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Information Fusion 中科院1区 JCR:Q1 SCIE EI PubMed JST
发文量 4,083
被引量 230,435
影响因子(2025版) 17.056

The journal is intended to present within a single forum all of the developments in the field of multi-sensor, multi-source, multi-process information fusion and thereby promote the synergism among the many disciplines that are contributing to its growth. The journal is the premier vehicle for disseminating information on all aspects of research and development in the field of information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome. The journal publishes original papers, letters to the Editors and from time to time invited review articles, in all areas related to the information fusion arena including, but not limited to, the following suggested topics: • Data/Image, Feature, Decision, and Multilevel Fusion • Multi-classifier/Decision Systems • Multi-Look Temporal Fusion • Multi-Sensor, Multi-Source Fusion System Architectures • Distributed and Wireless Sensor Networks • Higher Level Fusion Topics Including Situation Awareness And Management • Multi-Sensor Management and Real-Time Applications • Adaptive And Self-Improving Fusion System Architectures • Active, Passive, And Mixed Sensor Suites • Multi-Sensor And Distributed Sensor System Design • Fusion Learning In Imperfect, Imprecise And Incomplete Environments • Intelligent Techniques For Fusion Processing • Fusion System Design And Algorithmic Issues • Fusion System Computational Resources and Demands Optimization • Special Purpose Hardware Dedicated To Fusion Applications • Mining Remotely Sensed Multi-Spectral/Hyper-Spectral Image Data Bases • Information Fusion Applications in Intrusion Detection, Network Security, Information Security and Assurance arena • Applications such as Robotics, Space, Bio-medical, Transportation, Economics, and Financial Information Systems • Real-World Issues such as Computational Demands, Real-Time Constraints in the context of Fusion systems. Benefits to authors We also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more.

  • 主办单位： ELSEVIER
  • 出版地区： AMSTERDAM
  • 出版周期： 双月刊
  • 别名： INFORM FUSION;Inf. Fusion;环境遥感;INFORMATION FUSION
  • 国际标准连续出版物号/电子版： ISSN 1566-2535 / EISSN 1872-6305
  • 创刊时间： 2000年
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Information FusionData FusionImage FusionDeep LearningGroup Decision MakingMultimodal FusionLarge Language ModelsConsensus Reaching ProcessSensor FusionContrastive LearningSupervised LearningFederated LEARNINGGroup DecisionFusion NetworkMulti-view LearningFusion MethodsMachine LearningNatural Language ProcessingConsensus ModelModal Fusion

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