scholarly journals Accuracy test in identifying the splice site type of DNA sequences by using probabilistic neural networks and support vector machines

2014 ◽  
Vol 1 (1) ◽  
Author(s):  
Djati Kerami
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Splice Site ◽  
Dna Sequences ◽  
Support Vector ◽  
Probabilistic Neural Networks ◽  
Application Problem ◽  
Vector Machines ◽  
Accuracy Level

It has been known that Probabilistic Neural Networks as machine learning is very fast in it’s computation time and give a better accuracy comparing to another type of neural networks, on solving a real-world application problem. In the recent years, Support Vector Machines has become a popular model over other machine learning. It can be analyzed theoretically and can achieve a good performance at same time. This paper will describe the use of those machines learning to solve pattern recognition problems with a preliminary case study in detecting the type of splice site on the DNA sequences, particularity on the accuracy level. The results obtained show that Support Vector Machines have a good accuracy level about 95 % comparing to Probabilistic Neural Networks with 92 % approximately.

scholarly journals Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257901
Author(s):  
Yanjing Bi ◽  
Chao Li ◽  
Yannick Benezeth ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Computer Assisted ◽  
Long Distance ◽  
Relationship Analysis ◽  
Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

scholarly journals Machine Learning Prediction of Hospitalization due to COVID-19 based on Self-Reported Symptoms: A Study for Brazil*

2021 ◽  
Author(s):  
Igor Miranda ◽  
Gildeberto Cardoso ◽  
Madhurananda Pahar ◽  
Gabriel Oliveira ◽  
Thomas Niesler
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Health Professionals ◽  
Human Development Index ◽  
Mobile App ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Vector Machines ◽  
Timely Treatment

Predicting the need for hospitalization due to COVID-19 may help patients to seek timely treatment and assist health professionals to monitor cases and allocate resources. We investigate the use of machine learning algorithms to predict the risk of hospitalization due to COVID-19 using the patient's medical history and self-reported symptoms, regardless of the period in which they occurred. Three datasets containing information regarding 217,580 patients from three different states in Brazil have been used. Decision trees, neural networks, and support vector machines were evaluated, achieving accuracies between 79.1% to 84.7%. Our analysis shows that better performance is achieved in Brazilian states ranked more highly in terms of the official human development index (HDI), suggesting that health facilities with better infrastructure generate data that is less noisy. One of the models developed in this study has been incorporated into a mobile app that is available for public use.

scholarly journals Advanced machine learning methods in psychiatry: an introduction

2020 ◽  
Vol 33 (2) ◽  
pp. e100197 ◽  
Author(s):  
Tsung-Chin Wu ◽  
Zhirou Zhou ◽  
Hongyue Wang ◽  
Bokai Wang ◽  
Tuo Lin ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vector Machines ◽  
Artificial Neural

Mental health questions can be tackled through machine learning (ML) techniques. Apart from the two ML methods we introduced in our previous paper, we discuss two more advanced ML approaches in this paper: support vector machines and artificial neural networks. To illustrate how these ML methods have been employed in mental health, recent research applications in psychiatry were reported.

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