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Electronics
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Applied Deep Learning and Multimedia Electronics
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Applied Deep Learning and Multimedia Electronics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 9751

Special Issue Editors

Dr. Chih-Chang Yu

E-Mail Website
Guest Editor
Department of Information and Computer Engineering, Chung Yuan Christian University, Taoyuan 320314, Taiwan
Interests: pattern recognition; computer vision; deep learning; learning analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Hsu-Yung Cheng

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan
Interests: video analysis and understanding; machine learning; image processing; intelligent systems

Special Issue Information

Dear colleagues,

Deep learning has become one of the most successful research topics in various fields, such as artificial intelligence, internet of things, and multimedia processing. Thanks to the continuously increasing processing and sensing power of electronic devices, deep learning techniques can now be practically applied in many domains such as automotive, education, and manufacturing. Embedded multimedia processing technologies are applied to DSP processors, FPGA, and GPU devices in order to achieve edge computing.

In this Special Issue on “Applied Deep Learning and Multimedia Electronics”, we invite authors to submit original research articles and review articles related to applied deep learning technologies in all kinds of fields and technologies of deep learning in multimedia processing. We are particularly interested in presenting emerging technologies related to deep learning and multimedia electronics that may have a significant impact on this research field. We are open to papers addressing a broad range of topics, from foundational topics regarding theoretical issues of multimedia processing to novel algorithms improving deep learning problems, advanced and technological systems for interesting applications, and innovative approaches in edge computing. Topics of interest for this Special Issue include but are not limited to the following:

  • Machine learning and deep learning for multimedia processing;
  • Deep network algorithms and architectures for multi-modal data;
  • Deep learning algorithms for clustering and classification;
  • Deep learning algorithms for segmentation and data annotation;
  • Multimedia content analysis and recommendation;
  • Embedded multimedia applications for edge computing;
  • Deep learning-based cross-disciplinary applications such as agriculture, education, healthcare, smart factory, etc.;
  • Novel applications in robotic vision, AIoT, intelligent consumer electronics, etc.;
  • System and software architecture of AI-based systems.

Dr. Chih-Chang Yu
Dr. Hsu-Yung Cheng
Guest Editors

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. Electronics 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.

Keywords

  • deep learning
  • machine learning
  • artificial intelligence
  • multimedia processing
  • edge computing
  • Internet of Things

Published Papers (3 papers)

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Research

14 pages, 1398 KiB  
Article
Incorporating Multimedia Teaching Methods and Computational Thinking into the Baking Dessert Course
by Yen-Cheng Chen, Pei-Ling Tsui, Ching-Sung Lee, Ming-Chen Chiang and Bo-Kai Lan
Electronics 2022, 11(22), 3772; https://doi.org/10.3390/electronics11223772 - 17 Nov 2022
Cited by 1 | Viewed by 1767
Abstract
Rapid developments in motion media technology have prompted the dessert industry to incorporate both motion multimedia and social media into their marketing strategies. Modern consumption patterns have shifted dramatically toward motion multimedia, with data searching and cost-related decision-making gradually becoming a new type [...] Read more.
Rapid developments in motion media technology have prompted the dessert industry to incorporate both motion multimedia and social media into their marketing strategies. Modern consumption patterns have shifted dramatically toward motion multimedia, with data searching and cost-related decision-making gradually becoming a new type of consumption experience. As a result, the effective application of motion multimedia and computational thinking has become a critical skill in culinary education, as it improves students’ learning outcomes and enables them to enter the workforce with a practical modern skill. This study examines the learning outcomes of Chinese Culture University students enrolled in a dessert-making course that experimentally incorporated motion media and computational thinking into its curriculum. The results show that this approach significantly enhances students’ learning outcomes, especially in terms of creativity and teamwork, both of which are critical in dessert-making. This study makes a strong contribution to the literature by demonstrating that motion multimedia-based teaching methods and computational thinking boost learning outcomes in dessert-making education. Full article
(This article belongs to the Special Issue Applied Deep Learning and Multimedia Electronics)
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18 pages, 4232 KiB  
Article
Scene Classification, Data Cleaning, and Comment Summarization for Large-Scale Location Databases
by Hsu-Yung Cheng and Chih-Chang Yu
Electronics 2022, 11(13), 1947; https://doi.org/10.3390/electronics11131947 - 22 Jun 2022
Cited by 1 | Viewed by 1344
Abstract
This paper presents a framework that can automatically analyze the images and comments in user-uploaded location databases. The proposed framework integrates image processing and natural language processing techniques to perform scene classification, data cleaning, and comment summarization so that the cluttered information in [...] Read more.
This paper presents a framework that can automatically analyze the images and comments in user-uploaded location databases. The proposed framework integrates image processing and natural language processing techniques to perform scene classification, data cleaning, and comment summarization so that the cluttered information in user-uploaded databases can be presented in an organized way to users. For scene classification, RGB image features, segmentation features, and the features of discriminative objects are fused with an attention module to improve classification accuracy. For data cleaning, incorrect images are detected using a multilevel feature extractor and a multiresolution distance calculation scheme. Finally, a comment summarization scheme is proposed to overcome the problems of unstructured sentences and the improper usage of punctuation marks, which are commonly found in customer reviews. To validate the proposed framework, a system that can classify and organize scenes and comments for hotels is implemented and evaluated. Comparisons with existing related studies are also performed. The experimental results validate the effectiveness and superiority of the proposed framework. Full article
(This article belongs to the Special Issue Applied Deep Learning and Multimedia Electronics)
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13 pages, 1865 KiB  
Article
Facial Skincare Products’ Recommendation with Computer Vision Technologies
by Ting-Yu Lin, Hung-Tse Chan, Chih-Hsien Hsia and Chin-Feng Lai
Electronics 2022, 11(1), 143; https://doi.org/10.3390/electronics11010143 - 03 Jan 2022
Cited by 8 | Viewed by 5655
Abstract
Acne is a skin issue that plagues many young people and adults. Even if it is cured, it leaves acne spots or acne scars, which drives many individuals to use skincare products or undertake medical treatment. On the contrary, the use of inappropriate [...] Read more.
Acne is a skin issue that plagues many young people and adults. Even if it is cured, it leaves acne spots or acne scars, which drives many individuals to use skincare products or undertake medical treatment. On the contrary, the use of inappropriate skincare products can exacerbate the condition of the skin. In view of this, this work proposes the use of computer vision (CV) technology to realize a new business model of facial skincare products. The overall framework is composed of a finger vein identification system, skincare products’ recommendation system, and electronic payment system. A finger vein identification system is used as identity verification and personalized service. A skincare products’ recommendation system provides consumers with professional skin analysis through skin type classification and acne detection to recommend skincare products that finally improve skin issues of consumers. An electronic payment system provides a variety of checkout methods, and the system will check out by finger-vein connections according to membership information. Experimental results showed that the equal error rate (EER) comparison of the FV-USM public database on the finger-vein system was the lowest and the response time was the shortest. Additionally, the comparison of the skin type classification accuracy was the highest. Full article
(This article belongs to the Special Issue Applied Deep Learning and Multimedia Electronics)
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