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Applied Sciences
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Smart Computing and Big Data Analysis: Latest Advances and Applications
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Smart Computing and Big Data Analysis: Latest Advances and Applications

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 January 2022) | Viewed by 35500

Special Issue Editors

Prof. Dr. Jinho Kim

E-Mail Website
Guest Editor
Dept. of Computer Science, Kangwon National University, Chuncheon, Korea
Interests: big data; machine learning; smart computing
Prof. Dr. Young-ho Park

E-Mail Website
Guest Editor
Dept. of IT Engineering, Sookmyung Women's University, Seoul, Korea
Interests: big data; artificial intelligence

Special Issue Information

Dear Colleagues,

Smart computing and big data have become recently essential keywords for driving new technologies and innovative solutions in many applications, and are taking much attention from computer science and information technology as well as from social sciences and other disciplines. Big data analysis is a variety of advanced techniques which draws hidden rules, patterns, and knowledge from very large, diverse data to provide valuable insights and improvements in business, manufacturing, healthcare, etc.

Smart computing is also a hot topic which combines advanced computer technologies to create new systems, applications, and services in diverse application such as business, health-care, industrial systems, and so on. It provides people with intelligent, innovative, and convenient services by taking advantage of new hardware, communication, and advanced software technologies such as mobile devices, cloud computing, and big data analysis.

We invite the academic community and relevant industrial partners to submit papers to this Special Issue, on research results, application developments, and practical experiences in relevant fields and topics including (but not limited to) the following:

  • Novel techniques for big data and smart computing
  • Tools and systems for big data and smart computing
  • Machine learning for big data
  • Infrastructure and platform for smart computing
  • Big data analytics and social media
  • Cloud and grid computing
  • Mobile communications and networks
  • Application of big data and smart computing

Prof. Dr. Jinho Kim
Prof. Dr. Young-Hoon Park
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 analysis
  • data mining
  • machine learning
  • deep learning
  • smart computing
  • cloud computing
  • mobile communication
  • smart devices

Published Papers (12 papers)

