This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to *** than using traditio...
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This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to *** than using traditional machinelearning(ML)algorithms or hybrid signal processing techniques,a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML *** the proposed method,the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization(PSO)*** this purpose,power system failures are simulated by using the PSCA D-Python *** of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored ***,the proposed technique will be able to work on different systems,topologies,or data *** proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.
The present study has proposed three novel hybrid models by integrating three traditional ensemble models, such as random forest, logitboost, and naive bayes, and six newly developed ensemble models of rotation forest...
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The present study has proposed three novel hybrid models by integrating three traditional ensemble models, such as random forest, logitboost, and naive bayes, and six newly developed ensemble models of rotation forest (RF), such as decision tree (RF-DT), J48 (DF-J48), naive bayes tree (RF-NBT), neural network (RF-NN), M5P (RF-M5P) and REPTree (RF-REPTree), with three statistical models, i.e. weight of evidence, logistic regression and combination of WOE and LR. To predict the groundwater potential, nine groundwater potential conditioning parameters have been created. The Information Gain Ratio has been used to evaluate the impact of each parameter. The ROC curve has been used to validate the models. According to the findings, 15 to 30% of the study area has a very high or high groundwater potentiality. Furthermore, validation results revealed that RF based ensembles models outperformed other standalone models for groundwater potential modelling.
In software development systems, the maintenance process of software systems attracted the attention of researchers due to its importance in fixing the defects discovered in the software testing by using bug reports (...
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In software development systems, the maintenance process of software systems attracted the attention of researchers due to its importance in fixing the defects discovered in the software testing by using bug reports (BRs) which include detailed information like description, status, reporter, assignee, priority, and severity of the bug and other information. The main problem in this process is how to analyze these BRs to discover all defects in the system, which is a tedious and time-consuming task if done manually because the number of BRs increases dramatically. Thus, the automated solution is the best. Most of the current research focuses on automating this process from different aspects, such as detecting the severity or priority of the bug. However, they did not consider the nature of the bug, which is a multi-class classification problem. This paper solves this problem by proposing a new prediction model to analyze BRs and predict the nature of the bug. The proposed model constructs an ensemble machine learning algorithm using natural language processing (NLP) and machinelearning techniques. We simulate the proposed model by using a publicly available dataset for two online software bug repositories (Mozilla and Eclipse), which includes six classes: Program Anomaly, GUI, Network or Security, Configuration, Performance, and Test-Code. The simulation results show that the proposed model can achieve better accuracy than most existing models, namely, 90.42% without text augmentation and 96.72% with text augmentation.
This study investigates the impact of precipitation on Middle Eastern countries like Iran using precise methods such as stable isotope techniques. Stable isotope data for precipitation in Tehran were obtained from the...
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This study investigates the impact of precipitation on Middle Eastern countries like Iran using precise methods such as stable isotope techniques. Stable isotope data for precipitation in Tehran were obtained from the Global Network of Isotopes in Precipitation (GNIP) station and sampled for two periods: 1961-1987 and 2000-2004. Precipitation samples were collected, stored, and shipped to a laboratory for stable isotope analyses using the GNIP procedure. Several models, including artificial neural networks (ANNs), stepwise regression, and ensemblemachinelearning approaches, were applied to simulate stable isotope signatures in precipitation. Among the studied machinelearning models, XGboost showed the most accurate simulation with higher R-2 (0.84 and 0.86) and lower RMSE (1.97 and 12.54), NSE (0.83 and 0.85), AIC (517.44 and 965.57), and BIC values (531.42 and 979.55) for O-18 and H-2 compared to other models, respectively. The uncertainty in the simulations of the XGboost model was assessed using the bootstrap technique, indicating that this model accurately predicted stable isotope values. Various wavelet coherence analyses were applied to study the associations between stable isotope signatures and their controlling parameters. The BWC analysis results show coherence relationships, mainly ranging from 16 to 32 months for both & delta;O-18-temperature and & delta;H-2-temperature pairs with the highest average wavelet coherence (AWC). Temperature is the dominant predictor influencing stable isotope signatures of precipitation, while precipitation has lower impacts. This study provides valuable insights into the relationship between stable isotopes and climatological parameters of precipitation in Tehran.
Storm surge has recently emerged as a major concern. In case it occurs, we suffer from the damages it creates. To predict its occurrence, machinelearning technology can be considered. It can help ease the damages cre...
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ISBN:
(纸本)9789897585647
Storm surge has recently emerged as a major concern. In case it occurs, we suffer from the damages it creates. To predict its occurrence, machinelearning technology can be considered. It can help ease the damages created by storm surge, by predicting its occurrence, if a good dataset is provided. There are a number of machinelearningalgorithms giving promising results in the prediction, but using different dataset. Thus, it is hard to benchmark them. The goal of this paper is to examine the performance of machinelearningalgorithms, either single or ensemble, in predicting storm surge. Simulation result showed that ensemblealgorithms can efficiently provide optimal and satisfactory result. The accuracy of prediction reaches a level, which is better than that of single machinelearningalgorithms.
In view of high mortality associated with coronary artery disease (CAD), development of an early predicting tool will be beneficial in reducing the burden of the disease. The database comprising demographic, conventio...
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In view of high mortality associated with coronary artery disease (CAD), development of an early predicting tool will be beneficial in reducing the burden of the disease. The database comprising demographic, conventional, folate/xenobiotic genetic risk factors of 648 subjects (364 cases of CAD and 284 healthy controls) was used as the basis to develop CAD risk and percentage stenosis prediction models using ensemble machine learning algorithms (EMLA), multifactor dimensionality reduction (MDR) and recursive partitioning (RP). The EMLA model showed better performance than other models in disease (89.3%) and stenosis prediction (82.5%). This model depicted hypertension and alcohol intake as the key predictors of CAD risk followed by cSHMT C1420T, GCPII C1561T, diabetes, GSTT1, CYP1A1 m2, TYMs 5'-UTR 28 bp tandem repeat and MTRR A66G. MDR and RP models are in agreement in projecting increasing age, hypertension and cSHMTC1420T as the key determinants interacting in modulating CAD risk. Receiver operating characteristic curves exhibited clinical utility of the developed models in the following order: EMLA (C = 0.96) > RP (C = 0.83) > MDR (C = 0.80). The stenosis prediction model showed that xenobiotic pathway genetic variants i.e. CYP1A1 m2 and GSTT1 are the key determinants of percentage of stenosis. Diabetes, diet, alcohol intake, hypertension and MTRR A66G are the other determinants of stenosis. These eleven variables contribute towards 82.5% stenosis. To conclude, the EMLA model exhibited higher predictability both in terms of disease prediction and stenosis prediction. This can be attributed to higher number of iterations in EMLA model that can increase the prediction accuracy.
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