Search

Planned methods may be developed to improve the efficiency of building construction. The construction business is profoundly impacted by the prevalence of inaccurate cost and schedule prediction. The main strategy to improve the project performance is to evaluate the hybrid sustainable materials using the artificial neural network (ANN) method based on the effective factors in construction projects in Iraq. This strategy needs an effective method to classify the project input representation and specify the accurate activity of each factor. This paper uses a hybrid artificial neural network to correlate and classify the sustainable hybrid of construction projects to evaluate their performance. The contribution of this method is the selection of the Multi-Criteria Decision-Maker method (MCDM) based on time and cost-effective factors correlated with the artificial neural network method. A dynamic selection procedure for project materials may be created using the existing technique as an evolutionary model for successful project completion. The MCDM observed that the appropriate sustainable material was considered as the main factor with a rank of 0.823 for cost effect and 0.735 for time effect and the main influence factor in Iraqi projects was the building height. The results present superior functional cost evaluation results correlated with the selection of hybrid sustainable materials. [ABSTRACT FROM AUTHOR]

Copyright of Iraqi Journal of Physics is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

(1) Background: Acute asthma and bronchitis are common infectious diseases in children that affect lower respiratory tract infections (LRTIs), especially in preschool children (below six years). These diseases can be caused by viral or bacterial infections and are considered one of the main reasons for the increase in the number of deaths among children due to the rapid spread of infection, especially in low- and middle-income countries (LMICs). People sometimes confuse acute bronchitis and asthma because there are many overlapping symptoms, such as coughing, runny nose, chills, wheezing, and shortness of breath; therefore, many junior doctors face difficulty differentiating between cases of children in the emergency departments. This study aims to find a solution to improve the differential diagnosis between acute asthma and bronchitis, reducing time, effort, and money. The dataset was generated with 512 prospective cases in Iraq by a consultant pediatrician at Fallujah Teaching Hospital for Women and Children; each case contains 12 clinical features. The data collection period for this study lasted four months, from March 2022 to June 2022. (2) Methods: A novel method is proposed for merging two one-dimensional convolutional neural networks (2-1D-CNNs) and comparing the results with merging one-dimensional neural networks with long short-term memory (1D-CNNs + LSTM). (3) Results: The merged results (2-1D-CNNs) show an accuracy of 99.72% with AUC 1.0, then we merged 1D-CNNs with LSTM models to obtain the accuracy of 99.44% with AUC 99.96%. (4) Conclusions: The merging of 2-1D-CNNs is better because the hyperparameters of both models will be combined; therefore, high accuracy results will be obtained. The 1D-CNNs is the best artificial neural network technique for textual data, especially in healthcare; this study will help enhance junior and practitioner doctors' capabilities by the rapid detection and differentiation between acute bronchitis and asthma without referring to the consultant pediatrician in the hospitals. [ABSTRACT FROM AUTHOR]

Over the past 20 years, there has been a surge of diabetes cases in Iraq. Blood tests administered in the absence of professional medical judgment have allowed for the early detection of diabetes, which will fasten disease detection and lower medical costs. This work focuses on the use of a Long-Short Term Memory (LSTM) neural network for diabetes classification in Iraq. Some medical tests and body features were used as classification features. The most relevant features were selected using the Binary Dragon Fly Algorithm (BDA) Binary version of the selection method because the features either selected or not. To reduce the number of features that are used in prediction, features without effects will be eliminated. This effects the classification accuracy, which is very important in both the computation time of the method and the cost of medical test that the individual will take during annual check ups. This work found out that among 11 features, only five features are most relevant to the disease. These features provide a classification accuracy up to 98% among three classes: diabetic, non diabetic and pre-diabetic. [ABSTRACT FROM AUTHOR]

On 12 November 2017, an earthquake with a moment magnitude of 7.3 struck the west of Iran near the Iraq border. This event was followed about 9 and 12 months later by two large aftershocks of magnitude 5.9 and 6.3, which together triggered intensive seismic activity known as the 2017–2019 Kermanshah sequence. In this study, we analyse this sequence regarding the potential to forecast the spatial aftershock distribution based on information about the main shock and its largest aftershocks. Recent studies showed that classical Coulomb failure stress (CFS) maps are outperformed by alternative scalar stress quantities, as well as a distance-slip probabilistic model (R) and deep neural networks (DNN). In particular, the R-model performed best. However, these test results were based on the receiver operating characteristic (ROC) metric, which is not well suited for imbalanced data sets such as aftershock distributions. Furthermore, the previous analyses also ignored the potential impact of large secondary earthquakes. For the complex Kermanshah sequence, we applied the same forecast models but used the more appropriate MCC-F1 metric for testing. Similar to previous studies, we also observe that the receiver independent stress scalars yield better forecasts than the classical CFS values relying on the specification of receiver mechanisms. However, detailed analysis based on the MCC-F1 metric revealed that the performance depends on the grid size, magnitude cut-off and test period. Increasing the magnitude cut-off and decreasing the grid size and period reduce the performance of all methods. Finally, we found that the performance of the best methods improves when the source information of large aftershocks is additionally considered, with stress-based models outperforming the R model. Our results highlight the importance of accounting for secondary stress changes in improving earthquake forecasts. [ABSTRACT FROM AUTHOR]

