjin 300350, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1415-4073","authenticated-orcid":false,"given":"Zengqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, No. 38 Tongyan Road, Jinnan District, Tianjin 300350, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.firesaf.2016.10.003","article-title":"Reasoning on multi-sensor geographic smoke spread data for fire development and risk analysis","volume":"86","author":"Vandecasteele","year":"2016","journal-title":"Fire Saf. 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