Sage Science Review of Applied Machine Learning

Call for Papers

The Sage Science Review of Applied Machine Learning is pleased to invite submissions for publication in 2023. We welcome original research articles, reviews, and case studies in all areas of applied machine learning.

We encourage submissions on, but not limited to, the following topics:

• 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 for healthcare, finance, social media, and other applications

We welcome submissions from both academia and industry and encourage interdisciplinary research that combines machine learning with other fields such as engineering, biology, psychology, and economics. All submissions will be rigorously peer-reviewed, and accepted papers will be published online and indexed in major scientific databases.

Submission guidelines and instructions can be found on our website. The deadline for submissions is September 30, 2023. We look forward to receiving your contributions and to continuing to promote the advancement of applied machine learning.

Aim and Scope:

The aim of the Sage Science Review of Applied Machine Learning is to provide a platform for researchers, practitioners, and scholars to share their innovative research and applications in the field of machine learning. The journal seeks to promote the advancement of applied machine learning by publishing high-quality and original research papers, reviews, and case studies.

The scope of the journal includes a broad range of topics related to applied machine learning, 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 for healthcare, finance, social media, and other applications

The journal welcomes submissions from both academia and industry and encourages interdisciplinary research that combines machine learning with other fields such as engineering, biology, psychology, and economics. The Sage Science Review of Applied Machine Learning aims to contribute to the advancement of applied machine learning and its impact on society.

Peer Review Policy

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.

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.