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  1. Article ; Online: A Computational Model to Predict Consumer Behaviour During COVID-19 Pandemic.

    Safara, Fatemeh

    Computational economics

    2020  Volume 59, Issue 4, Page(s) 1525–1538

    Abstract: The knowledge-based economy has drawn increasing attention recently, particularly in online shopping applications where all the transactions and consumer opinions are logged. Machine learning methods could be used to extract implicit knowledge from the ... ...

    Abstract The knowledge-based economy has drawn increasing attention recently, particularly in online shopping applications where all the transactions and consumer opinions are logged. Machine learning methods could be used to extract implicit knowledge from the logs. Industries and businesses use the knowledge to better understand the consumer behavior, and opportunities and threats correspondingly. The outbreak of coronavirus (COVID-19) pandemic has a great impact on the different aspects of our daily life, in particular, on our shopping behaviour. To predict electronic consumer behaviour could be of valuable help for managers in government, supply chain and retail industry. Although, before coronavirus pandemic we have experienced online shopping, during the disease the number of online shopping increased dramatically. Due to high speed transmission of COVID-19, we have to observe personal and social health issues such as social distancing and staying at home. These issues have direct effect on consumer behaviour in online shopping. In this paper, a prediction model is proposed to anticipate the consumers behaviour using machine learning methods. Five individual classifiers, and their ensembles with Bagging and Boosting are examined on the dataset collected from an online shopping site. The results indicate the model constructed using decision tree ensembles with Bagging achieved the best prediction of consumer behavior with the accuracy of 95.3%. In addition, correlation analysis is performed to determine the most important features influencing the volume of online purchase during coronavirus pandemic.
    Keywords covid19
    Language English
    Publishing date 2020-11-05
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1477445-8
    ISSN 1572-9974 ; 0927-7099
    ISSN (online) 1572-9974
    ISSN 0927-7099
    DOI 10.1007/s10614-020-10069-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: A Computational Model to Predict Consumer Behaviour During COVID-19 Pandemic

    Safara, Fatemeh

    Comput Econ

    Abstract: The knowledge-based economy has drawn increasing attention recently, particularly in online shopping applications where all the transactions and consumer opinions are logged. Machine learning methods could be used to extract implicit knowledge from the ... ...

    Abstract The knowledge-based economy has drawn increasing attention recently, particularly in online shopping applications where all the transactions and consumer opinions are logged. Machine learning methods could be used to extract implicit knowledge from the logs. Industries and businesses use the knowledge to better understand the consumer behavior, and opportunities and threats correspondingly. The outbreak of coronavirus (COVID-19) pandemic has a great impact on the different aspects of our daily life, in particular, on our shopping behaviour. To predict electronic consumer behaviour could be of valuable help for managers in government, supply chain and retail industry. Although, before coronavirus pandemic we have experienced online shopping, during the disease the number of online shopping increased dramatically. Due to high speed transmission of COVID-19, we have to observe personal and social health issues such as social distancing and staying at home. These issues have direct effect on consumer behaviour in online shopping. In this paper, a prediction model is proposed to anticipate the consumers behaviour using machine learning methods. Five individual classifiers, and their ensembles with Bagging and Boosting are examined on the dataset collected from an online shopping site. The results indicate the model constructed using decision tree ensembles with Bagging achieved the best prediction of consumer behavior with the accuracy of 95.3%. In addition, correlation analysis is performed to determine the most important features influencing the volume of online purchase during coronavirus pandemic.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #917126
    Database COVID19

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  3. Article ; Online: Cumulant-based trapezoidal basis selection for heart sound classification.

    Safara, Fatemeh

    Medical & biological engineering & computing

    2015  Volume 53, Issue 11, Page(s) 1153–1164

    Abstract: Past decades witnessed the expansion of linear signal processing methods in numerous biomedical applications. However, the nonlinear behavior of biomedical signals revived the interest in nonlinear signal processing methods such as higher-order ... ...

    Abstract Past decades witnessed the expansion of linear signal processing methods in numerous biomedical applications. However, the nonlinear behavior of biomedical signals revived the interest in nonlinear signal processing methods such as higher-order statistics, in particular higher-order cumulants (HOC). In this paper, HOC are utilized toward heart sound classification. Heart sounds are presented by wavelet packet decomposition trees. Information measures are then defined based on HOC of wavelet packet coefficients, and three basis selection methods are proposed to prune the trees and preserve the most informative nodes for feature extraction. In addition, an approach is introduced to reduce the dimensionality of the search space from the whole wavelet packet tree to a trapezoidal sub-tree of it. This approach can be recommended for signals with a short frequency range. HOC features are extracted from the coefficients of selected nodes and fed into support vector machine classifier. Experimental data is a set of 59 heart sounds from different categories: normal heart sounds, mitral regurgitation, aortic stenosis, and aortic regurgitation. The promising results achieved indicate the capabilities of HOC of wavelet packet coefficients to capture nonlinear characteristics of the heart sounds to be used for basis selection.
    MeSH term(s) Algorithms ; Databases, Factual ; Heart Murmurs/physiopathology ; Heart Sounds/physiology ; Heart Valve Diseases/physiopathology ; Humans ; Phonocardiography/methods ; Signal Processing, Computer-Assisted
    Language English
    Publishing date 2015-11
    Publishing country United States
    Document type Journal Article
    ZDB-ID 282327-5
    ISSN 1741-0444 ; 0025-696X ; 0140-0118
    ISSN (online) 1741-0444
    ISSN 0025-696X ; 0140-0118
    DOI 10.1007/s11517-015-1394-4
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 294: Show issues Location:
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  4. Article: Improving the Accuracy of Diabetes Diagnosis Applications through a Hybrid Feature Selection Algorithm.

