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Journal Photo for Information Fusion
Peer reviewed only Open Access

Information Fusion (IF)

Publisher : ELSEVIER SCIENCE BV
Computer Science Information Systems Computer Vision
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e-ISSN 1872-6305
p-ISSN 1566-2535
Issue Frequency Bi-Monthly
Impact Factor 18.6
Est. Year 2000
Country Netherlands The
Language English
APC YES

Journal Descriptions

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

Information Fusion (IF) is :-

  • International, Peer-Reviewed, Open Access, Refereed, Computer Science, Information Systems, Computer Vision, Engineering, Artificial Intelligence , Online or Print , Bi-Monthly Journal

  • UGC Approved, ISSN Approved: P-ISSN P-ISSN: 1566-2535, E-ISSN: 1872-6305, Established: 2000, Impact Factor: 18.6
  • Does Not Provide Crossref DOI

  • Indexed in: Scopus, WoS

  • Not indexed in DOAJ, PubMed, UGC CARE

Indexing

Publications of IF

Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions
Ankita Gandhi March, 2023
Sentiment analysis (SA) has gained much traction In the field of artificial intelligence (AI) and natural language processing (NLP). There is growing demand to automate analysis of user sent...
Multimodal and multicontrast image fusion via deep generative models
Pietro Liò December, 2022
Recently, it has become progressively more evident that classic diagnostic labels are unable to accurately and reliably describe the complexity and variability of several clinical phenotypes...
Multimodal Co-learning: Challenges, applications with datasets, recent advances and future directions
Ketan Kotecha May, 2022
Multimodal deep learning systems that employ multiple modalities like text, image, audio, video, etc., are showing better performance than individual modalities (i.e., unimodal) systems. Mul...
Multimodal pedestrian detection using metaheuristics with deep convolutional neural network in crowded scenes
Ketan Kotecha July, 2023
Pedestrian detection (PD) is a vital computer vision (CV) problem that is highly employed in several real-time applications, namely autonomous driving methods, robotics, and security observi...