| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 371 | 43 | 58 | 16 |
Evolving Systems focuses on Artificial intelligence, Complex system, Data mining, Machine learning and Pattern recognition. As a part of the journal, discussions in Artificial intelligence involve topics like Artificial neural network, Fuzzy logic, Classifier (UML), Feature extraction and Deep learning. Fuzzy control system is a major topic of Fuzzy logic research.
The studies in Complex system featured incorporate elements of Algorithm, Mathematical optimization and Control theory. Control theory and Nonlinear system are all topics related to Control theory research discussed. The journal facilitates discussions on Data mining that incorporate concepts from other fields like Fuzzy classification, Neuro-fuzzy and Cluster analysis.
Some problems in Neuro-fuzzy that were presented in it overlapped with concepts under Fuzzy set operations and Defuzzification. Feature vector is a focus of the Pattern recognition works in the journal. The journal focused on Data stream mining research but expanded to cover Data stream.
The most cited papers are organized to reinforce research efforts on Complex system, Data mining, Artificial intelligence, Fuzzy logic and Data stream mining. The most cited articles explore issues in Complex system which can be linked to other research areas like Classifier (UML), Structure (mathematical logic), Annotation and Control theory. While Artificial intelligence is the focus of the journal papers, it also provides insights into the studies of Machine learning and Pattern recognition.
The objective of Evolving Systems is to combine knowledge in the areas of Artificial intelligence, Complex system, Pattern recognition, Fuzzy logic and Artificial neural network. It investigates Artificial intelligence research which frequently intersects with Machine learning. Evolving Systems focuses on Complex system but the discussions also offer insight into other areas such as Context (language use), Identification (information), Optimization algorithm, Mathematical optimization and Algorithm.
Pattern recognition research in Evolving Systems involves the investigation of Digital image studies, all of which are linked to disciplines such as Robustness (computer science). The studies on Fuzzy logic discussed can also contribute to research in the domains of Observer (quantum physics) and Data mining. While the primary focus in it is Data mining, it also dissects topics surrounding Random forest and Support vector machine as a whole.
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 Evolving Systems (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 Evolving Systems (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, 9.09% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 22.50% were posted by at least one author from the top 10 institutions publishing in the journal. Another 15.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 27.50% of all publications and 35.00% 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.
One aspect deeply connected but not covered in this article is the potential future career paths for authors and contributors in the various fields explored in the Evolving Systems journal. Many authors aren’t just contributing for the sake of academic curiosity, but to further their education and careers in their chosen fields. With a focus on cutting-edge topics such as Artificial Intelligence, Complex Systems, and Data Mining, there are abundant career opportunities for researchers contributing to these discussions. Specifically, for authors and contributors interested in pursuing a career in educational research, certain teaching credential programs could help cement their expertise and legitimacy in their field. For instance, in Vermont, several programs offer cost-effective ways to develop their teaching and research qualifications in AI and Data Science. One such path worth considering is the best teaching credential programs in Vermont. With the right educational and experiential preparation, one could potentially initiate or enhance a fulfilling and engaging career combining research in evolving systems with the ability to guide and shape the future generations of researchers in this field.
Taye Girma Debelee;Friedhelm Schwenker;Achim Ibenthal;Dereje Yohannes
(2020)Aikaterini Protogerou;Stavros Papadopoulos;Anastasios Drosou;Dimitrios Tzovaras
(2021)Daniel F. Leite;Igor Skrjanc;Fernando A. C. Gomide
(2020)Nikolaos Polatidis;Elias Pimenidis;Michalis Pavlidis;Spyridon Papastergiou
(2020)José de Jesús Rubio;Enrique Garcia;Gustavo Aquino;Carlos Aguilar-Ibañez
(2020)João Sauer;Viviana Cocco Mariani;Viviana Cocco Mariani;Leandro dos Santos Coelho;Leandro dos Santos Coelho;Matheus Henrique Dal Molin Ribeiro;Matheus Henrique Dal Molin Ribeiro
(2021)Samuel Rahimeto;Taye Girma Debelee;Dereje Yohannes;Friedhelm Schwenker
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