Published by: International Association of Computer Science & Information Technology
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 985 | 9 | 9 | 3 |
Artificial intelligence, Pattern recognition, Machine learning, Data mining and Computer vision are among the topics commonly tackled in the journal. The studies tackled, which mainly focus on Artificial intelligence, apply to Natural language processing as well.
The journal publications mostly deal with topics like Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Data mining. Most of the Artificial intelligence studies addressed in the published articles also intersect with Natural language processing. While Pattern recognition is the focus of the published articles, it also provides insights into the studies of Contextual image classification, Artificial neural network and Distribution (number theory).
The main points discussed in International Journal of Machine Learning and Computing deals with Artificial intelligence, Machine learning, Pattern recognition, Deep learning and Convolutional neural network. The journal investigates Artificial intelligence research which frequently intersects with Computer vision. International Journal of Machine Learning and Computing explores topics in Machine learning which can be helpful for research in disciplines like Facial recognition system, Trajectory planning, Learning methods and Adaptation (computer science).
Multi modal fusion research are fields of study within Pattern recognition but they also intertwine with concepts in Mass spectrometry. The Deep learning research presented in it explores the relationship between Multimedia and the closely related topic of Art therapy. The Convolutional neural network works featured in International Journal of Machine Learning and Computing incorporate elements from Question answering, Similarity (network science), Facial expression recognition and Measure (physics).
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 Machine Learning and Computing (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 Machine Learning and Computing (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, 100.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, nan% were posted by at least one author from the top 10 institutions publishing in the journal. Another nan% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included nan% of all publications and nan% 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.
To truly appreciate the real-world applications of these insightful research topics, it's essential to examine how they can be applied in various professional fields. For instance, teaching is a field that could significantly benefit from integrating artificial intelligence and machine learning techniques. Educators could develop tailor-made lessons that use pattern recognition to take note of students' learning patterns and adjust accordingly. Data mining could be used to analyze students' grades and feedback more effectively, identifying areas that need improvement in teaching methodologies. Moreover, upcoming educators can also equip themselves with knowledge in these areas. To learn more and possibly start a career with such knowledge at their fingertips, one could consider checking out the cheapest teaching credential program in Texas. Exploring a teaching credential program that incorporates modules about using machine learning techniques could certainly give an edge, making these educators pioneers in their field. Given the widespread understanding of the effectiveness of these technological tools, we could potentially see a growth in the demand for educators with such knowledge and skillsets. Harnessing the power of machine learning and computing in teaching could certainly revolutionize the field, offering more effective, tailored education services.
Sadeq Darrab;David Broneske
(2021)Cosmas Ifeanyi Nwakanma;Md. Sajjad Hossain;Jae-Min Lee
(2020)Yannan Xing;Weijie Ke;Gaetano Di Caterina;John Soraghan
(2021)Nazanin Zounemat Kermani;Xian Yang;Yike Guo
(2021)Michael Walch;Dimitris Karagiannis
(2020)Yoshihiro Mitani;Robert Fisher;Yusuke Fujita;Yoshihiko Hamamoto
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