    Li, Xiaohua / Zhang, Jusheng / Safara, Fatemeh

    Neural processing letters

    2021  Volume 55, Issue 1, Page(s) 153–169

    Abstract: Artificial intelligence is a future and valuable tool for early disease recognition and support in patient condition monitoring. It can increase the reliability of the cure and decision making by developing useful systems and algorithms. Healthcare ... ...

    Abstract Artificial intelligence is a future and valuable tool for early disease recognition and support in patient condition monitoring. It can increase the reliability of the cure and decision making by developing useful systems and algorithms. Healthcare workers, especially nurses and physicians, are overworked due to a massive and unexpected increase in the number of patients during the coronavirus pandemic. In such situations, artificial intelligence techniques could be used to diagnose a patient with life-threatening illnesses. In particular, diseases that increase the risk of hospitalization and death in coronavirus patients, such as high blood pressure, heart disease and diabetes, should be diagnosed at an early stage. This article focuses on diagnosing a diabetic patient through data mining techniques. If we are able to diagnose diabetes in the early stages of the disease, we can force patients to stay home and care for their health, so the risk of being infected with the coronavirus would be reduced. The proposed method has three steps: preprocessing, feature selection and classification. Several combinations of Harmony search algorithm, genetic algorithm, and particle swarm optimization algorithm are examined with K-means for feature selection. The combinations have not examined before for diabetes diagnosis applications. K-nearest neighbor is used for classification of the diabetes dataset. Sensitivity, specificity, and accuracy have been measured to evaluate the results. The results achieved indicate that the proposed method with an accuracy of 91.65% outperformed the results of the earlier methods examined in this article.
    Language English
    Publishing date 2021-03-27
    Publishing country Belgium
    Document type Journal Article
    ZDB-ID 1478375-7
    ISSN 1573-773X ; 1370-4621
    ISSN (online) 1573-773X
    ISSN 1370-4621
    DOI 10.1007/s11063-021-10491-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Wavelet packet entropy for heart murmurs classification.

    Safara, Fatemeh / Doraisamy, Shyamala / Azman, Azreen / Jantan, Azrul / Ranga, Sri

    Advances in bioinformatics

    2012  Volume 2012, Page(s) 327269

    Abstract: Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was ... ...

    Abstract Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was successfully used as a feature to distinguish different heart sounds. In this paper, new entropy was introduced to analyze heart sounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown. The entropy was previously introduced to analyze mammograms. Four common murmurs were considered including aortic regurgitation, mitral regurgitation, aortic stenosis, and mitral stenosis. Wavelet packet transform was employed for heart sound analysis, and the entropy was calculated for deriving feature vectors. Five types of classification were performed to evaluate the discriminatory power of the generated features. The best results were achieved by BayesNet with 96.94% accuracy. The promising results substantiate the effectiveness of the proposed wavelet packet entropy for heart sounds classification.
    Language English
    Publishing date 2012-11-25
    Publishing country Egypt
    Document type Journal Article
    ZDB-ID 2448875-6
    ISSN 1687-8035 ; 1687-8035
    ISSN (online) 1687-8035
    ISSN 1687-8035
    DOI 10.1155/2012/327269
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  6. Article: Multi-level basis selection of wavelet packet decomposition tree for heart sound classification

    Safara, Fatemeh / Doraisamy, Shyamala / Azman, Azreen / Jantan, Azrul / Abdullah Ramaiah, Asri Ranga

    Computers in Biology and Medicine. 2013 Oct. 1, v. 43, no. 10

    2013  

    Abstract: Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve ... ...

    Abstract Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve the most informative bases of a wavelet packet decomposition tree through removing less informative bases by applying three exclusion criteria: frequency range, noise frequency, and energy threshold. MLBS achieved an accuracy of 97.56% for classifying normal heart sound, aortic stenosis, mitral regurgitation, and aortic regurgitation. MLBS is a promising basis selection to be suggested for signals with a small range of frequencies.
    Keywords energy ; heart ; heart sounds ; heart valve diseases ; wavelet
    Language English
    Dates of publication 2013-1001
    Size p. 1407-1414.
    Publishing place Elsevier Ltd
    Document type Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2013.06.016
    Database NAL-Catalogue (AGRICOLA)

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    In stock of ZB MED Bonn / Germany

    Z 78/522: Show issues

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  7. Article ; Online: Multi-level basis selection of wavelet packet decomposition tree for heart sound classification.

