scholarly journals Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

2012 ◽  
Vol 6 (8) ◽  
pp. 2873-2888 ◽  
Author(s):  
Mohammad Zare ◽  
Hamid Reza Pourghasemi ◽  
Mahdi Vafakhah ◽  
Biswajeet Pradhan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Multilayer Perceptron ◽  
Landslide Susceptibility ◽  
Artificial Neural Network Model ◽  
Basic Function ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping

scholarly journals Detection of Lung Cancer using Multilayer Perceptron Neural Network

2017 ◽  
Vol 1 (4) ◽  
pp. 109
Author(s):  
Farzad Mirzakhani
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Artificial Neural Network ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Network Model ◽  
Neural Network Model ◽  
Multilayer Perceptron ◽  
Predictive Value ◽  
Artificial Neural Network Model

Introduction: Lung cancer is the most common cancer in terms of prevalence and mortality. The cancer can be detected once it is reached to a stage that is visible in the CT imaging. Eighty six percent of the patients with lung cancer because they are late understand their disease, surgery has little effect on their improvement. Therefore, the existence of an intelligent system that can detect lung cancer in the early stages is necessary. Methods: In this study, a lung cancer dataset of UCI database was used. This dataset consists of 32 samples, 57 variables and 3 classes (each class including 10, 9 and 13 samples). The data were normalized within the range 0 to 1. Then, to increase the detection speed, the dimensions of the data were reduced by using the Principal Components Analysis (PCA). Then, using a multilayer perceptron neural network, a model for classification and prediction of lung cancer was developed. Finally, the performance of the model was measured using sensitivity, specificity, positive predictive value and negative predictive value. It should be noted that all analyzes were done using Weka software. Results: After developing and evaluating an artificial neural network model, the developed model had a sensitivity of 66.7%, a 98.5% specificity, a positive predictive value of 75%, and a negative predictive value of 97.7%. Conclusion: In intelligent diagnostic systems, in addition to high accuracy of diagnosis, the speed of diagnosis and decision making is also important. Therefore, researchers increased the speed of the prediction model by reducing 57 variables to 8 variables using PCA. Also, the high sensitivity and high specificity of developed model demonstrates high power of artificial neural network model in detecting lung cancer.

Landslide susceptibility analysis using an artificial neural network model

2007 ◽  
Author(s):  
Shattri Mansor ◽  
Biswajeet Pradhan ◽  
Mohamed Daud ◽  
Normalina Jamaludin ◽  
Zailani Khuzaimah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Landslide Susceptibility ◽  
Artificial Neural Network Model ◽  
Susceptibility Analysis ◽  
Artificial Neural

scholarly journals Landslide susceptibility mapping of Phewa catchment using multilayer perceptron artificial neural network

2016 ◽  
Vol 4 ◽  
pp. 1-9 ◽  
Author(s):  
Ananta Man Singh Pradhan ◽  
Yun-Tae Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Geographical Information ◽  
Landslide Susceptibility Mapping ◽  
Mapping Technique ◽  
Inventory Map ◽  
Artificial Neural

The aim of this study was to prepare landslide susceptibility mapping technique using multilayer perceptron artificial neural network (MLP-ANN) and then to apply this method to Phewa catchment in western Nepal. To determine the effect of causative factors on landslides, data layers of aspect, elevation, slope, internal relief, slope shape, drainage proximity, drainage density, stream power index, topographic wetness index, sediment transport index, land cover and geology were analysed in R-statistical package and final map was produced using geographical information system environment. A GIS-based landslide inventory map of 88 landslide locations was prepared using data from previous reports and satellite image interpretation. A MLP-ANN model was generated from a training set consisting of ~70% randomly selected landslide in the inventory map, with the remaining ~30% landslides used for validation of the susceptibility map. According to analysis, the model had a success rate of 82.1% and the prediction accuracy of 91.4%, indicating a good performance.

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