| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 973 | 11 | 19 | 3 |
The journal aims to foster the development of research in Artificial intelligence, Algorithm, Artificial neural network, Data mining and Machine learning. The studies in Artificial intelligence featured incorporate elements of Natural language processing, Computer vision and Pattern recognition. The in-depth study on Algorithm also explores topics in the intersecting field of Theoretical computer science.
The most cited publications cover a variety of subjects, including Artificial intelligence, Intelligent decision support system, Multi-agent system, Logic programming and Machine learning. The journal papers with studies in Artificial intelligence featured incorporate elements of Development (topology), Negation and Natural language processing. While the journal publications focused on Logic programming, they were also able to explore topics like Moral disengagement, Social intuitionism, Moral reasoning, Trolley problem and Principle of double effect.
The concepts of Artificial intelligence, Wireless sensor network, Scheduling (computing), Wireless and Efficient energy use are tackled in International Journal of Reasoning-based Intelligent Systems. The journal explores topics in Artificial intelligence which can be helpful for research in disciplines like Machine learning and Natural language processing. Machine learning research presented in it encompasses a variety of subjects, including Set (abstract data type) and Text processing.
The studies on Scheduling (computing) discussed can also contribute to research in the domains of Multi-agent system, Load balancing (computing), Computer engineering and Interval (mathematics). Some problems in Wireless that were presented in the journal overlapped with concepts under Microcell, Information and Communications Technology and Plant state. The work on Efficient energy use tackled in the journal brings together disciplines like Genetic algorithm, Particle swarm optimization and Real-time computing.
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 International Journal of Reasoning-based Intelligent 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 International Journal of Reasoning-based Intelligent 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, 50.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 6.67% 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 20.00% of all publications and 73.33% 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.
With the increasing relevance and application of Artificial Intelligence (AI) and Machine Learning (ML) in various sectors, there is growing demand for professionals in these fields. A career in AI and ML can be incredibly rewarding, offering numerous opportunities in both research and application-based roles. From businesses leveraging data to gain insightful customer metrics, to educational institutions training the next generation of tech leaders, the potential for career growth and development is vast.
If education piques your interest, becoming an elementary school teacher specializing in these fields could be a great pathway. Teaching the younger generation about the fundamentals of AI and ML can provide a strong foundation for their future studies and career choices. Most importantly, it will contribute to the development of a technologically literate society. For information on how to pursue this career opportunity, check out our guide on how to become an elementary school teacher in Montana.
In the business sector, businesses are capitalizing on AI and ML to optimize operations, improve decision-making processes, and offer better customer experiences. There are several roles available such as data analysts, data scientists, machine learning specialists and more. Depending on your interests and skills, you could find rewarding opportunities in these areas.
The research sector also offers plentiful opportunities. As this article suggests, there's a high concentration of research happening in AI and ML, making it a great field for those interested in contributing to the theory and development of these areas.
Whether your interests lie in education, business, or research, Artificial Intelligence and Machine Learning offer a multitude of career options, ensuring a bright future for those who choose to specialize in these fields.
Sudipta Roy;Bhavya Patel;Debnath Bhattacharyya;Kushal Dhayal
(2021)Khalil Ibrahim Hamzaoui;Mohammed Berrajaa;Mostafa Azizi;Giuseppe Lipari
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