Acadlore Transactions on AI and Machine Learning (ATAIML) (ISSN 2957-9562) is an open access journal of computer science, artificial intelligence, machine and deep learning, graph neural networks, synthetic data, and other related fields, theory, methods, as well as interdisciplinary applications, algorithms, data and implementations on different platforms. ATAIML offers an advanced meeting place for studies related to AI and machine learning topics and their applications. We welcome original submissions in various forms, including reviews, regular research papers, and short communications as well as Special Issues on particular topics. The journal will have a special focus on the relation between AI and extended reality, to synthetic data, and to graph neural networks.
The aim of ATAIML is to encourage scientists to publish their concepts, theoretical and experimental results and coding as much detailed as possible. Therefore, the journal has no restrictions regarding the length of papers. Full details should be provided so that the results can be reproduced. In addition, the journal has the following features:
- Manuscripts regarding new and innovative research proposals and ideas are particularly welcome.
- Young scientists will find a forum to exchange ideas.
- Electronic files or software regarding the full details of the calculation and experimental procedure as well as source codes can be submitted as supplementary material.
The scope of the journal covers, but is not limited to the following topics:
- AI and sensorics
- AI and IoT
- AI and mixed reality
- AI and smart food, agriculture and forest
- AI and design, fashion and arts
- AI and psychology
- Ethical and law issues of AI
- Graph neural networks
- Machine Learning in biology, chemistry, physics
- Advanced sequential neural networks
- Bayesian learning
- Statistical and topological methods in machine learning
- AI and multimedia data
- Green AI and AI for a green world
- Data-centric AI and synthetic data
- AI and knowledge graphs
- AI and geoinformatics
- AI and ML use cases and applications
- Mathematical methods of deep learning and graph neural networks
- AI foundational standards
- Computational approaches and computational characteristics of AI systems
- Emerging AI technologies