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Abstract

The cost overrun in road construction projects in Iraq is one of the major problems that face the construction of new roads. To enable the concerned government agencies to predict the final cost of roads, the objective this paper suggested is to develop an early cost estimating model for road projects using a support vector machine based on (43) sets of bills of quantity collected in Baghdad city in Iraq. As cost estimates are required at the early stages of a project, consideration was given to the fact that the input data for the support vector machine model could be easily extracted from sketches or the project’s scope definition. The data were collected from contracts awarded by the Mayoralty of Baghdad for completed projects between 2010–2013. Mathematical equations were constructed using the Support Vector Machine Algorithm (SMO) technique. An average of accuracy (AA) (99.65%) and coefficient of determination (R2) (97.63%) for the model was achieved by the created prediction equations.
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Authors and Affiliations

Musaab Falih Hasan
1
ORCID: ORCID
Oday Hammody
2
ORCID: ORCID
Khaldoon Satea Albayati
3
ORCID: ORCID

  1. General Directorate of Education Baghdad Rusafa First, Ministry of Education, Iraq
  2. Civil Engineering Department, University of Technology, Baghdad, Iraq
  3. Iraqi Reinsurance Company, Ministry of Finance, Iraq
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Abstract

Laughter is one of the most important paralinguistic events, and it has specific roles in human conversation. The automatic detection of laughter occurrences in human speech can aid automatic speech recognition systems as well as some paralinguistic tasks such as emotion detection. In this study we apply Deep Neural Networks (DNN) for laughter detection, as this technology is nowadays considered state-of-the-art in similar tasks like phoneme identification. We carry out our experiments using two corpora containing spontaneous speech in two languages (Hungarian and English). Also, as we find it reasonable that not all frequency regions are required for efficient laughter detection, we will perform feature selection to find the sufficient feature subset.

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Authors and Affiliations

Gábor Gosztolya
András Beke
Tilda Neuberger
László Tóth

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