Cognitive Computation

Top Research Topics at Cognitive Computation?

The journal facilitates discussions on Artificial intelligence, Pattern recognition, Machine learning, Artificial neural network and Computer vision. The Artificial intelligence works featured in the journal incorporate elements from Cognition and Natural language processing. The studies tackled, which mainly focus on Cognition, apply to Cognitive science as well.

Cognitive Computation emphasizes research on Natural language processing, which includes concerns such as Sentiment analysis. The study on Pattern recognition presented is investigated in conjunction with research in Cluster analysis. Some problems in Machine learning that were presented in the journal overlapped with concepts under Classifier (UML) and Data mining.

The journal explores research in Deep learning and the adjacent study of Convolutional neural network.

• Artificial intelligence (57.93%)
• Pattern recognition (17.28%)
• Machine learning (17.17%)

What are the most cited papers published in the journal?

• An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels (666 citations)
• Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors (412 citations)
• What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle (332 citations)

Research areas of the most cited articles at Cognitive Computation:

The journal papers are organized to address concerns in the fields of Artificial intelligence, Pattern recognition, Cognition, Machine learning and Artificial neural network. The most cited publications facilitate discussions on Artificial intelligence that incorporate concepts from other fields like Computer vision and Natural language processing. While Machine learning is the focus of the published papers, it also provides insights into the studies of Data mining and Face (geometry).

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

• Artificial intelligence
• Machine learning
• Statistics

The previous edition focused in particular on these issues:

Cognitive Computation primarily focuses on research topics in Artificial intelligence, Machine learning, Pattern recognition, Deep learning and Artificial neural network. In addition to Artificial intelligence research, it aims to explore topics under Context (language use), Field (computer science) and Natural language processing. It holds forums on Natural language processing that merges themes from other disciplines such as Word (computer architecture), Task (project management) and Identification (information).

It focuses on Machine learning but the discussions also offer insight into other areas such as Graph (abstract data type), Cognition and Fuzzy logic. Pattern recognition research presented in it encompasses a variety of subjects, including Feature (computer vision) and Benchmark (computing). The studies in Sentiment analysis featured incorporate elements of Semantics, Social media and Affective computing.

The most cited articles from the last journal are:

• Deep Learning in Mining Biological Data (95 citations)
• Shallow Convolutional Neural Network for COVID-19 Outbreak Screening Using Chest X-rays. (42 citations)
• A Novel Semi-Supervised Convolutional Neural Network Method for Synthetic Aperture Radar Image Recognition (31 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 Cognitive Computation (based on the number of publications) are:

• Amir Hussain (57 papers) published 5 papers at the last edition, 1 more than at the previous edition,
• Erik Cambria (23 papers) published 2 papers at the last edition, 1 less than at the previous edition,
• Bin Luo (15 papers) published 4 papers at the last edition, 3 more than at the previous edition,
• Marcos Faundez-Zanuy (13 papers) absent at the last edition,
• Anna Esposito (10 papers) published 1 paper 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 Cognitive Computation (based on the number of publications) are:

• University of Stirling (51 papers) published 1 paper at the last edition the same number as at the previous edition,
• Nanyang Technological University (44 papers) published 7 papers at the last edition, 1 more than at the previous edition,
• Chinese Academy of Sciences (43 papers) published 6 papers at the last edition the same number as at the previous edition,
• Anhui University (25 papers) published 11 papers at the last edition, 10 more than at the previous edition,
• Central South University (19 papers) published 2 papers at the last edition, 2 less than at the previous 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 2021 edition, 6.38% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 19.32% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.39% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 15.34% of all publications and 57.95% 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.

Application and Career Perspectives in Cognitive Computation

With the rise in research topics such as Artificial Intelligence, Machine Learning, and Deep Learning, it is evident that the field of Cognitive Computation holds promising career prospects. Ranging from scientists, researchers, to educators, this discipline provides diverse roles.

For instance, if an individual possesses an in-depth understanding of these complex subjects, they might consider applying their knowledge as an educator. A quintessential example is the role of a high school art teacher where understanding Artificial Intelligence could revolutionize the traditional teaching methods. Schools in South Carolina, for instance, are actively seeking educators who can amalgamate technology with art. If you feel this might be your calling, here is a detailed guide on how to become a high school art teacher in South Carolina.

The prospects don't stop here. Many other career paths are centered around Cognitive Computation. For instance, AI specialists, Data Analysts, and Computational Linguists are some of the prominent roles that one can consider. So, whether you are a student contemplating a future career path, or a professional interested in transitioning into this exciting field, the wealth of possibilities in Cognitive Computation is truly unparalleled.

Career Networks and Opportunities

Conclusion