Journal of Computational Science

Top Research Topics at Journal of Computational Science?

Artificial intelligence, Mathematical optimization, Algorithm, Parallel computing and Distributed computing are among the topics commonly tackled in the journal. The research on Artificial intelligence featured in the journal combines topics in other fields like Machine learning, Data mining, Computer vision and Pattern recognition. Optimization problem is a major topic of Mathematical optimization research.

The journal focuses on Parallel computing research which is adjacent to topics in Scalability.

• Artificial intelligence (14.78%)
• Mathematical optimization (12.03%)
• Algorithm (11.01%)

What are the most cited papers published in the journal?

• Twitter mood predicts the stock market. (3202 citations)
• Modeling the spatial spread of infectious diseases: The GLobal Epidemic and Mobility computational model (344 citations)
• Chaotic bat algorithm (315 citations)

Research areas of the most cited articles at Journal of Computational Science:

The most cited articles generally zeroe in on subjects such as Artificial intelligence, Mathematical optimization, Algorithm, Data mining and Parallel computing. The most cited papers hold forums on Artificial intelligence that merge themes from other disciplines such as Machine learning and Pattern recognition. The most cited articles deal with Parallel computing in conjunction with Computational science and similar fields in Solver and Scalability.

What topics the last edition of the journal is best known for?

• Quantum mechanics
• Artificial intelligence
• Operating system

The previous edition focused in particular on these issues:

Journal of Computational Science is mainly concerned with subjects like Algorithm, Applied mathematics, Artificial intelligence, Parallel computing and Artificial neural network. The concepts on Algorithm presented in it can also apply to other research fields, including Data assimilation and Interpolation. It facilitates discussions on Applied mathematics that incorporate concepts from other fields like Parametric statistics, Boundary value problem, Stability (probability), Discretization and Function (mathematics).

It explores issues in Artificial intelligence which can be linked to other research areas like Machine learning and Pattern recognition. Topics in Parallel computing explored in it were investigated in conjunction with research in Scalability and Solver. The Solver works featured in it incorporate elements from Finite element method and Speedup.

The most cited articles from the last journal are:

• An artificial intelligence model considering data imbalance for ship selection in port state control based on detention probabilities (8 citations)
• The MVAPICH project: Transforming research into high-performance MPI library for HPC community (6 citations)
• Numerical study of the nonlinear anomalous reaction–subdiffusion process arising in the electroanalytical chemistry (5 citations)

Papers citation over time

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.

Top authors and change over time

The top authors publishing in Journal of Computational Science (based on the number of publications) are:

• Valeria V. Krzhizhanovskaya (14 papers) published 2 papers at the last edition the same number as at the previous edition,
• Peter M. A. Sloot (12 papers) published 1 paper at the last edition, 1 less than at the previous edition,
• Michael Lees (11 papers) published 1 paper at the last edition the same number as at the previous edition,
• Jack Dongarra (11 papers) published 2 papers at the last edition, 1 more than at the previous edition,
• Peter V. Coveney (10 papers) published 1 paper at the last edition the same number as at the previous edition.

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.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Journal of Computational Science (based on the number of publications) are:

• AGH University of Science and Technology (36 papers) published 4 papers at the last edition,
• University of Amsterdam (26 papers) published 1 paper at the last edition, 1 less than at the previous edition,
• Chinese Academy of Sciences (19 papers) published 2 papers at the last edition, 1 more than at the previous edition,
• Saint Petersburg State University of Information Technologies, Mechanics and Optics (19 papers) published 2 papers at the last edition the same number as at the previous edition,
• Technische Universität München (18 papers) published 2 papers at the last edition the same number as at the previous edition.

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.

Publication chance based on affiliation

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, 6.16% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 10.95% were posted by at least one author from the top 10 institutions publishing in the journal. Another 10.22% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 16.79% of all publications and 62.04% were from other institutions.

Returning Authors Index

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.

Returning Institution Index

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.

The experience to innovation index

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:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Why are These Research Topics Important?

Understanding why the Journal of Computational Science focuses on certain research topics, such as artificial intelligence, machine learning, and parallel computing, can help us appreciate the significance of these fields. Many of these topics have practical applications that shape our everyday lives, and recognizing their importance can expand our understanding of the technology around us.

For instance, artificial intelligence (AI) can improve our ability to make predictions and automate tasks. It can be used in diverse fields, ranging from digital marketing to healthcare, where AI algorithms can accurately predict disease outbreaks based on data patterns. Learn more about this in the featured course on teaching credential programs in Louisiana.

Similarly, other topics like parallel computing and machine learning can enhance our lives in exceptional ways. Parallel computing allows us to process large amounts of data quickly, making everything from weather forecasting to scientific research quicker and more efficient. Meanwhile, machine learning algorithms keep improving and making our interaction with digital devices more seamless and intuitive.

These are just a few reasons why these research topics are critical. By delving deeper and expanding knowledge in these subjects, we can make significant scientific strides and contribute positively to society.