TY -的盟Kilimci希拉尔泽盟——Guven Aykut盟——Uysal Mitat盟——Akyokus斯莱姆PY - 2019 DA - 2019/12/14 TI -情绪从使用深度学习物理和神经回路数据模型检测SP - 6434578六世- 2019 AB -如今,智能设备作为日常生活的一部分收集数据对用户的帮助下传感器。传感器数据通常是物理数据但移动应用程序收集超过物理数据设备的使用习惯和个人利益。收集的数据通常归类为个人,但它们包含有价值的信息对用户进行了分析和解释。个人数据分析的主要目的之一是对用户作出预测。收集的数据可以分为两大类:物理和行为数据。行为数据也称为神经回路的数据。物理和神经回路参数收集作为这项研究的一部分。物理数据包含用户的测量如心跳、睡眠质量、能量、运动/流动参数。神经回路数据包含击键模式像打字速度和打字错误。用户的情感/情绪状态也调查问日常问题。 Six questions are asked to the users daily in order to determine the mood of them. These questions are emotion-attached questions, and depending on the answers, users’ emotional states are graded. Our aim is to show that there is a connection between users’ physical/neurophysical parameters and mood/emotional conditions. To prove our hypothesis, we collect and measure physical and neurophysical parameters of 15 users for 1 year. The novelty of this work to the literature is the usage of both combinations of physical and neurophysical parameters. Another novelty is that the emotion classification task is performed by both conventional machine learning algorithms and deep learning models. For this purpose, Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) neural network are employed as deep learning methodologies. Multinomial Naïve Bayes (MNB), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), and Decision Integration Strategy (DIS) are evaluated as conventional machine learning algorithms. To the best of our knowledge, this is the very first attempt to analyze the neurophysical conditions of the users by evaluating deep learning models for mood analysis and enriching physical characteristics with neurophysical parameters. Experiment results demonstrate that the utilization of deep learning methodologies and the combination of both physical and neurophysical parameters enhances the classification success of the system to interpret the mood of the users. A wide range of comparative and extensive experiments shows that the proposed model exhibits noteworthy results compared to the state-of-art studies. SN - 1076-2787 UR - https://doi.org/10.1155/2019/6434578 DO - 10.1155/2019/6434578 JF - Complexity PB - Hindawi KW - ER -