Special Issue "Novel Machine Learning Approaches for Intelligent Big Data"

A special issue of Symmetry (ISSN 2073-8994).

Deadline for manuscript submissions: closed (31 March 2018).

Special Issue Editors

Prof. Dr. Gangman Yi
E-Mail Website
Guest Editor
Department of Multimedia Engineering, Dongguk University, Seoul, Korea
Interests: machine learning; intelligent Big Data; complex information; artificial intelligence
Prof. Dr. Yi Pan
E-Mail Website
Guest Editor
Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
Interests: deep learning, big data analytics, networking, cloud computing and bioinformatics

Special Issue Information

Dear Colleagues,

In recent years, there has been a substantial amount of work on intelligent big data (IBD) analytics using multi-core platforms based on large clusters of computers. Outcomes from those systems provide a huge amount of complex information that is too much for any single institution or computing center to handle. In particular, multimedia and individuals with smartphones and on social network sites will continue to fuel exponential growth. Recent developments in the field of machine learning offer powerful tools to handle intelligent big data. We believe that a cognitive formalism, such as machine learning architecture that combines artificial intelligence and deep learning, will cause a new leap forward in the current perception of information processing and management.

This Special Issue aims to foster the dissemination of high-quality research in methods, theories, techniques, and tools concerning active intelligent big data technology in the coming era. Its emerging applications and usages, which provide tailored and precise solutions, wherever and whenever they are active, are extremely concentrated. Original research articles are solicited on all aspects of, including traditional data processing formalisms that are inadequate to solve this problem, practical applications, new communication technology, and experimental prototypes.

Potential topics include, but are not limited to:

  • Advanced symmetric classification, regression and prediction for IBD

  • Applied clustering and Kernel methods for IBD

  • Deep learning and Data Science for IBD

  • Symmetry in Problem solving and planning for IBD

  • Symmetry in Data mining and Web mining for IBD

  • Symmetry in Information retrieval for IBD

  • Symmetry in Probabilistic Models and Methods for IBD

  • Symmetry in Natural language processing for IBD

  • Symmetry in Design and diagnosis for IBD

  • Vision and speech perception for IBD

  • Robotics and control for IBD

  • Bioinformatics for IBD

  • Industrial, financial and scientific applications of all kind.

  • Other symmetry issues in applied intelligent big data

Prof. Dr. Gangman Yi
Prof. Dr. Yi Pan
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 papers will be 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 1400 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.

Published Papers (9 papers)

