<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1990-8644</journal-id>
<journal-title><![CDATA[Conrado]]></journal-title>
<abbrev-journal-title><![CDATA[Conrado]]></abbrev-journal-title>
<issn>1990-8644</issn>
<publisher>
<publisher-name><![CDATA[Editorial Universo Sur]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1990-86442020000300079</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Independient educating optimum method of independent auditor report type prediction]]></article-title>
<article-title xml:lang="es"><![CDATA[Educando el método óptimo de la predicción del tipo de informe de auditor]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bakhshi]]></surname>
<given-names><![CDATA[Ali]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Yazdani]]></surname>
<given-names><![CDATA[Shohre]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Maleki]]></surname>
<given-names><![CDATA[Ali]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Islamic Azad University Department of Accounting ]]></institution>
<addr-line><![CDATA[Damavand ]]></addr-line>
<country>Iran</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Azad University Department of Statistics ]]></institution>
<addr-line><![CDATA[Firoozkuh ]]></addr-line>
<country>Iran</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2020</year>
</pub-date>
<volume>16</volume>
<numero>74</numero>
<fpage>79</fpage>
<lpage>92</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1990-86442020000300079&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1990-86442020000300079&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1990-86442020000300079&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Accountability requires the existence of reliable and valid information and auditing is one of fundamental bases for the accountability process. Thus educating the optimum method is of great importance. With the presence of a great volume of information, using the prediction methods can contribute the auditors in this respect. This research aims to compare a variety of methods of teaching of that. In current research, J48, random forest, vector machine and neural network have been used. Research population involves the corporates accepted by Tehran Stock Exchange in 2008-2017. Here, 19 financial and non-financial independent variables have been applied in two groups of test and training. Also, the independent auditor report has been classified into two groups of acceptable and conditional. Comparing the above-mentioned methods has indicated that random forest algorithm with the prediction accuracy average as 78.83% was the most optimum model to predict the report type in the both groups and other models involving J48, support vector machine, CART decision tree and finally, artificial neural network were of the most accuracy.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La rendición de cuentas requiere la existencia de información confiable y válida y la auditoría es una de las bases fundamentales para el proceso de rendición de cuentas. Por lo tanto, educar el método óptimo es de gran importancia. Con la presencia de un gran volumen de información, el uso de los métodos de predicción puede contribuir a los auditores a este respecto. Esta investigación tiene como objetivo comparar una variedad de métodos de enseñanza de eso. En la investigación actual, se han utilizado J48, bosque aleatorio, máquina de vectores y red neuronal. La población de investigación involucra a las empresas aceptadas por la Bolsa de Teherán en 2008-2017. Aquí, se han aplicado 19 variables independientes financieras y no financieras en dos grupos de prueba y capacitación. Además, el informe del auditor independiente se ha clasificado en dos grupos de aceptable y condicional. La comparación de los métodos mencionados anteriormente ha indicado que el algoritmo de bosque aleatorio con un promedio de precisión de predicción de 78.83% fue el modelo más óptimo para predecir el tipo de informe en ambos grupos y otros modelos que involucran J48, máquina de vectores de soporte, árbol de decisión CART y finalmente, La red neuronal artificial fue de la mayor precisión.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Auditor report type]]></kwd>
<kwd lng="en"><![CDATA[J48 algorithm]]></kwd>
<kwd lng="en"><![CDATA[random forest]]></kwd>
<kwd lng="en"><![CDATA[support vector machine]]></kwd>
<kwd lng="en"><![CDATA[CART decision tree]]></kwd>
<kwd lng="en"><![CDATA[artificial neural network.]]></kwd>
<kwd lng="es"><![CDATA[Tipo de informe de auditor]]></kwd>
<kwd lng="es"><![CDATA[algoritmo J48]]></kwd>
<kwd lng="es"><![CDATA[bosque aleatorio]]></kwd>
<kwd lng="es"><![CDATA[máquina de vectores de soporte]]></kwd>
<kwd lng="es"><![CDATA[árbol de decisión CART;, red neuronal artificial]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Abaszade]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Maftonian]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Babaee]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Fadaee]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Investigating accuracy of heuristic algorithms and linear logit regression in predicting auditor comment type]]></article-title>
<source><![CDATA[Modern Researches in Accounting Journal]]></source>
<year>2017</year>
<volume>4</volume>
<page-range>39-73</page-range></nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Abolfathi]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Taebi]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Providing transfer function for the population model]]></article-title>
<source><![CDATA[Journal of Social Sciences and Humanities Research]]></source>
<year>2019</year>
<volume>7</volume>
<numero>01</numero>
<issue>01</issue>
<page-range>64-9</page-range></nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Alikhani Dehaghi]]></surname>
<given-names><![CDATA[Ho.]]></given-names>
</name>
</person-group>
<source><![CDATA[Investigating effective elements in longevity of audit report release]]></source>
<year>2006</year>
<publisher-name><![CDATA[Shahid Beheshti University]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bagherpor Valashani]]></surname>
<given-names><![CDATA[M. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Saee]]></surname>
<given-names><![CDATA[M. J.]]></given-names>
</name>
<name>
<surname><![CDATA[Meshkani]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Bagheri]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<source><![CDATA[Predicting independent audit report in Iran: data mining approach]]></source>
