Estimation of Electrical Parameters of the Induction Machine Steady State Model Using Nameplate Data and Hunger Game Search Algorithm

In this paper, the Hunger Games Search (HGS) optimization algorithm is used to estimate the electrical parameters of the induction machine steady state model. Induction machine nameplate data is used as input to the proposed algorithm. The performance of the proposed method is confirmed by comparing the output characteristics obtained by estimating the motor parameters including torque, current and power factor in the steady state model of the induction machine with the values provided by the manufacturer. In addition, by evaluating and comparing the results of the proposed method with the results of previous research, it is shown that the proposed algorithm is a very effective and accurate method for the acceptable estimation of induction machine parameters

A Feasibility Study on Using the Facilities of Health Centers for Developing a Laboratory Network on Vectors and Reservoir Hosts of Cutaneous Leishmaniasis in Iran

Background: Cutaneous leishmaniasis is an ancient endemic disease in Iran and continues to be a growing health threat to community development and the environment. This paper explains how to use the facilities of health centers for developing a laboratory network on vectors and reservoir hosts of cutaneous leishmaniasis in Iran. Methods: A literature search was performed of the relevant multiple databases to include studies on vectors and reservoirs of cutaneous leishmaniasis in Iran. A team of experienced experts was performed. After holding several meetings, field visits and organizing workshops, the activities of laboratories were determined at three levels. Results: Entomological studies showed that 5 species of the genus Phlebotomus and 10 species of the genus Sergentomyia are active in the south, 4 species of the genus Phlebotomus and one species of the genus Sergentomyia in the central part and 5 species of the genus Phlebotomus and 2 species of the genus Sergentomyia in the north east. Reservoir hosts were identified in the study areas. The tasks of laboratories were regulated at different levels. Conclusion: The Iranian Ministry of Health and Medical Education should prioritize the employment of capable persons in the field of Medical Entomology and Vector Control. The survival of this laboratory network depends on hiring and employing interested and persistent people. The universities of Medical Sciences that have the facilities to set up this network will be a very effective partner in the control of the disease in high risk areas. The results can be used in neighboring countries.Fil: Reza Yaghoobi Ershadi, Mohammad. Tehran University of Medical Sciences; IránFil: Akhavan, Amir Ahmad. Tehran University of Medical Sciences; IránFil: Reza Shirzadi, Mohammad. Iranian Ministry of Health and Medical Education; IránFil: Zohreh Hosseini, Seyedeh. Tehran University of Medical Sciences; IránFil: Salomón, Oscar Daniel. Secretaria de Gobierno de Salud. Instituto Nacional de Medicina Tropical. Instituto Nacional de Medicina Tropical - Sede Tucumán; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; ArgentinaFil: Hanafi Bojd, Ahmad Ali. Tehran University of Medical Sciences; IránFil: Rassi, Yavar. Tehran University of Medical Sciences; Irá

A Feasibility Study on Using the Facilities of Health Centers for Developing a Laboratory Network on Vectors and Reservoir Hosts of Cutaneous Leishmaniasis in Iran

Background: Cutaneous leishmaniasis is an ancient endemic disease in Iran and continues to be a growing health threat to community development and the environment. This paper explains how to use the facilities of health centers for developing a laboratory network on vectors and reservoir hosts of cutaneous leishmaniasis in Iran. Methods: A literature search was performed of the relevant multiple databases to include studies on vectors and reservoirs of cutaneous leishmaniasis in Iran. A team of experienced experts was performed. After holding several meetings, field visits and organizing workshops, the activities of laboratories were determined at three levels. Results: Entomological studies showed that 5 species of the genus Phlebotomus and 10 species of the genus Sergentomyia are active in the south, 4 species of the genus Phlebotomus and one species of the genus Sergentomyia in the central part and 5 species of the genus Phlebotomus and 2 species of the genus Sergentomyia in the north east. Reservoir hosts were identified in the study areas. The tasks of laboratories were regulated at different levels. Conclusion: The Iranian Ministry of Health and Medical Education should prioritize the employment of capable persons in the field of Medical Entomology and Vector Control. The survival of this laboratory network depends on hiring and employing interested and persistent people. The universities of Medical Sciences that have the facilities to set up this network will be a very effective partner in the control of the disease in high risk areas. The results can be used in neighboring countries.Fil: Reza Yaghoobi Ershadi, Mohammad. Tehran University of Medical Sciences; IránFil: Akhavan, Amir Ahmad. Tehran University of Medical Sciences; IránFil: Reza Shirzadi, Mohammad. Iranian Ministry of Health and Medical Education; IránFil: Zohreh Hosseini, Seyedeh. Tehran University of Medical Sciences; IránFil: Salomón, Oscar Daniel. Secretaria de Gobierno de Salud. Instituto Nacional de Medicina Tropical. Instituto Nacional de Medicina Tropical - Sede Tucumán; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; ArgentinaFil: Hanafi Bojd, Ahmad Ali. Tehran University of Medical Sciences; IránFil: Rassi, Yavar. Tehran University of Medical Sciences; Irá

Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas

Land subsidence susceptibility mapping in South Korea using machine learning algorithms

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results

An Integrated Model for Multi-Mode Resource-Constrained Multi-Project Scheduling Problems Considering Supply Management with Sustainable Approach in the Construction Industry under Uncertainty Using Evidence Theory and Optimization Algorithms

In this study, the multi-mode resource-constrained multi-project scheduling problems (MMRCMPSPs) considering supply management and sustainable approach in the construction industry under uncertain conditions have been investigated using evidence theory to mathematical modeling and solving by multi-objective optimization algorithms. In this regard, a multi-objective mathematical model has been proposed, in which the first objective function aims to maximize a weighted selection of projects based on economic, environmental, technical, social, organizational, and competitive factors; the second objective function is focused on maximizing profit, and the third objective function is aimed at minimizing the risk of supply management. Moreover, various components, such as interest rates, carbon penalties, and other implementation limitations and additional constraints, have also been considered in the modeling and mathematical relationships to improve the model’s performance and make it more relevant to real-world conditions and related issues, leading to better practical applications. In the mathematical modeling adopted, the processing time of project activities has been considered uncertain, and the evidence theory has been utilized. This method can provide a flexible and rational approach based on evidence and knowledge in the face of uncertainty. In addition, to solve the proposed multi-objective mathematical model, metaheuristic optimization algorithms, such as the differential evolution (DE) algorithm based on the Pareto archive, have been used, and for evaluating the results, the non-dominated sorting genetic algorithm II (NSGA-II) has also been employed. Furthermore, the results have been compared based on multi-objective evaluation criteria, such as quality metric (QM), spacing metric (SM), and diversity metric (DM). It is worth noting that to investigate the performance and application of the proposed model, multiple evaluations have been conducted on sample problems with different dimensions, as well as a case study on residential apartment construction projects by a contracting company. In this respect, the answers obtained from solving the model using the multi-objective DE algorithm were better and superior to the NSGA-II algorithm and had a more favorable performance. Generally, the results indicate that using the integrated multi-objective mathematical model in the present research for managing and scheduling multi-mode resource-constrained multi-project problems, especially in the construction industry, can lead to an optimal state consistent with the desired objectives and can significantly improve the progress and completion of projects

A hybrid computational intelligence approach to groundwater spring potential mapping

© 2019 by the authors. This study proposes a hybrid computational intelligence model that is a combination of alternating decision tree (ADTree) classifier and AdaBoost (AB) ensemble, namely "AB-ADTree", for groundwater spring potential mapping (GSPM) at the Chilgazi watershed in the Kurdistan province, Iran. Although ADTree and its ensembles have been widely used for environmental and ecological modeling, they have rarely been applied to GSPM. To that end, a groundwater spring inventory map and thirteen conditioning factors tested by the chi-square attribute evaluation (CSAE) technique were used to generate training and testing datasets for constructing and validating the proposed model. The performance of the proposed model was evaluated using statistical-index-based measures, such as positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity accuracy, root mean square error (RMSE), and the area under the receiver operating characteristic (ROC) curve (AUROC). The proposed hybrid model was also compared with five state-of-the-art benchmark soft computing models, including singleADTree, support vector machine (SVM), stochastic gradient descent (SGD), logistic model tree (LMT), logistic regression (LR), and random forest (RF). Results indicate that the proposed hybrid model significantly improved the predictive capability of the ADTree-based classifier (AUROC = 0.789). In addition, it was found that the hybrid model, AB-ADTree, (AUROC = 0.815), had the highest goodness-of-fit and prediction accuracy, followed by the LMT (AUROC = 0.803), RF (AUC = 0.803), SGD, and SVM (AUROC = 0.790) models. Indeed, this model is a powerful and robust technique for mapping of groundwater spring potential in the study area. Therefore, the proposed model is a promising tool to help planners, decision makers, managers, and governments in the management and planning of groundwater resources

A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides

This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas

A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran)

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811)

Gis-based gully erosion susceptibility mapping: a comparison of computational ensemble data mining models

Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion. We used multi-collinearity statistical techniques in the modeling process, and checked model performance using statistical indexes including precision, recall, F-measure, Matthew correlation coefficient (MCC), receiver operatic characteristic curve (ROC), precision-recall graph (PRC), Kappa, root mean square error (RMSE), relative absolute error (PRSE), mean absolute error (MAE), and relative absolute error (RAE). Results show that rainfall, elevation, and river density are the most important factors for gully erosion susceptibility mapping in the study area. All three hybrid models that we tested significantly enhanced and improved the predictive power of REPTree (AUC=0.800), but the RS-REPTree (AUC= 0.860) ensemble model outperformed the Bag-REPTree (AUC= 0.841) and the AB-REPTree (AUC= 0.805) models. We suggest that decision makers, planners, and environmental engineers employ the RS-REPTree hybrid model to better manage gully erosion-prone areas in Iran