International Joint Conference on Neural Networks

Top Research Topics at International Joint Conference on Neural Network?

• Artificial intelligence (63.50%)
• Artificial neural network (51.83%)
• Pattern recognition (24.05%)

International Joint Conference on Neural Network was organized to reinforce research efforts on Artificial intelligence, Artificial neural network, Pattern recognition, Machine learning and Algorithm. The research on Artificial intelligence tackled can also make contributions to studies in the areas of Data mining and Computer vision. Topics in Artificial neural network explored in International Joint Conference on Neural Network were investigated in conjunction with research in Control theory and Nonlinear system.

International Joint Conference on Neural Network explores topics in Pattern recognition which can be helpful for research in disciplines like Contextual image classification, Speech recognition and Cluster analysis. The majority of Machine learning studies are focused on the issues of Unsupervised learning. While the event focused on Algorithm, it was also able to explore topics like Theoretical computer science and Mathematical optimization.

Issues in Feature extraction were discussed, taking into consideration concepts from other disciplines like Convolutional neural network and Feature (computer vision). The works on Recurrent neural network deal in particular with Recurrent neural nets. The in-depth study on Deep learning also explores topics in the intersecting field of Types of artificial neural networks.

What are the most cited papers published at the conference?

• Extreme learning machine: a new learning scheme of feedforward neural networks (2714 citations)
• Framewise phoneme classification with bidirectional LSTM and other neural network architectures (2109 citations)
• Theory of the backpropagation neural network (1536 citations)

Research areas of the most cited articles at International Joint Conference on Neural Network:

The most cited articles explore disciplines such as Artificial intelligence, Artificial neural network, Machine learning, Pattern recognition and Data mining. The studies tackled in the most cited publications, which mainly focus on Artificial intelligence, apply to Computer vision as well. The most cited articles explore research in Artificial neural network alongside concepts in Algorithm and other areas of study in Mathematical optimization.

Papers citation over time

A key indicator for each conference 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 at International Joint Conference on Neural Network (based on the number of publications) are:

• Jose C. Principe (36 papers) published 2 papers at the last edition, 2 less than at the previous edition,
• Tarek M. Taha (19 papers) published 1 paper at the last edition, 6 less than at the previous edition,
• Haibo He (18 papers) published 3 papers at the last edition, 1 less than at the previous edition,
• Jinglu Hu (18 papers) published 1 paper at the last edition, 4 less than at the previous edition,
• Edgar N. Sanchez (17 papers) absent at the last edition.

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

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing at International Joint Conference on Neural Network (based on the number of publications) are:

• Chinese Academy of Sciences (98 papers) published 51 papers at the last edition, 33 more than at the previous edition,
• Nanyang Technological University (62 papers) published 11 papers at the last edition the same number as at the previous edition,
• University of Technology, Sydney (54 papers) published 17 papers at the last edition the same number as at the previous edition,
• Federal University of Pernambuco (54 papers) published 16 papers at the last edition, 12 more than at the previous edition,
• University of São Paulo (52 papers) published 5 papers at the last edition, 3 less than at the previous edition.

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

Publication chance based on affiliation

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

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 2019 edition, 3.87% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 16.73% were posted by at least one author from the top 10 institutions publishing at the conference. Another 5.58% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 11.93% of all publications and 65.76% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of conferences they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same conference 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 conference 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 at a conference. The index includes the authors publishing at the last edition of a conference, 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.