About this Research Topic
Machine learning is currently a flourishing area of interest within the field of data processing and mining. Although machine learning has achieved some level of maturity in certain areas, the paradigm in data mining is undergoing constant change due to the continuous emergence of new algorithms (resulting in improvements in results and/or efficiency) and new approaches (e.g., ChatGTP4). The addition of perspectives from computer science to mathematical algorithms and approaches – including applications in support of the goals of the 2030 Agenda for Sustainable Development – bring new insights to the discipline. Furthermore, data mining has now become an multidisciplinary technology from which many different fields can benefit.
The goal of this Research Topic is to explore the latest trends and algorithms for application in multimodal data mining and processing, presenting solutions from these new machine learning techniques and algorithms, their application to different types of data, and their implementation and deployment within software applications. Submissions should offer a focus on aspects of foundations, theory, and proven properties, etc. The Research Topic will also welcome contributions that address the practical challenges to determining and optimizing algorithms to solve real-world problems – with data that might be incomplete, distorted or corrupted – across applications in such varied areas as engineering, industry, business, finance, and medicine.
Contributors are encouraged to submit research on any novel algorithm or application of machine learning, although particular topics of interest include:
- Recently-developed models
- Multimodal machine learning
- AI explainability
- AI-based cybersecurity
- Frugal machine learning
- AI for sustainability
- Embedded machine learning
- Transformers
- Language modelling
- Bias in machine learning
- Digital twins and the metaverse
- Domain-specific machine learning
Work on specific solutions within individual fields, including details on final implementation, will also be welcomed.
The goal of this Research Topic is to explore the latest trends and algorithms for application in multimodal data mining and processing, presenting solutions from these new machine learning techniques and algorithms, their application to different types of data, and their implementation and deployment within software applications. Submissions should offer a focus on aspects of foundations, theory, and proven properties, etc. The Research Topic will also welcome contributions that address the practical challenges to determining and optimizing algorithms to solve real-world problems – with data that might be incomplete, distorted or corrupted – across applications in such varied areas as engineering, industry, business, finance, and medicine.
Contributors are encouraged to submit research on any novel algorithm or application of machine learning, although particular topics of interest include:
- Recently-developed models
- Multimodal machine learning
- AI explainability
- AI-based cybersecurity
- Frugal machine learning
- AI for sustainability
- Embedded machine learning
- Transformers
- Language modelling
- Bias in machine learning
- Digital twins and the metaverse
- Domain-specific machine learning
Work on specific solutions within individual fields, including details on final implementation, will also be welcomed.
Keywords: multimodal machine learning, explainable AI, automated machine learning, embedded machine learning, language modeling, bias in machine learning, digital twins, metaverse, multi-task models
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
