Artificial neural networks and decision tree classifier performance on medium resolution ASTER data to detect gully networks in southern Italy

2006 ◽  
Author(s):  
A. Ghaffari ◽  
G. Priestnall ◽  
M. L. Clarke
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Southern Italy ◽  
Decision Tree Classifier ◽  
Aster Data ◽  
Medium Resolution ◽  
Tree Classifier ◽  
Classifier Performance ◽  
Artificial Neural

The Use of Quantitative Methods in Investment Decisions

2019 ◽  
pp. 256-275 ◽  
Author(s):  
Serkan Eti
Keyword(s):  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Quantitative Methods ◽  
Simulation Analysis ◽  
Investment Decision ◽  
Tree Algorithms ◽  
Artificial Neural

Quantitative methods are mainly preferred in the literature. The main purpose of this chapter is to evaluate the usage of quantitative methods in the subject of the investment decision. Within this framework, the studies related to the investment decision in which quantitative methods are taken into consideration. As for the quantitative methods, probit, logit, decision tree algorithms, artificial neural networks methods, Monte Carlo simulation, and MARS approaches are taken into consideration. The findings show that MARS methodology provides a more accurate results in comparison with other techniques. In addition to this situation, it is also concluded that probit and logit methodologies were less preferred in comparison with decision tree algorithms, artificial neural networks methods, and Monte Carlo simulation analysis, especially in the last studies. Therefore, it is recommended that a new evaluation for investment analysis can be performed with MARS method because it is understood that this approach provides better results.

Artificial neural networks and decision tree model analysis of liver cancer proteomes

2007 ◽  
Vol 361 (1) ◽  
pp. 68-73 ◽  
Author(s):  
John M. Luk ◽  
Brian Y. Lam ◽  
Nikki P.Y. Lee ◽  
David W. Ho ◽  
Pak C. Sham ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Liver Cancer ◽  
Decision Tree ◽  
Model Analysis ◽  
Decision Tree Model ◽  
Tree Model ◽  
Artificial Neural

scholarly journals A study and identification of COVID-19 viruses using N-grams with Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine

2020 ◽  
Author(s):  
Mohamed El Boujnouni
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Naive Bayes ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
The World ◽  
Artificial Neural

Abstract Coronavirus disease 2019 or COVID-19 is a global health crisis caused by a virus officially named as severe acute respiratory syndrome coronavirus 2 and well known with the acronym (SARS-CoV-2). This very contagious illness has severely impacted people and business all over the world and scientists are trying so far to discover all useful information about it, including its potential origin(s) and inter-host(s). This study is a part of this scientific inquiry and it aims to identify precisely the origin(s) of a large set of genomes of SARS-COV-2 collected from different geographic locations in all over the world. This research is performed through the combination of five powerful techniques of machine learning (Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine) and a widely known tool of language modeling (N-grams). The experimental results have shown that the majority of techniques gave the same global results concerning the origin(s) and inter-host(s) of SARS-COV-2. These results demonstrated that this virus has one zoonotic source which is Pangolin.

scholarly journals Machine Learning Classifiers for Modeling Soil Characteristics by Geophysics Investigations: A Comparative Study

2020 ◽  
Vol 10 (17) ◽  
pp. 5734
Author(s):  
Chee Soon Lim ◽  
Edy Tonnizam Mohamad ◽  
Mohammad Reza Motahari ◽  
Danial Jahed Armaghani ◽  
Rosli Saad
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Soil Characteristics ◽  
Machine Learning Techniques ◽  
Chi Square ◽  
Interaction Detection ◽  
Testing Accuracy ◽  
Artificial Neural

To design geotechnical structures efficiently, it is important to examine soil’s physical properties. Therefore, classifying soil with respect to geophysical parameters is an advantageous and popular approach. Novel, quick, cost, and time effective machine learning techniques can facilitate this classification. This study employs three kinds of machine learning models, including the Decision Tree, Artificial Neural Networks, and Bayesian Networks. The Decision tree models included the chi-square automatic interaction detection (CHAID), classification and regression trees (CART), quick, unbiased, and efficient statistical tree (QUEST), and C5; the Artificial Neural Networks models included Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF); and BN models included the Tree Augmented Naïve (TAN) and Markov Blanket, which were employed to predict the soil classifications using geophysics investigations and laboratory tests. The performance of each model was assessed through the accuracy, stability and gains. The results showed that while the BAYESIANMARKOV model achieved the highest overall accuracy (100%) in training phase, this model achieved the lowest accuracy (34.21%) in testing phases. Thus, this model had the worst stability. The QUEST had the second highest overall training accuracy (99.12%) and had the highest overall testing accuracy (94.74%). Thus, this model was somewhat stable and had an acceptable overall training and testing accuracy to predict the soil characteristics. The future studies can use the findings of this paper as a benchmark to classify the soil characteristics and select the best machine learning technique to perform this classification.

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