Neurocomputing

Top Research Topics at Neurocomputing?

The objective of the journal is to combine knowledge in the areas of Artificial intelligence, Pattern recognition, Artificial neural network, Machine learning and Control theory. The journal holds forums on Artificial intelligence that merges themes from other disciplines such as Data mining and Computer vision. Neurocomputing explores research in Data mining and the adjacent study of Cluster analysis.

Neurocomputing connects the study in Pattern recognition with the closely related area of Image (mathematics). Topics in Artificial neural network explored in the journal were investigated in conjunction with research in Stability (learning theory), Exponential stability, Mathematical optimization, Applied mathematics and Algorithm. It facilitates discussions on Control theory that incorporate concepts from other fields like Synchronization (computer science) and Bounded function.

• Artificial intelligence (49.82%)
• Pattern recognition (23.19%)
• Artificial neural network (21.82%)

What are the most cited papers published in the journal?

• Extreme learning machine: Theory and applications (7963 citations)
• Time series forecasting using a hybrid ARIMA and neural network model (2142 citations)
• The Self-Organizing Map (1660 citations)

Research areas of the most cited articles at Neurocomputing:

The most cited publications are mainly concerned with subjects like Artificial intelligence, Artificial neural network, Pattern recognition, Machine learning and Control theory. The most cited papers address concerns in the field of Artificial intelligence by exploring it in line with topics in Data mining which intersect with Cluster analysis subjects. The studies on Artificial neural network discussed at the most cited publications can also contribute to research in the domains of Stability (learning theory) and Mathematical optimization.

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

• Artificial intelligence
• Statistics
• Quantum mechanics

The previous edition focused in particular on these issues:

Neurocomputing primarily tackles Artificial intelligence, Pattern recognition, Artificial neural network, Deep learning and Convolutional neural network. It explores issues in Artificial intelligence which can be linked to other research areas like Machine learning and Natural language processing. Machine learning research presented in the journal encompasses a variety of subjects, including Adversarial system and Generalization.

It explores topics in Pattern recognition which can be helpful for research in disciplines like Domain (software engineering), Similarity (geometry), Block (data storage), Dual graph and Transformation (function). While Artificial neural network is the focus of Neurocomputing, it also provided insights into the studies of Edge device and Control theory, Tracking error, Control theory, Nonlinear system. The work on Convolutional neural network tackled in the journal brings together disciplines like Computer vision and Taxonomy (general).

The most cited articles from the last journal are:

• BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis (1 citations)
• An accurate and practical algorithm for internet traffic recovery problem (0 citations)
• TSPred: A framework for nonstationary time series prediction (0 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 Neurocomputing (based on the number of publications) are:

• Jinde Cao (103 papers) absent at the last edition,
• Huaguang Zhang (86 papers) absent at the last edition,
• Xinbo Gao (77 papers) absent at the last edition,
• Xuelong Li (68 papers) absent at the last edition,
• Tingwen Huang (68 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 Neurocomputing (based on the number of publications) are:

• Chinese Academy of Sciences (794 papers) absent at the last edition,
• Harbin Institute of Technology (363 papers) absent at the last edition,
• University of Electronic Science and Technology of China (309 papers) published 3 papers at the last edition, 48 less than at the previous edition,
• Xidian University (298 papers) published 1 paper at the last edition, 53 less than at the previous edition,
• Northeastern University (China) (294 papers) absent at the last 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, 30.56% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 28.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 20.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 0.00% of all publications and 52.00% 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 Opportunities in Neurocomputing

The diverse and rapid advancements in the field of neurocomputing have opened up several exciting career opportunities. From research positions in artificial intelligence to data mining and machine learning, there is a robust demand for professionals in this field. Students who have a passion for combining coding and neuroscience might consider a career in neurocomputing. In order to embark on this career path, an understanding of both basic neuroscience and computer programming languages is crucial.

For instance, individuals aspiring to be a high school computer science or history teacher might consider delving into the field, as demonstrating expertise in such an advanced field can bolster their career prospects. As this path combines scientific concepts with magical wonders of technology, a teacher with knowledge in neurocomputing can provide a more enriching experience for students. On this note, if you're looking for more specific guidance, you might want to check out how to become a high school history teacher in Oklahoma.

Ultimately, the field of neurocomputing allows ambitious professionals to find their niche and contribute to a rapidly growing body of knowledge. Whether in academia, research, education, or industry, the opportunities are vast. If you’re interested in the intersection of neuroscience and computer science, a career in neurocomputing might be just the opportunity you’re looking for!