| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Electronics and Electrical Engineering | 245 | 53 | 105 | 15 |
| Computer Science | 249 | 169 | 186 | 22 |
| Engineering and Technology | 530 | 27 | 42 | 16 |
Neural Processing Letters covers a variety of subjects, including Computational intelligence, Artificial intelligence, Artificial neural network, Complex system and Pattern recognition. While the journal focused on Computational intelligence, it was also able to explore topics like Recurrent neural network, Data mining, Convergence (routing), Exponential stability and Algorithm. Machine learning and Computer vision are some topics wherein Artificial intelligence research discussed in it have an impact.
The research on Artificial neural network tackled can also make contributions to studies in the areas of Lyapunov function, Control theory, Nonlinear system, Mathematical optimization and Applied mathematics. The presented studies in Control theory, Linear matrix inequality and Stability (learning theory) fall within the purview of Control theory but it also intertwines with topics in Synchronization (computer science). The work on Complex system addressed in it expands to the thematically related Topology.
In the journal, Feature (computer vision) and Cluster analysis are investigated in conjunction with one another to address concerns in Pattern recognition research.
The main points discussed in the published papers deal with Artificial neural network, Computational intelligence, Artificial intelligence, Complex system and Algorithm. While work presented in the journal papers provide substantial information on Artificial neural network, it also covers topics in Convergence (routing), Exponential stability, Mathematical optimization and Control theory. The journal publications with studies in Artificial intelligence featured incorporate elements of Machine learning and Pattern recognition.
The discussions in Neural Processing Letters mainly cover the fields of Computational intelligence, Artificial intelligence, Artificial neural network, Complex system and Pattern recognition. The research on Computational intelligence presented in Neural Processing Letters often intersects with areas of study such as
The journal holds forums on Artificial neural network that merges themes from other disciplines such as Function (mathematics), Convergence (routing) and Lyapunov function, Nonlinear system. Complex system research presented in Neural Processing Letters encompasses a variety of subjects, including Stability (learning theory), Recurrent neural network, Exponential stability and Synchronization. It addresses concerns in Pattern recognition which are intertwined with other disciplines, such as Pixel and 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 Processing Letters (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 Processing Letters (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, 5.96% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 11.62% were posted by at least one author from the top 10 institutions publishing in the journal. Another 8.10% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 17.61% of all publications and 62.68% 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.
Daniel Auge;Julian Hille;Julian Hille;Etienne Mueller;Alois C. Knoll
(2021)Unknown
(2023)Tianrong Rao;Xiaoxu Li;Min Xu
(2020)A. Pratap;Ramachandran Raja;Jehad O. Alzabut;Joseph Dianavinnarasi
(2020)A. Chandrasekar;T. Radhika;Quanxin Zhu
(2021)S. Pradeepa;K. R. Manjula;S. Vimal;Mohammad S. Khan
(2020)For students interested in expanding their knowledge beyond Computer Science, several related online degrees offer promising career paths. Engineering disciplines such as Mechanical Engineering provide a strong foundation in problem-solving and design principles. Exploring the online mechanical engineering degree options can be a cost-effective way to enter fields like robotics or manufacturing.
Physics is another complementary area that enhances analytical skills and scientific understanding. Pursuing a physics degree online can open doors to careers in research, technology development, or education, making it an excellent pairing with a computer science background.
Data Science remains one of the fastest-growing fields, merging statistics, machine learning, and software engineering. For those looking to specialize, earning a data scientist degree online offers competitive tuition rates and practical skills for roles in analytics and AI.
Finally, Electrical Engineering closely intersects with computer hardware and software, electronics, and communication systems. Students can find affordable programs by researching the electrical engineering online tuition costs, ensuring access to quality education that supports diverse tech careers.
