Next-Generation Machine Learning and Deep Learning Models for Complex Data, Vision, and Intelligent Applications
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 April 2026 | Viewed by 64
Special Issue Editor
Special Issue Information
Dear Colleagues,
The research community has shown a burgeoning interest in applying machine learning (ML) and deep learning (DL) methodologies to solve complex real-world problems across diverse fields. These advancements in machine learning continuously present new challenges and innovative solutions for a variety of intricate issues in applications, technologies, and theoretical constructs. Deep learning, a critical subset of machine learning, focuses on learning hierarchical representations of input data via multiple non-linear layers. In recent years, DL techniques have seen widespread application, achieving remarkable success across various domains.
In this Special Issue, we invite researchers and practitioners in the fields of machine learning and deep learning to disseminate their original and innovative ideas. We welcome submissions that include both theoretical advancements and practical applications aimed at solving complex data-related challenges using ML and DL algorithms. Topics of interest for this collection include, but are not limited to, the following:
- Advanced machine learning models and deep learning architectures (e.g., CNN, RNN, LSTM, GNN, Transfer Learning, Attention, and GCN);
- Novel methods for feature extraction and selection;
- Image analysis techniques (segmentation, classification, retrieval, and generation);
- Human action and gesture recognition;
- Development of intelligent and user-friendly interfaces;
- Handwriting analysis and recognition;
- Applications in healthcare and medical image analysis;
- Bioinformatics and computer vision;
- Explainable artificial intelligence (XAI).
Dr. Yoichi Tomioka
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning (ML)
- deep learning (DL)
- computer vision
- image segmentation
- feature extraction
- explainable AI (XAI)
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