Information Fusion

Top Research Topics at Information Fusion?

The scientific interests tackled in the journal are Artificial intelligence, Machine learning, Pattern recognition, Data mining and Computer vision. Most of the Artificial intelligence studies addressed also intersect with Fusion. Machine learning, which encompasses Ensemble learning and Artificial neural network, is the main subject of Information Fusion.

Many of the studies tackled connect Data mining with a similar field of study like Context (language use). Computer vision research is the primary subject tackled in it with a focus on Pixel. The Image fusion study featured falls within the wider field of Image (mathematics).

• Artificial intelligence (50.07%)
• Machine learning (16.78%)
• Pattern recognition (16.29%)

What are the most cited papers published in the journal?

• Multisensor data fusion: A review of the state-of-the-art (1288 citations)
• Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (910 citations)
• Diversity creation methods: a survey and categorisation (760 citations)

Research areas of the most cited articles at Information Fusion:

The most cited papers cover a variety of subjects, including Artificial intelligence, Image fusion, Computer vision, Pattern recognition and Machine learning. The journal articles with studies in Artificial intelligence featured incorporate elements of Data mining and Fusion. The published papers address concerns in Machine learning which are intertwined with other disciplines, such as Classifier (UML) and Majority rule.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Statistics
• Machine learning

The previous edition focused in particular on these issues:

The main research concerns discussed in Information Fusion are Artificial intelligence, Deep learning, Pattern recognition, Data science and Sensor fusion. Topics in Artificial intelligence were tackled in line with various other fields like Machine learning and Computer vision. In the journal, Segmentation, Image segmentation, Pulse repetition frequency, Field (computer science) and Signal are investigated in conjunction with one another to address concerns in Deep learning research.

Domain (software engineering), Multi-source, Feature (computer vision), Fusion and Transformation (function) are some topics wherein Pattern recognition research discussed in the journal have an impact. The work on Data science tackled in it brings together disciplines like Social network, Vagueness, Point (typography), Set (psychology) and Field (geography). The studies in Sensor fusion featured incorporate elements of Transfer of learning, Convolution, Granularity and Electromyography.

The most cited articles from the last journal are:

• Finding and removing Clever Hans: Using explanation methods to debug and improve deep models (4 citations)
• Early detection of cardiovascular autonomic neuropathy: A multi-class classification model based on feature selection and deep learning feature fusion (1 citations)
• Interpretable learning based Dynamic Graph Convolutional Networks for Alzheimer’s Disease analysis (1 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Top authors and change over time

The top authors publishing in Information Fusion (based on the number of publications) are:

• Belur V. Dasarathy (71 papers) absent at the last edition,
• Francisco Herrera (30 papers) published 2 papers at the last edition, 2 less than at the previous edition,
• Giancarlo Fortino (18 papers) published 1 paper at the last edition, 6 less than at the previous edition,
• Zeshui Xu (17 papers) absent at the last edition,
• Enrique Herrera-Viedma (15 papers) absent at the last edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Information Fusion (based on the number of publications) are:

• University of Granada (60 papers) published 2 papers at the last edition, 10 less than at the previous edition,
• King Abdulaziz University (46 papers) published 3 papers at the last edition, 5 less than at the previous edition,
• Sichuan University (38 papers) published 1 paper at the last edition, 9 less than at the previous edition,
• Chinese Academy of Sciences (30 papers) published 2 papers at the last edition, 3 less than at the previous edition,
• Xidian University (27 papers) published 1 paper at the last edition, 4 less than at the previous edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2022 edition, 5.26% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 33.33% were posted by at least one author from the top 10 institutions publishing in the journal. Another 16.67% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 16.67% of all publications and 33.33% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

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