Journal of Artificial Intelligence and Machine Learning in Management

Scope:

JAIMM welcomes original research articles, reviews, case studies, and technical notes related to the following topics, as well as other aspects of AI and ML in management:

1. Artificial intelligence and machine learning applications in business management
2. Data mining and predictive modeling in management
3. Natural language processing and speech recognition in Management
4. Optimization and decision support systems for management
5. Robotics and automation in management
6. Deep Learning and neural networks in Management
7. Computer vision and image processing in management
8. Intelligent agents and multi-agent systems in management
9. Intelligent transportation systems and logistics management
10. Smart Cities and urban planning with AI and ML

Peer review policy of JAMM:

The Journal of Artificial Intelligence and Machine Learning in Management (JAMM) follows a rigorous peer review process to ensure the quality and validity of the articles published in the journal. The peer review policy of JAMM is outlined below:

1. Initial Review: All submitted manuscripts are screened by the Editor-in-Chief to ensure they meet the journal's scope and standards. Manuscripts that pass this initial screening are assigned to an Associate Editor.

2. Peer Review: The Associate Editor selects at least two independent reviewers with expertise in the relevant field to evaluate the manuscript. The reviewers assess the manuscript's originality, significance, methodology, validity, and overall quality.

3. Decision: Based on the reviewers' comments, the Associate Editor makes a recommendation to the Editor-in-Chief, who makes the final decision on whether to accept, reject, or request revisions to the manuscript. If revisions are required, the manuscript is sent back to the authors for revision and resubmission.

4. Author Notification: The corresponding author is notified of the decision, and if accepted, the manuscript is scheduled for publication in a future issue of the journal.

5. Reviewer Anonymity: JAMM follows a double-blind peer review policy, where the identities of both the reviewers and authors are kept confidential.