Sewer systems are used to convey sewage and/or storm water to sewage treatment plants for disposal by a network of buried sewer pipes, gutters, manholes and pits. Unfortunately, the sewer pipe deteriorates with time leading to the collapsing of the pipe with traffic disruption or clogging of the pipe causing flooding and environmental pollution. Thus, the management and maintenance of the buried pipes are important tasks that require information about the changes of the current and future sewer pipes conditions. In this research, the study was carried on in Baghdad, Iraq and two deteriorations model's multinomial logistic regression and neural network deterioration model NNDM are used to predict sewers future conditions. The results of the deterioration models' application showed that NNDM gave the highest overall prediction efficiency of 93.6% by adapting the confusion matrix test, while multinomial logistic regression was inconsistent with the data. The error in prediction of related model was due to its inability to reflect the dependent variable (condition classes) ordered nature.
HS Saeed, SS Abdul-Jabbar, SG Mohammed, EA Abed, HS Ibrahem, Solid State Technology, 2020
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In this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts invo
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The proposal of nonlinear models is one of the most important methods in time series analysis, which has a wide potential for predicting various phenomena, including physical, engineering and economic, by studying the characteristics of random disturbances in order to arrive at accurate predictions.
In this, the autoregressive model with exogenous variable was built using a threshold as the first method, using two proposed approaches that were used to determine the best cutting point of [the predictability forward (forecasting) and the predictability in the time series (prediction), through the threshold point indicator]. B-J seasonal models are used as a second method based on the principle of the two proposed approaches in dete
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Unconfined Compressive Strength is considered the most important parameter of rock strength properties affecting the rock failure criteria. Various research have developed rock strength for specific lithology to estimate high-accuracy value without a core. Previous analyses did not account for the formation's numerous lithologies and interbedded layers. The main aim of the present study is to select the suitable correlation to predict the UCS for hole depth of formation without separating the lithology. Furthermore, the second aim is to detect an adequate input parameter among set wireline to determine the UCS by using data of three wells along ten formations (Tanuma, Khasib, Mishrif, Rumaila, Ahmady, Maudud, Nahr Um
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The aim of this research is to adopt a close range photogrammetric approach to evaluate the pavement surface condition, and compare the results with visual measurements. This research is carried out on the road of Baghdad University campus in AL-Jaderiyiah for evaluating the scaling, surface texture for Portland cement concrete and rutting, surface texture for asphalt concrete pavement. Eighty five stereo images of pavement distresses were captured perpendicular to the surface using a DSLR camera. Photogrammetric process was carried out by using ERDAS IMAGINE V.8.4. The results were modeled by using a relationship between the photogrammetric and visual techniques and selected the highest coefficient of determination (R2). The first techniqu
... Show MoreThe aim of this research is to adopt a close range photogrammetric approach to evaluate the pavement surface condition, and compare the results with visual measurements. This research is carried out on the road of Baghdad University campus in AL-Jaderiyiah for evaluating the scaling, surface texture for Portland cement concrete and rutting, surface texture for asphalt concrete pavement. Eighty five stereo images of pavement distresses were captured perpendicular to the surface using a DSLR camera. Photogrammetric process was carried out by using ERDAS IMAGINE V.8.4. The results were modeled by using a relationship between the photogrammetric and visual techniques and selected the highest coefficient of determinatio
... Show MoreArtificial neural networks usage, as a developed technique, increased in many fields such as Auditing business. Contemporary auditor should cope with the challenges of the technology evolution in the business environment by using computerized techniques such as Artificial neural networks, This research is the first work made in the field of modern techniques of the artificial neural networks in the field of auditing; it is made by using thesample of neural networks as a sample of the artificial multi-layer Back Propagation neural networks in the field of detecting fundamental mistakes of the financial statements when making auditing. The research objectives at offering a methodology for the application of theartificial neural networks wi
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