Data Science and Engineering is a premier open access journal published by SPRINGERNATURE, dedicated to advancing the fields of data science, artificial intelligence, computational mechanics, and information systems. Since its inception in 2016, this journal has rapidly established itself as a leader in the academic community, boasting an impressive Q1 ranking in multiple computer science categories, including Artificial Intelligence, Software, and Information Systems. With a commitment to disseminating high-quality research, it caters to a diverse audience of researchers, professionals, and students eager to explore the intersection of data and technology. The journal's robust global reach, combined with its respected reputation, empowers authors to share their findings widely, facilitating breakthroughs and innovations across the digital landscape. Join the vibrant community of scholars contributing to this integral field of study, and stay informed with the latest research by accessing the journal freely online.

International Journal of Machine Learning and Cybernetics, published by SPRINGER HEIDELBERG, serves as a pivotal platform for scholars and practitioners in the fields of Artificial Intelligence, Computer Vision, and Software Engineering. With its impressive Q2 and Q1 rankings in esteemed categories such as Artificial Intelligence and Computer Vision and Pattern Recognition, this journal boasts a commendable reputation within the academic community, further solidified by its Scopus rankings that place it in the top percentiles of its category. This journal covers critical advancements and research trends spanning the years from 2010 to 2024, facilitating knowledge exchange that drives innovation in machine learning and cybernetics. Although it operates under a subscription model, it remains accessible for extensive readership, comprising researchers, professionals, and students eager to advance their understanding in this dynamic and rapidly evolving discipline. Stay at the forefront of technology by engaging with the latest findings and methodologies published in this influential journal, identified by its ISSN 1868-8071 and E-ISSN 1868-808X.

SIAM Journal on Mathematics of Data Science is an esteemed publication within the fields of applied mathematics and data science, published by SIAM PUBLICATIONS. This journal serves as a vital platform for researchers and practitioners, dedicated to disseminating high-quality research that addresses complex mathematical problems arising in the context of data science. The journal aims to bridge the gap between rigorous mathematical theory and practical applications, fostering interdisciplinary collaboration among mathematicians, data scientists, and statisticians. With its commitment to excellence, the SIAM Journal on Mathematics of Data Science contributes significantly to advancing the understanding and development of mathematical methodologies that analyze and interpret large datasets effectively. Researchers and professionals will find it an invaluable resource with its comprehensive articles, insightful reviews, and original research papers, which represent the forefront of innovative mathematical approaches in the evolving landscape of data science. For those interested in contributing to this dynamic field, the journal provides an array of access options tailored to diverse audiences.

ACM Transactions on Intelligent Systems and Technology, published by the Association for Computing Machinery (ACM), is a leading peer-reviewed journal dedicated to the rapid dissemination of innovative research in the fields of Artificial Intelligence and Theoretical Computer Science. With an impressive Impact Factor and a strong H-Index, it boasts a premier standing in the academic community, being ranked Q1 in both AI and Theoretical Computer Science categories as of 2023. This journal serves as a critical platform for researchers, professionals, and students aiming to contribute to and stay informed about the latest developments in intelligent systems and technology. While there are currently no open-access options, readers can explore invaluable insights into current trends and methodologies from 2010 to 2024, making it an essential resource for anyone passionate about advancing their knowledge in these rapidly evolving domains. The journal continues to foster collaboration and innovation, reflecting the forefront of research where technology intersects with intelligent systems.

The Journal of Experimental & Theoretical Artificial Intelligence, published by Taylor & Francis Ltd, serves as a premier platform for researchers and professionals interested in advancing the fields of artificial intelligence, software engineering, and theoretical computer science. Established in 1989, this peer-reviewed journal features original research and comprehensive reviews that explore innovative methodologies and emerging trends within these dynamic domains. With an impact factor that reflects its relevance, the journal currently ranks in Q3 across its categories, affirming its contributions to the scholarly community. The journal's commitment to quality and rigor invites submissions that bridge experimental and theoretical frameworks, thus fostering a deeper understanding of AI's applications and implications. Researchers and students can access insightful articles that drive critical discussions and inspire future work, making this journal an essential resource for anyone looking to delve into the complexities of artificial intelligence and its surrounding disciplines.

