Deep Learning Models and Their Applications

Dear Colleagues,

In recent years, deep learning has emerged as one of the most transformative technologies in artificial intelligence, driving advancements across a wide range of domains. This Special Issue aims to highlight recent innovations, theoretical advancements, and practical applications of deep learning models, offering a platform for researchers and practitioners to share their latest findings and insights.

We welcome high-quality, original contributions that explore the development, optimization, and deployment of deep neural networks in various fields. Topics of interest include, but are not limited to, the following areas:

• Novel deep learning architectures (e.g., CNNs, RNNs, Transformers, GNNs);
• Efficient training techniques, model compression, and knowledge distillation;
• Transfer learning, self-supervised learning, and continual learning;
• Explainability and interpretability of deep learning models;
• Multimodal and hybrid deep learning systems (e.g., CLIP, Flamingo, BEiT-3);
• Edge and federated deep learning for real-time or distributed environments;
• Retrieval-augmented generation (RAG).

We are particularly interested in papers demonstrating deep learning applications across a wide spectrum of domains, such as

• Computer vision (e.g., image classification, object detection, medical imaging);
• Natural language processing (e.g., sentiment analysis, machine translation, question answering);
• Speech and audio processing;
• Time-series forecasting and anomaly detection;
• Smart healthcare, IoT, robotics, and smart cities;
• Financial prediction, cybersecurity, and industrial automation.

This Special Issue welcomes submissions that bridge theoretical advances with real-world implementations, including case studies and system-level evaluations. Interdisciplinary studies, applications using large-scale datasets, and efforts at benchmarking with state-of-the-art methods are also welcome.

Each submission will undergo rigorous peer review to ensure technical quality, originality, and relevance to the Special Issue theme. The aim is to provide a comprehensive and up-to-date overview of deep learning techniques that are shaping modern intelligent systems.

We invite researchers, engineers, and industry professionals to contribute to this timely and impactful collection and help to advance the state of the art in deep learning applications.

Dr. Chinchih Chang
Dr. Chien-Chang Chen
Dr. Sean Hsiao
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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.

• deep learning
• neural networks
• artificial intelligence applications
• deep learning model
• computer vision
• NLP