Machine Perception and Learning

Dear Colleagues,

Machine perception and learning are highly interdisciplinary and draw on findings in psychology, neuroscience, machine learning, computer vision, and behavioral economics. The mission of this field is to enable machines to perceive and understand the real world in order for them to intelligently generate multimodal content and perform robustly in challenging tasks. Recently, researchers have started to apply a range of machine learning- and AI-based methods to a wide variety of data sources, including multispectral, medical imagery, camera images, live webcam streams and video data. The recurring objective is to design efficient and accurate algorithms for the automatic extraction of semantic information from the data source. There is clear scope for the further development of such approaches to enhance the performance of associated technologies, which is the key aim of this journal, such as machine learning, deep learning, and transfer learning methods and AI models.

We welcome original and well-grounded research papers on all aspects of the foundations of machine perception and learning. The contributions may be theoretical, methodological, algorithmic, empirical, integrative (connecting ideas and methods across machine perception and learning), or critical (e.g., principled analyses and arguments that draw attention to goals, assumptions, or approaches). The submissions should place emphasis on the demonstrated or potential impact of the research in addressing pressing societal challenges, e.g., health, food, environment, education, governance, among others. All submissions will be evaluated and scored for the significance and novelty of the contributions (research problems or questions addressed, methods, experiments, analyses), theoretical and/or empirical soundness of the claims, and clarity of exposition.

The topics of interest include, but are not limited to:

• AI-related brain and cognitive science;
• Machine perception and human–machine interaction;
• Machine learning and data mining;
• Multimodal emotion recognition;
• Pattern recognition and computer vision;
• Signal processing and recognition;
• Medical image processing;
• Semi-supervised and weakly supervised learning;
• Intelligent information processing;
• Natural language processing;
• Network intelligence and mobile computing;
• Intelligent control and decision;
• Robotics and intelligent systems;
• Auto-ML;
• Information fusion from disparate sources.

Prof. Dr. Yi Ding
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-anonymized 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.