The Journal of Advanced Computational Intelligence and Intelligent Informatics, published by FUJI TECHNOLOGY PRESS LTD, stands as a pivotal platform in the fields of Artificial Intelligence, Computer Vision, and Human-Computer Interaction. Established in 1997, this Open Access journal has been providing accessible insights into the latest advancements in computational intelligence and informatics since 2007. With its ISSN 1343-0130 and E-ISSN 1883-8014, this journal invites a diverse readership, including researchers, professionals, and students eager to explore innovative methodologies and applications. Despite its current Q4 ranking in the relevant categories, the journal remains committed to contributing valuable knowledge to the academic community and enhancing the global discourse in computational technologies. With its focus on fostering communication and collaboration among scholars, the journal plays an essential role in driving forward the understanding of intelligent systems and their applications in various domains.

Applied Computing Review is a prominent academic journal published by the Association for Computing Machinery (ACM), an esteemed organization known for advancing the computing profession. Focusing on the intersection of practical applications and theoretical foundations, this journal serves as a vital platform for disseminating research in the field of applied computing, facilitating knowledge sharing among researchers, professionals, and students alike. With an ISSN of 1559-6915, the journal encompasses a wide array of topics including software engineering, data analytics, and application development, addressing current trends and challenges in the industry. Although it does not offer open access, its rigorous peer-review process ensures high-quality publications that significantly contribute to the discipline. Positioned within a competitive landscape, Applied Computing Review is dedicated to fostering innovation and providing insightful perspectives that inspire further research, thereby solidifying its importance in the field of applied computing.

MACHINE LEARNING is a premier academic journal published by SPRINGER, dedicated to advancing the field of artificial intelligence and software engineering. Established in 1986, this journal has consistently showcased pioneering research and innovative methodologies that facilitate the understanding and application of machine learning techniques across various domains. With an impressive impact factor reflecting its high-quality publications, it is classified within the top quartile (Q1) in both Artificial Intelligence and Software categories as of 2023, underscoring its critical role in these fields. The journal boasts significant Scopus rankings, placing it at the 45th and 54th positions, respectively, in Computer Science - Software and Computer Science - Artificial Intelligence, further validating its stature as a vital resource for researchers, professionals, and students alike. While not an open-access journal, MACHINE LEARNING ensures that its comprehensive peer-reviewed articles are widely circulated, fostering academic dialogue and innovation. The journal continues to be a go-to platform for cutting-edge research, providing invaluable insights and contributions that drive the future of machine learning.

Quantum Machine Intelligence is a leading academic journal published by Springer Nature, focusing on the rapidly evolving intersection of quantum computing and artificial intelligence. With an impressive impact factor reflected in its prestigious ranking in various categories—Q1 in Applied Mathematics, Computational Theory and Mathematics, and Theoretical Computer Science, alongside Q2 in Artificial Intelligence and Software—this journal serves as a vital platform for disseminating innovative research from 2019 to 2024. Researchers, professionals, and students are encouraged to engage with the journal’s content, which features high-quality peer-reviewed articles that explore theoretical foundations and practical applications of quantum technologies in machine intelligence. Although the journal operates under traditional subscription models, it is committed to advancing open academic discourse and accessibility in the digital age. With Scopus rankings that place it among the top echelons of its fields, the journal is an essential resource for anyone interested in the transformative potential of quantum algorithms and AI.

Big Data Research, published by Elsevier, is a leading academic journal dedicated to the exploration and advancement of Big Data methodologies and technologies. With an ISSN of 2214-5796, and a commendable impact reflected in its Scopus rankings—ranking Q2 in Computer Science Applications and Q1 in Information Systems—this journal offers a prominent platform for researchers and practitioners to share innovative findings in the realm of data science, analytics, and management. Since its inception in 2014, Big Data Research has fostered a multidisciplinary approach, addressing cutting-edge topics crucial for both academic inquiry and real-world applications. The journal's objectives include advancing the understanding of data-intensive systems, promoting essential methodologies for big data analytics, and enhancing data-driven decision-making processes across various industries. As an open access journal, Big Data Research is committed to disseminating knowledge widely, allowing researchers, professionals, and students to stay at the forefront of developments in this fast-evolving field. Its influence in the academic community is further underscored by a strong commitment to quality and relevance, making it an essential resource for anyone interested in the transformative power of big data.