IEEE Transactions on Neural Networks and Learning Systems

Top Research Topics at IEEE Transactions on Neural Networks?

IEEE Transactions on Neural Networks primarily tackles Artificial neural network, Artificial intelligence, Pattern recognition, Control theory and Algorithm. The concepts on Artificial neural network presented in the journal can also apply to other research fields, including Stability (learning theory), Mathematical optimization and Nonlinear system. The journal focuses on Mathematical optimization as well as the interrelated topic of Convergence (routing).

The studies on Nonlinear system discussed can also contribute to research in the domains of Adaptive system and Optimal control. While work presented in it provided substantial information on Artificial intelligence, it also covered topics in Machine learning and Computer vision. The research on Pattern recognition tackled can also make contributions to studies in the areas of Contextual image classification and Cluster analysis.

The presentations discussing Control theory offer insights in topics such as Control theory, Adaptive control, Lyapunov function, Exponential stability and Control system. More specifically, the research on Recurrent neural network in the journal is related to Recurrent neural nets.

• Artificial neural network (48.24%)
• Artificial intelligence (43.74%)
• Pattern recognition (19.30%)

What are the most cited papers published in the journal?

• Identification and control of dynamical systems using neural networks (7240 citations)
• Training feedforward networks with the Marquardt algorithm (6010 citations)
• A comparison of methods for multiclass support vector machines (5486 citations)

Research areas of the most cited articles at IEEE Transactions on Neural Networks:

Artificial neural network, Artificial intelligence, Control theory, Pattern recognition and Nonlinear system are the main subjects of interest in the journal publications. While work presented in the journal publications provide substantial information on Artificial neural network, it also covers topics in Algorithm and Mathematical optimization. The published articles deal with Artificial intelligence in conjunction with Machine learning and similar fields in Training set.

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

• Artificial intelligence
• Artificial neural network
• Machine learning

The previous edition focused in particular on these issues:

The journal investigates areas of study like Artificial intelligence, Artificial neural network, Pattern recognition, Control theory and Machine learning. Deep learning, Convolutional neural network, Feature extraction, Feature (computer vision) and Discriminative model are all aspects of Artificial intelligence research featured in the journal. While it focused on Artificial neural network, it was also able to explore topics like Stability (learning theory), Convergence (routing), Function (mathematics), Algorithm and Robustness (computer science).

Issues in Pattern recognition were discussed, taking into consideration concepts from other disciplines like Image (mathematics), Representation (mathematics) and Cluster analysis. It features Control theory research that overlaps with concepts in Bounded function. Feature (machine learning) is a key component of Machine learning research discussed in it.

The most cited articles from the last journal are:

• A Comprehensive Survey on Graph Neural Networks (1871 citations)
• A Survey on Knowledge Graphs: Representation, Acquisition and Applications (239 citations)
• A Survey of the Usages of Deep Learning for Natural Language Processing (161 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 IEEE Transactions on Neural Networks (based on the number of publications) are:

• Xuelong Li (92 papers) published 23 papers at the last edition, 9 more than at the previous edition,
• Jun Wang (82 papers) published 13 papers at the last edition, 10 more than at the previous edition,
• Dacheng Tao (75 papers) published 13 papers at the last edition, 4 more than at the previous edition,
• Jinde Cao (73 papers) published 19 papers at the last edition, 10 more than at the previous edition,
• C. L. Philip Chen (63 papers) published 23 papers at the last edition, 9 more than at the previous 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 IEEE Transactions on Neural Networks (based on the number of publications) are:

• Chinese Academy of Sciences (301 papers) published 80 papers at the last edition, 41 more than at the previous edition,
• Nanyang Technological University (187 papers) published 32 papers at the last edition, 21 more than at the previous edition,
• City University of Hong Kong (172 papers) published 34 papers at the last edition, 12 more than at the previous edition,
• Southeast University (164 papers) published 36 papers at the last edition, 14 more than at the previous edition,
• Tsinghua University (132 papers) published 35 papers at the last edition, 17 more 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 2021 edition, 14.25% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 30.75% were posted by at least one author from the top 10 institutions publishing in the journal. Another 17.96% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 18.62% of all publications and 32.66% 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.

Career Pathways in IEEE Transactions on Neural Networks Domain

Given the coverage of the journal on key topics like Artificial intelligence, Machine learning, and Pattern recognition, it opens up numerous career options in research and academia. Opportunities may range from artificial intelligence specialists to software developers focusing on machine learning algorithms, pattern recognition researchers, and even educators teaching middle school to tertiary level mathematics and computer studies. For those considering a career in education, particularly in teaching middle school math with a focus on integrating machine learning and AI concepts into the curriculum, Georgia, USA is one state opening up to this innovative approach to education. For more information on how to pursue this career pathway, you can refer how long does it take to become a middle school math teacher in Georgia

Beyond direct research and academia, the disciplines covered by the IEEE Transactions on Neural Networks also offer potential career pathways in industries that rely heavily on these technologies. These include sectors such as manufacturing, logistics, finance, and healthcare, where automation, prediction models, image recognition, and data optimization are in high demand.