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Applied Sciences
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Latest Advances and Prospects in Big Data
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Latest Advances and Prospects in Big Data

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 (31 March 2023) | Viewed by 12386

Special Issue Editors

Prof. Dr. Kwan-Hee Yoo

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Guest Editor
Department of Computer Science, Chungbuk National University, 1, Chungdae-ro, Seowon-gu, Cheongju-si, Chungcheongbuk-do 28644, Korea
Interests: big data analysis; data visualization; visual analytics; smart manufacturing; virtual reality; augmented reality
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Carson K. Leung

E-Mail Website
Guest Editor
Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
Interests: AI; data mining and analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hyung-Jeong Yang

E-Mail Website
Guest Editor
Department of Electronics and Computer Engineering, Chonnam National University, 77 Yongbong-ro, Gwangju 61186, Korea
Interests: multimedia data mining; bioinformatics; medical data analysis; SNS data analysis
Prof. Dr. Nakhoon Baek

E-Mail
Guest Editor
School of Computer Science and Engineering, Kyungpook National University, Sangyeok-dong, Buk-gu, Daegu 41566, Korea
Interests: high performance computing; cloud computing; real-time computer graphics

Special Issue Information

Dear Colleagues,

Big data has become a core technology to provide innovative solutions in many fields. It refers to the large and complex amount of structured and unstructured data that grow at high-speed rates. The development of big data will enhance the discovery of useful information, such as hidden patterns and unknown correlations, that can be useful in many fields, including healthcare, manufacturing, social life, etc. However, there are still many challenges and issues that need to be solved in the area of big data. This Special Issue aims to highlight the latest advances and prospects in big data processing and analysis.

Hence, we invite the academic community and relevant industrial partners to submit papers to this Special Issue on relevant fields and topics, including (but not limited to) the following:

  • Novel algorithms for big data analysis;
  • Big data preprocessing techniques (acquisition, integration, cleaning, and transformation);
  • Data mining, machine learning, and deep learning for big data analysis;
  • Application of computer vision techniques in big data analysis;
  • Big database engineering and applications;
  • Visualization and visual analytics for supporting the big data analysis process;
  • Privacy issues in big data applications;
  • Scientific data analysis and visualization;
  • Cloud computing applications for big data processing;
  • High performance computing for big data;
  • Big data analysis: case studies and applications.

Prof. Dr. Kwan-Hee Yoo
Prof. Dr. Carson K. Leung
Prof. Dr. Hyung-Jeong Yang
Prof. Dr. Nakhoon Baek
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

  • big data
  • big data preprocessing
  • big data analysis
  • big data visualization
  • data mining
  • machine learning
  • artificial intelligence
  • computer vision
  • deep learning
  • cloud computing

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

Published Papers (3 papers)

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Research

16 pages, 1345 KiB  
Article
Identifying a Correlation among Qualitative Non-Numeric Parameters in Natural Fish Microbe Dataset Using Machine Learning
by Hideaki Shima, Yuho Sato, Kenji Sakata, Taiga Asakura and Jun Kikuchi
Appl. Sci. 2022, 12(12), 5927; https://doi.org/10.3390/app12125927 - 10 Jun 2022
Cited by 6 | Viewed by 1893
Abstract
Recent technical innovations and developments in computer-based technology have enabled bioscience researchers to acquire comprehensive datasets and identify unique parameters within experimental datasets. However, field researchers may face the challenge that datasets exhibit few associations among any measurement results (e.g., from analytical instruments, [...] Read more.
Recent technical innovations and developments in computer-based technology have enabled bioscience researchers to acquire comprehensive datasets and identify unique parameters within experimental datasets. However, field researchers may face the challenge that datasets exhibit few associations among any measurement results (e.g., from analytical instruments, phenotype observations as well as field environmental data), and may contain non-numerical, qualitative parameters, which make statistical analyses difficult. Here, we propose an advanced analysis scheme that combines two machine learning steps to mine association rules between non-numerical parameters. The aim of this analysis is to identify relationships between variables and enable the visualization of association rules from data of samples collected in the field, which have less correlations between genetic, physical, and non-numerical qualitative parameters. The analysis scheme presented here may increase the potential to identify important characteristics of big datasets. Full article
(This article belongs to the Special Issue Latest Advances and Prospects in Big Data)
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22 pages, 3914 KiB  
Article
Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory
by Tola Pheng, Tserenpurev Chuluunsaikhan, Ga-Ae Ryu, Sung-Hoon Kim, Aziz Nasridinov and Kwan-Hee Yoo
Appl. Sci. 2022, 12(2), 735; https://doi.org/10.3390/app12020735 - 12 Jan 2022
Cited by 6 | Viewed by 4225
Abstract
In the manufacturing industry, the process capability index (Cpk) measures the level and capability required to improve the processes. However, the Cpk is not enough to represent the process capability and performance of the manufacturing processes. In other words, considering that the smart [...] Read more.
In the manufacturing industry, the process capability index (Cpk) measures the level and capability required to improve the processes. However, the Cpk is not enough to represent the process capability and performance of the manufacturing processes. In other words, considering that the smart manufacturing environment can accommodate the big data collected from various facilities, we need to understand the state of the process by comprehensively considering diverse factors contained in the manufacturing. In this paper, a two-stage method is proposed to analyze the process quality performance (PQP) and predict future process quality. First, we propose the PQP as a new measure for representing process capability and performance, which is defined by a composite statistical process analysis of such factors as manufacturing cycle time analysis, process trajectory of abnormal detection, statistical process control analysis, and process capability control analysis. Second, PQP analysis results are used to predict and estimate the stability of the production process using a long short-term memory (LSTM) neural network, which is a deep learning algorithm-based method. The present work compares the LSTM prediction model with the random forest, autoregressive integrated moving average, and artificial neural network models to convincingly demonstrate the effectiveness of our proposed approach. Notably, the LSTM model achieved higher accuracy than the other models. Full article
(This article belongs to the Special Issue Latest Advances and Prospects in Big Data)
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13 pages, 12925 KiB  
Article
Stress Analysis with Dimensions of Valence and Arousal in the Wild
by Thi-Dung Tran, Junghee Kim, Ngoc-Huynh Ho, Hyung-Jeong Yang, Sudarshan Pant, Soo-Hyung Kim and Guee-Sang Lee
Appl. Sci. 2021, 11(11), 5194; https://doi.org/10.3390/app11115194 - 3 Jun 2021
Cited by 10 | Viewed by 4637
Abstract
In the field of stress recognition, the majority of research has conducted experiments on datasets collected from controlled environments with limited stressors. As these datasets cannot represent real-world scenarios, stress identification and analysis are difficult. There is a dire need for reliable, large [...] Read more.
In the field of stress recognition, the majority of research has conducted experiments on datasets collected from controlled environments with limited stressors. As these datasets cannot represent real-world scenarios, stress identification and analysis are difficult. There is a dire need for reliable, large datasets that are specifically acquired for stress emotion with varying degrees of expression for this task. In this paper, we introduced a dataset for Stress Analysis with Dimensions of Valence and Arousal of Korean Movie in Wild (SADVAW), which includes video clips with diversity in facial expressions from different Korean movies. The SADVAW dataset contains continuous dimensions of valence and arousal. We presented a detailed statistical analysis of the dataset. We also analyzed the correlation between stress and continuous dimensions. Moreover, using the SADVAW dataset, we trained a deep learning-based model for stress recognition. Full article
(This article belongs to the Special Issue Latest Advances and Prospects in Big Data)
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