| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 376 | 144 | 156 | 15 |
The journal primarily tackles Theory of computation, Artificial intelligence, Algorithm, Distributed computing and Theoretical computer science. The study on Theory of computation presented in the journal intersects with subjects under the field of Set (abstract data type). Journal of Computer Science and Technology focuses on Artificial intelligence but the discussions also offer insight into other areas such as Natural language processing, Machine learning, Computer vision and Pattern recognition.
It features Distributed computing research that overlaps with concepts in Computer network.
The journal papers generally zeroe in on subjects such as Artificial intelligence, Theory of computation, Data mining, Algorithm and Theoretical computer science. The studies on Artificial intelligence discussed at the most cited publications can also contribute to research in the domains of Natural language processing, Machine learning, Computer vision and Pattern recognition. While Data mining is the focus of the most cited articles, it also provides insights into the studies of Software, Information retrieval and Cluster analysis.
The primary areas of discussion in the journal are Artificial intelligence, Theory of computation, Machine learning, Deep learning and Pattern recognition. While Artificial intelligence is the focus of it, it also provided insights into the studies of Computer vision and Identification (information). Topics in Theory of computation explored in Journal of Computer Science and Technology were investigated in conjunction with research in Process (computing), Training set, Theoretical computer science, Noise (video) and Set (abstract data type).
Research in Field (computer science) and the interrelating topic of Classifier (UML) and Random forest were among the subjects of interest in the Machine learning studies discussed in the journal. The Pattern recognition works featured in it incorporate elements from Video tracking, Frame (networking) and Energy (signal processing). The research on Word error rate featured in Journal of Computer Science and Technology combines topics in other fields like Data mining and Cluster analysis.
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 Journal of Computer Science and Technology (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 Journal of Computer Science and Technology (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, 17.91% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 27.27% were posted by at least one author from the top 10 institutions publishing in the journal. Another 27.27% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 21.82% of all publications and 23.64% 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.
In light of the Journal's critical research areas, it's important to underline the potential implications and speculate about the direction of future work. The Journal's consistent attention to areas like Artificial Intelligence, Theory of computation, and Algorithm could reshape the tech industry's future outlook. Rising emphasis on Machine Learning and Deep Learning illustrates a growing preference for autonomous systems in technology.
Artificial Intelligence (AI) is not simply restricted to tech domains; it's at the forefront of several verticals, including education, healthcare, banking, and more. This overarching theme nods towards a future where AI plays an integral role in various facets of everyday life. For instance, the intersection of AI and education hints at a world where the conventional roles of educators could be remodeled, with AI stepping in to personalize and augment learning experiences. If you're interested in exploring how AI might change the role of a high school history teacher in Utah, learn more about it how much does a high school history teacher make in utah.
The spotlight on the Theory of Computation and Algorithm indicates a continued investment in the very fabric of computer science: using mathematical and logical methodologies to describe computation. This focus could steer upcoming empirical research towards devising more efficient computational models and algorithms.
Moreover, the growing interest in Machine and Deep Learning research underscores the push towards developing intelligent systems that can learn and improve from experience without being explicitly programmed. This research area's evolution could witness leaps in robotic automation, self-driving cars, and sophisticated prediction models.
Fittingly, the Journal's primary focus aligns with the constantly evolving landscape of Computer Science and Technology, echoing the push towards a more automated and AI-led future. As researchers continue to advance within these fields, we can only speculate about the transformative possibilities for our societal and technological future.
Robert B. Ross;George Amvrosiadis;Philip Carns;Charles D. Cranor
(2020)Dun Liang;Yuan Chen Guo;Shao Kui Zhang;Tai Jiang Mu
(2020)Yu-Jie Yuan;Yu-Kun Lai;Tong Wu;Lin Gao
(2021)André Brinkmann;Kathryn Mohror;Weikuan Yu;Philip Carns
(2020)Yuan Huang;Nan Jia;Hao-Jie Zhou;Xiang-Ping Chen
(2020)Haikun Liu;Di Chen;Hai Jin;Xiaofei Liao
(2021)Lie-Huang Zhu;Bao-Kun Zheng;Bao-Kun Zheng;Meng Shen;Meng Shen;Feng Gao
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