Advanced Research in Artificial Neural Networks

Dear Colleagues,

Over the years, numerous efforts have been made to understand—and ultimately emulate—the way humans solve problems, with the goal of achieving intelligent behavior in artificial systems. Among these efforts, artificial neural networks stand out as one of the most successful approaches, offering a simplified model of one of Nature’s most complex organs: the brain. Through interconnected nodes and learning processes based on examples, neural networks have demonstrated remarkable performance across a wide variety of research domains.

Although research in this field has always been present and active, neural networks have experienced a major resurgence driven by the rapid development of deep learning, a modern evolution of neural network methodologies. Deep learning builds upon foundational principles while leveraging new learning algorithms, multilayer architectures, and vastly expanded computational power, enabling unprecedented performance in complex tasks across scientific and industrial fields. This growing body of knowledge highlights the importance of advancing research in this area.

Suggested themes and article types for submission

In this Special Issue, original research articles and review papers are welcome. Research areas may include (but are not limited to) the following:

1. Novel learning paradigms, architectures, and training algorithms for artificial neural networks and deep learning.
2. Advanced applications of neural networks in areas such as image and video analysis, pattern recognition, time-series modeling or forecasting, or real-time decision-making systems.
3. Theoretical advances, interpretability studies, computational optimizations (i.e., green machine learning-aligned approaches), and new methodological frameworks within the neural network domain.

We are pleased to invite you to contribute to the Special Issue “Advanced Research in Artificial Neural Networks”, in which we aim to present cutting-edge theoretical developments—such as emerging learning paradigms, innovative architectures, and novel optimization strategies—as well as recent scientific contributions where neural network and deep learning models are applied to achieve state-of-the-art results in diverse application areas.

Researchers and practitioners are warmly welcomed to submit their contributions to this Special Issue.

Dr. Marcos Gestal
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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences 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 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.