| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 31 | 272 | 370 | 74 |
The journal mostly deals with topics like Artificial intelligence, Machine learning, Data mining, Artificial neural network and Air pollution. The majority of Artificial intelligence studies are focused on the issues of Deep learning. Air pollution research featured in the journal incorporates concerns from various other topics such as Pollutant and Air quality index.
The published articles mainly deal with areas of study such as Artificial intelligence, Machine learning, Data mining, Algorithm and Data science. The featured Artificial intelligence studies in the journal papers mainly concentrate on Field (computer science) but also cover areas of interest in Deep learning. The published articles facilitate discussions on Data mining that incorporate concepts from other fields like Classifier (UML) and Clustering high-dimensional data, Cluster analysis.
Artificial Intelligence Review is organized to address concerns in the fields of Artificial intelligence, Deep learning, Machine learning, Field (computer science) and Algorithm. Topics in Artificial intelligence were tackled in line with various other fields like Context (language use), Natural language processing and Pattern recognition. Some problems in Machine learning that were presented in it overlapped with concepts under Feature extraction and Process (engineering).
In Artificial Intelligence Review, Information retrieval and Data science are investigated in conjunction with one another to address concerns in Field (computer science) research. Optimization problem is a focus of the presented Algorithm works and it dives deep in Optimization problem. The studies on Optimization problem discussed can also contribute to research in the domains of Metaheuristic and Benchmark (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 Artificial Intelligence Review (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 Artificial Intelligence Review (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, 6.72% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 5.60% were posted by at least one author from the top 10 institutions publishing in the journal. Another 2.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 10.80% of all publications and 81.60% 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.
Artificial Intelligence Review, through its wide-ranging research and investigative articles, provides insights not only within the field of artificial intelligence, but its broad implications and applications across various disparate fields of study, including teaching and education.
For instance, the principles of machine learning and data mining have been instrumental in enhancing teaching methodologies and curricula. This has led to the development of more effective teaching strategies and tools, particularly in early childhood education. Research in this space is continually growing, with AI systems now capable of addressing individual learning needs, styles, and pace, effectively personalizing education.
An intriguing case study that illustrates this intersection between AI and education can be seen in the modern preschool classroom. The process of molding young minds involves an understanding of complex cognitive, social, and emotional attributes. Artificial intelligence and machine learning can be harnessed to aid teachers in making this process more efficient, systematic, and individual-oriented.
To know more about the role of educators in shaping the future of our children, especially in the early years, reading about how to step into the profession of early childhood education can provide interesting insights. Learn more about how to enter this crucial calling by assessing the resourceful guide on how do you become a preschool teacher in Massachusetts.
The symbiosis between AI and diverse fields continues to evolve, therefore it is indispensable to not only investigate AI in isolation, but to appreciate the plethora of ways it is influencing, changing and bettering other spheres of human enterprise.
Asifullah Khan;Anabia Sohail;Umme Zahoora;Aqsa Saeed Qureshi
(2020)Nantheera Anantrasirichai;David R. Bull
(2021)Saeid Asgari Taghanaki;Kumar Abhishek;Joseph Paul Cohen;Julien Cohen-Adad
(2021)Guoguang Du;Kai Wang;Shiguo Lian;Kaiyong Zhao
(2021)H. A. Alsattar;H. A. Alsattar;A. A. Zaidan;B. B. Zaidan
(2020)Iuliia Kotseruba;John K. Tsotsos
(2020)Unknown
(2022)Unknown
(2022)Laith Mohammad Abualigah;Ali Diabat;Ali Diabat
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