| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 980 | 9 | 20 | 3 |
The scientific interests tackled in the journal are Wavelet, Artificial intelligence, Pattern recognition, Mathematical analysis and Wavelet transform. It focuses on Wavelet as well as the interrelated topic of Algorithm. The studies tackled, which mainly focus on Algorithm, apply to Mathematical optimization as well.
Some problems in Artificial intelligence that were presented in the journal overlapped with concepts under Machine learning and Computer vision. The works on Pattern recognition deal in particular with Hyperspectral imaging. The research on Mathematical analysis tackled can also make contributions to studies in the areas of Pure mathematics and Applied mathematics.
The Wavelet transform study tackling the subject of Continuous wavelet transform is the focus of it. The journal concentrated on Discrete wavelet transform research, specifically Harmonic wavelet transform and Lifting scheme. The study on Stationary wavelet transform presented in International Journal of Wavelets, Multiresolution and Information Processing intersects with subjects under the field of Cascade algorithm.
The published papers generally zeroe in on subjects such as Wavelet, Artificial intelligence, Wavelet transform, Pattern recognition and Algorithm. The most cited articles deal with Artificial intelligence in conjunction with Computer vision and similar fields in Noise. While the most cited papers focused on Wavelet transform, they were also able to explore topics like Feature extraction and Mathematical analysis.
The foci of the journal are Artificial intelligence, Applied mathematics, Wavelet, Pattern recognition and Pure mathematics. While work presented in the journal provided substantial information on Artificial intelligence, it also covered topics in Machine learning and Computer vision. It explores topics in Applied mathematics which can be helpful for research in disciplines like Uncertainty principle, Convergence (routing), Regression, Dimensionality reduction and Bioheat transfer.
Wavelet transform and Multiresolution analysis are some of the facets of Wavelet tackled in the journal. Artificial neural network and Regression analysis are some topics wherein Pattern recognition research discussed in International Journal of Wavelets, Multiresolution and Information Processing have an impact. It deals with Pure mathematics in conjunction with Frame (networking) and similar fields in Locally compact group, Space (mathematics), Hilbert space and Mathematical analysis.
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 Wavelets, Multiresolution and Information Processing (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 Wavelets, Multiresolution and Information Processing (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, 6.82% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 19.51% were posted by at least one author from the top 10 institutions publishing in the journal. Another 4.88% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 19.51% of all publications and 56.10% 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.
While the journal extensively covers complex research topics like Wavelets, Artificial Intelligence, Pattern Recognition, Mathematical Analysis, and others, a section discussing the practical and educational implications of these studies in a straightforward manner can be beneficial. Particularly, this can help high school teachers engage their students better by applying these advanced concepts to teaching high school subjects. For instance, if you're wondering about how these complex topics could be integrated into a school curriculum, consider the case of a history teacher. Modern research in fields like Artificial Intelligence and pattern recognition can be used to create intelligent tutoring systems or to pattern students' learning behaviors, respectively. By understanding these real-world applications, teachers can use them to enhance their teaching methods and strategies. Additionally, understanding how such high-level concepts can be applied in real-life helps students to better appreciate the practicality of what they are learning, thus fostering greater interest and engagement in their studies. Doing so inspires students who aspire to become researchers and authors who might potentially contribute to journals like International Journal of Wavelets, Multiresolution and Information Processing. If you are interested in becoming a teacher and weaving these advanced concepts into your lessons, you might find our guide on how to become a high school history teacher in Pennsylvania particularly useful. This guide provides insightful information on the requisites and procedures for becoming a certified high school teacher.
Yanting Li;Junwei Jin;Liang Zhao;Huaiguang Wu
(2021)Yuqing Wan;Raymond Yiu Keung Lau;Yain-Whar Si
(2020)Lina Yang;Pu Wei;Cheng Zhong;Xichun Li
(2020)Maosheng Wei;Jun Wu;Lina Yang;Yuanyan Tang
(2021)Guang Yi Chen;Adam Krzyzak
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