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Information Fusion (IF)

Publisher :

ELSEVIER SCIENCE BV

Scopus Profile
Peer reviewed only
Scopus Profile
Open Access
  • Computer Science
  • Information Systems
  • Computer Vision

    • +2

e-ISSN :

1872-6305

Issue Frequency :

Bi-Monthly

Impact Factor :

18.6

p-ISSN :

1566-2535

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 - 1566-2535, E-ISSN - 1872-6305, Established in - 2000, Impact Factor - 18.6

Not Provide Crossref DOI

Indexed in Scopus, WoS

Not indexed in DOAJ, PubMed, UGC CARE

Indexing

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Scopus
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WoS
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Elsevier

+13 More

Publications of IF

Research Article
  • dott image Kinjal Adhvaryu
  • dott image March, 2023

Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions

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...

Research Article
  • dott image Giovanna Maria Dimitri
  • dott image December, 2022

Multimodal and multicontrast image fusion via deep generative models

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...

Research Article
  • dott image Anil Rahate
  • dott image May, 2022

Multimodal Co-learning: Challenges, applications with datasets, recent advances and future directions

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...

Research Article
  • dott image Deepak Kumar Jain
  • dott image July, 2023

Multimodal pedestrian detection using metaheuristics with deep convolutional neural network in crowded scenes

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...

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