Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.4
2.2
Journals
Symmetry
Special Issues
Symmetry/Asymmetry in Data Sciences and Machine Learning for Multidisciplinary Research
share announcement

Symmetry/Asymmetry in Data Sciences and Machine Learning for Multidisciplinary Research

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 8980

Special Issue Editors

Dr. Song-Kyoo (Amang) Kim

E-Mail Website
Guest Editor
School of Applied Sciences, Macao Polytechnic Institute, R. de Luis Gonzaga Gomes, Macao
Interests: probability theory; data sciences; machine learning; game theory; blockchain governance game; biometircs; applied statisitcs
Special Issues, Collections and Topics in MDPI journals
Dr. Chan Yeob Yeun

E-Mail Website
Guest Editor
Center for Cyber-Physical Systems, EECS Department, Khalifa University, Abu Dhabi, United Arab Emirates
Interests: cyber security; AI for cyber security; blockchain security; IoT security

Special Issue Information

Dear Colleagues,

We would like to invite you to submit your work to the Special Issue “Advances in Data Sciences and Machine Learning for Multidisciplinary Research” which is aligned with symmetry/asymmetry phenomena. This Special Issue is seeking high-quality contributions in both theoretical and applied data sciences/statistics techniques to solve practical applications.

This Special Issue of Symmetry features articles about solving real-world problems by designing new data centric techniques, including data sciences, statistical analysis, and machine learning with reflection of symmetry/asymmetry phenomena. We are soliciting contributions to cover practically any range of topics, including engineering, economics, marketing, biometrics, human behavior, business analytics, telecommunications, cybersecurity, the Internet of Things, and mechatronics. The main criterion for submissions is developing innovative data centric methods to tackle real-world problems. There is no page limit, but contributions must demonstrate knowledge of the theatrical background and the practicality of the topic of the submission.

Submit your paper and select the Journal “Symmetry” and the Special Issue “Symmetry/Asymmetry in Data Sciences and Machine Learning for Multidisciplinary Research” via: MDPI submission system. Our papers will be published on a rolling basis and we will be pleased to receive your submission once you have finished it.

Dr. Song-Kyoo (Amang) Kim
Dr. Chan Yeob Yeun
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. Symmetry is an international peer-reviewed open access monthly 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

  • data sciences
  • applied statistics
  • business analytics
  • artificial intelligence
  • machine learning
  • deep learning
  • engineering (mechanical, civil, chemical)
  • emerging technologies (telecommunication, Blockchain, IoT, cybersecurity)
  • medicine (medical engineering, biometrics)
  • management (human resources, operations management, finance)

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 5191 KiB  
Article
Reversible Data Hiding Using an Improved Pixel Value Ordering and Complementary Strategy
by Rajeev Kumar, Neeraj Kumar and Ki-Hyun Jung
Symmetry 2022, 14(12), 2477; https://doi.org/10.3390/sym14122477 - 22 Nov 2022
Cited by 2 | Viewed by 1484
Abstract
Reversible data hiding (RDH) schemes based on pixel value ordering have gained significant popularity due to their unique capability of providing high-quality marked images with a decent embedding capacity, while also enabling secret information extraction and the lossless recovery of the original images [...] Read more.
Reversible data hiding (RDH) schemes based on pixel value ordering have gained significant popularity due to their unique capability of providing high-quality marked images with a decent embedding capacity, while also enabling secret information extraction and the lossless recovery of the original images at the receiving side. However, the marked image quality may be distorted severely when the pixel value ordering (PVO) method is employed in a layer-wise manner to increase the embedding capacity. In this paper, a new high-capacity RDH scheme using a complementary strategy is introduced to overcome the limitation of the image quality in the case of layer-wise embedding. The proposed RDH scheme first divides the cover image into non-overlapping blocks of 2 × 2 pixels uniformly and then sorts the pixels of each block according to their intensity values. The secret data are then embedded into two layers. In the first layer, the minimum value of the block is decreased and the maximum value is increased by either 1 or 2 to embed the secret data bits. The second layer is used as a complement to the first layer and is in symmetry with the first layer. In the second layer, the proposed RDH scheme increases the minimum valued pixel and decreases the maximum valued pixel in order to minimize the distortion resulting from the first layer embedding and to embed an additional amount of the secret data. As a result, the proposed RDH scheme significantly increases the embedding capacity, which is clearly evident from the conducted experimental results. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Data Sciences and Machine Learning for Multidisciplinary Research)
►▼ Show Figures