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Research

14 pages, 3103 KiB  
Article
Classification of Sleep Stage with Biosignal Images Using Convolutional Neural Networks
by Moon-Jeung Joe and Seung-Chan Pyo
Appl. Sci. 2022, 12(6), 3028; https://doi.org/10.3390/app12063028 - 16 Mar 2022
Cited by 3 | Viewed by 2204
Abstract
Clinicians and researchers divide sleep periods into different sleep stages to analyze the quality of sleep. Despite advances in machine learning, sleep-stage classification is still performed manually. The classification process is tedious and time-consuming, but its automation has not yet been achieved. Another [...] Read more.
Clinicians and researchers divide sleep periods into different sleep stages to analyze the quality of sleep. Despite advances in machine learning, sleep-stage classification is still performed manually. The classification process is tedious and time-consuming, but its automation has not yet been achieved. Another problem is low accuracy due to inconsistencies between somnologists. In this paper, we propose a method to classify sleep stages using a convolutional neural network. The network is trained with EEG and EOG images of time and frequency domains. The images of the biosignal are appropriate as inputs to the network, as these are natural inputs provided to somnologists in polysomnography. To validate the network, the sleep-stage classifier was trained and tested using the public Sleep-EDFx dataset. The results show that the proposed method achieves state-of-the-art performance on the Sleep-EDFx (accuracy 94%, F1 94%). The results demonstrate that the classifier is able to learn features described in the sleep scoring manual from the sleep data. Full article
(This article belongs to the Special Issue Smart Computing and Big Data Analysis: Latest Advances and Applications)
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18 pages, 3333 KiB  
Article
Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model
by You Wang, Ruxue Jia, Fang Dai and Yunxia Ye
Appl. Sci. 2022, 12(4), 2190; https://doi.org/10.3390/app12042190 - 19 Feb 2022
Cited by 7 | Viewed by 1813
Abstract
Traffic flow is used as an essential indicator to measure the performance of the road network and a pivotal basis for road classification. However, the combined prediction model of traffic flow based on seasonal characteristics has been given little attention at present. Because [...] Read more.
Traffic flow is used as an essential indicator to measure the performance of the road network and a pivotal basis for road classification. However, the combined prediction model of traffic flow based on seasonal characteristics has been given little attention at present. Because the seasonal autoregressive integrated moving average model (SARIMA) has superior linear fitting characteristics, it is often used to process seasonal time series. In contrast, the non-autoregressive dynamic neural network (NAR) has a vital memory function and nonlinear interpretation capabilities. They are suitable for constructing combined forecasting models. The traffic flow time series of a highway in southwest China is taken as the research object in this paper. Combining the SARIMA (0,1,2) (0,1,2)12 model and the NAR model with 15 hidden layer neurons and fourth-order delay, two combined models are constructed: the linear and nonlinear component combination method is realized by the SARIMA-NAR combination model 1, and the MSE weight combination method is used by the SARIMA-NAR combination model 2. We calculated that the prediction accuracy of SARIMA-NAR combined model 1 is as high as 0.92, and the prediction accuracy of SARIMA-NAR combined model 2 is 0.90. In addition, the traffic flow forecast under the influence of the epidemic is also discussed. Through a comprehensive comparison of multiple indicators, the results show that the SARIMA-NAR combined model 1 has better road traffic flow fitting and prediction effects and is suitable for the greater volatility of traffic flow during the epidemic. This model improves the effectiveness and reliability of traffic flow forecasting, and the forecasting process is more convenient and efficient. Full article
(This article belongs to the Special Issue Smart Computing and Big Data Analysis: Latest Advances and Applications)
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20 pages, 2634 KiB  
Article
A Deep Learning Approach to Analyze Airline Customer Propensities: The Case of South Korea
by So-Hyun Park, Mi-Yeon Kim, Yeon-Ji Kim and Young-Ho Park
Appl. Sci. 2022, 12(4), 1916; https://doi.org/10.3390/app12041916 - 12 Feb 2022
Cited by 8 | Viewed by 4291
Abstract
In the airline industry, customer satisfaction occurs when passengers’ expectations are met through the airline experience. Considering that airline service quality is the main factor in obtaining new and retaining existing customers, airline companies are applying various approaches to improve the quality of [...] Read more.
In the airline industry, customer satisfaction occurs when passengers’ expectations are met through the airline experience. Considering that airline service quality is the main factor in obtaining new and retaining existing customers, airline companies are applying various approaches to improve the quality of the physical and social servicescapes. It is common to use data analysis techniques for analyzing customer propensity in marketing. However, their application to the airline industry has traditionally focused solely on surveys; hence, there is a lack of attention paid to deep learning techniques based on survey results. This study has two purposes. The first purpose is to find the relationship between various factors influencing customer churn risk and satisfaction by analyzing the airline customer data. For this, we applied deep learning techniques to the survey data collected from the users who have used mostly Korean airplanes. To the best of our knowledge, this is the one of the few attempts at applying deep learning to analyze airline customer propensities. The second purpose is to analyze the influence of the social servicescape, including the viewpoints of the cabin crew and passengers using aircraft, on airline customer propensities. The experimental results demonstrated that the proposed method of considering human services increased the accuracy of predictive models by up to 10% and 9% in predicting customer churn risk and satisfaction, respectively. Full article
