Be the first to learn about Machine Learning Science and Technology Impact Factor – The most important things you should know before your next publication.
Nobody can deny the significance of machine learning science and technology impact factor. The journal impact factor is becoming more used as a metric for assessing the quality of scientific research.
Every publisher or researcher must understand the machine learning science and technology impact factor of a specific journal, since it shows the ranking. The machine learning science and technology impact factor is a measurement of a journal’s relative relevance in its area.
Journals publish their machine learning science and technology impact factor report at a particular period. Because the world nowadays runs on technology, publications have enlarged their topic area categories. Most of the journal accepts more or less all types of subject. Machine learning science and technology impact factor is a trendy and in-demand research topic these days. There are several journals dedicated to machine learning science and technology impact factor, artificial intelligence, deep learning, and other related topics.
So if your research area is related to machine learning, you should examine the machine learning science and technology impact factor of the journal before deciding to publish.
Machine learning is a subset of artificial intelligence(AI). Positive impact of technology on business factor machine learning journals accept and publish publications that give substantial evidence for assertions regarding machine learning science and technology impact factor issues or techniques through empirical investigations, theoretical analysis, or comparisons to psychological phenomena.
Machine Learning: Science and technology is an open-access renowned journal. This journal is dedicated to all aspects of machine learning.
So, this article focuses on the details of “Machine Learning Science and technology Impact Factor”.In addition to the Machine Learning Science and Technology Impact Factor, the following journals have been given special attention-
- Machine Learning Journal Impact Factor
- Machine Learning with Applications Impact Factor
- Nature Machine Intelligence Impact Factor
- Science Journal Impact Factor
Before moving to the details of the Machine Learning Science and Technology Impact Factor let’s take a short look at the impact factor.
What is the Impact Factor?
The impact factor (IF) also known as the journal factor is a measurement of how many times an average article in a journal was referenced in a given year. It is used to determine a journal’s importance or rank by counting the number of times its articles are referenced.
What Is the Importance of the Impact Factor?
To know machine learning science and technology impact factor first you need to know the impact factor.
The Impact Factor is a metric that is used to assess the relative relevance of journals within certain fields. The IF of the journal with the most review articles will be greater. The greater the Impact factor, the better the journal’s ranking. It’s one tool for comparing journals in a given subject group.
Academicians value the Impact Factor more than any other criteria when choosing a journal to publish in. It also aids in determining the veracity of the facts presented in that publication. Therefore, the impact factor is crucial in machine learning science and technology impact factor.
How to Calculate Impact Factor
The most well-known source for journal impact factors is Thomson Scientific’s yearly release, Journal Citation Reports (JCR).
If you want to do it on your own, then use the following formula. The IF is calculated during two years by dividing the number of times papers published in that journal have been referenced by the total number of citable articles.
IFx=Citationsx-1 +Citations x-2Publications x-1 +Publicationsx-2
To calculate the impact factor of an “ABC” journal for the year 2020, for example, we would do the following:
IF2020=Citations2019 +Citations 2018Publications 2019 +Publications2018
If the Journal, for example, has the following citations and publications value:
Citations2019 = 80
Citations2018 = 60
Publications2019= 30
Publications2018= 40
IF( 2020)= (80 + 60) / (30 + 40) = 2
This IF value indicates that, on average, papers published in the “ABC” Journal in the years 2018 and 2019 earned 2 (two) citations apiece in the year 2020.
Machine Learning Science and Technology Impact Factor
Machine Learning: Science and Technology (MLST) is a versatile open-access journal that bridges the application of machine learning across science with the advancement of machine learning methods and theories inspired by physical insights and contributed greatly in machine learning science and technology impact factor. Articles must specifically fit into one of the following categories:
- Improve the status of machine learning-driven scientific applications
- Produce conceptual, methodological, or theoretical breakthroughs in machine learning with scientific issues as applications, inspiration, or motivation
Not only do machine learning science and technology impact factors require a significant number of papers and citations, but most other journal impact factors do as well. Individual authors create a significantly lower number of articles on average.
Let’s mention some important points about the journal machine learning: science and technology impact factor. Besides machine learning science and technology impact factors, this information is also important to know.
- Types of articles: Machine Learning: Science and Technology impact factor accept Research papers, letters, technical notes, which helps a lot to learn machine learning science and technology impact factor.
- Frequency: Each year, four issues of Machine Learning: Science and Technology impact factor are published. (Quarterly)
- Review by peers: Machine Learning: Science and Technology impact factor use a double-anonymous peer-review procedure, which ensures that authors remain anonymous to reviewers throughout the process.
- Medium: Online
- Identifier: ISSN 2632-2153
- Ethics: Machine Learning: Science and Technology impact factor is a member of the Committee for Publishing Ethics and adheres to the highest standards of publication and research ethics (COPE), which helps learning machine learning science and technology impact factor a breeze.
