Machine Learning: Innovation, Implementation, and Impact

Dear Colleagues,

Machine learning (ML) has become one of the most transformative forces across science, technology, and society. From predictive analytics and autonomous systems to personalized medicine and intelligent decision-support tools, ML drives innovation at an unprecedented pace. However, rapid progress also raises critical questions: How can innovative ML models be reliably implemented in real-world systems? What methodologies ensure fairness, interpretability, and sustainability? And what is the long-term impact of these technologies on industries, research, and society?

This Special Issue aims to provide a multidisciplinary platform for researchers, engineers, and practitioners to present original contributions, case studies, and critical reviews addressing both theoretical advances and practical applications of machine learning. By bridging innovation, implementation, and impact, this issue seeks to illuminate the path toward responsible, effective, and future-ready ML solutions.

This Special Issue welcomes contributions covering, but not limited to, the following areas:

1. Innovations in Machine Learning

• Novel algorithms, architectures, and training methods;
• Advances in deep learning, reinforcement learning, and transfer learning;
• Zero-shot learning, few-shot learning, continual learning, and life-long learning;
• Hybrid and interdisciplinary approaches (e.g., physics-informed ML, neuro-symbolic systems).

2. Implementation and Deployment

• Scalable and efficient ML systems for real-world environments;
• Edge and federated learning for distributed applications;
• ML model lifecycle management: deployment, monitoring, and updating;
• Robustness, reliability, and explainability in implementation.

3. Impact and Applications

• Domain-specific case studies in healthcare, finance, transportation, manufacturing, energy, and education;
• Societal and ethical implications: fairness, transparency, and accountability;
• ML for sustainable development and climate change mitigation;
• Policy, governance, and standardization issues in the adoption of machine learning.

Dr. Lei Zhou
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• machine learning
• deep learning
• zero-shot learning
• few-shot learning
• continual learning
• life-long learning
• artificial intelligence
• implementation
• explainability
• ethics
• big data analytics
• edge computing
• real-world