Symmetry

Research

14 pages, 7942 KiB

Open AccessArticle

Vegetation Greening for Winter Oblique Photography Using Cycle-Consistence Adversarial Networks

by Xiaowei Xue, Chunxue Wu, Ze Sun, Yan Wu and Neal N. Xiong

Symmetry 2018, 10(7), 294; https://doi.org/10.3390/sym10070294 - 20 Jul 2018

Cited by 5 | Viewed by 3487

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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17 pages, 2225 KiB

Open AccessArticle

Fuzzy Association Rule Based Froth Surface Behavior Control in Zinc Froth Flotation

by Jin Zhang, Zhaohui Tang, Mingxi Ai and Weihua Gui

Symmetry 2018, 10(6), 216; https://doi.org/10.3390/sym10060216 - 13 Jun 2018

Cited by 11 | Viewed by 4072

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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16 pages, 2159 KiB

Open AccessArticle

Data Decision and Drug Therapy Based on Non-Small Cell Lung Cancer in a Big Data Medical System in Developing Countries

by Jia Wu, Yanlin Tan, Zhigang Chen and Ming Zhao

Symmetry 2018, 10(5), 152; https://doi.org/10.3390/sym10050152 - 10 May 2018

Cited by 20 | Viewed by 4290

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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12 pages, 922 KiB

Open AccessArticle

Clickbait Convolutional Neural Network

by Hai-Tao Zheng, Jin-Yuan Chen, Xin Yao, Arun Kumar Sangaiah, Yong Jiang and Cong-Zhi Zhao

Symmetry 2018, 10(5), 138; https://doi.org/10.3390/sym10050138 - 1 May 2018

Cited by 53 | Viewed by 9749

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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30 pages, 12505 KiB

Open AccessArticle

Big Data Analysis for Personalized Health Activities: Machine Learning Processing for Automatic Keyword Extraction Approach

by Jun-Ho Huh

Symmetry 2018, 10(4), 93; https://doi.org/10.3390/sym10040093 - 2 Apr 2018

Cited by 58 | Viewed by 11132

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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13 pages, 2706 KiB

Open AccessArticle

Oversampling Techniques for Bankruptcy Prediction: Novel Features from a Transaction Dataset

by Tuong Le, Mi Young Lee, Jun Ryeol Park and Sung Wook Baik

Symmetry 2018, 10(4), 79; https://doi.org/10.3390/sym10040079 - 22 Mar 2018

Cited by 66 | Viewed by 9067

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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20 pages, 3809 KiB

Open AccessArticle

Hierarchical Meta-Learning in Time Series Forecasting for Improved Interference-Less Machine Learning

by David Afolabi, Sheng-Uei Guan, Ka Lok Man, Prudence W. H. Wong and Xuan Zhao

Symmetry 2017, 9(11), 283; https://doi.org/10.3390/sym9110283 - 18 Nov 2017

Cited by 21 | Viewed by 6841

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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18 pages, 1590 KiB

Open AccessArticle

Study on an Airport Gate Reassignment Method and Its Application

by Wu Deng, Bo Li and Huimin Zhao

Symmetry 2017, 9(11), 258; https://doi.org/10.3390/sym9110258 - 2 Nov 2017

Cited by 8 | Viewed by 5384

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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9 pages, 1798 KiB

Open AccessArticle

Exploiting the Formation of Maximal Cliques in Social Networks

by Fei Hao, Doo-Soon Park and Zheng Pei

Symmetry 2017, 9(7), 100; https://doi.org/10.3390/sym9070100 - 29 Jun 2017

Cited by 6 | Viewed by 4349

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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