Cultural heritage food experiences in tourism destination hold a boundless impression on tourists' loyalty towards a destination. The researchers conducted a survey on 100 international tourists and used multiple regression analysis to verify the factors affecting the intentional loyalty on heritage food consumption. Consequently, this study encountered that both Food Culture Involvement and Malaysian Food Culture had impact on tourists' Intentional Loyalty that involves revisiting Malaysia and repurchasing MHF. Also, this research outlines substantive recommendations on marketing strategies specifically for food providers to market MHF in establishing Malaysia as the distinctive food tourism destination in the eyes of the world.
Multilayer perceptron network (MLP) has been recognized as a powerful tool for many applications including classification. Selection of the activation functions in the multilayer perceptron (MLP) network plays an essential role on the network performance. This paper presents a comparison study of two commonly used MLP activation function; sigmoid and hyperbolic tangent for weather classification. Meteorological data such as solar radiation, ambient temperature, current, surface temperature, voltage, wind direction and wind speed data are acquired from a photovoltaic (PV) system. Then, the meteorological data are input to the MLP network to classify the weather condition. In this study, weather conditions are classified into four types; rain, cloudy, dry day and storm. Levenberg-Marquardt algorithm is used to train the MLP network since it is the fastest training and ensure the best converges towards a minimum error. Experimental results show that hyperbolic tangent activation function is more efficient compared to sigmoid activation function. The MLP network using hyperbolic tangent function has achieved higher classification accuracy with less number of hidden nodes compared to sigmoid activation function.
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