Automation and Machine Learning (AML)
Automation and Machine Learning (AML) is a journal dedicated to promoting and accelerating the dissemination of new research findings. There is a vast array of exciting research activities in this field worldwide. The journal aims to provide academicians and scientists around the globe with a platform to share, promote, and discuss various emerging issues and developments in different areas of automation and machine learning.
Aims & Scope
Automation and Machine Learning (AML) is a journal dedicated to promoting and accelerating the dissemination of new research findings. There is a vast array of exciting research activities in this field worldwide. The journal aims to provide academicians and scientists around the globe with a platform to share, promote, and discuss various emerging issues and developments in different areas of automation and machine learning.
Aims
- To promote and accelerate the dissemination of new research findings across the fields of automation and machine learning globally.
- To provide academicians and scientists worldwide with a platform to share, promote, and discuss emerging issues and developments in all areas of automation and machine learning.
- To foster academic exchange, collaboration, and innovation in interdisciplinary research at the intersection of automation systems and intelligent machine learning technologies.
- To advance knowledge and practical solutions for intelligent automation, machine learning applications, and related technologies through rigorous scholarly publication.
Scope
The journal covers a wide range of topics related to automation and machine learning, including but not limited to:
Automation Systems and Control Engineering
- Industrial automation and control systems, including process control, PLC programming, and SCADA systems
- Robotics and autonomous systems, including robot control, motion planning, and industrial robotics
- Intelligent control and optimization, including adaptive control, fuzzy control, and model predictive control
- Automation in manufacturing and smart factories, including Industry 4.0, digital twins, and automated production lines
Machine Learning and Intelligent Applications
- Machine learning algorithms and models, including supervised/unsupervised learning, deep learning, and reinforcement learning
- Machine learning for automation, including predictive maintenance, fault detection, and quality control
- Computer vision and pattern recognition, including image/video analysis, object detection, and visual inspection
- Natural language processing and intelligent systems, including text mining, chatbots, and decision support systems