Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach
نویسندگان
چکیده
منابع مشابه
Emotional state classification from EEG data using machine learning approach
Recently, emotion classification from EEG data has attracted much attention with the rapid development of dry electrode techniques, machine learning algorithms, and various real-world applications of brain– computer interface for normal people. Until now, however, researchers had little understanding of the details of relationship between different emotional states and various EEG features. To ...
متن کاملSpatiotemporal Estimation of PM2.5 Concentration Using Remotely Sensed Data, Machine Learning, and Optimization Algorithms
PM 2.5 (particles <2.5 μm in aerodynamic diameter) can be measured by ground station data in urban areas, but the number of these stations and their geographical coverage is limited. Therefore, these data are not adequate for calculating concentrations of Pm2.5 over a large urban area. This study aims to use Aerosol Optical Depth (AOD) satellite images and meteorological data from 2014 to 2017 ...
متن کاملEnhancing Learning from Imbalanced Classes via Data Preprocessing: A Data-Driven Application in Metabolomics Data Mining
This paper presents a data mining application in metabolomics. It aims at building an enhanced machine learning classifier that can be used for diagnosing cachexia syndrome and identifying its involved biomarkers. To achieve this goal, a data-driven analysis is carried out using a public dataset consisting of 1H-NMR metabolite profile. This dataset suffers from the problem of imbalanced classes...
متن کاملAnalyzing Behavioral Markers of Autistic Children Using Eye Tracking Data
Introduction: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that occurs in the early years of life and is characterized by social impairment, verbal and non-verbal communication difficulties as well as stereotypical behaviors. Rehabilitating autistic children at the early stages of growth, in which their brain is highly flexible, yields to enhanced treatment process and provid...
متن کاملMachine Learning Models for Housing Prices Forecasting using Registration Data
This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: JMIR mHealth and uHealth
سال: 2021
ISSN: 2291-5222
DOI: 10.2196/24465