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  • Open Access

    ARTICLE

    Research on the Freezing Phenomenon of Quantum Correlation by Machine Learning

    Xiaoyu Li1, Qinsheng Zhu2, *, Yiming Huang1, Yong Hu2, Qingyu Meng2, Chenjing Su1, Qing Yang2, Shaoyi Wu2, Xusheng Liu3

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2143-2151, 2020, DOI:10.32604/cmc.2020.010865

    Abstract Quantum correlation shows a fascinating nature of quantum mechanics and plays an important role in some physics topics, especially in the field of quantum information. Quantum correlations of the composite system can be quantified by resorting to geometric or entropy methods, and all these quantification methods exhibit the peculiar freezing phenomenon. The challenge is to find the characteristics of the quantum states that generate the freezing phenomenon, rather than only study the conditions which generate this phenomenon under a certain quantum system. In essence, this is a classification problem. Machine learning has become an effective method for researchers to study… More >

  • Open Access

    ARTICLE

    Efficient Virtual Resource Allocation in Mobile Edge Networks Based on Machine Learning

    Li Li1,*, Yifei Wei1, Lianping Zhang2, Xiaojun Wang3

    Journal of Cyber Security, Vol.2, No.3, pp. 141-150, 2020, DOI:10.32604/jcs.2020.010764

    Abstract The rapid growth of Internet content, applications and services require more computing and storage capacity and higher bandwidth. Traditionally, internet services are provided from the cloud (i.e., from far away) and consumed on increasingly smart devices. Edge computing and caching provides these services from nearby smart devices. Blending both approaches should combine the power of cloud services and the responsiveness of edge networks. This paper investigates how to intelligently use the caching and computing capabilities of edge nodes/cloudlets through the use of artificial intelligence-based policies. We first analyze the scenarios of mobile edge networks with edge computing and caching abilities,… More >

  • Open Access

    ARTICLE

    Classification-Based Fraud Detection for Payment Marketing and Promotion

    Shuo He1,∗, Jianbin Zheng1,†, Jiale Lin2,‡, Tao Tang1,§, Jintao Zhao1,¶, Hongbao Liu1,ll

    Computer Systems Science and Engineering, Vol.35, No.3, pp. 141-149, 2020, DOI:10.32604/csse.2020.35.141

    Abstract Nowadays, many payment service providers use the discounts and other marketing strategies to promote their products. This also raises the issue of people who deliberately take advantage of such promotions to reap financial benefits. These people are known as ‘scalper parties’ or ‘econnoisseurs’ which can constitute an underground industry. In this paper, we show how to use machine learning to assist in identifying abnormal scalper transactions. Moreover, we introduce the basic methods of Decision Tree and Boosting Tree, and show how these classification methods can be applied in the detection of abnormal transactions. In addition, we introduce a graph computing… More >

  • Open Access

    ARTICLE

    Intelligent Choice of Machine Learning Methods for Predictive Maintenance of Intelligent Machines

    Marius Becherer, Michael Zipperle, Achim Karduck

    Computer Systems Science and Engineering, Vol.35, No.2, pp. 81-89, 2020, DOI:10.32604/csse.2020.35.081

    Abstract Machines are serviced too often or only when they fail. This can result in high costs for maintenance and machine failure. The trend of Industry 4.0 and the networking of machines opens up new possibilities for maintenance. Intelligent machines provide data that can be used to predict the ideal time of maintenance. There are different approaches to create a forecast. Depending on the method used, appropriate conditions must be created to improve the forecast. In this paper, results are compiled to give a state of the art of predictive maintenance. First, the different types of maintenance and economic relationships are… More >

  • Open Access

    ARTICLE

    A Survey and Systematic Categorization of Parallel K-Means and Fuzzy-C-Means Algorithms

    Ahmed A. M. Jamel1,∗, Bahriye Akay2,†

    Computer Systems Science and Engineering, Vol.34, No.5, pp. 259-281, 2019, DOI:10.32604/csse.2019.34.259

    Abstract Parallel processing has turned into one of the emerging fields of machine learning due to providing consistent work by performing several tasks simultaneously, enhancing reliability (the presence of more than one device ensures the workflow even if some devices disrupted), saving processing time and introducing low cost and high-performance computation units. This research study presents a survey of parallel K-means and Fuzzy-c-means clustering algorithms based on their implementations in parallel environments such as Hadoop, MapReduce, Graphical Processing Units, and multi-core systems. Additionally, the enhancement in parallel clustering algorithms is investigated as hybrid approaches in which K-means and Fuzzy-c-means clustering algorithms… More >