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Research

Open AccessArticle
Vegetation Greening for Winter Oblique Photography Using Cycle-Consistence Adversarial Networks
Symmetry 2018, 10(7), 294; https://doi.org/10.3390/sym10070294 - 20 Jul 2018
Cited by 1
Abstract
A 3D city model is critical for the construction of a digital city. One of the methods of building a 3D city model is tilt photogrammetry. In this method, oblique photography is crucial for generating the model because the visual quality of photography [...] Read more.
A 3D city model is critical for the construction of a digital city. One of the methods of building a 3D city model is tilt photogrammetry. In this method, oblique photography is crucial for generating the model because the visual quality of photography directly impacts the model’s visual effect. Yet, sometimes, oblique photography does not have good visual quality due to a bad season or defective photographic equipment. For example, for oblique photography taken in winter, vegetation is brown. If this photography is employed to generate the 3D model, the result would be bad visually. Yet, common methods for vegetation greening in oblique photography rely on the assistance of the infrared band, which is not available sometimes. Thus, a method for vegetation greening in winter oblique photography without the infrared band is required, which is proposed in this paper. The method was inspired by the work on CycleGAN (Cycle-consistence Adversarial Networks). In brief, the problem of turning vegetation green in winter oblique photography is considered as a style transfer problem. Summer oblique photography generally has green vegetation. By applying CycleGAN, winter oblique photography can be transferred to summer oblique photography, and the vegetation can turn green. Yet, due to the existence of “checkerboard artifacts”, the original result cannot be applied for real production. To reduce artifacts, the generator of CycleGAN is modified. As the final results suggest, the proposed method unlocks the bottleneck of vegetation greening when the infrared band is not available and artifacts are reduced. Full article
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data)
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Open AccessArticle
Fuzzy Association Rule Based Froth Surface Behavior Control in Zinc Froth Flotation
Symmetry 2018, 10(6), 216; https://doi.org/10.3390/sym10060216 - 13 Jun 2018
Cited by 1
Abstract
Froth flotation is a vital mineral concentration process. Froth surface behavior is the knowledge about flotation working condition. However, in computer vision aided froth surface behavior control, there are still two challenges that need to be tackled seriously. Against the difficulty in the [...] Read more.
Froth flotation is a vital mineral concentration process. Froth surface behavior is the knowledge about flotation working condition. However, in computer vision aided froth surface behavior control, there are still two challenges that need to be tackled seriously. Against the difficulty in the froth surface behavior representation, this paper proposes to combine the bubble size distribution (BSD) and froth velocity distribution. As far as we know, this is the first time that the froth velocity distribution is presented. Against the difficulty in the adaptive generation of the optimal froth surface behavior feature (optimal setpoint), this paper introduces the fuzzy apriori to mine the association rule between the current working condition and the optimal setpoint. Then, a fuzzy inference module is constructed to generate optimal setpoint for current working condition adaptively. Many validation experiments and comparison experiments demonstrate the superiority and robustness of the proposed methods. Full article
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data)
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Open AccessArticle
Data Decision and Drug Therapy Based on Non-Small Cell Lung Cancer in a Big Data Medical System in Developing Countries
Symmetry 2018, 10(5), 152; https://doi.org/10.3390/sym10050152 - 10 May 2018
Cited by 3
Abstract
In many developing or underdeveloped countries, limited medical resources and large populations may affect the survival of mankind. The research for the medical information system and recommendation of effective treatment methods may improve diagnosis and drug therapy for patients in developing or underdeveloped [...] Read more.
In many developing or underdeveloped countries, limited medical resources and large populations may affect the survival of mankind. The research for the medical information system and recommendation of effective treatment methods may improve diagnosis and drug therapy for patients in developing or underdeveloped countries. In this study, we built a system model for the drug therapy, relevance parameter analysis, and data decision making in non-small cell lung cancer. Based on the probability analysis and status decision, the optimized therapeutic schedule can be calculated and selected, and then effective drug therapy methods can be determined to improve relevance parameters. Statistical analysis of clinical data proves that the model of the probability analysis and decision making can provide fast and accurate clinical data. Full article
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data)
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Open AccessArticle
Clickbait Convolutional Neural Network
Symmetry 2018, 10(5), 138; https://doi.org/10.3390/sym10050138 - 01 May 2018
Cited by 8
Abstract
With the development of online advertisements, clickbait spread wider and wider. Clickbait dissatisfies users because the article content does not match their expectation. Thus, clickbait detection has attracted more and more attention recently. Traditional clickbait-detection methods rely on heavy feature engineering and fail [...] Read more.
