Predicting students blood pressure by Artificial Neuron Network: Facebook predict students blood pressure

• Published in: 2014 Science and Information Conference (SAI) pp. 430 - 437
• Main Authors: Khan, Shazada Muhammad Umair, Shaikh, Javaria Manzoor
• Format: Conference Proceeding Journal Article
• Language: English
• Published: The Science and Information (SAI) Organization 01.08.2014
• Subjects: Artificial neural networks; Artificial Neuron Network (ANN); Blood pressure; Blood Pressure (BP); Educational institutions; Emotions; Facebook; Hanyang University; Human; Learning theory; Mathematical model; Mathematical models; Networks; Neural networks; Neurons; Students; Support vector machine classification; Vectors
• ISBN: 0989319334; 9780989319331
• DOI: 10.1109/SAI.2014.6918223

Emotion signifies the core value when a person comes in contact with multidimensional situation. Primary emotion has a capacity to observe when subject is contact with varying arising situation. Our research aims to present how primary emotion help to predict the human blood pressure (BP). Facebook is a Social Networking Sites (SNS) that provide emotionally rich environments and one of the most popular social sites for extracting information related to primary emotions. To extract the information of human emotion using Facebook, vector model is applied here. To predict the primary emotion using Facebook status, we adopt Artificial Neuron Network (ANN) since Facebook status updated by active users that helps to forecast the upcoming BP. The textual data is classified by vector model and ANN is used to predict human BP. The data set comprises of BP and human emotion gathered from Facebook updated post based on formal text from volunteer students at Hanyang University. The outcome shows that ANN can be prosperously applied for prediction of BP through primary emotion. The prediction result shows that there is 25% variation among the correlation coefficient variation of happy emotion instead of 18% of angry emotion.