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About the Journal
The Sage Science Review of Applied Machine Learning is a peer-reviewed academic journal that publishes original research articles, reviews, and case studies in the field of applied machine learning. The journal aims to provide a platform for researchers, practitioners, and scholars to share their innovative research and applications in the field of machine learning.
The journal covers a broad range of topics related to applied machine learning, including machine learning algorithms and models, deep learning and neural networks, natural language processing, computer vision, and image processing, data mining and knowledge discovery, statistical learning, reinforcement learning, transfer learning, explainable and interpretable machine learning, and machine learning for healthcare, finance, social media, and other applications.
Current Issue
Vol. 7 No. 1 (2024): Sage Science Review of Applied Machine Learning-2024-1
Published:
2024-01-05
Articles
Call for Papers
The Sage Science Review of Applied Machine Learning
The Sage Science Review of Applied Machine Learning invites submissions for our 2024 edition, welcoming pioneering research articles, reviews, and case studies across all facets of applied machine learning.
We invite contributions on topics including, but not limited to:
- Machine learning algorithms and models
- Deep learning and neural networks
- Natural language processing
- Computer vision and image processing
- Data mining and knowledge discovery
- Statistical learning
- Reinforcement learning
- Transfer learning
- Explainable and interpretable machine learning
- Machine learning applications in healthcare, finance, social media, and more
Topics for Contributions
| Topic |
|---|
| Machine learning algorithms and models |
| Deep learning and neural networks |
| Natural language processing |
| Computer vision and image processing |
| Data mining and knowledge discovery |
| Statistical learning |
| Reinforcement learning |
| Transfer learning |
| Explainable and interpretable machine learning |
| Machine learning applications in healthcare, finance, social media, and more |
We seek submissions from both academia and industry, encouraging interdisciplinary research that integrates machine learning with domains like engineering, biology, psychology, and economics. All submissions will undergo a rigorous peer-review process, and accepted papers will be published online and indexed in leading scientific databases.
Submission Deadline: December 30, 2024. Visit our website for detailed submission guidelines. We look forward to your contributions as we drive forward the field of applied machine learning.
Aim and Scope:
Our mission at the Sage Science Review of Applied Machine Learning is to offer a dedicated platform for researchers and practitioners to share transformative advancements in machine learning. The journal prioritizes original, high-quality research, reviews, and case studies that significantly contribute to applied machine learning.
Our scope encompasses diverse applied machine learning topics, including:
- Machine learning algorithms and models
- Deep learning and neural networks
- Natural language processing
- Computer vision and image processing
- Data mining and knowledge discovery
- Statistical learning
- Reinforcement learning
- Transfer learning
- Explainable and interpretable machine learning
- Applications across healthcare, finance, social media
Peer Review Policy
Publishing timeline
4 days
Time to first decision ⓘ
40 days
Review time ⓘ
62 days
Submission to acceptance ⓘ
3 days
Acceptance to publication ⓘ
The Sage Science Review of Applied Machine Learning is committed to publishing high-quality and original research articles, reviews, and case studies in the field of applied machine learning. To ensure the quality of the articles published in our journal, we operate a rigorous peer-review process.
All submissions to the journal undergo a double-blind peer-review process. Two independent reviewers are assigned to each submission, and their comments and feedback are provided to the authors to help them improve their manuscript. The reviewers are selected based on their expertise in the topic area and their relevant research experience.
We aim to provide a timely and constructive review process. We expect reviewers to provide unbiased, constructive, and helpful feedback to authors. We also ask authors to respond to reviewers' comments in a timely manner and make revisions to their manuscripts accordingly.
Peer-Review Process
All submissions undergo a double-blind peer-review process.
Two independent reviewers are assigned to each submission.
Reviewers provide comments and feedback to help authors improve their manuscript.
Reviewers are selected based on their expertise in the topic area and research experience.
We adhere to the highest standards of peer review ethics, including confidentiality, objectivity, and integrity. We do not disclose the identity of reviewers to authors, and we do not disclose the identity of authors to reviewers. In cases of potential conflicts of interest, we follow a transparent policy of disclosure and, if necessary, we seek additional reviewers. Our peer-review process is critical to maintaining the quality and reputation of our journal. We are committed to providing authors with fair and constructive feedback, and to upholding the highest standards of academic publishing.
Ethical Standards in Peer Review
Adherence to the highest standards of confidentiality, objectivity, and integrity.
Reviewer identities are not disclosed to authors.
Author identities are not disclosed to reviewers.
In cases of conflicts of interest, a transparent disclosure policy is followed, and additional reviewers are assigned if needed.