With the development of online advertisements, clickbait spread wider and wider. Clickbait dissatisfies users because the article content does not match their expectation. Thus, clickbait detection has attracted more and more attention recently. Traditional clickbait-detection methods rely on heavy feature engineering and fail to distinguish clickbait from normal headlines precisely because of the limited information in headlines. A convolutional neural network is useful for clickbait detection, since it utilizes pretrained Word2Vec to understand the headlines semantically, and employs different kernels to find various characteristics of the headlines. However, different types of articles tend to use different ways to draw users’ attention, and a pretrained Word2Vec model cannot distinguish these different ways. To address this issue, we propose a clickbait convolutional neural network (CBCNN) to consider not only the overall characteristics but also specific characteristics from different article types. Our experimental results show that our method outperforms traditional clickbait-detection algorithms and the TextCNN model in terms of precision, recall and accuracy. Full article
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data)
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Open AccessArticle
Big Data Analysis for Personalized Health Activities: Machine Learning Processing for Automatic Keyword Extraction Approach
Symmetry 2018, 10(4), 93; https://doi.org/10.3390/sym10040093 - 02 Apr 2018
Cited by 18
Abstract
The obese population is increasing rapidly due to the change of lifestyle and diet habits. Obesity can cause various complications and is becoming a social disease. Nonetheless, many obese patients are unaware of the medical treatments that are right for them. Although a [...] Read more.
The obese population is increasing rapidly due to the change of lifestyle and diet habits. Obesity can cause various complications and is becoming a social disease. Nonetheless, many obese patients are unaware of the medical treatments that are right for them. Although a variety of online and offline obesity management services have been introduced, they are still not enough to attract the attention of users and are not much of help to solve the problem. Obesity healthcare and personalized health activities are the important factors. Since obesity is related to lifestyle habits, eating habits, and interests, I concluded that the big data analysis of these factors could deduce the problem. Therefore, I collected big data by applying the machine learning and crawling method to the unstructured citizen health data in Korea and the search data of Naver, which is a Korean portal company, and Google for keyword analysis for personalized health activities. It visualized the big data using text mining and word cloud. This study collected and analyzed the data concerning the interests related to obesity, change of interest on obesity, and treatment articles. The analysis showed a wide range of seasonal factors according to spring, summer, fall, and winter. It also visualized and completed the process of extracting the keywords appropriate for treatment of abdominal obesity and lower body obesity. The keyword big data analysis technique for personalized health activities proposed in this paper is based on individual’s interests, level of interest, and body type. Also, the user interface (UI) that visualizes the big data compatible with Android and Apple iOS. The users can see the data on the app screen. Many graphs and pictures can be seen via menu, and the significant data values are visualized through machine learning. Therefore, I expect that the big data analysis using various keywords specific to a person will result in measures for personalized treatment and health activities. Full article
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data)
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Open AccessArticle
Oversampling Techniques for Bankruptcy Prediction: Novel Features from a Transaction Dataset
Symmetry 2018, 10(4), 79; https://doi.org/10.3390/sym10040079 - 22 Mar 2018
Cited by 12
Abstract
In recent years, weakened by the fall of economic growth, many enterprises fell into the crisis caused by financial difficulties. Bankruptcy prediction, a machine learning model, is a great utility for financial institutions, fund managers, lenders, governments, and economic stakeholders. Due to the [...] Read more.
In recent years, weakened by the fall of economic growth, many enterprises fell into the crisis caused by financial difficulties. Bankruptcy prediction, a machine learning model, is a great utility for financial institutions, fund managers, lenders, governments, and economic stakeholders. Due to the number of bankrupt companies compared to that of non-bankrupt companies, bankruptcy prediction faces the problem of imbalanced data. This study first presents the bankruptcy prediction framework. Then, five oversampling techniques are used to deal with imbalance problems on the experimental dataset which were collected from Korean companies in two years from 2016 to 2017. Experimental results show that using oversampling techniques to balance the dataset in the training stage can enhance the performance of the bankruptcy prediction. The best overall Area Under the Curve (AUC) of this framework can reach 84.2%. Next, the study extracts more features by combining the financial dataset with transaction dataset to increase the performance for bankruptcy prediction and achieves 84.4% AUC. Full article
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data)
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Open AccessArticle
Hierarchical Meta-Learning in Time Series Forecasting for Improved Interference-Less Machine Learning
Symmetry 2017, 9(11), 283; https://doi.org/10.3390/sym9110283 - 18 Nov 2017
Cited by 3
Abstract
