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  • 1
    Language: English
    In: Expert Systems With Applications, 2009, Vol.36(7), pp.10485-10493
    Description: Probabilistic latent semantic analysis (PLSA) is a double structure mixture model which has got a wide application in text and web mining. This method is capable of establishing hidden semantic relations among the observed features, using a number of latent variables. In this approach, the selection of the correct number of latent variables is critical. In the most of the previous researches, the number of latent topics was selected based on the number of invoked classes. This paper presents a method, based on backward elimination approach, which is capable of unsupervised order selection in PLSA. This method starts with a model having a number of components more than the needed value, and then prunes the mixtures to reach their optimum size. During the elimination process, proper selection of some latent variables which must be deleted is the most essential problem, and its relation to the final performance of the pruned model is straightforward. To treat this problem, we introduce a new combined pruning method which selects the best options for removal, while keeping a low computational cost, at all. We conducted some experiments on two datasets from Reuters-21578 corpus. The obtained results show that this algorithm leads to an optimized number of latent variables and in turn achieves better clustering performance compared to the conventional model selection methods. It also shows superiority over the case in which a PLSA model with a fixed number of latent variables, equal to the real number of clusters, is exploited.
    Keywords: Model Selection ; Document Clustering ; Plsa ; Bayesian Information Criterion (Bic) ; Em Algorithm ; Computer Science
    ISSN: 0957-4174
    E-ISSN: 1873-6793
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  • 2
    Language: English
    In: Journal of the American College of Cardiology, 29 October 2013, Vol.62(18), pp.B157-B157
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jacc.2013.08.1263 Byline: Mohsen Sharifi, Wilbur K. Freeman, Nikki Rezania, Tahereh Emami, Jean Chatham, Frederic Schwartz Author Affiliation: (1) Arizona Cardiovascular Consultants & A.T. Still University, Mesa, AZ (2) A.T.Still University, Mesa, AZ (3) Arizona Cardiovascular Consultants & Vein Clinic, Mesa, AZ (4) Arizona Cardiovascular Consultants, Mesa, AZ (5) A.T.still University, Mesa, AZ
    Keywords: Medicine
    ISSN: 0735-1097
    E-ISSN: 1558-3597
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  • 3
    Language: English
    In: Journal of the American College of Cardiology, 29 October 2013, Vol.62(18), pp.B157-B157
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jacc.2013.08.1262 Byline: Mohsen Sharifi, Wilbur K. Freeman, Jean Chatham, Tahereh Emami, Curt Bay, Frederic Schwartz Author Affiliation: (1) Arizona Cardiovascular Consultants & A.T. Still University, Mesa, AZ (2) A.T.Still University, Mesa, AZ (3) Arizona Cardiovascular Consultants, Mesa, AZ (4) A.T.Still University, Mesa, AZ
    Keywords: Medicine
    ISSN: 0735-1097
    E-ISSN: 1558-3597
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  • 4
    In: Critical Care Medicine, 2015, Vol.43(12 Suppl 1), pp.137-137
    ISSN: 0090-3493
    Source: Copyright © 2013 Lippincott Williams & Wilkins. All rights reserved.〈img src=http://exlibris-pub.s3.amazonaws.com/LWW%20logo.png style="vertical-align:middle;margin-left:7px"〉
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  • 5
    In: International Journal of Asian Social Science, 2016, Vol.6(4), pp.224-230
    Description: Despite the importance of personal and professional characteristics in the educational performance, to date, there is no study to profound the effects of these characteristics on the educational performance among faculty members of Iran?s universities and higher education instructions. Therefore, with focus on personal and professional characteristics, the present study aimed to compare the educational performance among faculty members of agricultural colleges in western of Iran. The statistical population of this study consisted of all faculty members in the agricultural colleges of universities of Ilam, Razi and Kurdistan at Iran, which 116 faculty members were selected as the sample using the proportionate stratified random sampling method. To gather required data, one questionnaire was designed. The data was analyzed using descriptive and inferential statistics with SPSSWin20 software. Results showed that the present status of educational performance among faculty members of agricultural colleges in western part of Iran was weak. Results of mean comparisons showed that there was significant difference between educational performance based on age, work experience, academic degree, educational group and gender variables. Findings of this study can pave the way for formulating sound programs in higher agricultural education system to promote educational performance among faculty members of agricultural colleges.
    Keywords: Social Sciences (General);
    ISSN: 22265139
    E-ISSN: 22244441
    Source: CrossRef
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  • 6
    Language: English
    In: Expert Systems With Applications, January 2011, Vol.38(1), pp.610-619
    Description: Most of the existing classification methods, used for voice pathology assessment, are built based on labeled pathological and normal voice signals. This paper studies the problem of building a classifier using labeled and unlabeled data. We propose a novel learning technique, called Partitioning and Biased Support Vector Machine Classification (PBSVM), which tries to utilize all the available data in two steps: (1) a new heuristically partition-based algorithm, which extracts high quality pathological and normal samples from an unlabeled set, and (2) a more principle approach based on biased formulation of support vector machine, which is fairly robust to mislabeling and unbalance data problem. Experiments with wavelet-based energy features extracted from sustained vowels show that the new recognition scheme is highly feasible and significantly outperform the baseline classical SVM classifier, especially in the situation where the labeled training data is small.
