Comparison of computational intelligence models for cuttings transport in horizontal and deviated wells

2016 ◽  
Vol 146 ◽  
pp. 832-837 ◽  
Author(s):  
Erman Ulker ◽  
Mehmet Sorgun
Keyword(s):  
Computational Intelligence ◽  
Cuttings Transport ◽  
Intelligence Models ◽  
Deviated Wells

scholarly journals GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

2019 ◽  
Vol 11 (24) ◽  
pp. 7118 ◽  
Author(s):  
Viet-Tien Nguyen ◽  
Trong Hien Tran ◽  
Ngoc Anh Ha ◽  
Van Liem Ngo ◽  
Al-Ansari Nadhir ◽  
...  
Keyword(s):  
Land Use ◽  
Risk Management ◽  
Land Use Planning ◽  
Computational Intelligence ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Statistical Measures ◽  
Susceptibility Maps ◽  
Intelligence Models ◽  
Landslide Susceptibility Maps

Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development.

Pre-Diagnosis of Chronic Diseases by the Application of Computational Intelligence Models

2020 ◽  
Vol 24 (3) ◽  
Author(s):  
Patricio Reyes León ◽  
Julio César Salgado Ramírez ◽  
José Luis Velázquez Rodríguez
Keyword(s):  
Chronic Diseases ◽  
Computational Intelligence ◽  
Intelligence Models

scholarly journals Empirical Study of Homogeneous and Heterogeneous Ensemble Models for Software Development Effort Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Mahmoud O. Elish ◽  
Tarek Helmy ◽  
Muhammad Imtiaz Hussain
Keyword(s):  
Empirical Study ◽  
Software Development ◽  
Computational Intelligence ◽  
Accurate Estimation ◽  
Development Effort ◽  
Effort Estimation ◽  
Ensemble Models ◽  
Intelligence Models ◽  
Software Development Effort ◽  
Software Development Effort Estimation

Accurate estimation of software development effort is essential for effective management and control of software development projects. Many software effort estimation methods have been proposed in the literature including computational intelligence models. However, none of the existing models proved to be suitable under all circumstances; that is, their performance varies from one dataset to another. The goal of an ensemble model is to manage each of its individual models’ strengths and weaknesses automatically, leading to the best possible decision being taken overall. In this paper, we have developed different homogeneous and heterogeneous ensembles of optimized hybrid computational intelligence models for software development effort estimation. Different linear and nonlinear combiners have been used to combine the base hybrid learners. We have conducted an empirical study to evaluate and compare the performance of these ensembles using five popular datasets. The results confirm that individual models are not reliable as their performance is inconsistent and unstable across different datasets. Although none of the ensemble models was consistently the best, many of them were frequently among the best models for each dataset. The homogeneous ensemble of support vector regression (SVR), with the nonlinear combiner adaptive neurofuzzy inference systems-subtractive clustering (ANFIS-SC), was the best model when considering the average rank of each model across the five datasets.

A Review of Computational Intelligence Models for Brain Tumour Classification and Prediction

2021 ◽  
Vol 13 (4) ◽  
pp. 18-39
Author(s):  
Justice Kwame Appati ◽  
Godfred Akwetey Brown ◽  
Michael Agbo Tettey Soli ◽  
Ismail Wafaa Denwar
Keyword(s):  
Genetic Algorithm ◽  
Brain Tumor ◽  
Literature Review ◽  
Computational Intelligence ◽  
Model Comparison ◽  
Performance Metrics ◽  
Empirical Studies ◽  
Tumor Classification ◽  
Estimation Accuracy ◽  
Intelligence Models

This review aims to systematically analyze ML models from four aspects: type of ML technique, estimation accuracy, model comparison, and estimation context. A systematic literature review of empirical studies was conducted on the ML models published in the last decades. Fifty-one primary studies relevant to the objective of this research were revealed. After investigating these studies, five ML techniques have been employed in brain tumor classification and prediction. Ultimately, the estimation accuracy of these ML models could be regarded and accepted and outperformed non-ML models. ML models have been revealed to be useful in brain tumor classification and prediction. Genetic algorithm among the ML models achieved an accuracy of 100%. Nevertheless, ML models are still restricted in the industry, so initiative and encouragement are needed to make ML models easier. Further work is required on these ML models to verify the accuracy and consider other performance metrics other than the accuracy.

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