| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 446 | 78 | 99 | 13 |
IEEE Systems, Man, and Cybernetics Magazine aims to foster the development of research in Cybernetics, Artificial intelligence, Human–computer interaction, Data science and Operations research. While work presented in IEEE Systems, Man, and Cybernetics Magazine provided substantial information on Cybernetics, it also covered topics in Management science, Engineering ethics, Systems science, Cognitive science and Big data. It explores topics in Artificial intelligence which can be helpful for research in disciplines like Machine learning, Task (project management) and Computer vision.
Studies on Human–computer interaction discussed in it link to the field of Wearable computer. The work on Data science addressed in IEEE Systems, Man, and Cybernetics Magazine expands to the thematically related Data modeling.
The published papers facilitate discussions on Artificial intelligence, Human–computer interaction, Data modeling, Big data and Computational intelligence. Most of the works presented in the journal publications deal with Artificial intelligence but they intersect with the subject of Task (project management). In addition to Big data research, the most cited publications aim to explore topics under Emerging technologies and Commerce.
The objective of the journal is to combine knowledge in the areas of Cybernetics, Artificial intelligence, Telecommunications, Engineering management and Machine learning. Topics in Cybernetics were tackled in line with various other fields like Radio-frequency identification, Plan (drawing), Theoretical computer science and Identification (information). The study on Artificial intelligence presented is investigated in conjunction with research in Pattern recognition.
Concepts in Smart grid, as well as related topics in Renewable energy, Electricity, Information and Communications Technology and Cyber-physical system, are covered in the Telecommunications research presented in the journal. The journal holds forums on Engineering management that merges themes from other disciplines such as Coaching and Social system. The featured Machine learning research zeroes in on concepts in Artificial neural network and Interpretability but also tackles themes under Structure (mathematical logic) and Symbolic regression.
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 IEEE Systems, Man, and Cybernetics Magazine (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 IEEE Systems, Man, and Cybernetics Magazine (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, 0.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 42.86% were posted by at least one author from the top 10 institutions publishing in the journal. Another 0.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 21.43% of all publications and 35.71% 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.
Having covered various research areas within the IEEE Systems, Man, and Cybernetics Magazine, it would be helpful to understand the routes one could take to contribute to such a prestigious publication. Often, a career in research requires extensive education, internships and hands-on training. Different fields of study may have slightly varying paths, but there are common steps involved. Firstly, a bachelor's degree is a prerequisite – typically in a science-related major, such as cybernetics, engineering or computer science. Further specialization is often necessary by way of a Master's or Doctorate degree. Relevant internships can pave the way towards finding initial research roles or assistantships. Those aiming to become lead researchers usually progress through more junior research positions, gaining experience and contributing to increased responsibility over time. Strong analytical, problem-solving and communication skills are vital, along with a continually updated understanding of evolving technologies and methodologies. For those interested particularly in the area of human-computer interaction, and considering a shift from educational to research roles, a guide on {How to Become a Preschool Teacher in Nevada} provides valuable insight that may apply to potential pathways into research roles. The skills and experiences gained in educational roles can certainly apply to research - particularly in fields like human-computer interaction where understanding of learning and behavioural patterns is key. The road towards a research career may seem lengthy, but the potential to contribute to impactful topics in publications such as the IEEE Systems, Man, and Cybernetics Magazine can make the journey worthwhile. The evolving world of cybernetics and related fields promises a dynamic and rewarding career for those up for the challenge.
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(2020)For students considering advanced education in Computer Science, exploring the variety of online degree options is essential. Those interested in further specialization might want to look into easy masters degree programs that offer flexibility without compromising quality.
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