scholarly journals A Comparative Study of Machine Learning Techniques to Simulate Land Use Changes

2018 ◽  
Vol 47 (1) ◽  
pp. 53-62 ◽  
Author(s):  
Mohammad Ahmadlou ◽  
Mahmoud Reza Delavar ◽  
Anahid Basiri ◽  
Mohammad Karimi
2020 ◽  
pp. 1096-1117
Author(s):  
Rodrigo Ibañez ◽  
Alvaro Soria ◽  
Alfredo Raul Teyseyre ◽  
Luis Berdun ◽  
Marcelo Ricardo Campo

Progress and technological innovation achieved in recent years, particularly in the area of entertainment and games, have promoted the creation of more natural and intuitive human-computer interfaces. For example, natural interaction devices such as Microsoft Kinect allow users to explore a more expressive way of human-computer communication by recognizing body gestures. In this context, several Supervised Machine Learning techniques have been proposed to recognize gestures. However, scarce research works have focused on a comparative study of the behavior of these techniques. Therefore, this chapter presents an evaluation of 4 Machine Learning techniques by using the Microsoft Research Cambridge (MSRC-12) Kinect gesture dataset, which involves 30 people performing 12 different gestures. Accuracy was evaluated with different techniques obtaining correct-recognition rates close to 100% in some results. Briefly, the experiments performed in this chapter are likely to provide new insights into the application of Machine Learning technique to facilitate the task of gesture recognition.


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