Advances in Machine Learning and Deep Learning: Innovations and Applications

Dear Colleagues,

We invite you to submit your latest research and reviews that explore innovative developments and practical applications to the Special Issue entitled "Advances in Machine Learning and Deep Learning: Innovations and Applications". This Special Issue aims to showcase cutting-edge research that not only advances the theoretical foundations of machine learning and deep learning but also demonstrates their applicability across various domains. This issue seeks to emphasize both the technical advancements and the transformative potential of these models in real-world applications. Machine learning and deep learning represent a core area of artificial intelligence that focuses on the development of systems that can learn from data, identify patterns, and make decisions. While machine learning relies on algorithms to parse data, learn from that data, and make informed decisions based on what it has learned, deep learning processes data through layers to make sense of it, which makes it capable of learning from data that are unstructured. Topics of interest may include, but are not limited to, the following:

• Novel machine learning models and algorithms;
• Deep learning architectures and optimization techniques;
• Application of machine learning in healthcare, finance, autonomous vehicles, and more;
• Deep learning applications in computer vision, speech recognition, and natural language processing;
• Interpretability and explainability of machine learning and deep learning models;
• Scalability and efficiency enhancements in computational models;
• Advances in unsupervised, supervised, and semi-supervised learning;
• Integrative AI systems combining machine learning and deep learning with other AI technologies.

We welcome original research papers, review articles, case studies, and short communications that contribute to the discourse in the field. Submissions should provide insightful analyses and robust methodologies, and contribute significantly to the advancement of machine learning and deep learning.

Dr. Dongping Zhu
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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 algorithms
• deep learning architectures
• artificial intelligence applications
• neural networks
• supervised learning
• unsupervised learning
• reinforcement learning
• computer vision
• natural language processing
• speech recognition
• data mining techniques
• model optimization
• big data analytics
• explainable AI