    Safara, Fatemeh / Doraisamy, Shyamala / Azman, Azreen / Jantan, Azrul / Abdullah Ramaiah, Asri Ranga

    Computers in biology and medicine

    2013  Volume 43, Issue 10, Page(s) 1407–1414

    Abstract: Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve ... ...

    Abstract Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve the most informative bases of a wavelet packet decomposition tree through removing less informative bases by applying three exclusion criteria: frequency range, noise frequency, and energy threshold. MLBS achieved an accuracy of 97.56% for classifying normal heart sound, aortic stenosis, mitral regurgitation, and aortic regurgitation. MLBS is a promising basis selection to be suggested for signals with a small range of frequencies.
    MeSH term(s) Heart Sounds/physiology ; Heart Valve Diseases/physiopathology ; Humans ; Phonocardiography/classification ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Support Vector Machine
    Language English
    Publishing date 2013-10
    Publishing country United States
    Document type Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2013.06.016
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 653: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

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  8. Article ; Online: Wavelet Packet Entropy for Heart Murmurs Classification

    Fatemeh Safara / Shyamala Doraisamy / Azreen Azman / Azrul Jantan / Sri Ranga

    Advances in Bioinformatics, Vol

    2012  Volume 2012

    Abstract: Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was ... ...

    Abstract Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was successfully used as a feature to distinguish different heart sounds. In this paper, new entropy was introduced to analyze heart sounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown. The entropy was previously introduced to analyze mammograms. Four common murmurs were considered including aortic regurgitation, mitral regurgitation, aortic stenosis, and mitral stenosis. Wavelet packet transform was employed for heart sound analysis, and the entropy was calculated for deriving feature vectors. Five types of classification were performed to evaluate the discriminatory power of the generated features. The best results were achieved by BayesNet with 96.94% accuracy. The promising results substantiate the effectiveness of the proposed wavelet packet entropy for heart sounds classification.
    Keywords Biology (General) ; QH301-705.5 ; Statistics ; HA1-4737
    Subject code 410
    Language English
    Publishing date 2012-01-01T00:00:00Z
    Publisher Hindawi Publishing Corporation
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Candida colonization and species identification by two methods in NICU newborn

    Narges Sadat Taherzadeh / Farideh Zaini / Roshanak Daie Ghazvini / Sasan Rezaie / Mahmoud Mahmoudi / Maliheh Kadivar / Fatemeh Sadat Nayeri / Mahin Safara / Parivash Kordbacheh

    Tehran University Medical Journal, Vol 73, Iss 11, Pp 819-

    2016  Volume 826

    Abstract: Background: Over the last two decades invasive candidiasis has become an increasing problem in neonatal intensive care units (NICUs). Colonization of skin and mucous membranes with Candida spp. is important factor in the pathogenesis of neonatal ... ...

    Abstract Background: Over the last two decades invasive candidiasis has become an increasing problem in neonatal intensive care units (NICUs). Colonization of skin and mucous membranes with Candida spp. is important factor in the pathogenesis of neonatal infection and several colonized sites are major risk factors evoking higher frequencies of progression to invasive candidiasis. The aim of this study was to detect Candida colonization in NICU patients. Methods: This cross-sectional study was conducted on 93 neonates in NICUs at Imam Khomeini and Children Medical Center Hospitals in Tehran. Cutaneous and mucous membrane samples obtained at first, third, and seventh days of patients’ stay in NICUs during nine months from August 2013 to May 2014. The samples were primarily cultured on CHROMagar Candida medium. The cultured media were incubated at 35°C for 48h and evaluated based on colony color produced on CHROMagar Candida. In addition, isolated colonies were cultured on Corn Meal Agar medium supplemented with tween 80 for identification of Candida spp. based on their morphology. Finally, polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method was performed for definite identification of isolated species. Results: Colonization by Candida spp. was occurred in 20.43% of neonates. Fifteen and four patients colonized with one and two different Candida spp., respectively. Isolated Candida spp. identified as; C. parapsilosis (n: 10), C. albicans (n: 7), C. tropicalis (n: 3), C. guilliermondii (n: 2), and C. krusei (n: 1). In present study non-albicans Candia species were dominant (69.56%) and C. parapsilosis was the most frequent isolate (43.47%). Using Fisher's exact test, the correlation between fungal colonization with low birth weight, low gestational age, and duration of hospital stay was found to be statistically significant (P=0.003). Conclusion: The results of this study imply to the candida species colonization of neonates. Neonates in NICU are at the highest risk for severe infection with Candida parapsilosis. Therefore, isolation of C. parapsilosis as the most common species (43.47%) in present study was noteworthy.
    Keywords candida ; candida parapsilosis ; colonization ; neonatal intensive care units ; polymerase chain reaction ; Medicine (General) ; R5-920
    Subject code 610
    Language Persian
    Publishing date 2016-02-01T00:00:00Z
    Publisher Tehran University of Medical Sciences
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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