Innovative Deep Transfer Learning Techniques and Their Use in Real-World Applications
A special issue of Mathematical and Computational Applications (ISSN 2297-8747).
Deadline for manuscript submissions: 15 June 2026
Special Issue Editors
Interests: machine learning; deep learning; pattern recognition; medical imaging; computer vision; bioinformatics; NLP
Interests: machine learning; deep learning; pattern recognition; medical imaging; computer vision; bioinformatics; NLP
Interests: corporate finance; corporate governance; quantitative finance; sustainable development
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the last couple of decades, the use of deep learning models has increased exponentially due to the fact of demonstrated improved predictive ability. This not only expands to research outcomes as quality academic publications but also for the direct benefit to the public by their use in real-world applications. Examples of real-world applications could include image recognition/classification, disease diagnosis, natural language processing, finance trends, etc. The proposed Special Issue focuses specifically on recent advancements in the use of deep transfer learning techniques and their application to real-world problems. Deep transfer learning models exploit the pre-trained models on a dataset to be fine-tuned to deal with other datasets, maybe of the same nature or in some cases entirely of a different nature, but with similar characterization in terms of the type of data. A major benefit of using these approaches is to demonstrate how previously trained models can be fine-tuned without requiring the training from scratch, thus reducing the time of training with improved predictive capability and increased generalizability. The objective is to bring leading scientists and researchers together and create an interdisciplinary platform of computational theories, methodologies, and techniques related to deep transfer learning and their applications to real-world problems.
In this Special Issue, original research articles, reviews and substantive applications related to deep transfer learning are welcome. Research areas may include (but are not limited to) the following:
- Novel Architectures of algorithms for deep transfer learning;
- Cross-domain and cross-model transfer learning;
- Few-shot, one-shot, and zero-shot learning via transfer learning;
- Detection of fake media, deepfakes, and misinformation using transfer learning;
- Transfer learning for cybersecurity and digital forensics;
- Transfer learning under data scarcity or imbalanced data conditions;
- Application in computer vision, NLP, healthcare, and finance.
We look forward to receiving your contributions.
Dr. Muhammad Asad Arshed
Prof. Dr. Atif Alvi
Prof. Dr. Ştefan Cristian Gherghina
Guest Editors
Manuscript Submission Information
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Keywords
- deep transfer learning
- pretrained models
- domain adaptation
- fake media detection
- few-shot learning
- real-world applications
- cross-domain learning
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