  • Open Access

    ARTICLE

    Extreme Learning Machine with Elastic Net Regularization

    Lihua Guo*

    Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 421-427, 2020, DOI:10.32604/iasc.2020.013918

    Abstract Compared with deep neural learning, the extreme learning machine (ELM) can be quickly converged without iteratively tuning hidden nodes. Inspired by this merit, an extreme learning machine with elastic net regularization (ELM-EN) is proposed in this paper. The elastic net is a regularization method that combines LASSO and ridge penalties. This regularization can keep a balance between system stability and solution's sparsity. Moreover, an excellent optimization method, i.e., accelerated proximal gradient, is used to find the minimum of the system optimization function. Various datasets from UCI repository and two facial expression image datasets are used to validate the efficiency of… More >

  • Open Access

    ARTICLE

    Automated Inspection of Char Morphologies in Colombian Coals Using Image Analysis

    Deisy Chaves1,5,*, Maria Trujillo1, Edward Garcia2, Juan Barraza2, Edward Lester3, Maribel Barajas4, Billy Rodriguez4, Manuel Romero4, Laura Fernández-Robles5

    Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 397-405, 2020, DOI:10.32604/iasc.2020.013916

    Abstract Precise automated determination of char morphologies formed by coal during combustion can lead to more efficient industrial control systems for coal combustion. Commonly, char particles are manually classified following the ICCP decision tree which considers four morphological features. One of these features is unfused material, and this class of material not characteristic of Colombian coals. In this paper, we propose new machine learning algorithms to classify the char particles in an image based system. Our hypothesis is that supervised classification methods can outperform the 4 ‘class’ ICCP criteria. In this paper we evaluate several morphological features and specifically assess the… More >

  • Open Access

    ARTICLE

    Sentiment Analysis System in Big Data Environment

    Wint Nyein Chan1, Thandar Thein2

    Computer Systems Science and Engineering, Vol.33, No.3, pp. 187-202, 2018, DOI:10.32604/csse.2018.33.187

    Abstract Nowadays, Big Data, a large volume of both structured and unstructured data, is generated from Social Media. Social Media are powerful marketing tools and social big data can offer the business insights. The major challenge facing social big data is attaining efficient techniques to collect a large volume of social data and extract insights from the huge amount of collected data. Sentiment Analysis of social big data can provide business insights by extracting the public opinions. The traditional analytic platforms need to be scaled up for analyzing a large volume of social big data. Social data are by nature shorter… More >

  • Open Access

    ARTICLE

    Online And Offline Scheduling Schemes to Maximize the Weighted Delivered Video Packets Towards Maritime Cpss

    Tingting Yang1, Hailong Feng1, Chengming Yang2, Ge Guo3, Tieshan Li1

    Computer Systems Science and Engineering, Vol.33, No.2, pp. 157-164, 2018, DOI:10.32604/csse.2018.33.157

    Abstract In this paper, the online and offline scheduling schemes towards maritime Cyber Physical Systems (CPSs), to transmit video packets generating from the interior of vessel. During the sailing from the origin port to destination port, the video packets could be delivered via the infostations shoreside. The video packets have their respective release times, deadlines, weights and processing time. The video packets only could be successfully transmitted before their deadlines. A mathematic job-machine problem is mapped. Facing distinguished challenges with unique characteristics imposed in maritime scenario, we focus on the heterogeneous networking and resource optimal scheduling technology to provide valuable insights… More >

  • Open Access

    ARTICLE

    Automated and Precise Event Detection Method for Big Data in Biomedical Imaging with Support Vector Machine

    Lufeng Yuan, Erlin Yao, Guangming Tan

    Computer Systems Science and Engineering, Vol.33, No.2, pp. 105-113, 2018, DOI:10.32604/csse.2018.33.105

    Abstract This paper proposes a machine learning based method which can detect certain events automatically and precisely in biomedical imaging. We detect one important and not well-defined event, which is called flash, in fluorescence images of Escherichia coli. Given a time series of images, first we propose a scheme to transform the event detection on region of interest (ROI) in images to a classification problem. Then with supervised human labeling data, we develop a feature selection technique to utilize support vector machine (SVM) to solve this classification problem. To reduce the time in training SVM model, a parallel version of SVM… More >

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