Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (324)
  • Open Access

    ARTICLE

    A Cryptograph Domain Image Retrieval Method Based on Paillier Homomorphic Block Encryption

    Wenjia Xu1, Shijun Xiang1,*, Vasily Sachnev2

    CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 285-295, 2018, DOI:10.3970/cmc.2018.01719

    Abstract With the rapid development of information network, the computing resources and storage capacity of ordinary users cannot meet their needs of data processing. The emergence of cloud computing solves this problem but brings data security problems. How to manage and retrieve ciphertext data effectively becomes a challenging problem. To these problems, a new image retrieval method in ciphertext domain by block image encrypting based on Paillier homomophic cryptosystem is proposed in this paper. This can be described as follows: According to the Paillier encryption technology, the image owner encrypts the original image in blocks, obtains the image in ciphertext domain,… More >

  • Open Access

    ARTICLE

    Adversarial Learning for Distant Supervised Relation Extraction

    Daojian Zeng1,3, Yuan Dai1,3, Feng Li1,3, R. Simon Sherratt2, Jin Wang3,*

    CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 121-136, 2018, DOI:10.3970/cmc.2018.055.121

    Abstract Recently, many researchers have concentrated on using neural networks to learn features for Distant Supervised Relation Extraction (DSRE). These approaches generally use a softmax classifier with cross-entropy loss, which inevitably brings the noise of artificial class NA into classification process. To address the shortcoming, the classifier with ranking loss is employed to DSRE. Uniformly randomly selecting a relation or heuristically selecting the highest score among all incorrect relations are two common methods for generating a negative class in the ranking loss function. However, the majority of the generated negative class can be easily discriminated from positive class and will contribute… 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 >

  • Open Access

    ARTICLE

    Research on damage distribution and permeability distribution of coal seam with slotted borehole

    Yi Xue1,2, Feng Gao1,2,3, Xingguang Liu4, Xiru Li2

    CMC-Computers, Materials & Continua, Vol.47, No.2, pp. 127-141, 2015, DOI:10.3970/cmc.2015.047.127

    Abstract In order to study the effect of high pressure water jet cutting technology on the permeability of single coal seam, we use the damage variable to describe the fracture distribution of coal seam, develop the 3-D finite element program based on the damage theory, and then analyze the damage distribution of coal seam after drilling and slotting. Using MTS815 rock mechanics testing system and the permeability test system, we conduct the permeability test and get the relationship between permeability and damage. Based on the damage distribution of coal seam after drilling and slotting and the permeability change law, we analyze… More >

Displaying 321-330 on page 33 of 324. Per Page