- Copyright: Authors who publish in Machine Learning: Science and Technology impact factor keep their copyright and publish under a CC BY license (Creative Commons Attribution Licence). This helps to acknowledge your contribution in machine learning science and technology impact factor.
- Publisher: IOP Publishing
- Editor-in-Chief: Anatole von Lilienfeld, University of Vienna, Austria
- Publication fee: The highest fee charged by this journal is 1640 GBP as publication fees (article processing charges or APCs). There are no submission fees, therefore APCs only applies to papers that are approved for publication.
When it comes to machine learning science and technology impact factors, this journal hasn’t published a report recently. Machine Learning: Science and Technology: ISSN number is 2632-2153 where all information available. Latest description: Volume 1, number 1 (March 1, 2020).
However, machine learning science and technology impact factor reports can be easily calculated by the above mentioned formula. If you know how to calculate the impact factor, you can easily calculate that report. Also, the “Web of Science Master Journal List” provides you with a detailed profile of your desired journal.
Machine Learning Journal Impact Factor | 2.940 (2020)
Machine Learning Journal is a global forum for research on computational learning techniques. Machine Learning Journal publishes research papers on a wide range of learning approaches that have been applied to a variety of learning issues. Machine Learning Journal is a peer-reviewed scientific journal. It has been publishing since 1986.
Papers in the Machine Learning Journal cover study on problems and techniques, applications research, and research methodological concerns. Either by actual investigations, theoretical analysis, or comparisons to psychological phenomena, papers making assertions regarding learning difficulties or approaches give substantial evidence.
Application papers demonstrate how to use learning approaches to tackle real-world issues. Research methodology articles help researchers better understand how to perform machine learning research.
Besides the Machine Learning Journal impact factor, let’s look at some important information about Machine Learning Journal.
- Editor-in-Chief: Hendrik Blockeel
- Publishing model: Hybrid (Transformative Journal)
- Latest Issue: Volume 110, Issue 9, September 2021
- Publisher: Kluwer/Springer (USA)
- ISSN: 1573-0565
- Fee: Authors must pay an article processing fee to publish open access in Machine Learning. The article processing fee is $2780.00.
- Ethics: The journal is dedicated to publishing information with the greatest level of integrity.
- Plagiarism: To filter contributions, the journal may utilize plagiarism detection tools. If plagiarism is discovered, the COPE plagiarism standards will be followed.
- Peer Review: Single-blind
You can also find machine learning journal details profile from Web of Science Master Journal List.
Machine Learning with Applications Impact Factor
Machine Learning with Applications is a peer-reviewed, open-access journal. Data mining, computer vision, natural language processing (NLP), intelligent systems, neural networks, AI-based artificial intelligence vs software engineering, bioinformatics, and its applications in engineering, medicine, biology, education, business, and social sciences are all covered by the journal.
Like all other journals, “Machine learning with applications impact factor” depends on a significant number of papers and citations.
Machine Learning with Applications journal ensures that :
- Application papers should show how machine learning may be utilized to tackle real-world issues.
- Papers on research methods should show how current machine learning research may be improved.
- Both normal articles and technical notes are accepted by MLWA (technical notes are limited to a maximum of 10 pages)
Let’s take a look at some important points about the journal Machine learning with applications. Besides Machine learning with applications impact factor, this information is also important to know.
- Latest issue: Volume 6, In progress (15 December 2021)
- ISSN: 2666-8270
- Editor-in-Chief: Prof. Dr. Binshan Lin, PhD
- Review Process: Peer review (Single blind)
- Abstracting and indexing: Directory of Open Access Journals (DOAJ)
- Fees: No subscription charges. This journal’s article publishing fee is USD 2200, without taxes.
When it comes to telling about Machine learning with applications impact factors, this journal hasn’t published a report in a while. Machine learning with applications: ISSN number is 2666-8270 where all information available.
However, Machine learning with applications impact factor reports can be easily calculated by the above formula. If you know how to calculate the impact factor, you can easily calculate that report. Also, the “Web of Science Master Journal List” provides you a detailed profile of your desired journal.
Next let’s discuss nature’s machine intelligence impact factor.
Nature Machine Intelligence Impact Factor | 16.65 (2020)
Nature Machine Intelligence is a groundbreaking scientific publication focused on machine learning and artificial intelligence (with optional open access) and greatly contributed to nature machine intelligence impact factor . It was developed by Nature Research in reaction to the 2010s machine learning boom.
Artificial Intelligence (Q1), Computer Networks and Communications (Q1), Computer Vision and Pattern Recognition (Q1), Human-Computer Interaction (Q1), and Software (Q1),human-robot interaction, computer vision, natural language processing, genetic and evolutionary computing, neuro-inspired computing, are all topics covered in Nature Machine Intelligence (Q1) and greatly influenced the knowledge regarding nature machine intelligence impact factor.