(This article belongs to the Special Issue Smart Computing and Big Data Analysis: Latest Advances and Applications)
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12 pages, 4640 KiB  
Article
Dual-ISM: Duality-Based Image Sequence Matching for Similar Image Search
by Hye-Jin Lee, Yongjin Kwon and Sun-Young Ihm
Appl. Sci. 2022, 12(3), 1609; https://doi.org/10.3390/app12031609 - 03 Feb 2022
Viewed by 1333
Abstract
In this paper, we propose the duality-based image sequence matching method, which is called Dual-ISM, a subsequence matching method for searching for similar images. We first extract feature points from the given image data and configure the feature vectors as one data sequence. [...] Read more.
In this paper, we propose the duality-based image sequence matching method, which is called Dual-ISM, a subsequence matching method for searching for similar images. We first extract feature points from the given image data and configure the feature vectors as one data sequence. Next, the feature vectors are configured in the form of a disjoint window, and a low-dimensional transformation is carried out. Subsequently, the query image that is entered to construct the candidate set is similarly subjected to a low-dimensional transformation, and the low-dimensional transformed window of the data sequence and window that are less than the allowable value, ε, is regarded as the candidate set using a distance calculation. Finally, similar images are searched in the candidate set using the distance calculation that are based on the original feature vector. Full article
(This article belongs to the Special Issue Smart Computing and Big Data Analysis: Latest Advances and Applications)
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17 pages, 1940 KiB  
Article
UCLAONT: Ontology-Based UML Class Models Verification Tool
by Adel Rajab, Abdul Hafeez, Asadullah Shaikh, Abdullah Alghamdi, Mana Saleh Al Reshan, Mohammed Hamdi and Khairan Rajab
Appl. Sci. 2022, 12(3), 1397; https://doi.org/10.3390/app12031397 - 28 Jan 2022
Viewed by 1802
Abstract
The software design model performs an important role in modern software engineering methods. Especially in Model-Driven Engineering (MDE), it is treated as an essential asset of software development; even programming language code is produced by the models. If the model has errors, then [...] Read more.
The software design model performs an important role in modern software engineering methods. Especially in Model-Driven Engineering (MDE), it is treated as an essential asset of software development; even programming language code is produced by the models. If the model has errors, then they can propagate into the code. Model verification tools check the presence of errors in the model. This paper shows how a UML class model verification tool has been built to support complex models and unsupported elements such as XOR constraints and dependency relationships. This tool uses ontology for verifying the UML class model. It takes a class model in XMI format and generates the OWL file. Performs verification of model in two steps: (1) uses the ontology-based algorithm to verify association multiplicity constraints; and (2) uses ontology reasoner for the verification of XOR constraints and dependency relationships. The results show the proposed tool improves the verification efficiency and supports the verification of UML class model elements that have not been supported by any existing tool. Full article
(This article belongs to the Special Issue Smart Computing and Big Data Analysis: Latest Advances and Applications)
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27 pages, 6678 KiB  
Article
A Shallow Domain Knowledge Injection (SDK-Injection) Method for Improving CNN-Based ECG Pattern Classification
by Soyeon Oh and Minsoo Lee
Appl. Sci. 2022, 12(3), 1307; https://doi.org/10.3390/app12031307 - 26 Jan 2022
Cited by 5 | Viewed by 1438
Abstract
ECG pattern classification for identifying the progress status of various heart diseases is a typical nonlinear problem. Therefore, deep learning-based automatic ECG diagnosis is being widely studied, and for this purpose, the CNN is mainly used to classify ECG patterns. In this case, [...] Read more.
ECG pattern classification for identifying the progress status of various heart diseases is a typical nonlinear problem. Therefore, deep learning-based automatic ECG diagnosis is being widely studied, and for this purpose, the CNN is mainly used to classify ECG patterns. In this case, it is hard to expect any further improvement in accuracy after optimizing the parameters. We propose a shallow domain knowledge injection method that can improve the accuracy of the existing parameter-optimized CNN. The proposed method can improve the accuracy by effectively injecting shallow domain knowledge, that can be acquired by non-medical experts, into the existing parameter-optimized CNN. The experiments show that the proposed method can be applied to both heart disease diagnoses and general ECG classification tasks, while improving the existing accuracy for both types of tasks. Full article
(This article belongs to the Special Issue Smart Computing and Big Data Analysis: Latest Advances and Applications)
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23 pages, 7995 KiB  
Article
Hybrid Deep Reinforcement Learning for Pairs Trading
by Sang-Ho Kim, Deog-Yeong Park and Ki-Hoon Lee
Appl. Sci. 2022, 12(3), 944; https://doi.org/10.3390/app12030944 - 18 Jan 2022
Cited by 11 | Viewed by 6683
