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  • Open Access

    ARTICLE

    A Nonuniform Clustering Routing Algorithm Based on an Improved K-Means Algorithm

    Xinliang Tang1, Man Zhang1, Pingping Yu1, Wei Liu2, Ning Cao3, *, Yunfeng Xu4

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1725-1739, 2020, DOI:10.32604/cmc.2020.010272

    Abstract In a large-scale wireless sensor network (WSN), densely distributed sensor nodes process a large amount of data. The aggregation of data in a network can consume a great amount of energy. To balance and reduce the energy consumption of nodes in a WSN and extend the network life, this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm. The algorithm uses a clustering method to form and optimize clusters, and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN. To ensure that the cluster head (CH) selection… More >

  • Open Access

    ARTICLE

    A Multi-Feature Weighting Based K-Means Algorithm for MOOC Learner Classification

    Yuqing Yang1,2, Dequn Zhou1,*, Xiaojiang Yang1,3,4

    CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 625-633, 2019, DOI:10.32604/cmc.2019.05246

    Abstract Massive open online courses (MOOC) have recently gained worldwide attention in the field of education. The manner of MOOC provides a new option for learning various kinds of knowledge. A mass of data miming algorithms have been proposed to analyze the learner’s characteristics and classify the learners into different groups. However, most current algorithms mainly focus on the final grade of the learners, which may result in an improper classification. To overcome the shortages of the existing algorithms, a novel multi-feature weighting based K-means (MFWK-means) algorithm is proposed in this paper. Correlations between the widely used feature grade and other… More >

  • Open Access

    ARTICLE

    A Machine Learning Approach for MRI Brain Tumor Classification

    Ravikumar Gurusamy1, Dr Vijayan Subramaniam2

    CMC-Computers, Materials & Continua, Vol.53, No.2, pp. 91-108, 2017, DOI:10.3970/cmc.2017.053.091

    Abstract A new method for the denoising, extraction and tumor detection on MRI images is presented in this paper. MRI images help physicians study and diagnose diseases or tumors present in the brain. This work is focused towards helping the radiologist and physician to have a second opinion on the diagnosis. The ambiguity of Magnetic Resonance (MR) image features is solved in a simpler manner. The MRI image acquired from the machine is subjected to analysis in the work. The real-time data is used for the analysis. Basic preprocessing is performed using various filters for noise removal. The de-noised image is… More >

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