journal of Machine Intelligence and Applications

The Journal of Machine Intelligence and Applications is a peer-reviewed, open-access international journal dedicated to publishing original research that bridges the gap between theoretical machine intelligence and real-world implementation. The journal seeks to advance the state-of-the-art in algorithms, architectures, and systems that exhibit intelligent behaviour, with a specific emphasis on deployable solutions across diverse sectors.
The journal covers a broad spectrum of topics at the intersection of computer science, engineering, and applied sciences. Submissions are encouraged in, but not limited to, the following areas:
Foundational Machine Intelligence Research in this area should focus on novel algorithms and theoretical advancements that push the boundaries of what machines can learn. Key topics include deep learning architectures, reinforcement learning strategies, unsupervised and self-supervised learning paradigms, and neuro-symbolic AI. We particularly welcome papers that offer new insights into model interpretability, robustness, and efficiency. 
Applied Machine Learning and Industry 4.0 This section highlights the transformation of industries through intelligent automation. We seek case studies and technical papers on smart manufacturing, predictive maintenance, autonomous robotics, and supply chain optimization. Contributions should detail the integration of AI into existing workflows, quantifying improvements in productivity, safety, and resource management.