| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 22 | 739 | 1273 | 86 |
The journal mainly tackles studies in Artificial intelligence, Artificial neural network, Pattern recognition, Machine learning and Algorithm. Neural Computing and Applications explores issues in Artificial intelligence which can be linked to other research areas like Data mining and Computer vision. The studies on Artificial neural network discussed can also contribute to research in the domains of Mathematical optimization and Control theory, Nonlinear system.
It facilitated presentations on Mathematical optimization research, particularly Optimization problem and Particle swarm optimization. The study on Control theory presented is investigated in conjunction with research in Fuzzy logic. In the journal, Feature (computer vision) and Cluster analysis are investigated in conjunction with one another to address concerns in Pattern recognition research.
The journal papers primarily focus on research topics in Artificial intelligence, Artificial neural network, Machine learning, Mathematical optimization and Pattern recognition. The journal papers facilitate discussions on Artificial intelligence that incorporate concepts from other fields like Genetic algorithm and Data mining. The Artificial neural network research presented in the most cited papers focuses mostly on Control theory and, on occasion, topics in Fuzzy logic.
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.
The top authors publishing in Neural Computing and Applications (based on the number of publications) are:
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.
Only papers with recognized affiliations are considered
The top affiliations publishing in Neural Computing and Applications (based on the number of publications) are:
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.
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, 5.14% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 10.69% were posted by at least one author from the top 10 institutions publishing in the journal. Another 5.20% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 12.26% of all publications and 71.85% were from other institutions.
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.
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.
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:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Neural computing is not confined to research areas like artificial intelligence or machine learning. It also finds relevance in the field of education. For instance, this interdisciplinary approach allows us to better understand and improve the learning process. With the right blend of neural computing and educational techniques, it’s possible to assess a student's responsiveness to various learning strategies and customize a learning path for each student as per his or her cognitive abilities.
This element of customization could be crucial in locations where the student-teacher ratio is imbalanced. The issue of mass education can be somewhat mitigated if a semi-automated system is employed to handle repetitive tasks. For example, tasks such as checking multiple-choice assessments could be automated, providing teachers with more time to handle complex tasks that require human judgment.
At the level of teacher education, neural computing applications could be integrated to enhance teaching patterns leading to an optimized learning environment. If you're interested in pursuing a career in education, you might find our guide on how to become a teacher in montana with a bachelor's degree helpful. This guide helps you navigate the steps needed to become a qualified teacher and how neural computing can be an effective tool in your teaching arsenal.
Long Wen;Xinyu Li;Liang Gao
(2020)Unknown
(2022)Shu Lih Oh;Yuki Hagiwara;U. Raghavendra;Rajamanickam Yuvaraj
(2020)Ibrar Yaqoob;Khaled Salah;Raja Jayaraman;Yousof Al-Hammadi
(2021)Hamdi Altaheri;Ghulam Muhammad;Mansour Alsulaiman;Syed Umar Amin
(2021)Unknown
(2022)Kazi Md. Rokibul Alam;Nazmul H. Siddique;Hojjat Adeli
(2020)Laith Mohammad Abualigah;Mohammad Shehab;Mohammad Alshinwan;Hamzeh Alabool
(2020)Mohammed Azmi Al-Betar;Mohammed Azmi Al-Betar;Zaid Abdi Alkareem Alyasseri;Zaid Abdi Alkareem Alyasseri;Mohammed A. Awadallah;Iyad Abu Doush;Iyad Abu Doush
(2021)Erwan Le Merrer;Patrick Pérez;Gilles Trédan
(2020)Exploring related online degrees can open up diverse career opportunities for students interested in Computer Science. Fields like mechanical engineering, physics, data science, and electrical engineering often overlap with computer science skills, offering multiple pathways to specialize or broaden your expertise.
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