<year>2012</year>
<conf-name><![CDATA[ 10thNational Accounting Conference]]></conf-name>
<conf-loc>Iran </conf-loc>
</nlm-citation>
</ref>
<ref id="B5">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Barkhordarian]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Hashemi]]></surname>
<given-names><![CDATA[S. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Hosseini]]></surname>
<given-names><![CDATA[S. M.]]></given-names>
</name>
</person-group>
<source><![CDATA[Predicting conditional audit comment using multilayer perceptron neural network and decision tree]]></source>
<year>2011</year>
<publisher-name><![CDATA[Isfahan University]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B6">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Boshen]]></surname>
<given-names><![CDATA[J. K.]]></given-names>
</name>
<name>
<surname><![CDATA[Smith]]></surname>
<given-names><![CDATA[J. R.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Does size matter? The influence of large clients on office-level auditor reporting decisions]]></article-title>
<source><![CDATA[Journal of Accounting an Economics]]></source>
<year>2009</year>
<volume>30</volume>
<page-range>375-400</page-range></nlm-citation>
</ref>
<ref id="B7">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Carcello]]></surname>
<given-names><![CDATA[J. V.]]></given-names>
</name>
<name>
<surname><![CDATA[Hermanson]]></surname>
<given-names><![CDATA[D. R.]]></given-names>
</name>
<name>
<surname><![CDATA[Neal]]></surname>
<given-names><![CDATA[T. L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Auditor reporting behavior when GAAS lack specificity: the case of SAS No.59]]></article-title>
<source><![CDATA[Journal of Accounting and Public Policy]]></source>
<year>2003</year>
<volume>22</volume>
<page-range>63-81</page-range></nlm-citation>
</ref>
<ref id="B8">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Efstathios]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Spathis]]></surname>
<given-names><![CDATA[Ch.]]></given-names>
</name>
<name>
<surname><![CDATA[Nanopoulos]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Manolopoulos]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Identifying Qualified Auditors Opinions: A Data Mining Approach]]></article-title>
<source><![CDATA[Journal of Emerging Technologies in Accounting]]></source>
<year>2007</year>
<volume>4</volume>
<page-range>183-97</page-range></nlm-citation>
</ref>
<ref id="B9">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gaganis]]></surname>
<given-names><![CDATA[Ch.]]></given-names>
</name>
<name>
<surname><![CDATA[Pasiouras]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Doumpos]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Probabilistic Neural Networks for the Identification of Qualified Audit Opinions]]></article-title>
<source><![CDATA[Expert Systems with Applications]]></source>
<year>2007</year>
<volume>32</volume>
<page-range>114-24</page-range></nlm-citation>
</ref>
<ref id="B10">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hasas Yegane]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Taqavifard]]></surname>
<given-names><![CDATA[M. T.]]></given-names>
</name>
<name>
<surname><![CDATA[Mohammadpor]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Using probabilistic neural network to identify audit comment type]]></article-title>
<source><![CDATA[Theory and Practice Journal]]></source>
<year>2014</year>
<volume>1</volume>
<page-range>131-59</page-range></nlm-citation>
</ref>
<ref id="B11">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ireland]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[An Empirical Investigation of Determinants of Audit Reports in the UK]]></article-title>
<source><![CDATA[Journal of Business Finance and Accounting]]></source>
<year>2003</year>
<volume>30</volume>
<numero>78</numero>
<issue>78</issue>
<page-range>975-1015</page-range></nlm-citation>
</ref>
<ref id="B12">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Khajavi]]></surname>
<given-names><![CDATA[Sh.]]></given-names>
</name>
<name>
<surname><![CDATA[Kazemnejad]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Dehghani Saadi]]></surname>
<given-names><![CDATA[A. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Momtazian]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Investigating different methods of selecting predictive variables in predicting auditor comment type]]></article-title>
<source><![CDATA[Accounting Journal]]></source>
<year>2018</year>
<volume>7</volume>
<page-range>81-102</page-range></nlm-citation>
</ref>
<ref id="B13">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kirkos]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Spathis]]></surname>
<given-names><![CDATA[C. N.]]></given-names>
</name>
<name>
<surname><![CDATA[Yannis]]></surname>
<given-names><![CDATA[A. M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Identifying Qualified Auditors opinion: A Data Mining Approach]]></article-title>
<source><![CDATA[Journal of Emerging Technologies in Accounting]]></source>
<year>2007</year>
<volume>4</volume>
<page-range>183-97</page-range></nlm-citation>
</ref>
<ref id="B14">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Porheydari]]></surname>
<given-names><![CDATA[O.]]></given-names>
</name>
<name>
<surname><![CDATA[Azami]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Identifying auditor comment type using neural networks]]></article-title>
<source><![CDATA[Accounting Knowledge Journal]]></source>
<year>2010</year>
<volume>3</volume>
<page-range>77-97</page-range></nlm-citation>
</ref>
<ref id="B15">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Russell]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Nurvich]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
</person-group>
<source><![CDATA[Artificial Intelligence Modern Approach]]></source>
<year>2008</year>
<publisher-name><![CDATA[Naghous Publishing]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B16">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tambunan]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The Effectiveness of the Problem Solving Strategy and the Scientific Approach to Students&#8217; Mathematical Capabilities in High Order Thinking Skills]]></article-title>
<source><![CDATA[International Electronic Journal of Mathematics Education]]></source>
<year>2019</year>
<volume>14</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>293-302</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
