About the Journal
Aims & Scope
Artificial Intelligence and Machine Learning(AIML) is an international, peer-reviewed, open-access journal dedicated to publishing high-quality, original research articles, review papers, and case studies in the rapidly evolving fields of artificial intelligence (AI) and machine learning (ML). The journal aims to serve as a premier platform for researchers, academicians, and industry professionals to share cutting-edge research, innovative methodologies, and practical applications.
Scope of the Journal:
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Fundamental AI and ML Algorithms:
- Supervised, unsupervised, and semi-supervised learning
- Reinforcement learning and deep reinforcement learning
- Neural networks and deep learning
- Probabilistic models and statistical learning
- Optimization methods and techniques
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Applications of AI and ML:
- Natural language processing and understanding
- Computer vision and image processing
- Speech recognition and synthesis
- AI in healthcare and biomedical applications
- Autonomous systems and robotics
- AI for social good and public policy
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AI and ML Infrastructure and Tools:
- Scalable and distributed AI and ML systems
- High-performance computing for AI and ML
- Cloud-based AI and ML platforms
- Software frameworks and libraries for AI and ML
- Data engineering and data pipelines
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Ethics and Fairness in AI and ML:
- Ethical implications of AI and ML technologies
- Fairness, accountability, and transparency in AI and ML
- Bias detection and mitigation
- Privacy-preserving machine learning
- Regulatory and policy aspects of AI and ML
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Interdisciplinary AI and ML Research:
- AI and ML in cognitive science and neuroscience
- AI and ML in economics and finance
- AI and ML in environmental science
- AI and ML in social sciences and humanities
- Cross-disciplinary applications of AI and ML
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AI and ML in Industry:
- AI and ML in manufacturing and automation
- AI and ML in logistics and supply chain management
- AI and ML in marketing and customer service
- AI and ML in cybersecurity
- AI and ML in financial technology (FinTech)
Submission Types: The journal welcomes a variety of submission types, including but not limited to:
- Original research articles
- Comprehensive review papers
- Case studies and application reports
- Technical notes and short communications
By facilitating the dissemination of knowledge and fostering collaboration, the Artificial Intelligence and Machine Learning journal aims to contribute significantly to the advancement and practical implementation of AI and ML technologies.
Publication Frequency
The journal is published online continuously in annual general issues. Articles included in special issues are published at one time, and the issue is then closed to additional articles.
Peer Review Type
Double anonymized
Reviewer identity is not made visible to author, author identity is not made visible to reviewer, reviewer and author identity is visible to (decision-making) editor.
Open Access Type
Full Gold Open Access
The final version of an article is freely and permanently accessible for everyone, immediately after publication.
No subscription fees are charged for any part of a journal that is fully gold open access.
Archiving Policy
We focus on making content discoverable and accessible through indexing services. Content is also archived to ensure long-term availability. The journal is indexed by the following services:
Google Scholar, the journal is available for harvesting via OAI-PMH.
To ensure the permanency of the journal, we utilize the Portico archiving system to create permanent archives for the purposes of preservation and restoration.
If the journal is not indexed by your preferred service, please let us know by emailing: office@esdpub.com
Detailed information on policies and guidelines regarding our open access policy, copyrights and licenses, etc. can be viewed in our resource list.