Journal of Informetrics - Elsevier - Impact Factor

Journal of Informetrics - Elsevier | 2024 Impact Factor:3.5 | Cite Score: 7.0 | Q1

Impact Factor and Journal Rank of Journal of Informetrics

About: The International Journal of Machine Learning and Cybernetics (IJMLC) serves as a broad forum for rapid dissemination of the latest advancements at the intersection of machine learning and cybernetics. It focuses on the key research problems emerging from these fields and provides a platform for researchers, engineers, and practitioners to publish innovative research and applications.
Objective: The primary objective of the journal is to advance the understanding and application of machine learning and cybernetics. It aims to explore and disseminate innovative research that discovers fundamental functional relationships and describes complex interactions and interrelationships between systems. By fostering collaboration and knowledge exchange, the journal contributes to solving complex problems across various domains.
Interdisciplinary Focus: IJMLC emphasizes the intersection of machine learning and cybernetics, exploring how these disciplines can collectively address complex challenges in various domains. It provides insights into discovering fundamental functional relationships and describing complex interactions and interrelationships between systems. By integrating advancements from both fields, the journal facilitates interdisciplinary research and applications.
Global Reach and Impact: With a global readership and contributions from leading researchers and practitioners worldwide, IJMLC significantly impacts the fields of machine learning and cybernetics. It publishes innovative research that advances the understanding and application of these technologies across international boundaries. The journal contributions are instrumental in driving advancements and solutions to complex problems in diverse domains.
High Standards and Rigorous Review: IJMLC upholds high academic standards through a rigorous peer-review process. Each manuscript undergoes comprehensive evaluation by experts in the field to ensure methodological rigor, scientific accuracy, and originality of contributions. This rigorous review process ensures that only high-quality and impactful research is published, maintaining the integrity and credibility of the journal.
Significance: IJMLC plays a crucial role in advancing machine learning and cybernetics by providing a platform for researchers, engineers, and practitioners to publish innovative research and applications. By fostering collaboration and knowledge exchange, the journal facilitates the development and application of these technologies in solving complex problems across various domains. Researchers and practitioners rely on IJMLC to explore new frontiers and push the boundaries of machine learning and cybernetics.

Journal Home: Journal Homepage

Editor-in-Chief: Mu-Hsuan Huang

Scope: The Journal of Informetrics (JOI) publishes rigorous, high-quality research on the quantitative aspects of information science. The main focus of the journal is on topics in bibliometrics, scientometrics, webometrics, patentometrics, altmetrics, and research evaluation. It serves as a premier forum for scholars and researchers interested in understanding and advancing quantitative methodologies in information science. Here is an overview of its key focus areas and scope:
1. Bibliometrics:
Quantitative analysis of bibliographic data.
Research on citation analysis, co-citation analysis, and bibliometric mapping.
2. Scientometrics:
Quantitative studies of science and scientific research.
Research on scientific productivity, collaboration patterns, and citation impact.
3. Webometrics:
Quantitative analysis of the World Wide Web and online information.
Research on web link analysis, web impact factors, and social network analysis of online platforms.
4. Patentometrics:
Quantitative analysis of patent data.
Research on patent citation networks, technological innovation, and patent impact assessment.
5. Altmetrics:
Alternative metrics for assessing research impact.
Research on social media metrics, usage statistics, and other non-traditional indicators of scholarly influence.
6. Research Evaluation:
Methods and metrics for evaluating research performance.
Research on assessment frameworks, performance metrics, and benchmarking in scholarly communication.