A Novel Cultural Crowd Model Toward Cognitive Artificial Intelligence  

作　　者：Fatmah Abdulrahman Baothman Osama Ahmed Abulnaja Fatima Jafar Muhdher 

机构地区：[1]Faculty of Computing and Information Technology,King Abdulaziz University,Jeddah,21431,Kingdom of Saudi Arabia

出　　处：《Computers, Materials & Continua》2021年第12期3337-3363,共27页计算机、材料和连续体（英文）

基　　金：This project is funded by the Deanship of Scientific research(DSR),King Abdulaziz University,Jeddah,under Grant No.(DF-593-165-1441).Therefore,the authors gratefully acknowledge the technical and financial support of the DSR.

摘　　要：Existing literature shows cultural crowd management has unforeseen issues due to four dynamic elements;time,capacity,speed,and culture.Crosscultural variations are increasing the complexity level because each mass and event have different characteristics and challenges.However,no prior study has employed the six Hofstede Cultural Dimensions(HCD)for predicting crowd behaviors.This study aims to develop the Cultural Crowd-Artificial Neural Network(CC-ANN)learning model that considers crowd’s HCD to predict their physical(distance and speed)and social(collectivity and cohesion)characteristics.The model was developed towards a cognitive intelligent decision support tool where the predicted characteristics affect the estimated regulation plan’s time and capacity.We designed the experiments as four groups to analyze the proposed model’s outcomes and extract the interrelations between the HCD of crowd’s grouped individuals and their physical and social characteristics.Furthermore,the extracted interrelations were verified with the dataset’s statistical correlation analysis with a P-value<0.05.Results demonstrate that the predicted crowd’s characteristics were positively/negatively affected by their considered cultural features.Similarly,analyzing outcomes identified the most influential HCD for predicting crowd behavior.The results also show that the CC-ANN model improves the prediction and learning performance for the crowd behavior because the achieved accepted level of accuracy does not exceed 10 to 20 epochs in most cases.Moreover,the performance improved by 90%,93%respectively in some cases.Finally,all prediction best cases were related to one or more cultural features with a low error of 0.048,0.117,0.010,and 0.014 mean squared error,indicating a novel cultural learning model.

关 键 词：Cultural crowds learning model artificial neural network hHofstede cultural dimensions predicting group behaviors crowd management 

分 类 号：G12[文化科学]