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Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS

Catena (Giessen), 2017-02, Vol.149, p.52-63

2017

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A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran

The Science of the total environment, 2018-06, Vol.627, p.744-755

2018

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Bagging based Support Vector Machines for spatial prediction of landslides

Environmental earth sciences, 2018-02, Vol.77 (4), Article 146

2018

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A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling

Bulletin of engineering geology and the environment, 2019-06, Vol.78 (4), p.2865-2886

2019

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A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods

Journal of hydrology (Amsterdam), 2019-06, Vol.573, p.311-323

2019

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A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)

Environmental modelling & software : with environment data news, 2016-10, Vol.84, p.240-250

2016

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Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods

Theoretical and applied climatology, 2017-04, Vol.128 (1-2), p.255-273

2017

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Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

Mathematical problems in engineering, 2021, Vol.2021, p.1-15

2021

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A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area, India

International journal of sediment research, 2018-06, Vol.33 (2), p.157-170

2018

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Development of advanced artificial intelligence models for daily rainfall prediction

Atmospheric research, 2020-06, Vol.237, p.104845, Article 104845

2020

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Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS

Natural hazards (Dordrecht), 2016-08, Vol.83 (1), p.97-127

2016

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Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India

Environmental processes, 2017-09, Vol.4 (3), p.711-730

2017

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Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis

The Science of the total environment, 2019-08, Vol.679, p.172-184

2019

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GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment

Water (Basel), 2020-03, Vol.12 (3), p.683

2020

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Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling

Forests, 2019-02, Vol.10 (2), p.157

2019

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A comparative study of sequential minimal optimization-based support vector machines, vote feature intervals, and logistic regression in landslide susceptibility assessment using GIS

Environmental earth sciences, 2017-05, Vol.76 (10), p.1-15, Article 371

2017

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Development of Next Generation Stevia Sweetener: Rebaudioside M

Foods, 2014-02, Vol.3 (1), p.162-175

2014

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Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

Applied sciences, 2020-04, Vol.10 (7), p.2469

2020

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Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

Symmetry (Basel), 2020-06, Vol.12 (6), p.1022

2020

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Non-Caloric Sweeteners, Sweetness Modulators, and Sweetener Enhancers

Annual review of food science and technology, 2012-04, Vol.3 (1), p.353-380

2012

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A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

Water (Basel), 2020-01, Vol.12 (1), p.239

2020

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Performance assessment of artificial neural network using chi-square and backward elimination feature selection methods for landslide susceptibility analysis

Environmental earth sciences, 2021-10, Vol.80 (20), Article 686

2021

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Estimation of the undrained shear strength of sensitive clays using optimized inference intelligence system

Neural computing & applications, 2022-05, Vol.34 (10), p.7835-7849

2022

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Groundwater Potential Mapping Using GIS‐Based Hybrid Artificial Intelligence Methods

Ground water, 2021-09, Vol.59 (5), p.745-760

2021

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A new diterpene glycoside from Stevia rebaudiana

Molecules (Basel, Switzerland), 2011-04, Vol.16 (4), p.2937-2943

2011

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Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS

A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran

Bagging based Support Vector Machines for spatial prediction of landslides

A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling

A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods

A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)

Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods

Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area, India

Development of advanced artificial intelligence models for daily rainfall prediction

Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS

Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India

Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis

GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment

Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling

A comparative study of sequential minimal optimization-based support vector machines, vote feature intervals, and logistic regression in landslide susceptibility assessment using GIS

Development of Next Generation Stevia Sweetener: Rebaudioside M

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

Non-Caloric Sweeteners, Sweetness Modulators, and Sweetener Enhancers

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

Performance assessment of artificial neural network using chi-square and backward elimination feature selection methods for landslide susceptibility analysis

Estimation of the undrained shear strength of sensitive clays using optimized inference intelligence system

Groundwater Potential Mapping Using GIS‐Based Hybrid Artificial Intelligence Methods

A new diterpene glycoside from Stevia rebaudiana

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