Abstract
Pairs trading is an investment strategy that exploits the short-term price difference (spread) between two co-moving stocks. Recently, pairs trading methods based on deep reinforcement learning have yielded promising results. These methods can be classified into two approaches: (1) indirectly determining trading actions [...] Read more.
Pairs trading is an investment strategy that exploits the short-term price difference (spread) between two co-moving stocks. Recently, pairs trading methods based on deep reinforcement learning have yielded promising results. These methods can be classified into two approaches: (1) indirectly determining trading actions based on trading and stop-loss boundaries and (2) directly determining trading actions based on the spread. In the former approach, the trading boundary is completely dependent on the stop-loss boundary, which is certainly not optimal. In the latter approach, there is a risk of significant loss because of the absence of a stop-loss boundary. To overcome the disadvantages of the two approaches, we propose a hybrid deep reinforcement learning method for pairs trading called HDRL-Trader, which employs two independent reinforcement learning networks; one for determining trading actions and the other for determining stop-loss boundaries. Furthermore, HDRL-Trader incorporates novel techniques, such as dimensionality reduction, clustering, regression, behavior cloning, prioritized experience replay, and dynamic delay, into its architecture. The performance of HDRL-Trader is compared with the state-of-the-art reinforcement learning methods for pairs trading (P-DDQN, PTDQN, and P-Trader). The experimental results for twenty stock pairs in the Standard & Poor’s 500 index show that HDRL-Trader achieves an average return rate of 82.4%, which is 25.7%P higher than that of the second-best method, and yields significantly positive return rates for all stock pairs. Full article
(This article belongs to the Special Issue Smart Computing and Big Data Analysis: Latest Advances and Applications)
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14 pages, 599 KiB  
Article
SHAT: A Novel Asynchronous Training Algorithm That Provides Fast Model Convergence in Distributed Deep Learning
by Yunyong Ko and Sang-Wook Kim
Appl. Sci. 2022, 12(1), 292; https://doi.org/10.3390/app12010292 - 29 Dec 2021
Cited by 3 | Viewed by 2871
Abstract
The recent unprecedented success of deep learning (DL) in various fields is underlied by its use of large-scale data and models. Training a large-scale deep neural network (DNN) model with large-scale data, however, is time-consuming. To speed up the training of massive DNN [...] Read more.
The recent unprecedented success of deep learning (DL) in various fields is underlied by its use of large-scale data and models. Training a large-scale deep neural network (DNN) model with large-scale data, however, is time-consuming. To speed up the training of massive DNN models, data-parallel distributed training based on the parameter server (PS) has been widely applied. In general, a synchronous PS-based training suffers from the synchronization overhead, especially in heterogeneous environments. To reduce the synchronization overhead, asynchronous PS-based training employs the asynchronous communication between PS and workers so that PS processes the request of each worker independently without waiting. Despite the performance improvement of asynchronous training, however, it inevitably incurs the difference among the local models of workers, where such a difference among workers may cause slower model convergence. Fro addressing this problem, in this work, we propose a novel asynchronous PS-based training algorithm, SHAT that considers (1) the scale of distributed training and (2) the heterogeneity among workers for successfully reducing the difference among the local models of workers. The extensive empirical evaluation demonstrates that (1) the model trained by SHAT converges to the higher accuracy up to 5.22% than state-of-the-art algorithms, and (2) the model convergence of SHAT is robust under various heterogeneous environments. Full article
(This article belongs to the Special Issue Smart Computing and Big Data Analysis: Latest Advances and Applications)
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13 pages, 14758 KiB  
Article
Scalable Fog Computing Orchestration for Reliable Cloud Task Scheduling
by Jongbeom Lim
Appl. Sci. 2021, 11(22), 10996; https://doi.org/10.3390/app112210996 - 19 Nov 2021
Cited by 5 | Viewed by 1819
Abstract
As Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices are becoming increasingly popular in the era of the Fourth Industrial Revolution, the orchestration and management of numerous fog devices encounter a scalability problem. In fog computing environments, to embrace various [...] Read more.
As Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices are becoming increasingly popular in the era of the Fourth Industrial Revolution, the orchestration and management of numerous fog devices encounter a scalability problem. In fog computing environments, to embrace various types of computation, cloud virtualization technology is widely used. With virtualization technology, IoT and IIoT tasks can be run on virtual machines or containers, which are able to migrate from one machine to another. However, efficient and scalable orchestration of migrations for mobile users and devices in fog computing environments is not an easy task. Naïve or unmanaged migrations may impinge on the reliability of cloud tasks. In this paper, we propose a scalable fog computing orchestration mechanism for reliable cloud task scheduling. The proposed scalable orchestration mechanism considers live migrations of virtual machines and containers for the edge servers to reduce both cloud task failures and suspended time when a device is disconnected due to mobility. The performance evaluation shows that our proposed fog computing orchestration is scalable while preserving the reliability of cloud tasks. Full article
