Communications for Statistical Applications and Methods is a vital academic journal dedicated to advancing the field of statistics, with a particular focus on practical applications and methodologies. Published by the Korean Statistical Society, this journal has become a significant resource for researchers, practitioners, and students engaged in statistical sciences and its diverse applications in various fields including finance and modeling. Operating without an Open Access format, the journal is accessible through institutional subscriptions, allowing a broad audience to benefit from its insights. The journal covers works from its inception in 2017 to 2024, and although it currently ranks in the Q4 and Q3 quartiles across various mathematical and statistical categories, its commitment to quality research makes it a noteworthy platform for emerging trends and innovations. The journal not only serves to disseminate knowledge but also fosters collaboration among statisticians, ensuring that crucial advancements in statistical applications are communicated effectively.

EXPERT SYSTEMS WITH APPLICATIONS is a premier journal published by PERGAMON-ELSEVIER SCIENCE LTD, dedicated to showcasing cutting-edge research in the fields of Artificial Intelligence, Computer Science Applications, and Engineering. With an esteemed Q1 ranking across multiple categories, this journal not only reflects high-quality scholarship but also plays a pivotal role in advancing the application of expert systems and intelligent technologies in various sectors. Operating from the United Kingdom, it features a wide range of insightful articles that address complex challenges and offer innovative solutions through interdisciplinary approaches. Researchers and practitioners can access invaluable resources to inform their work, contributing to the journal's strong reputation and growing readership. With its convergence of relevant technologies from 1990 to 2025, EXPERT SYSTEMS WITH APPLICATIONS stands as an essential source for those aiming to push the boundaries of knowledge in these thriving fields, making it a key platform for both established experts and emerging scholars.

Big Data Mining and Analytics, published by TSINGHUA UNIVERSITY PRESS, stands at the forefront of interdisciplinary research in the fields of Artificial Intelligence, Computer Networks and Communications, Computer Science Applications, and Information Systems. With an impressive Q1 ranking in multiple categories as of 2023, this journal serves as a critical platform for researchers and professionals eager to explore innovative techniques and methodologies related to big data analytics. Since its transition to Open Access in 2018, Big Data Mining and Analytics has aimed to increase the visibility and accessibility of its cutting-edge research, making permanent strides in the global academic landscape. Housed in Beijing, China, and actively embracing the converged years from 2018 to 2024, the journal aims to cultivate a rich discourse on emerging trends and applications, ensuring its relevance in a rapidly evolving technological environment. Join a vibrant community of scholars dedicated to advancing the frontiers of knowledge in big data.

Journal of Big Data, published by SPRINGERNATURE, is a leading academic journal dedicated to advancing the understanding and application of big data technologies and methodologies across various domains. Since its inception in 2014, this Open Access journal has gained recognition for its rigorous peer-reviewed research, boasting impressive rankings in multiple categories in Scopus, including Q1 in Computer Networks and Communications and Q1 in Information Systems. With its impactful contributions, the journal is positioned at the forefront of scholarly work on big data, addressing critical topics such as data analytics, storage, visualization, and applied data science. Its global reach and commitment to disseminating knowledge ensure that researchers, professionals, and students have equitable access to groundbreaking findings that empower advancements in technology and information systems. The Journal of Big Data continues to foster collaboration and inspire new research avenues leading to tangible impacts in the field.

ENVIRONMENTAL AND ECOLOGICAL STATISTICS, published by SPRINGER, stands as a premier journal dedicated to advancing the fields of environmental science and statistical methodologies. With an ISSN of 1352-8505 and an E-ISSN of 1573-3009, this journal has continually provided a platform for innovative research and interdisciplinary studies since its inception in 1994. Operating from the Netherlands, it enjoys a significant impact within the academic community, reflected in its impressive Q2 rankings across various categories including Environmental Science and Statistics. The journal maintains a strong focus on the application of statistical techniques to ecological and environmental problems, fostering an environment for discourse that is both robust and insightful. Although it does not currently offer open access, the depth and quality of research published within its pages position it as a vital resource for researchers, professionals, and students alike, eager to understand and address the complexities of environmental data analysis up to the year 2024.