Let’s mention some important points about the journal Nature Machine Intelligence. Besides knowing the Nature Machine Intelligence Impact Factor, the following information is also essential to know.
- Publication Type: Journal
- H-index: 16
- Overall Rank/Ranking: 302
- Publisher: Springer Nature Switzerland AG
- Country: Switzerland
- ISSN: 2522-5839 (online)
- Chief Editor: Liesbeth Venema
Nature Machine Intelligence Impact Factor score for 2020 is 16.65, which, according to its definition, is computed in 2021.
Nature Machine Intelligence Impact Factor score prediction for 2021: If the rising trend continues, the joule impact score as well as nature machine intelligence impact factor score may climb in 2021 as well.
You can also find the Nature Machine Intelligence journal details profile from “Web of Science Master Journal List”. Check it to learn more about nature machine intelligence impact factor.
Science Journal Impact Factor | 41.845 (2019)
Science Journal is a peer-reviewed academic journal of the American Association for the Advancement of Science and one of the world’s top academic journals. Science Journal was originally published in 1880 and now has a weekly circulation of about 130,000 subscribers.
Science Journal has worldwide authorship, and its papers routinely rank among the most referenced studies in the world. Science Journal strives to publish the most prominent articles in their areas, as well as studies that will considerably improve scientific understanding.
During the previous three years, 116524 publications have mentioned science (Preceding 2020). Science Journal impact factor (IF) for 2019 is 41.845, which is calculated in 2020 according to its criteria. When compared to the previous year 2018, Science Journal IF has grown by a factor of 0.81, with an estimated percentage change of 1.97 percent, indicating a growing trend.
The h-index of Science Journal is 1186. It implies that Science Journal’s 1186 articles have received more than 1186 citations.
Let’s mention some important points about the journal Science Journal. Besides the Science Journal impact factor, this information is also important to know.
- Publication Type: Journal
- Subject Area, Categories, Scope: History and Philosophy of Science (Q1); Multidisciplinary (Q1)
- H-index: 1186
- Overall Rank/Ranking: 49
- SCImago Journal Rank (SJR): 12.556
- Impact Score: 13.08
- Impact Factor: 41.845 (2019)
- Publisher: American Association for the Advancement of Science
- Country: United States
- ISSN / eISSN:1095-9203/ 0036-8075
- Publication fee: Color drawings will set you back $650 for the first color figure and $450 for each subsequent color figure. In addition, there is a charge for using color in reprints that is similar.
- Acceptance rate: Science currently accepts less than 7% of all original research papers submitted due to strong competition for space in the journal.
It’s critical to realize that the acceptance/rejection rate of manuscripts differs amongst journals. Some journals use all manuscript submissions as the basis for calculating acceptance rates. On the other hand, few people think about the only papers sent for peer review, and even fewer people think about keeping track of total submissions accurately. As a result, it can only offer an approximate estimate.
The easiest method to find out the acceptance rate is to contact the associated editor or go to the Journal/official Conference’s website.
You can also find the Science Journal details profile from “Web of Science Master Journal List”.
In the end, I just want to say that the Journal Impact Factor has unquestionably become one of the most widely used and controversial statistics accessible.
If you are interested in the journal of Machine Learning: Science and technology impact factor, then it would be a good choice undoubtedly. Though, recently machine learning science and technology impact factor has not been published. Using the impact factor formula, you can calculate the machine learning science and technology impact factors. However, before publishing your next research paper, do your homework and investigate journals and their impact factors thoroughly.
Frequently Asked Questions (FAQ)
1. What is a good impact factor?
In most disciplines, a score of 10 or above is regarded as exceptional, while a score of 3 is considered good, and the average score is less than 1. This is a general rule. The wild card to keep in mind is that impact factor and journal comparison are most useful in the same subject.
2. What does the impact factor mean?
The impact factor (IF) is a measurement of how many times an average article in a journal was referenced in a given year. It is used to determine a journal’s importance or rank by counting the number of times its articles are referenced.
3. What are high-impact factor journals?
The following are only a few of the top impact factor journals:
- CA-A Cancer Journal For Clinicians
- New England Journal of Medicine
- Nature Review Drug Discovery
- Chemical Review
- Nature Reviews Molecular Cell Biology
- Nature Biotechnology
- Nature Material
- Science
4. What are the TOP journals for Machine Learning?
Some TOP Computer Science Journals for Machine Learning, Data Mining & Artificial Intelligence –
-
- IEEE Transactions on Pattern Analysis and Machine Intelligence
Impact score: 25.25
- Pattern Recognition
Impact score: 17.32
- IEEE Transactions on Neural Networks and Learning Systems
Impact score: 16.17
- Neurocomputing
Impact score: 15.18
- Information Sciences
Impact score: 14.36
