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  • Open Access

    ARTICLE

    Feature Selection Method Based on Class Discriminative Degree for Intelligent Medical Diagnosis

    Shengqun Fang1, Zhiping Cai1,*, Wencheng Sun1, Anfeng Liu2, Fang Liu3, Zhiyao Liang4, Guoyan Wang5

    CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 419-433, 2018, DOI:10.3970/cmc.2018.02289

    Abstract By using efficient and timely medical diagnostic decision making, clinicians can positively impact the quality and cost of medical care. However, the high similarity of clinical manifestations between diseases and the limitation of clinicians’ knowledge both bring much difficulty to decision making in diagnosis. Therefore, building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical domain. In this paper, we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease… More >

  • Open Access

    ARTICLE

    Semi-Supervised Learning with Generative Adversarial Networks on Digital Signal Modulation Classification

    Ya Tu1, Yun Lin1, Jin Wang2,3,*, Jeong-Uk Kim4

    CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 243-254, 2018, DOI:10.3970/cmc.2018.01755

    Abstract Deep Learning (DL) is such a powerful tool that we have seen tremendous success in areas such as Computer Vision, Speech Recognition, and Natural Language Pro-cessing. Since Automated Modulation Classification (AMC) is an important part in Cognitive Radio Networks, we try to explore its potential in solving signal modula-tion recognition problem. It cannot be overlooked that DL model is a complex mod-el, thus making them prone to over-fitting. DL model requires many training data to combat with over-fitting, but adding high quality labels to training data manually is not always cheap and accessible, especially in More >

  • Open Access

    ARTICLE

    Interobserver variability in the classification of congenital coronary abnormalities: A substudy of the anomalous connections of the coronary arteries registry

    Athanasios Koutsoukis1, Xavier Halna du Fretay2, Patrick Dupouy3, Phalla Ou4, Jean-Pierre Laissy4, Jean-Michel Juliard5, Fabien Hyafil6, Pierre Aubry5

    Congenital Heart Disease, Vol.12, No.6, pp. 726-732, 2017, DOI:10.1111/chd.12504

    Abstract Objective: The diagnosis of anomalous connections of the coronary arteries (ANOCOR) requires an appropriate identification for the management of the patients involved. We studied the observer variability in the description and classification of ANOCOR between a nonexpert group of physicians and a group of expert physicians, using the ANOCOR cohort.
    Patients and design: Consecutive patients identified by 71 referring cardiologists were included in the ANOCOR cohort. Anomalous connection was diagnosed by invasive and/or computed tomography coronary angiography. Angiographic images were reviewed by an angiographic committee with experience in this field. Both investigators and angiographic committee filled out… More >

  • Open Access

    ARTICLE

    Brake Fault Diagnosis Through Machine Learning Approaches – A Review

    Alamelu Manghai T.M.1, Jegadeeshwaran R2, Sugumaran V.3

    Structural Durability & Health Monitoring, Vol.11, No.1, pp. 43-67, 2017, DOI:10.3970/sdhm.2017.012.043

    Abstract Diagnosis is the recognition of the nature and cause of a certain phenomenon. It is generally used to determine cause and effect of a problem. Machine fault diagnosis is a field of finding faults arising in machines. To identify the most probable faults leading to failure, many methods are used for data collection, including vibration monitoring, thermal imaging, oil particle analysis, etc. Then these data are processed using methods like spectral analysis, wavelet analysis, wavelet transform, short-term Fourier transform, high-resolution spectral analysis, waveform analysis, etc., The results of this analysis are used in a root 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 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 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 More >

  • Open Access

    ABSTRACT

    A New Quadtree-based Image Compression Technique using Pattern Matching Algorithm

    F. Keissarian1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.12, No.4, pp. 137-144, 2009, DOI:10.3970/icces.2009.012.137

    Abstract In this paper, a new image compression technique is proposed in which variable block size technique is adopted, using quadtree decomposition, for coding images at low bit rates. The proposed algorithm decomposes the host image into blocks of variable sizes according to histogram analysis of the block residuals. Variable block sizes are then encoded at different rates based on their visual activity levels. To preserve edge integrity, a high-detail block is coded by a set of parameters associated with the pattern appearing inside the block. The use of these parameters at the receiver together with More >

  • Open Access

    ARTICLE

    Classification and estimation of genetic diversity in Nopal Opuntia spp based on phenotypic descriptors and genetic-molecular markers

    García-Zambrano2 Eduardo A, Adriana Gutiérrez-Díez, Gilberto E Salinas-García, Elizabeth Cárdenas-Cerda, Rigoberto E Vázquez-Alvarado, Francisco Zavala-García, Jesús Martínez de la Cerda

    Phyton-International Journal of Experimental Botany, Vol.75, pp. 125-135, 2006, DOI:10.32604/phyton.2006.75.125

    Abstract The objective of this investigation was to develop and apply molecular markers (type RAPD) for estimating the genetic diversity of 100 Opuntia accessions from the Germoplasma Bank of the FAUANL. The molecular data were evaluated by multivariate analysis using the methods of Ward and UPGMA. These results were compared with phenotypic data taken from the same accessions, already evaluated by the above mentioned methods. The protocol was modified for the extraction of the Opuntia DNA to reduce the quantity of polysaccharides and their influence in its quality. Later on, a protocol was developed for the generation of More >

  • Open Access

    ARTICLE

    Water quality in the basin of the Amajac river, Hidalgo, Mexico: Diagnosis and prediction

    Amado Alvarez1,2, Enrique Rubiños Panta1, Francisco Gavi Reyes1, Juan José Alarcón Cabañero2, Elizabeth Hernández Acosta3, Carlos Ramírez Ayala1, Enrique Mejía Saenz1, Francisco Pedrero Salcedo2, Emilio Nicolas Nicolas2, Enrique Salazar Sosa4

    Phyton-International Journal of Experimental Botany, Vol.75, pp. 71-83, 2006, DOI:10.32604/phyton.2006.75.071

    Abstract A water quality index as a standardized method to compare the category in an integral way, between locations and through time, of the different water, river and stream storages of the Amajac river basin was developed. In addition, it is possible to predict the degree of contamination and establish planning strategies in the management of the water resources for the river basin in issue. The used methodology was based in the quantitative expression of water quality. Three samplings were made (2005-2006) and Dissolved Oxygen, Coliform in feaces, pH, Oxygen Biochemical Demand, Nitrates, Total Phosphorus, Turbidity… More >

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