0952-1976
Published by: Elsevier
https://www.journals.elsevier.com/engineering-applications-of-artificial-intelligence
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 56 | 573 | 826 | 57 |
| Engineering and Technology | 94 | 219 | 345 | 45 |
The main research concerns discussed in Engineering Applications of Artificial Intelligence are Artificial intelligence, Machine learning, Artificial neural network, Data mining and Fuzzy logic. While the journal focused on Artificial intelligence, it was also able to explore topics like Process (engineering), Computer vision and Pattern recognition. Feature extraction and Classifier (UML) are some of the facets of Pattern recognition tackled in Engineering Applications of Artificial Intelligence.
The concepts on Artificial neural network presented in it can also apply to other research fields, including Algorithm and Nonlinear system. The Fuzzy logic study tackled is a key component of adjacent topics in the area of Control theory. The research on Neuro-fuzzy discussed in Engineering Applications of Artificial Intelligence draws on the closely related field of Adaptive neuro fuzzy inference system.
The most cited articles aim to foster the development of research in Artificial intelligence, Artificial neural network, Machine learning, Genetic algorithm and Data mining. The journal articles explore issues in Artificial intelligence which can be linked to other research areas like Algorithm, Computer vision and Pattern recognition. The journal articles explore research in Genetic algorithm alongside concepts in Particle swarm optimization and other areas of study in Metaheuristic, Differential evolution and Evolutionary algorithm.
The main points discussed in the journal deals with Artificial intelligence, Algorithm, Pattern recognition, Machine learning and Artificial neural network. Artificial intelligence study tackled is connected to the field of Computer vision. The research on Algorithm featured in the journal combines topics in other fields like Support vector machine, Robustness (computer science) and Benchmark (computing).
The journal focuses on Machine learning research which is adjacent to topics in Process (engineering). The study on Segmentation presented in Engineering Applications of Artificial Intelligence intersects with the topics under Image (mathematics).
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 Engineering Applications of Artificial Intelligence (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 Engineering Applications of Artificial Intelligence (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, 9.27% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 8.67% were posted by at least one author from the top 10 institutions publishing in the journal. Another 2.48% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 16.41% of all publications and 72.45% 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 (AI) is revolutionizing various sectors, including education. Its applications in education extend beyond virtual classroom settings, offering numerous opportunities for teachers, students, and administrators alike. For instance, AI can transform traditional curriculum into customized and individual learning paths that take advantage of the strengths and weaknesses of each student. Furthermore, it can help teachers manage their time better by automating administrative tasks like grading and lesson planning.
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The main sections covered in this guide include the need for AI in education, the technologies involved, their applications in various aspects of education, and the challenges faced in their implementation. The guide aims to equip educators with the necessary tools to effectively utilize AI in their classrooms, thereby enhancing the quality of education imparted and nurturing a generation of students who are adept in a world that increasingly relies on AI.
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