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  • Open Access

    ARTICLE

    An Entropy-Based Model for Recommendation of Taxis’ Cruising Route

    Yizhi Liu1, 2, Xuesong Wang1, 2, Jianxun Liu1, 2, *, Zhuhua Liao1, 2, Yijiang Zhao1, 2, Jianjun Wang1, 2

    Journal on Artificial Intelligence, Vol.2, No.3, pp. 137-148, 2020, DOI:10.32604/jai.2020.010620

    Abstract Cruising route recommendation based on trajectory mining can improve taxidrivers' income and reduce energy consumption. However, existing methods mostly recommend pick-up points for taxis only. Moreover, their performance is not good enough since there lacks a good evaluation model for the pick-up points. Therefore, we propose an entropy-based model for recommendation of taxis' cruising route. Firstly, we select more positional attributes from historical pick-up points in order to obtain accurate spatial-temporal features. Secondly, the information entropy of spatial-temporal features is integrated in the evaluation model. Then it is applied for getting the next pick-up points and further recommending a series… More >

  • Open Access

    ARTICLE

    COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining

    Yixian Zhang1, Jieren Cheng2, *, Yifan Yang2, Haocheng Li2, Xinyi Zheng2, Xi Chen2, Boyi Liu3, Tenglong Ren4, Naixue Xiong5

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1415-1434, 2020, DOI:10.32604/cmc.2020.011316

    Abstract With the spread and development of new epidemics, it is of great reference value to identify the changing trends of epidemics in public emotions. We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining. A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed. Establish a “Scrapy-Redis-Bloomfilter” distributed crawler framework to collect data. The system can judge the positive and negative emotions of the reviewer based on the comments, and can also reflect the… More >

  • Open Access

    ARTICLE

    A Distributed Approach of Big Data Mining for Financial Fraud Detection in a Supply Chain

    Hangjun Zhou1, *, Guang Sun1, 2, Sha Fu1, Xiaoping Fan1, Wangdong Jiang1, Shuting Hu1, Lingjiao Li1

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1091-1105, 2020, DOI:10.32604/cmc.2020.09834

    Abstract Supply Chain Finance (SCF) is important for improving the effectiveness of supply chain capital operations and reducing the overall management cost of a supply chain. In recent years, with the deep integration of supply chain and Internet, Big Data, Artificial Intelligence, Internet of Things, Blockchain, etc., the efficiency of supply chain financial services can be greatly promoted through building more customized risk pricing models and conducting more rigorous investment decision-making processes. However, with the rapid development of new technologies, the SCF data has been massively increased and new financial fraud behaviors or patterns are becoming more covertly scattered among normal… More >

  • Open Access

    ARTICLE

    Using Audiometric Data to Weigh and Prioritize Factors that Affect Workers’ Hearing Loss through Support Vector Machine (SVM) Algorithm

    Hossein ElahiShirvan1, MohammadReza Ghotbi-Ravandi2, Sajad Zare3,*, Mostafa Ghazizadeh Ahsaee4

    Sound & Vibration, Vol.54, No.2, pp. 99-112, 2020, DOI:10.32604/sv.2020.08839

    Abstract Workers’ exposure to excessive noise is a big universal work-related challenges. One of the major consequences of exposure to noise is permanent or transient hearing loss. The current study sought to utilize audiometric data to weigh and prioritize the factors affecting workers’ hearing loss based using the Support Vector Machine (SVM) algorithm. This cross sectional-descriptive study was conducted in 2017 in a mining industry in southeast Iran. The participating workers (n = 150) were divided into three groups of 50 based on the sound pressure level to which they were exposed (two experimental groups and one control group). Audiometric tests… More >

  • Open Access

    ARTICLE

    MINE: A Method of Multi-Interaction Heterogeneous Information Network Embedding

    Dongjie Zhu1, Yundong Sun1, Xiaofang Li2, Haiwen Du3, Rongning Qu2, Pingping Yu4, *, Xuefeng Piao1, Russell Higgs5, Ning Cao6

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1343-1356, 2020, DOI:10.32604/cmc.2020.010008