    Keywords: Laryngeal Pathology Detection ; Partially Learning ; Support Vector Machine ; Computer Science
    ISSN: 0957-4174
    E-ISSN: 1873-6793
    Source: ScienceDirect Journals (Elsevier)
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  • 7
    Language: English
    In: International Journal of Agricultural Management and Development, 01 June 2015, Vol.5(2), pp.81-87
    Description: The main purpose of this study was to investigate effect of access to health facilities on the health status of rural households at Kermanshah Province. Statistical pupation of this study consisted of all heads of rural households at Bisotoun district in Harsin Township, Kermanshah Province, Iran (N=1020), that 278 of them were selected by the cluster random sampling method. The main instrument of this study was a questionnaire which its validity confirmed by a panel of experts and its reliability was calculated by Cronbach's alpha coefficient. The results showed that the health status of rural households was medium at the given district. The results of multiple regression analysis showed that 36% of the dependent variable (health status) is explained by five following variables: level of water availability, level of sewer system availability, and level of access to health services (e.g. toilet, bath, health home, etc.), access to resources and communication and information channels and the level of waste disposal system availability. The results of this study can be considered by authorities and politicians so that they can pay more attention to the rural health issues.
    Keywords: Health ; Healthcare Facilities ; Rural Households ; Rural Development ; Agriculture
    ISSN: 2159-5852
    E-ISSN: 2159-5860
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  • 8
    Language: English
    Description: The main purpose of this study was to investigate effect of access to health facilities on the health status of rural households at Kermanshah Province. Statistical pupation of this study consisted of all heads of rural households at Bisotoun district in Harsin Township,...
    Keywords: Community/Rural/Urban Development ; Health Economics and Policy ; Health ; Healthcare Facilities ; Rural Households ; Rural Development ; Agriculture
    ISSN: 2159-5860
    E-ISSN: 2159-5860
    Source: AgEcon Search: Research in Agricultural and Applied Economics
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  • 9
    Language: English
    In: 2014 21th Iranian Conference on Biomedical Engineering (ICBME), November 2014, pp.343-347
    Description: In this paper we implement state of the art factor analysis based methods and fused their scores to gain a channel robust speaker recognition system. These two methods are joint factor analysis (JFA) and i-Vector which define low-dimensional speaker and channel dependent spaces. For score fusion we propose a simple weight computation without training step. We experiment our method on two conditions; 1) in channel matched training and test channel (telephone in training phase/telephone in test phase) task and 2) the channel mismatched condition (telephone training phase/microphone, GSM and VOIP in test phase) task. Our strategies outperform a state-of-the-art GMM-UBM based system. We obtained more than 4% absolute EER improvement for both channel dependent and channel independent condition compared to the standard GMM-UBM based method. Simulation also results that the combined system based on i-Vector and JFA gives better performance than all implemented method.
    Keywords: Speaker Recognition ; Vectors ; Training ; Feature Extraction ; Biomedical Engineering ; Speech ; Mathematical Model ; Gaussian Mixture Model ; Joint Factor Analysis ; I-Vector ; Adaptive T-Norm ; Robust Speaker Recognition
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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  • 10
    Language: English
    In: Cellular and molecular biology (Noisy-le-Grand, France), 05 August 2019, Vol.65(6), pp.22-27
    Description: Salinity is one of the main obstacles to the production of crops in dry regions of the world. This study focuses on the effects of different strains of plant growth promoting rhizobacteria (PGPR) isolated from native soils on the physiological responses of wheat and barley plants under normal and salt stress conditions. Soil samples were collected from a field in Ilam province, in Iran and bacterial isolates were isolated and screened for salt tolerance, included siderophore and ACC-deaminase production and phosphate solubilizing. Thereafter a two-years greenhouse experiment was conducted as a completely randomized block design with four replications. The applied treatments included bacterial inoculation at five levels (B0: non-inoculation, B1: Siderophore producing + salt-tolerant bacteria, B2: phosphate solubilizing + salt-tolerant bacteria, B3: ACC-deaminase producing + salt-tolerant bacteria, B4: Barvar-2 biological fertilizer, B5: Biofarm-2 biological fertilizer) and salt stress at three levels (S1: 0 dS/m, S2: 4 dS/m, S3: 8 dS/m). Results showed that phosphate solubilizing+ salt-tolerant bacteria resulted in the highest barley grain yield at 4 dS/m salinity level and had no significant difference with ACC-deaminase producing + salt-tolerant bacteria and Barvar-2 biological fertilizer and Biofarm-2 biological fertilizer. The highest proline content in wheat and barley observed in Siderophore producing+ salt-tolerant bacteria at 8 dS/m by 17.48 and 23.42, respectively, followed by phosphate solubilizing+ salt-tolerant bacteria by 16.53 and 19.78. Therefore, the application of isolated growth promoting bacteria can be recommended as an effective biofertilizer in Ilam province.
    Keywords: Microorganisms ; Physiological Traits ; Promotion ; Tolerance.
    E-ISSN: 1165-158X
    Source: MEDLINE/PubMed (U.S. National Library of Medicine)
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