| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Mathematics | 372 | 14 | 19 | 8 |
| Computer Science | 991 | 8 | 9 | 3 |
The topics of Applied mathematics, Mathematical optimization, Artificial intelligence, Mathematical analysis and Finite element method are the focal point of discussions in the journal. The study on Artificial intelligence presented is investigated in conjunction with research in Computer vision. The field of Structural engineering is the anchor for the Finite element method studies presented in it.
The journal publications are organized to address concerns in the fields of Fuzzy logic, Mathematical optimization, Simulation, Mechanics and Nonlinear system. While Simulation is the focus of the most cited articles, it also provides insights into the studies of Synapse, Simulation software, Calcium and Space exploration. The most cited publications deal with Nonlinear system in conjunction with Heat transfer and similar fields in Mathematical analysis.
The main points discussed in International Journal of Modeling, Simulation, and Scientific Computing deals with Applied mathematics, Coronavirus disease 2019 (COVID-19), 2019-20 coronavirus outbreak, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Artificial intelligence. While Applied mathematics is the focus of it, it also provided insights into the studies of Convergence (routing), Variable (mathematics), Uniqueness, Numerical analysis and Order (group theory). The studies in Variable (mathematics) featured incorporate elements of Fractional differential, Stability (probability) and Fuzzy logic.
While the primary focus in the journal is Coronavirus disease 2019 (COVID-19), it also dissects topics surrounding Virology and Pandemic as a whole. It facilitates discussions on Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that incorporate concepts from other fields like Term (time), Epidemic model and Basic reproduction number. The in-depth study on Artificial intelligence also explores topics in the intersecting field of Machine learning.
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 International Journal of Modeling, Simulation, and Scientific Computing (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 International Journal of Modeling, Simulation, and Scientific Computing (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, 14.14% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 16.47% were posted by at least one author from the top 10 institutions publishing in the journal. Another 11.76% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 11.76% of all publications and 60.00% 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.
Hamadjam Abboubakar;Pushpendra Kumar;Norodin A. Rangaig;Sachin Kumar
(2021)Hamadjam Abboubakar;Pushpendra Kumar;Vedat Suat Erturk;Anoop Kumar
(2021)Soufiane Bentout;Salih Djilali;Behzad Ghanbari
(2021)Muhammad Altaf Khan;Saif Ullah;Yasir Khan;Muhammad Farhan
(2020)Pushpendra Kumar;Vedat Suat Erturk;Abdullahi Yusuf;Tukur Abdulkadir Sulaiman
(2021)Soufiane Bentout;Behzad Ghanbari;Behzad Ghanbari;Salih Djilali;Lakshmi Narayan Guin
(2021)Zouaoui Bekri;Vedat Suat Erturk;Pushpendra Kumar
(2021)Aliya Shaheen;Jinyong Sheng;Sadia Arshad;Ozlem Defterli
(2020)For those interested in pursuing Computer Science in the USA, exploring online degree options offers flexibility and accessibility. Many students look for the best accredited self-paced online colleges to balance studies with work or personal commitments effectively. These programs allow learners to progress at their own speed while ensuring quality education.
Cost is another significant factor. Affordable education is within reach through cheap online master's programs that provide advanced knowledge without the financial burden often associated with traditional on-campus degrees. Such programs are ideal for career advancement in tech fields where a master's degree can open doors.
For those just starting out or seeking quicker entry into the workforce, options like the easiest associate degree to get can provide foundational skills in computer science and related areas. These degrees are designed to be approachable yet valuable for entry-level positions.
Choosing a reputable institution matters hugely, making good online colleges a priority for ensuring your degree holds value in the job market. Accreditation and program quality are key considerations for long-term career success in tech.