Statistical Analysis and Data Mining is a leading journal published by WILEY, dedicated to exploring the latest advancements in statistical methods and data mining techniques. With an ISSN of 1932-1864 and an E-ISSN of 1932-1872, this journal serves as a significant platform for researchers and professionals in statistical analysis, computer science applications, and information systems. Covering a wide range of topics from innovative analytical methodologies to emerging data mining algorithms, the journal aims to disseminate high-quality research that contributes to the evolving landscape of data science. Ranked in the Q2 category for the fields of Analysis, Computer Science Applications, and Information Systems in 2023, it emphasizes its relevance and impact within academia. While it offers limited Open Access options, the insights shared in this publication are integral for those wishing to stay ahead in fast-paced research and data-driven industries. Since its inception in 2008 and continuing through 2024, Statistical Analysis and Data Mining invites submissions that reflect rigorous empirical research coupled with practical implications, making it a vital resource for the academic community.

Japanese Journal of Statistics and Data Science, published by SPRINGERNATURE, is a leading academic journal dedicated to the advancement of statistical methodologies and data science applications, with a focus on fostering innovative research and discourse within the field. Since its inception in 2018, the journal has sought to bridge theory and practice, embracing emerging trends and interdisciplinary approaches that contribute to the ever-evolving landscape of statistics, probability, and computational theory. Hailing from Germany, the journal holds an impressive Q3 ranking in both Computational Theory and Mathematics and Statistics and Probability, reflecting its commitment to high-quality, impactful research. With an accessible ISSN of 2520-8756 and E-ISSN 2520-8764, the journal invites a global audience of researchers, professionals, and students to explore its rich array of articles and findings, all aimed at furthering knowledge and application in the realm of data science.

Journal of Algorithms & Computational Technology, published by SAGE PUBLICATIONS LTD, serves as a noteworthy platform for scholars and practitioners in the realms of applied mathematics, computational mathematics, and numerical analysis. With an ISSN of 1748-3018 and an E-ISSN of 1748-3026, this Open Access journal has been disseminating high-quality research since 2007, ensuring that significant advancements in algorithmic techniques and computational methodologies are readily accessible to the global academic community. Based in the United Kingdom, this journal has steadily established itself within specialized quartiles, notably achieving Q3 ranking in Computational Mathematics and Q4 in both Applied Mathematics and Numerical Analysis for 2023, reflecting its growing influence in these fields. As the journal converges from 2011 to 2024, it aims to cater to the needs of researchers, professionals, and students by publishing innovative research that not only addresses theoretical frameworks but also provides practical applications. By leveraging its open-access model, the Journal of Algorithms & Computational Technology fosters a collaborative environment where knowledge can flourish, allowing for the continuous evolution of algorithms that drive technological advancement.

JOURNAL OF CLASSIFICATION, published by Springer, stands as a premier academic platform dedicated to the advancement of classification methodologies across various disciplines. With an ISSN of 0176-4268 and E-ISSN of 1432-1343, this esteemed journal has been pivotal since its inception in 1984, showcasing influential research that continues to shape the fields of Library and Information Sciences, Mathematics, Psychology, and Statistics. As reflected in its 2023 quartile rankings—Q2 in Library and Information Sciences, Mathematics (miscellaneous), and Psychology (miscellaneous), as well as Q3 in Statistics, Probability and Uncertainty—the journal is recognized for its high standard of scholarly contributions. Notably, its Scopus rankings highlight its significant impact, particularly in Mathematics (ranked #12/90) and Psychology (ranked #21/97), placing it among the elite publications in these fields. Although not an open access journal, it provides invaluable insights for researchers, professionals, and students seeking to deepen their understanding of classification theory and its practical applications. With convergence expected through 2024, the journal is well-positioned to further contribute to interdisciplinary discussions and innovations, solidifying its importance within the academic community.

COMPUTATIONAL STATISTICS, published by Springer Heidelberg, is a prominent international journal that bridges the fields of computational mathematics and statistical analysis. Since its inception in 1996, this journal has served as a critical platform for disseminating high-quality research and advancements in statistical methodologies and computational techniques. Operating under Germany's esteemed scholarly tradition, it holds a commendable Q2 ranking in key categories such as Computational Mathematics and Statistics and Probability, reflecting its significant impact and relevance in the academic community. Although it does not offer Open Access, the journal remains a vital resource for researchers, professionals, and students seeking to enhance their understanding of the intricate interplay between computation and statistical inference. Each issue features rigorously peer-reviewed articles that contribute to the development of innovative methodologies and applications, thereby solidifying its role in shaping the future of computational statistics.