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  • Open Access

    ARTICLE

    Analysis of Bus Ride Comfort Using Smartphone Sensor Data

    Hoong-Chor Chin1, Xingting Pang1, Zhaoxia Wang2,3,*

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 455-463, 2019, DOI:10.32604/cmc.2019.05664

    Abstract Passenger comfort is an important indicator that is often used to measure the quality of public transport services. It may also be a crucial factor in the passenger’s choice of transport mode. The typical method of assessing passenger comfort is through a passenger interview survey which can be tedious. This study aims to investigate the relationship between bus ride comfort based on ride smoothness and the vehicle’s motion detected by the smartphone sensors. An experiment was carried out on a bus fixed route within the University campus where comfort levels were rated on a 3-point scale and recorded at 5-second… More >

  • Open Access

    ARTICLE

    A Comparative Study of Machine Learning Methods for Genre Identification of Classical Arabic Text

    Maha Al-Yahya1, *

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 421-433, 2019, DOI:10.32604/cmc.2019.06209

    Abstract The purpose of this study is to evaluate the performance of five supervised machine learning methods for the task of automated genre identification of classical Arabic texts using text most frequent words as features. We design an experiment for comparing five machine-learning methods for the genre identification task for classical Arabic text. We set the data and the stylometric features and vary the classification method to evaluate the performance of each method. Of the five machine learning methods tested, we can conclude that Support Vector Machine (SVM) are generally the most effective. The contribution of this work lies in the… More >

  • Open Access

    ARTICLE

    Relation Extraction for Massive News Texts

    Libo Yin1, Xiang Meng2, Jianxun Li3, Jianguo Sun2,*

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 275-285, 2019, DOI:10.32604/cmc.2019.05556

    Abstract With the development of information technology including Internet technologies, the amount of textual information that people need to process daily is increasing. In order to automatically obtain valuable and user-informed information from massive amounts of textual data, many researchers have conducted in-depth research in the area of entity relation extraction. Based on the existing research of word vector and the method of entity relation extraction, this paper designs and implements an method based on support vector machine (SVM) for extracting English entity relationships from massive news texts. The method converts sentences in natural language into a form of numerical matrix… More >

  • Open Access

    ARTICLE

    Application of Image Compression to Multiple-Shot Pictures Using Similarity Norms With Three Level Blurring

    Mohammed Omari1,*, Souleymane Ouled Jaafri1

    CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 753-775, 2019, DOI:10.32604/cmc.2019.06576

    Abstract Image compression is a process based on reducing the redundancy of the image to be stored or transmitted in an efficient form. In this work, a new idea is proposed, where we take advantage of the redundancy that appears in a group of images to be all compressed together, instead of compressing each image by itself. In our proposed technique, a classification process is applied, where the set of the input images are classified into groups based on existing technique like L1 and L2 norms, color histograms. All images that belong to the same group are compressed based on dividing… More >

  • Open Access

    ARTICLE

    A Learning Based Brain Tumor Detection System

    Sultan Noman Qasem1,2, Amar Nazar3, Attia Qamar4, Shahaboddin Shamshirband5,6,*, Ahmad Karim4

    CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 713-727, 2019, DOI:10.32604/cmc.2019.05617

    Abstract Brain tumor is one of the most dangerous disease that causes due to uncontrollable and abnormal cell partition. In this paper, we have used MRI brain scan in comparison with CT brain scan as it is less harmful to detect brain tumor. We considered watershed segmentation technique for brain tumor detection. The proposed methodology is divided as follows: pre-processing, computing foreground applying watershed, extract and supply features to machine learning algorithms. Consequently, this study is tested on big data set of images and we achieved acceptable accuracy from K-NN classification algorithm in detection of brain tumor. 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

    Reversible Data Hiding in Encrypted Image Based on Block Classification Permutation

    Qun Mo1, Heng Yao1, Fang Cao2, Zheng Chang3, Chuan Qin1,*

    CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 119-133, 2019, DOI:10.32604/cmc.2019.05770

    Abstract Recently, reversible data hiding in encrypted image (RDHEI) has attracted extensive attention, which can be used in secure cloud computing and privacy protection effectively. In this paper, a novel RDHEI scheme based on block classification and permutation is proposed. Content owner first divides original image into non-overlapping blocks and then set a threshold to classify these blocks into smooth and non-smooth blocks respectively. After block classification, content owner utilizes a specific encryption method, including stream cipher encryption and block permutation to protect image content securely. For the encrypted image, data hider embeds additional secret information in the most significant bits… More >

  • Open Access

    ARTICLE

    Weighted Sparse Image Classification Based on Low Rank Representation

    Qidi Wu1, Yibing Li1, Yun Lin1,*, Ruolin Zhou2

    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 91-105, 2018, DOI: 10.3970/cmc.2018.02771

    Abstract The conventional sparse representation-based image classification usually codes the samples independently, which will ignore the correlation information existed in the data. Hence, if we can explore the correlation information hidden in the data, the classification result will be improved significantly. To this end, in this paper, a novel weighted supervised spare coding method is proposed to address the image classification problem. The proposed method firstly explores the structural information sufficiently hidden in the data based on the low rank representation. And then, it introduced the extracted structural information to a novel weighted sparse representation model to code the samples in… More >

  • Open Access

    ARTICLE

    A New Minimax Probabilistic Approach and Its Application in Recognition the Purity of Hybrid Seeds

    Liming Yang1, Yongping Gao2, Qun Sun3

    CMES-Computer Modeling in Engineering & Sciences, Vol.104, No.6, pp. 493-506, 2015, DOI:10.3970/cmes.2015.104.493

    Abstract Minimax probability machine (MPM) has been recently proposed and shown its advantage in pattern recognition. In this paper, we present a new minimax probabilistic approach (MPA),which can provide an explicit lower bound on prediction accuracy. Applying the Chebyshev-Cantelli inequality, the MPA is posed as a second order cone program formulation and solved effectively. Following that, this method is exploited directly to recognize the purity of hybrid seeds using near-infrared spectroscopic data. Experimental results in different spectral regions show that the proposed MPA is competitive with the existing minimax probability machine and support vector machine in generalization, while requires less computational… More >

  • Open Access

    ARTICLE

    Classification and Optimization Model of Mesoporous Carbons Pore Structure and Adsorption Properties Based on Support Vector Machine

    Zhen Yang1, Xingsheng Gu2, Xiaoyi Liang1,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.74, No.3&4, pp. 161-182, 2011, DOI:10.3970/cmes.2011.074.161

    Abstract Mesoporous carbons are synthesized by organic-organic self-assembly of triblock copolymer F127 and a new type of carbon precursor as resorcinol-furfural oligomers. Some factors will impact the mesoporous carbons pore structure and properties were studied. The main factors, such as the ratio of triblock copolymer F127 and oligomers, degree of polymerizstry of resorcinol-furfural oligomers, the ratio of resorcinol-furfural oligomers - F/R, and their mutual relations were identified. Aimed at balancing the complex characteristic of mesoporous structure and adsorption properties, a classification and optimization model based on support vector machine is developed. The optimal operation conditions of Barret-Joyner-Halenda (BJH) adsorption cumulative volume… More >

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