Figure 1

21 pages, 8069 KiB  
Article
Image Encryption Algorithm Using 2-Order Bit Compass Coding and Chaotic Mapping
by Jinlin Chen, Yiquan Wu, Yeguo Sun and Chunzhi Yang
Symmetry 2022, 14(7), 1482; https://doi.org/10.3390/sym14071482 - 20 Jul 2022
Cited by 1 | Viewed by 1685
Abstract
This paper proposes a novel image encryption algorithm based on an integer form of chaotic mapping and 2-order bit compass diffusion technique. Chaotic mapping has been widely used in image encryption. If the floating-point number generated by chaotic mapping is applied to image [...] Read more.
This paper proposes a novel image encryption algorithm based on an integer form of chaotic mapping and 2-order bit compass diffusion technique. Chaotic mapping has been widely used in image encryption. If the floating-point number generated by chaotic mapping is applied to image encryption algorithm, it will slow encryption and increase the difficulty of hardware implementation. An innovative pseudo-random integer sequence generator is proposed. In chaotic system, the result of one-iteration is used as the shift value of two binary sequences, the original symmetry relationship is changed, and then XOR operation is performed to generate a new binary sequence. Multiple iterations can generate pseudo-random integer sequences. Here integer sequences have been used in scrambling of pixel positions. Meanwhile, this paper demonstrates that there is an inverse operation in the XOR operation of two binary sequences. A new pixel diffusion technique based on bit compass coding is proposed. The key vector of the algorithm comes from the original image and is hidden by image encryption. The efficiency of our proposed method in encrypting a large number of images is evaluated using security analysis and time complexity. The performance evaluation of algorithm includes key space, histogram differential attacks, gray value distribution(GDV),correlation coefficient, PSNR, entropy, and sensitivity. The comparison between the results of coefficient, entropy, PSNR, GDV, and time complexity further proves the effectiveness of the algorithm. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Data Sciences and Machine Learning for Multidisciplinary Research)
►▼ Show Figures

Figure 1

16 pages, 2432 KiB  
Article
Clustering with Missing Features: A Density-Based Approach
by Kun Gao, Hassan Ali Khan and Wenwen Qu
Symmetry 2022, 14(1), 60; https://doi.org/10.3390/sym14010060 - 2 Jan 2022
Cited by 10 | Viewed by 3113
Abstract
Density clustering has been widely used in many research disciplines to determine the structure of real-world datasets. Existing density clustering algorithms only work well on complete datasets. In real-world datasets, however, there may be missing feature values due to technical limitations. Many imputation [...] Read more.
Density clustering has been widely used in many research disciplines to determine the structure of real-world datasets. Existing density clustering algorithms only work well on complete datasets. In real-world datasets, however, there may be missing feature values due to technical limitations. Many imputation methods used for density clustering cause the aggregation phenomenon. To solve this problem, a two-stage novel density peak clustering approach with missing features is proposed: First, the density peak clustering algorithm is used for the data with complete features, while the labeled core points that can represent the whole data distribution are used to train the classifier. Second, we calculate a symmetrical FWPD distance matrix for incomplete data points, then the incomplete data are imputed by the symmetrical FWPD distance matrix and classified by the classifier. The experimental results show that the proposed approach performs well on both synthetic datasets and real datasets. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Data Sciences and Machine Learning for Multidisciplinary Research)
►▼ Show Figures

Figure 1

12 pages, 565 KiB  
Article
An Improved Controlled Random Search Method
by Vasileios Charilogis, Ioannis Tsoulos, Alexandros Tzallas and Nikolaos Anastasopoulos
Symmetry 2021, 13(11), 1981; https://doi.org/10.3390/sym13111981 - 20 Oct 2021
Cited by 3 | Viewed by 1433
Abstract
A modified version of a common global optimization method named controlled random search is presented here. This method is designed to estimate the global minimum of multidimensional symmetric and asymmetric functional problems. The new method modifies the original algorithm by incorporating a new [...] Read more.
A modified version of a common global optimization method named controlled random search is presented here. This method is designed to estimate the global minimum of multidimensional symmetric and asymmetric functional problems. The new method modifies the original algorithm by incorporating a new sampling method, a new termination rule and the periodical application of a local search optimization algorithm to the points sampled. The new version is compared against the original using some benchmark functions from the relevant literature. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Data Sciences and Machine Learning for Multidisciplinary Research)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop