Performance Comparison of Support Vector Machine, K-Nearest-Neighbor, Artificial Neural Networks, and Recurrent Neural networks in Gender Recognition from Voice Signals

2019 ◽  
Author(s):  
Fulya Akdeniz ◽  
Yasar Becerikli
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Recurrent Neural Networks ◽  
Nearest Neighbor ◽  
Performance Comparison ◽  
Support Vector ◽  
Gender Recognition ◽  
K Nearest Neighbor ◽  
Artificial Neural

Intelligent Prediction of Differential Pipe Sticking by Support Vector Machine Compared With Conventional Artificial Neural Networks: An Example of Iranian Offshore Oil Fields

2012 ◽  
Vol 27 (04) ◽  
pp. 586-595 ◽  
Author(s):  
Reza Jahanbakhshi ◽  
Reza Keshavarzi ◽  
Mahdi Aliyari Shoorehdeli ◽  
Abolqasem Emamzadeh
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Oil Fields ◽  
Support Vector ◽  
Artificial Neural ◽  
Offshore Oil

IMPROVEMENT OF DOSE ESTIMATION PROCESS USING ARTIFICIAL NEURAL NETWORKS

2018 ◽  
Vol 184 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Gal Amit ◽  
Hanan Datz
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
Physical Parameters ◽  
Machine Method ◽  
Algorithm Performance ◽  
Artificial Neural ◽  
First Time ◽  
Automatic Algorithm

Abstract We present here for the first time a fast and reliable automatic algorithm based on artificial neural networks for the anomaly detection of a thermoluminescence dosemeter (TLD) glow curves (GCs), and compare its performance with formerly developed support vector machine method. The GC shape of TLD depends on numerous physical parameters, which may significantly affect it. When integrated into a dosimetry laboratory, this automatic algorithm can classify ‘anomalous’ (having any kind of anomaly) GCs for manual review, and ‘regular’ (acceptable) GCs for automatic analysis. The new algorithm performance is then compared with two kinds of formerly developed support vector machine classifiers—regular and weighted ones—using three different metrics. Results show an impressive accuracy rate of 97% for TLD GCs that are correctly classified to either of the classes.

Predicting Students Grades Using Artificial Neural Networks and Support Vector Machine

2019 ◽  
pp. 751-766
Author(s):  
Sajid Umair ◽  
Muhammad Majid Sharif
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Student Performance ◽  
Semantic Analysis ◽  
Vital Role ◽  
Support Vector ◽  
Data Set ◽  
Important Research Topic ◽  
Artificial Neural

Prediction of student performance on the basis of habits has been a very important research topic in academics. Studies show that selection of the correct data set also plays a vital role in these predictions. In this chapter, the authors took data from different schools that contains student habits and their comments, analyzed it using latent semantic analysis to get semantics, and then used support vector machine to classify the data into two classes, important for prediction and not important. Finally, they used artificial neural networks to predict the grades of students. Regression was also used to predict data coming from support vector machine, while giving only the important data for prediction.

Modeling river discharge time series using support vector machine and artificial neural networks

2016 ◽  
Vol 75 (8) ◽  
Author(s):  
Mohammad Ali Ghorbani ◽  
Rahman Khatibi ◽  
Arun Goel ◽  
Mohammad Hasan FazeliFard ◽  
Atefeh Azani
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
River Discharge ◽  
Support Vector ◽  
Discharge Time ◽  
Artificial Neural
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