| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 377 | 116 | 138 | 15 |
Artificial intelligence, Algorithm, Programming language, Theoretical computer science and Distributed computing are the subjects of interest in the journal. The research on Artificial intelligence featured in the journal combines topics in other fields like Natural language processing, Computer vision and Pattern recognition.
The most cited papers facilitate discussions on Algorithm, Artificial intelligence, Programming language, Theoretical computer science and Data mining. The works on Artificial intelligence tackled in the most cited papers bring together disciplines like Machine learning, Computer vision and Pattern recognition.
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 The Computer Journal (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 The Computer Journal (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, 21.77% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 8.25% were posted by at least one author from the top 10 institutions publishing in the journal. Another 4.47% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 5.84% of all publications and 81.44% 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.
The study of computer science and its related branches has often surpassed perceived boundaries and opened new research avenues. This expansion of knowledge shows potential for many more research directions. One such emerging area of interest is the intersection of Artificial Intelligence, Machine Learning, and Education. This harmony can help in better understanding and transforming learning methodologies, thus benefiting the education sector as a whole.
As an illustration, making use of AI to help develop art curriculums in schools could bring beautifully unexplored perspectives to traditional education. It would allow teachers to utilize technology effectively and provide a more engaging learning environment. Prospective art educators, may find guidance on the career path from external sources such as learning how to become an elementary art teacher in Michigan.
Similar intersections can be explored in areas of theoretical computing and pattern recognition which have vast implications in security, data analysis, and businesses. The potential for growth and advancement is great, and the community continues to push the boundaries of existing knowledge.
Xiaoyan Li;Xiaoyan Li;Cheng-Kuan Lin;Jianxi Fan;Xiaohua Jia
(2021)May Altulyan;May Altulyan;Lina Yao;Xianzhi Wang;Chaoran Huang
(2021)Colin Boyd;Kai Gellert
(2021)Amjad Osmani;Jamshid Bagherzadeh Mohasefi;Farhad Soleimanian Gharehchopogh
(2020)Jiafu Jiang;Linyu Tang;Ke Gu;Ke Gu;WeiJia Jia
(2020)Mengjie Lv;Baolei Cheng;Jianxi Fan;Xi Wang
(2021)Yi Yi;Jianxi Fan;Baolei Cheng;Yan Wang
(2021)Juan Li;Dan-dan Xiao;Ting Zhang;Chun Liu
(2021)Stéphane Devismes;Anissa Lamani;Franck Petit;Pascal Raymond
(2021)Wei Guo;Sujuan Qin;Jun Lu;Fei Gao
(2020)Tzu-Liang Kung;Hon-Chan Chen;Chia-Hui Lin;Lih-Hsing Hsu
(2021)Hao Lin;Zhen Zhao;Fei Gao;Willy Susilo
(2021)Sandeep Kumar Sood;Vaishali Sood;Isha Mahajan;Sahil
(2020)Pursuing a Computer Science degree in the USA opens up numerous opportunities, and exploring related online degrees can offer flexibility and affordability. For students seeking cost-effective options, many highly rated online universities provide accredited programs that don’t compromise on quality.
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