    Abstract Interactivity is the most significant feature of network data, especially in social networks. Existing network embedding methods have achieved remarkable results in learning network structure and node attributes, but do not pay attention to the multiinteraction between nodes, which limits the extraction and mining of potential deep interactions between nodes. To tackle the problem, we propose a method called MultiInteraction heterogeneous information Network Embedding (MINE). Firstly, we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm. Secondly, we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse… More >

  • Open Access

    ARTICLE

    A Hybrid Approach of TLBO and EBPNN for Crop Yield Prediction Using Spatial Feature Vectors

    Preeti Tiwari1, *, Piyush Shukla1

    Journal on Artificial Intelligence, Vol.1, No.2, pp. 45-58, 2019, DOI:10.32604/jai.2019.04444

    Abstract The prediction of crop yield is one of the important factor and also challenging, to predict the future crop yield based on various criteria’s. Many advanced technologies are incorporated in the agricultural processes, which enhances the crop yield production efficiency. The process of predicting the crop yield can be done by taking agriculture data, which helps to analyze and make important decisions before and during cultivation. This paper focuses on the prediction of crop yield, where two models of machine learning are developed for this work. One is Modified Convolutional Neural Network (MCNN), and the other model is TLBO (Teacher… More >

  • Open Access

    ABSTRACT

    Risk modeling by CHAID decision tree algorithm

    A.S. Koyuncugil1, N. Ozgulbas2

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.11, No.2, pp. 39-46, 2009, DOI:10.3970/icces.2009.011.039

    Abstract In this paper, a data mining model for detecting financial and operational risk indicators by CHAID Decision Tree is presenting. The identification of the risk factors by clarifying the relationship between the variables defines the discovery of knowledge from the financial and operational variables. Automatic and estimation oriented information discovery process coincides the definition of data mining. During the formation of model; an easy to understand, easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background is targeted by the discovery of the implicit relationships between the data and the identification of… More >

  • Open Access

    ABSTRACT

    Network-Marketing: Intelligent Data Control System using Data Mining Technique

    H.S. Fadewar1, S.B. Jagtap1, G.N.Shinde2

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.9, No.1, pp. 57-66, 2009, DOI:10.3970/icces.2009.009.057

    Abstract Data Mining (DM) techniques are well-known for providing flexible and efficient analytical tools for data processing. In this paper, we propose intelligent data control system design and specifications as an example of DM application in marketing data processing. E-Marketing businesses are using Data Mining to identify patterns in customers' buying behavior; identify profitable customer segments; increase marketing return rates; prevent loss of valuable customers; estimate credit risk; identify fraudulent activity and much more. More >

  • Open Access

    ARTICLE

    Mining of Data from Evolutionary Algorithms for Improving Design Optimization

    Y.S. Lian1, M.S. Liou2

    CMES-Computer Modeling in Engineering & Sciences, Vol.8, No.1, pp. 61-72, 2005, DOI:10.3970/cmes.2005.008.061

    Abstract This paper focuses on integration of computational methods for design optimization based on data mining and knowledge discovery. We propose to use radial basis function neural networks to analyze the large database generated from evolutionary algorithms and to extract the cause-effect relationship, between the objective functions and the input design variables. The aim is to improve the optimization process by either reducing the computation cost or improving the optimal. Also, it is hoped to provide designers with the salient design pattern about the problem under consideration, from the physics-based simulations. The proposed technique is applied to both academic problems and… More >

  • Open Access

    ARTICLE

    Research on Privacy Preserving Data Mining

    Pingshui Wang1,*, Tao Chen1,2, Zecheng Wang1

    Journal of Information Hiding and Privacy Protection, Vol.1, No.2, pp. 61-68, 2019, DOI:10.32604/jihpp.2019.05943

    Abstract In recent years, with the explosive development in Internet, data storage and data processing technologies, privacy preservation has been one of the greater concerns in data mining. A number of methods and techniques have been developed for privacy preserving data mining. This paper provided a wide survey of different privacy preserving data mining algorithms and analyzed the representative techniques for privacy preservation. The existing problems and directions for future research are also discussed. More >

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