| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 709 | 22 | 25 | 7 |
The main points discussed in the journal deals with Computational intelligence, Artificial intelligence, Machine learning, Data mining and Algorithm. While Progress in Artificial Intelligence primarily focused on Computational intelligence, it also opened dialogues on the discipline of Set (abstract data type). Artificial intelligence research presented in Progress in Artificial Intelligence encompasses a variety of subjects, including Natural language processing and Pattern recognition.
Supervised learning and Multiclass classification are all aspects of Machine learning discussed in it. It focuses on Data mining but the discussions also offer insight into other areas such as Preprocessor and Multi-label classification. More specifically, the research on Algorithm in Progress in Artificial Intelligence is related to Local search (optimization).
Discussions in Progress in Artificial Intelligence are anchored in the subject of Classifier (UML) and the similar topic of Decision tree.
The most cited articles are mainly concerned with subjects like Computational intelligence, Artificial intelligence, Machine learning, Data mining and Data stream mining. The journal papers tackle research in various disciplines, including Computational intelligence and Cloud testing. The journal articles explore topics in Data stream mining which can be helpful for research in disciplines like Focus (computing) and Data science.
Progress in Artificial Intelligence investigates areas of study like Computational intelligence, Artificial intelligence, Machine learning, Deep learning and Benchmark (computing). The concepts on Computational intelligence presented in the journal can also apply to other research fields, including Segmentation, Data mining, Classifier (UML), Cluster analysis and Ranking. In Progress in Artificial Intelligence, Natural language processing, Sample (statistics) and Pattern recognition are investigated in conjunction with one another to address concerns in Artificial intelligence research.
Topics in Machine learning were tackled in line with various other fields like Class (biology) and Bayesian probability. The studies on Deep learning discussed can also contribute to research in the domains of Brainstorming and Set (psychology). The research on Benchmark (computing) featured in Progress in Artificial Intelligence combines topics in other fields like Local optimum, Algorithm, Chaotic, Intrusion detection system and Continuous optimization.
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 Progress in 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 Progress in 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, 20.93% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 14.71% were posted by at least one author from the top 10 institutions publishing in the journal. Another 2.94% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 29.41% of all publications and 52.94% 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.
While the Progress in Artificial Intelligence journal primarily focuses on the various aspects of Artificial Intelligence, Machine Learning, and Data Mining, it's also worth noting that these technologies and research findings have broader applications, like in special education. For example, AI and Machine Learning algorithms can play a substantial role in creating custom learning plans for students with diverse learning abilities. The specific use of AI in special education requires correlate knowledge both on AI technologies and educational sector, which can also be obtained through obtaining certain credentials.
Those interested in applying AI to create learning solutions in the special education sector may want to consider attaining a special education credential maine online. This credential provides comprehensive insight into the unique needs of students in special education and can be combined with the AI research discussed here to develop innovative learning approaches. This cross-disciplinary approach could potentially pave the way for more inclusive education that is more amenable to students' various needs.
Continued research in AI and its various applications is vital for technological progress and for improving diverse sectors like education, healthcare, and many others. Progress in Artificial Intelligence continues to spotlight the latest research and innovative solutions in the field, fostering a comprehensive understanding of these technologies and their potential applications.
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