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Applied Sciences
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Visualization Technologies in Deep Learning
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Visualization Technologies in Deep Learning

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (10 April 2023) | Viewed by 2348

Special Issue Editors

Prof. Dr. Zhiguang Zhou

E-Mail Website
Guest Editor
School of Media and Design, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: artificial intelligence; big data; machine learning; data mining; virtual reality; human–computer interaction
Prof. Dr. Fangfang Zhou

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Central South University, Changsha 410083, China
Interests: visualization; visual analytics
Dr. Chenhui Li

E-Mail Website
Guest Editor
School of Computer Science and Technology, East China Normal University, Shanghai, China
Interests: information visualization; computer graphics; intelligent design; deep learning

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to visualization technologies in deep learning. Over recent years, deep learning has been widely applied in a variety of fields ranging from facial recognition and strategy games to shopping recommendations and answering questions. However, the effectiveness and efficiency of the training models are still difficult to manage because they serve as black boxes, providing little insight into how, why, and when they are successful. Visualization is an effective way to present the internal features in the objects and events, which will be quite suitable for enabling users to obtain insights into the training courses available and the usefulness of models. Thus, we are pleased to announce this Special Issue, “Visualization Technologies in Deep Learning”, in which works focusing on DL4Vis (deep-learning-based visualization) and VIS4DL (visualization methods for the interpretation of deep learning models) are welcome. Hope that this Special Issue will assist the promotion and utilization of visualization and deep learning applications.

Prof. Dr. Zhiguang Zhou
Prof. Dr. Fangfang Zhou
Dr. Chenhui Li
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. 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.

Keywords

  • deep learning
  • visualization
  • visual analytics
  • human–computer interaction
  • data mining
  • machine learning

Published Papers (1 paper)

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Research

20 pages, 13753 KiB  
Article
A New Framework for Visual Classification of Multi-Channel Malware Based on Transfer Learning
by Zilin Zhao, Shumian Yang and Dawei Zhao
Appl. Sci. 2023, 13(4), 2484; https://doi.org/10.3390/app13042484 - 15 Feb 2023
Cited by 8 | Viewed by 2012
Abstract
With the continuous development and popularization of the Internet, there has been an increasing number of network security problems appearing. Among them, the rapid growth in the number of malware and the emergence of variants have seriously affected the security of the Internet. [...] Read more.
With the continuous development and popularization of the Internet, there has been an increasing number of network security problems appearing. Among them, the rapid growth in the number of malware and the emergence of variants have seriously affected the security of the Internet. Traditional malware detection methods require heavy feature engineering, which seriously affects the efficiency of detection. Existing deep-learning-based malware detection methods have problems such as poor generalization ability and long training time. Therefore, we propose a malware classification method based on transfer learning for multi-channel image vision features and ResNet convolutional neural networks. Firstly, the features of malware samples are extracted and converted into grayscale images of three different types. Then, the grayscale image sizes are processed using the bilinear interpolation algorithm to make them uniform in size. Finally, the three grayscale images are synthesized into three-dimensional RGB images, and the RGB images processed using data enhancement are used for training and classification. For the classification model, we used the previous ImageNet dataset (>10 million) and trained all the parameters of ResNet after loading the weights. For the evaluations, an experiment was conducted using the Microsoft BIG benchmark dataset. The experimental results showed that the accuracy on the Microsoft dataset reached 99.99%. We found that our proposed method can better extract the texture features of malware, effectively improve the accuracy and detection efficiency, and outperform the compared models on all performance metrics. Full article
(This article belongs to the Special Issue Visualization Technologies in Deep Learning)
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