Construction cost estimation is one of the essential processes in construction management. Project cost is a complex engineering problem due to various factors affecting the construction industry. Accurate cost estimation is important in construction management and significantly impacts project performance. Artificial intelligence (AI) models have been effectively implemented in construction management studies in recent years owing to their capability to deal with complex problems. In this research, extreme gradient boosting is developed as an advanced input selector algorithm and coupled with three AI models, including random forest (RF), artificial neural network (ANN), and support vector machine (SVM) for cost estimation. Datasets were gathered based on a survey conducted on 90 building projects in Iraq. Statistical indicators and graphical methods were used to evaluate the developed models. Several input predictors were used, and XGBoost highlighted inflation as the most crucial parameter. The results indicated that the best prediction was attained by XGBoost-RF using six input parameters, with r-squared and the mean absolute percentage error equal to 0.87 and 0.25, respectively. The comparison results revealed that all AI models showed good prediction performance when applied to datasets affected by more than two parameters. The outcomes of this research revealed an optimistic strategy that can help decision makers select the influencing parameters in the early phases of project management. Also, developing a prediction model with high precision results can assist the project's estimators in decreasing the errors in the cost estimation process. [ABSTRACT FROM AUTHOR]

Modelling drought is vital to water resources management, particularly in arid areas, to reduce its effects. Drought severity and frequency are significantly influenced by climate change. In this study, a novel hybrid methodology was built, data preprocessing and artificial neural network (ANN) combined with the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA), to forecast standard precipitation index (SPI) based on climatic factors. Additionally, the marine predators algorithm (MPA) and the slime mould algorithm (SMA) were used to validate the performance of the CPSOCGSA algorithm. Climatic factors data from 1990 to 2020 were employed to create and evaluate the SPI 1, SPI 3, and SPI 6 models for Al-Kut City, Iraq. The results indicated that data preprocessing methods improve data quality and find the best predictors scenario. The performance of CPSOCGSA-ANN is better than MPA-ANN and SMA-ANN algorithms based on various statistical criteria (i.e., R2, MAE, and RMSE). The proposed methodology yield R2 = 0.93, 0.93, and 0.88 for SPI 1, SPI 3, and SPI 6, respectively. [ABSTRACT FROM AUTHOR]

This work focuses on the problem of constructing accurate prediction models using an adaptive neuro-fuzzy inference system (ANFIS) from data that are scarce and poorly scaled. Many real-world problems have limited data to work with and yet require models with high prediction accuracy. Due to the fuzzy nature of many data sets, it is appropriate to construct prediction models using fuzzy inference systems (FIS), rather than traditional artificial neural network (ANN) models. However, the scarcity of the data raises serious concerns when attempting to construct models of this type. In this paper, we address the problem of constructing accurate prediction models using ANFIS in circumstances where capturing large amounts of data is difficult. We propose a new approach, using robust data expansion methods to enhance the data carefully. This approach provides the learning algorithm with enough representative data to allow all of the parameters (premise and consequent) to be optimised. We compare and contrast the effectiveness in reducing prediction error for three specific expansion models using radial basis function (RBF) kernels. When expanding data, for which the underlying smoothness properties are not known, there is often a danger of over-smoothing. This is usually noticeable at turning points and can occur when fitting the data using polynomials or cubic splines for example. To avoid over-smoothing, we introduce a modified multiquadric approach that allows smooth, near-interpolation while maintaining the desirable shape-preserving properties of the data. The proposed method is explained through two case studies — the first to predict gasoline consumption for the oil/fuel industry in Iraq and the second for predicting natural gas consumption in Iran. This is the first study that uses adjustable, shape-preserving re-sampling methods for optimising ANFIS models in low data environments. • ANFIS networks capture complexities not easily found using traditional ANNs. • Model complexity versus data sample size is a significant practical problem. • Data augmentation methods can be constructed using robust continuous models. • Multiquadric RBFs can be easily tuned to provide robust, accurate data expansion. • Complex models can be built with confidence using reliable data expansion. [ABSTRACT FROM AUTHOR]