Guide for reviewers

Call for Reviewers: Invitation to Join the Peer-Review Process for Optimizations in Applied Machine Learning:

We are pleased to announce the opening of the peer-review process for all submitted papers to our journal, Optimizations in Applied Machine Learning. As part of our ongoing commitment to maintaining the highest academic standards, we are now seeking qualified individuals to serve as peer reviewers.

If you are interested in contributing to the academic rigor of our journal by serving as a reviewer, we invite you to submit your application. Please send your current CV, highlighting relevant academic qualifications and research experience, to us at oaml@mri-pub.com. We look forward to collaborating with experts in the field to advance knowledge and innovation in applied machine learning.

Reviewer Qualifications:

To ensure the quality and relevance of our peer-review process, we are seeking individuals who meet the following criteria:

• PhD or equivalent academic qualifications in a relevant field.
• Active researchers with a demonstrated track record in the areas related to applied machine learning, including but not limited to machine learning algorithms, optimization techniques, AI-driven systems, and data analytics.
• Publication record: At least one recent peer-reviewed publication in a reputable journal or conference related to the field of applied machine learning or a closely associated discipline.

Reviewer Benefits:

As a reviewer, you will gain several professional benefits, including but not limited to:

• Knowledge Enrichment: Stay at the forefront of cutting-edge research in applied machine learning by reviewing high-quality submissions.
• Experience and Exposure: Expand your expertise and network within the academic community. Reviewers gain insights into new developments in the field, often ahead of general publication.
• Academic Reputation: Serve as a trusted expert in your field and contribute to building your professional reputation within the global research community.
• APC Discounts: After providing valuable review comments, you will be eligible for a discount on article processing charges (APC) for your own publications in Optimizations in Applied Machine Learning.
• Editorial Board Consideration: Outstanding reviewers may be invited to join the journal’s editorial board as part of our continued commitment to academic excellence.

Reviewer Responsibilities:

As a reviewer for Optimizations in Applied Machine Learning, we ask that you adhere to the following responsibilities:

• Confidentiality: Manuscripts submitted for review must be treated as confidential documents. The contents must not be disclosed or discussed with third parties without prior consent from the editorial team.
• Constructive Feedback: Provide detailed, constructive feedback aimed at improving the quality of the manuscript. Simple accept/reject decisions without explanation or justification will not be sufficient. Clear, actionable suggestions for authors are essential.
• Timely Responses: Please complete your review within two to three weeks of receiving the manuscript. If you are unable to meet this timeframe, kindly inform us as early as possible.
• Ethical Integrity: Do not use or replicate any data obtained during the review process for your own research or personal benefit.

If you have any questions regarding reviewer registration, the peer-review process, or the journal’s submission guidelines, please feel free to contact us at oaml@mri-pub.com.

We look forward to your valuable contributions to ensuring the continued success of Optimizations in Applied Machine Learning.