(This article belongs to the Special Issue Smart Computing and Big Data Analysis: Latest Advances and Applications)
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17 pages, 3701 KiB  
Article
Improvement of Business Productivity by Applying Robotic Process Automation
by Younggeun Hyun, Dongseop Lee, Uri Chae, Jindeuk Ko and Jooyeoun Lee
Appl. Sci. 2021, 11(22), 10656; https://doi.org/10.3390/app112210656 - 12 Nov 2021
Cited by 6 | Viewed by 5136
Abstract
Digitalization has been bringing about various changes and innovations not only in our daily life but also in our business environment. In the manufacturing industry, robots have been used for automation for a long time, resulting in innovation in terms of the faster [...] Read more.
Digitalization has been bringing about various changes and innovations not only in our daily life but also in our business environment. In the manufacturing industry, robots have been used for automation for a long time, resulting in innovation in terms of the faster operation process and higher product quality. Robotics Process Automation (RPA) can be said to have brought this innovation in the productivity improvement of many industries into the business office. The purpose of this study is to improve business productivity by applying RPA named CoPA. It is based on Domain-Specific Languages (DSLs) and Model-Driven Engineering (MDE) coupled with MS Office. CoPA has been replaced to perform the repetitive patterned tasks (especially document work) done by many people in an office. For the applications of business productivity, CoPA has been implemented to revise five government project proposals requiring quite strict writing standards. The improvement of business productivity obtained by CoPA has been compared to the performance of 10 employees who are familiar with MS Office. The paper explains the method of CoPA coupled with MS Office as well as the agile method of human collaboration. It is clearly shown that CoPA as a business RPA can improve business productivity in terms of time consumption and document quality. Full article
(This article belongs to the Special Issue Smart Computing and Big Data Analysis: Latest Advances and Applications)
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21 pages, 11694 KiB  
Article
A Photo Identification Framework to Prevent Copyright Infringement with Manipulations
by Doyoung Kim, Suwoong Heo, Jiwoo Kang, Hogab Kang and Sanghoon Lee
Appl. Sci. 2021, 11(19), 9194; https://doi.org/10.3390/app11199194 - 02 Oct 2021
Cited by 2 | Viewed by 2001
Abstract
In recent years, copyright infringement has been one of the most serious problems that hamper the development of the culture and arts industry. Due to the limitations of existing image search services, these infringements have not been properly identified and the number of [...] Read more.
In recent years, copyright infringement has been one of the most serious problems that hamper the development of the culture and arts industry. Due to the limitations of existing image search services, these infringements have not been properly identified and the number of infringements has been increasing continuously. To uncover these infringements and handle big data extracted from copyright photos, we propose a photo copyright identification framework to accurately handle manipulations of stolen photos. From a collage of cropped photos, regions of interest (RoIs) are detected to reduce the influence of cropping and identify each photo by Image RoI Detection. Binary descriptors for quick database search are generated from the RoIs by Image Hashing robustly to geometric and color manipulations. The matching results of Image Hashing are verified by measuring their similarity using the proposed Image Verification to reduce false positives. Experimental results demonstrate that the proposed framework outperforms other image retrieval methods in identification accuracy and significantly reduces the false positive rate by 2.8%. This framework is expected to identify copyright infringements in practical situations and have a positive effect on the copyright market. Full article
(This article belongs to the Special Issue Smart Computing and Big Data Analysis: Latest Advances and Applications)
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16 pages, 4930 KiB  
Article
Expert Recommendation for Answering Questions on Social Media
by Kyoungsoo Bok, Heesub Song, Dojin Choi, Jongtae Lim, Deukbae Park and Jaesoo Yoo
Appl. Sci. 2021, 11(16), 7681; https://doi.org/10.3390/app11167681 - 20 Aug 2021
Cited by 4 | Viewed by 2111
Abstract
In this paper, we propose a method for recommending experts to appropriately answer questions based on social activity analysis on social media. By analyzing various social activities performed on social media, the user’s interests are identified. Through the human relation analysis of the [...] Read more.
In this paper, we propose a method for recommending experts to appropriately answer questions based on social activity analysis on social media. By analyzing various social activities performed on social media, the user’s interests are identified. Through the human relation analysis of the users of a particular interest field and by considering the response speed and answer quality of the user, we determine the influence of a user. An expert group is matched by analyzing the content of queries by a user and using a hierarchical structure of words. For a user question, the accuracy of an expert recommendation is enhanced by incorporating the question content and sublevel words based on the hierarchical structure of words. Various evaluations have demonstrated that the performance of the proposed method is superior to existing methods. Full article
(This article belongs to the Special Issue Smart Computing and Big Data Analysis: Latest Advances and Applications)
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