Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter
}
TY - CHAP
T1 - Prediction of the attention area in ambient intelligence tasks
AU - Shafi, Jawad
AU - Angelov, Plamen Parvanov
AU - Umair, Muhammad
PY - 2016/2/3
Y1 - 2016/2/3
N2 - With recent advances in Ambient Intelligence (AmI), it is becoming possible to provide support to a human in an AmI environment. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) model based scheme, named as prediction of the attention area using ANFIS (PAA_ANFIS), which predicts the human attention area on visual display with ordinary web camera. The PAA_ANFIS model was designed using trial and error based on various experiments in simulated gaming environment. This study was conducted to illustrate that ANFIS is effective with hybrid learning, for the prediction of eye-gaze area in the environment. PAA_ANFIS results show that ANFIS has been successfully implemented for predicting within different learning context scenarios in a simulated environment. The performance of the PAA_ANFIS model was evaluated using standard error measurements techniques. The Matlab® simulation results indicate that the performance of the ANFIS approach is valuable, accurate and easy to implement. The PAA_ANFIS results are based on analysis of different model settings in our environment. To further validate the PAA_ANFIS, forecasting results are then compared with linear regression. The comparative results show the superiority and higher accuracy achieved by applying the ANFIS, which is equipped with the capability of generating linear relationship and the fuzzy inference system in input-output data. However, it should be noted that an increase in the number of membership functions (MF) will increase the system response time.
AB - With recent advances in Ambient Intelligence (AmI), it is becoming possible to provide support to a human in an AmI environment. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) model based scheme, named as prediction of the attention area using ANFIS (PAA_ANFIS), which predicts the human attention area on visual display with ordinary web camera. The PAA_ANFIS model was designed using trial and error based on various experiments in simulated gaming environment. This study was conducted to illustrate that ANFIS is effective with hybrid learning, for the prediction of eye-gaze area in the environment. PAA_ANFIS results show that ANFIS has been successfully implemented for predicting within different learning context scenarios in a simulated environment. The performance of the PAA_ANFIS model was evaluated using standard error measurements techniques. The Matlab® simulation results indicate that the performance of the ANFIS approach is valuable, accurate and easy to implement. The PAA_ANFIS results are based on analysis of different model settings in our environment. To further validate the PAA_ANFIS, forecasting results are then compared with linear regression. The comparative results show the superiority and higher accuracy achieved by applying the ANFIS, which is equipped with the capability of generating linear relationship and the fuzzy inference system in input-output data. However, it should be noted that an increase in the number of membership functions (MF) will increase the system response time.
KW - attention modelling
M3 - Chapter
SN - 9783319272665
T3 - Studies in Computational Intelligence
SP - 33
EP - 56
BT - Innovative issues in intelligent systems
A2 - Sgurev, Vassil
A2 - Yager, Ronald
A2 - Kacprzyk, Janusz
A2 - Jotsov, Vladimir
PB - Springer
CY - Berlin
ER -