The importance of an interference-less machine learning scheme in time series prediction is crucial, as an oversight can have a negative cumulative effect, especially when predicting many steps ahead of the currently available data. The on-going research on noise elimination in time series [...] Read more.
The importance of an interference-less machine learning scheme in time series prediction is crucial, as an oversight can have a negative cumulative effect, especially when predicting many steps ahead of the currently available data. The on-going research on noise elimination in time series forecasting has led to a successful approach of decomposing the data sequence into component trends to identify noise-inducing information. The empirical mode decomposition method separates the time series/signal into a set of intrinsic mode functions ranging from high to low frequencies, which can be summed up to reconstruct the original data. The usual assumption that random noises are only contained in the high-frequency component has been shown not to be the case, as observed in our previous findings. The results from that experiment reveal that noise can be present in a low frequency component, and this motivates the newly-proposed algorithm. Additionally, to prevent the erosion of periodic trends and patterns within the series, we perform the learning of local and global trends separately in a hierarchical manner which succeeds in detecting and eliminating short/long term noise. The algorithm is tested on four datasets from financial market data and physical science data. The simulation results are compared with the conventional and state-of-the-art approaches for time series machine learning, such as the non-linear autoregressive neural network and the long short-term memory recurrent neural network, respectively. Statistically significant performance gains are recorded when the meta-learning algorithm for noise reduction is used in combination with these artificial neural networks. For time series data which cannot be decomposed into meaningful trends, applying the moving average method to create meta-information for guiding the learning process is still better than the traditional approach. Therefore, this new approach is applicable to the forecasting of time series with a low signal to noise ratio, with a potential to scale adequately in a multi-cluster system due to the parallelized nature of the algorithm. Full article
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data)
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Open AccessArticle
Study on an Airport Gate Reassignment Method and Its Application
Symmetry 2017, 9(11), 258; https://doi.org/10.3390/sym9110258 - 02 Nov 2017
Cited by 2
Abstract
Bad weather, mechanical failures, air control, and crew members of the discomfort health are very likely to cause flight delays. If these events occur, decision-makers of airport operation must rediscover the flight schedules through reassigning gates to these flights, delaying flights, and canceling [...] Read more.
Bad weather, mechanical failures, air control, and crew members of the discomfort health are very likely to cause flight delays. If these events occur, decision-makers of airport operation must rediscover the flight schedules through reassigning gates to these flights, delaying flights, and canceling flights. Therefore, it is important to study the recovery strategy with the feasibility and the least cost for delayed flights and to improve the airport operation efficiency. In this paper, a mathematical model of gate reassignment based on the objectives of the loss of passengers, airport operating, and airlines, and the most important index of disturbance value of the gate reassignment for delayed flights is constructed. Then, the genetic algorithm (GA) and ant colony optimization (ACO) algorithm are combined in order to propose a two-stage hybrid(GAOTWSH) algorithm, which is used to solve the constructed mathematical model of gate reassignment for delayed flights. The test data from the operations of the one airport is used to simulate and demonstrate the performance of the constructed mathematical model of gate reassignment for irregular flights. The results show that the proposed GAOTWSH algorithm has better optimization performance and the constructed gate reassignment model is feasible and effective. The study provides a new idea and method for irregular flights. Full article
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data)
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Open AccessArticle
Exploiting the Formation of Maximal Cliques in Social Networks
Symmetry 2017, 9(7), 100; https://doi.org/10.3390/sym9070100 - 29 Jun 2017
Cited by 3
Abstract
In social networking analysis, there exists a fundamental problem called maximal cliques enumeration(MCE), which has been extensively investigated in many fields, including social networks, biological science, etc. As a matter of fact, the formation principle of maximal cliques that can help us to [...] Read more.
In social networking analysis, there exists a fundamental problem called maximal cliques enumeration(MCE), which has been extensively investigated in many fields, including social networks, biological science, etc. As a matter of fact, the formation principle of maximal cliques that can help us to speed up the detection of maximal cliques from social networks is often ignored by most existing research works. Aiming to exploit the formation of maximal cliques in social networks, this paper pioneers a creative research issue on the detection of bases of maximal cliques in social networks. We propose a formal concept analysis-based approach for detecting the bases of maximal cliques and detection theorem. It is believed that our work can provide a new research solution and direction for future topological structure analysis in various complex networking systems. Full article
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data)
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