Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (18)
  • Open Access

    ARTICLE

    OPPR: An Outsourcing Privacy-Preserving JPEG Image Retrieval Scheme with Local Histograms in Cloud Environment

    Jian Tang, Zhihua Xia*, Lan Wang, Chengsheng Yuan, Xueli Zhao

    Journal on Big Data, Vol.3, No.1, pp. 21-33, 2021, DOI:10.32604/jbd.2021.015892

    Abstract As the wide application of imaging technology, the number of big image data which may containing private information is growing fast. Due to insufficient computing power and storage space for local server device, many people hand over these images to cloud servers for management. But actually, it is unsafe to store the images to the cloud, so encryption becomes a necessary step before uploading to reduce the risk of privacy leakage. However, it is not conducive to the efficient application of image, especially in the Content-Based Image Retrieval (CBIR) scheme. This paper proposes an outsourcing privacypreserving JPEG CBIR scheme. We… More >

  • Open Access

    ARTICLE

    Hybridization of Fuzzy and Hard Semi-Supervised Clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search

    Soukaina Mjahed1,*, Khadija Bouzaachane1, Ahmad Taher Azar2,3, Salah El Hadaj1, Said Raghay1

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 459-494, 2020, DOI:10.32604/cmes.2020.010791

    Abstract This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the “Higgs machine learning challenge 2014” data set. This unsupervised detection goes in this paper analysis through 4 steps: (1) selection of the most informative features from the considered data; (2) definition of the number of clusters based on the elbow criterion. The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters; (3) proposition of a new approach for hybridization of both hard and fuzzy clustering… More >

  • Open Access

    ARTICLE

    Probiotics enhance the recovery of gut atrophy in experimental malnutrition

    DIANA B. DOCK*, JOSÉ E. AGUILAR-NASCIMENTO**, MARCIA Q. LATORRACA*

    BIOCELL, Vol.28, No.2, pp. 143-150, 2004, DOI:10.32604/biocell.2004.28.143

    Abstract AIM: The aim of this study was to evaluate the effect of probiotics on the recovery of the bowel atrophy induced by malnutrition in rats. METHODS: Twenty-and-six Wistar rats (200-250g) were fed with either a normoproteic (sham group, n=6) or a free-protein diet (n=20) during 12 days. Twelve malnourished rats were randomized to recover during 15 days with either a hydrolyzed diet (control group, n=6) or the same diet enriched with probiotics (Streptococcus thermophilus and Lactobacillus helveticus; probiotic group, n=6). RESULTS: Probiotic group showed similar gain of body, liver and bowel weight than controls. At the jejunum, both the villus… More >

  • Open Access

    ARTICLE

    Evapotranspiration and energy balance measurements over a soybean field in the semiarid sowthwestern region of Buenos Aires province (Argentina)

    Cargnel MD1, AL Orchansky2, RE Brevedan2, SS Baioni2, MN Fioretti2

    Phyton-International Journal of Experimental Botany, Vol.86, pp. 181-189, 2017, DOI:10.32604/phyton.2017.86.181

    Abstract Two field experiments were carried out in a semiarid region of Argentina over a soybean (Glycine max L. Merrill) field. The sites of study were San Adolfo (39˚ 23’ S, 62˚ 22’ W, 22 m.a.s.l.) and Nueva Roma (38˚ 29’ S, 62˚ 39’ W, 70 m.a.s.l.). Soybeans were planted on Jan 4 (San Adolfo) and Nov 27 (Nueva Roma) in 0.75 m wide rows and at 400000 pl/ha during two consecutive growing seasons. Energy balance and evapotranspiration (ET) were estimated during the reproductive stages from full bloom (R2) to full maturity (R8). In Nueva Roma ET or latent heat flux… More >

  • Open Access

    ARTICLE

    Numerical Study of Trapped Solid Particles Displacement From the Elbow of an Inclined Oil Pipeline

    Dingqian Ding1,2, Yongtu Liang1,*, Yansong Li1,3, Jianfei Sun1, Dong Han1, Jing Liu4

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.1, pp. 273-290, 2019, DOI:10.32604/cmes.2019.07228

    Abstract The solid particle impurities generated by pipe wall corrosion might deposit at the elbow of hilly pipelines during the production shutdown of oil pipelines. These solid particle impurities will seriously affect the safety of the pipeline operation and the quality of the petroleum products. Thus, it is necessary to study the methods of removing these trapped particles from pipelines. At present, the most common way to remove these solid particle impurities is pigging oil pipelines periodically by utilizing the mechanical pigging method, while the frequent pigging operation will increase the cost and risk of pipeline operation. It is very convenient… More >

  • Open Access

    ARTICLE

    Ground-Based Cloud Recognition Based on Dense_SIFT Features

    Zhizheng Zhang1, Jing Feng1,*, Jun Yan2, Xiaolei Wang1, Xiaocun Shu1

    Journal of New Media, Vol.1, No.1, pp. 1-9, 2019, DOI:10.32604/jnm.2019.05937

    Abstract Clouds play an important role in modulating radiation processes and climate changes in the Earth's atmosphere. Currently, measurement of meteorological elements such as temperature, air pressure, humidity, and wind has been automated. However, the cloud's automatic identification technology is still not perfect. Thus, this paper presents an approach that extracts dense scale-invariant feature transform (Dense_SIFT) as the local features of four typical cloud images. The extracted cloud features are then clustered by K-means algorithm, and the bag-of-words (BoW) model is used to describe each ground-based cloud image. Finally, support vector machine (SVM) is used for classification and recognition. Based on… More >

  • Open Access

    ARTICLE

    New Generation Model of Word Vector Representation Based on CBOW or Skip-Gram

    Zeyu Xiong1,*, Qiangqiang Shen1, Yueshan Xiong1, Yijie Wang1, Weizi Li2

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 259-273, 2019, DOI:10.32604/cmc.2019.05155

    Abstract Word vector representation is widely used in natural language processing tasks. Most word vectors are generated based on probability model, its bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. Recently, neural-network language models CBOW and Skip-Gram are developed as continuous-space language models for words representation in high dimensional real-valued vectors. These vector representations have recently demonstrated promising results in various NLP tasks because of their superiority in capturing syntactic and contextual regularities in language. In this paper, we propose a new strategy based on optimization in contiguous… More >

  • Open Access

    ARTICLE

    Paragraph Vector Representation Based on Word to Vector and CNN Learning

    Zeyu Xiong1,*, Qiangqiang Shen1, Yijie Wang1, Chenyang Zhu2

    CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 213-227, 2018, DOI:10.3970/cmc.2018.01762

    Abstract Document processing in natural language includes retrieval, sentiment analysis, theme extraction, etc. Classical methods for handling these tasks are based on models of probability, semantics and networks for machine learning. The probability model is loss of semantic information in essential, and it influences the processing accuracy. Machine learning approaches include supervised, unsupervised, and semi-supervised approaches, labeled corpora is necessary for semantics model and supervised learning. The method for achieving a reliably labeled corpus is done manually, it is costly and time-consuming because people have to read each document and annotate the label of each document. Recently, the continuous CBOW model… More >

Displaying 11-20 on page 2 of 18. Per Page