1530-9827
Published by: The American Society of Mechanical Engineers (ASME)
http://computingengineering.asmedigitalcollection.asme.org/journal.aspx
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 497 | 31 | 46 | 12 |
| Engineering and Technology | 799 | 35 | 54 | 10 |
The foci of Journal of Computing and Information Science in Engineering are Artificial intelligence, Computer Aided Design, Engineering drawing, Algorithm and Systems engineering. Journal of Computing and Information Science in Engineering focuses on Artificial intelligence but the discussions also offer insight into other areas such as Machine learning, Computer vision and Pattern recognition. Research on Computer Aided Design addressed in the journal frequently intersections with the field of CAD.
The work on Engineering drawing tackled in it brings together disciplines like Engineering design process and Machining. It connects the study in Systems engineering with the closely related area of New product development.
The journal publications mainly deal with areas of study such as Computer Aided Design, Systems engineering, Artificial intelligence, Engineering design process and New product development. The most cited articles hold forums on Computer Aided Design that merge themes from other disciplines such as Tolerance analysis, CAD, Engineering drawing, Manufacturing engineering and Software engineering. The studies on Artificial intelligence discussed at the journal papers can also contribute to research in the domains of Algorithm, Data structure, Computer vision and Pattern recognition.
Journal of Computing and Information Science in Engineering generally zeroes in on subjects such as Artificial intelligence, Artificial neural network, Numerical analysis, Polygon mesh and Vibration. The work on Artificial intelligence tackled in Journal of Computing and Information Science in Engineering brings together disciplines like Defect free and Component (UML). Some problems in Artificial neural network that were presented in the journal overlapped with concepts under Lower extremity joint, Surface (mathematics), Residual and Pattern recognition.
It holds forums on Numerical analysis that merges themes from other disciplines such as Acoustics, Electromagnetic flowmeter, Calibration (statistics), Extreme learning machine and Linear least squares. Topics in Polygon mesh were tackled in line with various other fields like Base (topology), Quadrilateral, B-spline and Kernel (statistics). The studies on Vibration discussed can also contribute to research in the domains of Automotive engineering, Motion planning, Control theory and Deep neural networks.
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 Computing and Information Science in Engineering (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 Computing and Information Science in Engineering (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 2022 edition, 4.55% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 19.05% were posted by at least one author from the top 10 institutions publishing in the journal. Another 14.29% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 23.81% of all publications and 42.86% 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.
For those who are interested in the disciplines covered by the Journal of Computing and Information Science in Engineering such as Artificial Intelligence, Systems Engineering, and Computer Aided Design, there are a myriad of career options available. One such area could be teaching these subjects in a private school setting. Becoming a teacher of these highly specialized subjects in private schools requires certain qualifications and prerequisites. Different states and different schools have varying regulations regarding these requirements. For instance, the private school teacher requirements in Pennsylvania may differ in some respects from those of other states. In general, an aspiring private school teacher must have a bachelor’s degree in the subject they plan to teach. This is a universal requirement and applies to aspiring teachers in every state. However, private schools typically look for candidates with additional qualifications, such as a master’s degree or higher in the subject they intend to teach or in education. In addition, many private schools prefer their teachers to have previous teaching experience and even insist upon it. This could include student teaching during college, being a graduate teaching assistant, or experiences teaching at public or other private schools. Potential private school teachers should also be aware that many states require private school teachers to complete a teacher preparation program. This program, usually completed during the bachelor’s degree, includes both instruction in teaching methodologies and pedagogies, as well as a period of supervised teaching practice. For those interested in teaching computer science and related subjects in a private school setting, understanding these requirements is crucial to achieving success in this career path.
Prahar M. Bhatt;Rishi K. Malhan;Pradeep Rajendran;Brual C. Shah
(2021)L. Siddharth;Lucienne T. M. Blessing;Kristin L. Wood;Jianxi Luo
(2022)Junchuan Shi;Tianyu Yu;Kai Goebel;Kai Goebel;Kai Goebel;Dazhong Wu
(2021)Ziyang Zhang;Junchuan Shi;Tianyu Yu;Aaron Santomauro
(2020)Vivian Wong;Max Ferguson;Kincho Law;Yung-Tsun Tina Lee
(2021)Shengjun Liu;Tao Liu;Qiang Zou;Weiming Wang;Weiming Wang
(2021)Ziyang Zhang;Laxmi Poudel;Zhenghui Sha;Wenchao Zhou
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