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  • Open Access

    ARTICLE

    Comparative Variance and Multiple Imputation Used for Missing Values in Land Price DataSet

    Longqing Zhang1, Liping Bai1,*, Xinwei Zhang2, Yanghong Zhang2, Feng Sun2, Changcheng Chen2

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1175-1187, 2019, DOI:10.32604/cmc.2019.06075

    Abstract Based on the two-dimensional relation table, this paper studies the missing values in the sample data of land price of Shunde District of Foshan City. GeoDa software was used to eliminate the insignificant factors by stepwise regression analysis; NORM software was adopted to construct the multiple imputation models; EM algorithm and the augmentation algorithm were applied to fit multiple linear regression equations to construct five different filling datasets. Statistical analysis is performed on the imputation data set in order to calculate the mean and variance of each data set, and the weight is determined according… More >

  • Open Access

    ARTICLE

    Image Augmentation-Based Food Recognition with Convolutional Neural Networks

    Lili Pan1, Jiaohua Qin1,*, Hao Chen2, Xuyu Xiang1, Cong Li1, Ran Chen1

    CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 297-313, 2019, DOI:10.32604/cmc.2019.04097

    Abstract Image retrieval for food ingredients is important work, tremendously tiring, uninteresting, and expensive. Computer vision systems have extraordinary advancements in image retrieval with CNNs skills. But it is not feasible for small-size food datasets using convolutional neural networks directly. In this study, a novel image retrieval approach is presented for small and medium-scale food datasets, which both augments images utilizing image transformation techniques to enlarge the size of datasets, and promotes the average accuracy of food recognition with state-of-the-art deep learning technologies. First, typical image transformation techniques are used to augment food images. Then transfer More >

  • Open Access

    ARTICLE

    An Improved Memory Cache Management Study Based on Spark

    Suzhen Wang1, Yanpiao Zhang1, Lu Zhang1, Ning Cao2, *, Chaoyi Pang3

    CMC-Computers, Materials & Continua, Vol.56, No.3, pp. 415-431, 2018, DOI:10.3970/cmc.2018.03716

    Abstract Spark is a fast unified analysis engine for big data and machine learning, in which the memory is a crucial resource. Resilient Distribution Datasets (RDDs) are parallel data structures that allow users explicitly persist intermediate results in memory or on disk, and each one can be divided into several partitions. During task execution, Spark automatically monitors cache usage on each node. And when there is a RDD that needs to be stored in the cache where the space is insufficient, the system would drop out old data partitions in a least recently used (LRU) fashion… More >

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