Transactions on Computational and Scientific Methods

Top Research Topics at Transactions on Computational and Scientific Methods ?

The aim of Transactions on Computational and Scientific Methods is to expand the discussion of research in Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Pattern recognition (psychology). It links adjacent topics like Artificial intelligence with Machine learning. The research on Pattern recognition featured in the journal combines topics in other fields like Facial recognition system and Feature (computer vision).

Research on Computer vision presented in the journal focuses, in particular, on Segmentation, Image segmentation, Pixel and Face (geometry).

• Artificial intelligence (88.92%)
• Pattern recognition (29.17%)
• Computer vision (26.18%)

Research areas of the most cited articles at Transactions on Computational and Scientific Methods :

The published papers mainly deal with areas of study such as Artificial intelligence, Pattern recognition, Computer vision, Pattern recognition (psychology) and Artificial neural network. The journal papers hold forums on Artificial intelligence that merge themes from other disciplines such as Algorithm, Machine learning and Speech recognition. While the journal papers focused on Pattern recognition, they were also able to explore topics like Signature (logic) and Handwriting.

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

• Artificial intelligence
• Statistics
• Machine learning

The previous edition focused in particular on these issues:

The main points discussed in the journal deals with Artificial intelligence, Computer vision, Deep learning, Pattern recognition and Image (mathematics). The in-depth study on Artificial intelligence also explores topics in the intersecting field of Task (project management). The journal covers Deep learning research under the subject of Machine learning.

Issues in Pattern recognition were discussed, taking into consideration concepts from other disciplines like Artificial neural network and Identification (information).

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 Transactions on Computational and Scientific Methods (based on the number of publications) are:

• Patrick S. P. Wang (51 papers) published 3 papers at the last edition,
• Yuan Yan Tang (40 papers) published 1 paper at the last edition,
• Frank Y. Shih (31 papers) published 5 papers at the last edition, 3 more than at the previous edition,
• Bin Fang (23 papers) published 4 papers at the last edition, 3 more than at the previous edition,
• Ching Y. Suen (20 papers) published 3 papers at the last 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 Transactions on Computational and Scientific Methods (based on the number of publications) are:

• The University of Texas at Austin (34 papers) published 4 papers at the last edition, 2 less than at the previous edition,
• Northeastern University (25 papers) published 3 papers at the last edition, 2 more than at the previous edition,
• Columbia University (24 papers) published 6 papers at the last edition, 4 more than at the previous edition,
• National Chiao Tung University (17 papers) absent at the last edition,
• Hong Kong Baptist University (15 papers) published 2 papers at the last 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, 16.97% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 13.81% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.18% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 16.02% of all publications and 62.98% 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.

Career Opportunities and Advancements in the Field of Artificial Intelligence and Pattern Recognition

Artificial Intelligence (AI) and Pattern Recognition are complex fields of study that boast several research opportunities and career advancements. The potential for growth in these areas is vast, considering their extensive application across various industries. To build a career in these fields, it is crucial to have the right qualifications and skills, including a comprehensive understanding of concepts such as machine learning, deep learning, computer vision, and algorithms.

Specifically, for those interested in the dimension of education and training related to AI and Pattern Recognition, becoming a special education teacher might be a fascinating path. This career could offer the opportunity to utilize these advanced knowledge areas in developing unique instructional strategies and learning models. The state of Wyoming, for example, provides an online pathway towards obtaining your special education credential in Wyoming online, allowing you to equip yourself with the necessary credential to serve in this capacity.

Moreover, with continual breakthroughs and advancements in AI and Pattern Recognition, professionals have plenty of opportunities to contribute to research that could pioneer new techniques. Participating in top-ranked publications such as the Transactions on Computational and Scientific Methods can be a significant step in that regard.

In conclusion, with the right blend of education, skills, and experience, AI and Pattern Recognition can provide a fruitful and rewarding career path for aspiring professionals and researchers.