Application of Machine Learning Techniques for Simplifying the Association Problem in a Video Surveillance System

2005 ◽  
pp. 509-518 ◽  
Author(s):  
Blanca Rodríguez ◽  
Óscar Pérez ◽  
Jesús García ◽  
José M. Molina
Keyword(s):  
Machine Learning ◽  
Video Surveillance ◽  
Surveillance System ◽  
Machine Learning Techniques ◽  
Video Surveillance System ◽  
Learning Techniques

scholarly journals Evaluating the Surveillance System for Spotted Fever in Brazil Using Machine-Learning Techniques

2017 ◽  
Vol 5 ◽  
Author(s):  
Diego Montenegro Lopez ◽  
Flávio Luis de Mello ◽  
Cristina Maria Giordano Dias ◽  
Paula Almeida ◽  
Milton Araújo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surveillance System ◽  
Spotted Fever ◽  
Machine Learning Techniques ◽  
Learning Techniques

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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