0169-7439
Published by: Elsevier
https://www.journals.elsevier.com/chemometrics-and-intelligent-laboratory-systems
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Chemistry | 424 | 54 | 85 | 19 |
The journal was organized to reinforce research efforts on Artificial intelligence, Pattern recognition, Statistics, Partial least squares regression and Algorithm. The studies in Artificial intelligence featured incorporate elements of Machine learning and Data mining. The journal connects the study in Data mining with the closely related area of Process (computing).
Research on Pattern recognition presented in Chemometrics and Intelligent Laboratory Systems focuses, in particular, on Linear discriminant analysis and Support vector machine. The journal aims to address concerns in Statistics, specifically in the areas of Multivariate statistics, Regression and Calibration (statistics). The work on Partial least squares regression tackled in the journal brings together disciplines like Regression analysis, Principal component regression, Linear regression and Chemometrics.
Chemometrics and Intelligent Laboratory Systems focuses on Algorithm as well as the interrelated topic of Mathematical optimization. Chromatography, Biological system and Calibration are some topics wherein Analytical chemistry research discussed in the journal have an impact.
The published papers investigate studies in Artificial intelligence, Statistics, Pattern recognition, Partial least squares regression and Data mining. The journal publications hold forums on Artificial intelligence that merge themes from other disciplines such as Machine learning, Multivariate statistics and Chemometrics. The most cited articles focus on Partial least squares regression but sometimes tackle the closely related topic of Algorithm which is concerned with Mathematical optimization and Wavelet transform.
Chemometrics and Intelligent Laboratory Systems aims to foster the development of research in Artificial intelligence, Pattern recognition, Data mining, Algorithm and Deep learning. The research on Artificial intelligence featured in it combines topics in other fields like Machine learning and Chemometrics. The featured Pattern recognition studies mainly concentrate on Feature (computer vision) but also cover areas of interest in Calibration (statistics).
In addition to Data mining research, it aims to explore topics under Process (computing), Partial least squares regression, Multivariate statistics, Principal component analysis and Feature selection. Chemometrics and Intelligent Laboratory Systems addresses concerns in Partial least squares regression which are intertwined with other disciplines, such as Mean squared error and Regression analysis. While the journal focused on Multivariate statistics, it was also able to explore topics like MATLAB and Regression.
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 Chemometrics and Intelligent Laboratory Systems (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 Chemometrics and Intelligent Laboratory Systems (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, 6.06% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 10.97% were posted by at least one author from the top 10 institutions publishing in the journal. Another 12.26% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 12.90% of all publications and 63.87% 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.
Jean-Michel Roger;Alessandra Biancolillo;Federico Marini
(2020)Puneet Mishra;Puneet Mishra;Jean Michel Roger;Douglas N. Rutledge;Douglas N. Rutledge;Alessandra Biancolillo
(2020)Puneet Mishra;Jean Michel Roger;Federico Marini;Alessandra Biancolillo
(2021)Jing Liang;Maogang Li;Yao Du;Chunhua Yan
(2020)Zhining Shi;Christopher W.K. Chow;Rolando Fabris;Jixue Liu
(2020)Agnieszka Martyna;Alicja Menżyk;Alessandro Damin;Aleksandra Michalska
(2020)Jiahua Tan;Yan Sun;Li Ma;Heying Feng
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