| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Neuroscience | 405 | 10 | 8 | 5 |
The main research concerns discussed in Computational Intelligence and Neuroscience are Artificial intelligence, Pattern recognition, Artificial neural network, Machine learning and Algorithm. Computational Intelligence and Neuroscience explores research in Artificial intelligence and the adjacent study of Computer vision. Topics in Pattern recognition explored in Computational Intelligence and Neuroscience were investigated in conjunction with research in Image processing, Feature (computer vision) and Electroencephalography.
Brain–computer interface is a key component of Electroencephalography research discussed in the journal. The in-depth study on Artificial neural network also explores topics in the intersecting field of Data mining. Data mining research discussed connects with the study of Cluster analysis.
Discussions in Computational Intelligence and Neuroscience are anchored in the subject of Algorithm and the similar topic of Mathematical optimization.
The most cited papers mainly deal with areas of study such as Artificial intelligence, Electroencephalography, Machine learning, Artificial neural network and Pattern recognition. Aside from discussions in Artificial intelligence, the published papers also deal with the subject of Signal processing which intersects with Feature extraction disciplines. Toolbox, Speech recognition and Healthy subjects are some topics wherein Electroencephalography research discussed in the journal publications has an impact.
The foci of the journal are Artificial intelligence, Artificial neural network, Pattern recognition, Deep learning and Convolutional neural network. The Artificial intelligence study tackled is a key component of adjacent topics in the area of Machine learning. The journal holds forums on Artificial neural network that merges themes from other disciplines such as Genetic algorithm, Convergence (routing) and Data mining, Big data.
In addition to Pattern recognition research, the journal aims to explore topics under Feature (machine learning), Autoencoder and Robustness (computer science). Studies on Deep learning discussed in the journal link to the field of Recurrent neural network. The Convolutional neural network works featured in it incorporate elements from Image processing, Algorithm and Convolution.
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 Computational Intelligence and Neuroscience (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 Computational Intelligence and Neuroscience (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, 12.35% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 8.29% were posted by at least one author from the top 10 institutions publishing in the journal. Another 6.08% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 16.30% of all publications and 69.34% 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.
As the field of Computational Intelligence and Neuroscience continues to grow, so does the demand for professionals with expertise in this area. Career opportunities in this industry are vast and varied, ranging from data scientists and software developers to machine learning engineers and research scientists. A subfield that is gaining prominence in this domain is Speech Language Pathology, which leverages the principles of Artificial Intelligence and Neuroscience for the treatment of speech and language disorders.
Furthermore, the state Tennessee offers abundant opportunities for aspiring speech pathologists. Prospective candidates must fulfill certain speech pathologist requirements in Tennessee to qualify for such roles. This includes attaining a master's degree in Speech-Language Pathology, completing a clinical fellowship, and passing a national examination.
With increasing adoption of AI and machine learning techniques in healthcare, the role of a speech pathologist has evolved to include more computational aspects. Therefore, professionals with a foundational understanding of Computational Intelligence and Neuroscience will find themselves at a strategic advantage in the job market.
In the rapidly advancing realm of Computational Intelligence and Neuroscience, new job roles emerge constantly, making it a vibrant and promising field of study and work.
Simon Wein;Gustavo Deco;Gustavo Deco;Ana Maria Tomé;Markus Goldhacker
(2021)Yvonne Höller;Yvonne Höller;Kevin H G Butz;Aljoscha C Thomschewski;Elisabeth V Schmid
(2020)Sergio Leonardo Mendes;Walter Hugo Lopez Pinaya;Pedro Mario Pan;João Ricardo Sato
(2021)Alberto Antonietti;Dario Martina;Claudia Casellato;Egidio D'Angelo
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