RETRACTED ARTICLE: Cloud based u-healthcare network with QoS guarantee for mobile health service

Chung, Kyungyong; Park, Roy C.

doi:10.1007/s10586-017-1120-0

Cited by 26 publications

(13 citation statements)

References 29 publications

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“…User information can be accessed with a user's name, age, sex, and date of birth. When a user has access, it is possible to find the monthly records of depression [47]. In terms of context information, a user answers questions, each of which has a value recorded.…”

Section: Risky-on the Basis Of The Depression Scale 'Ces-d' Of Nationalmentioning

confidence: 99%

Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression

Baek

Chung

2020

IEEE Access

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Depression is a mental illness influenced by various factors, including stress in everyday life, physical activities, and physical diseases. It accompanies such symptoms as continuous depression, sleep disorder, and suicide attempts. In the healthcare, it is necessary to predict diverse situations accurately. Accordingly, in order to care for mental health, it is necessary to recognize individuals' situations and continue to manage them. In the area of mental diseases and treatment, research has been conducted to find a patient's state with the use of big data and to monitor the worst situation. Mental illnesses typically have depression. Research on Mental healthcare using artificial intelligence do conduct on prediction based on patients' voice, word choice, and conversation length. However, there is not much research on situation prediction in order to prevent depression. Therefore, this study proposes the context-DNN model for predicting depression risk using multiple-regression. The context of the proposed context-DNN consists of the information to predict situations and environments influencing depression in consideration of context information. Each context information related to predictor variables of depression becomes an input of DNN, and variable for depression prediction becomes an output of DNN. For DNN connection, the regression analysis to predict the risk of depression is used so as to predict the potential context influencing the risk of depression. According to the performance evaluation, the proposed model was evaluated to have the best performance in regression analysis and comparative analysis with DNN. INDEX TERMS Deep neural network, context, depression risk, mental health, multiple regression, healthcare, deep learning, context information.

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Section: Risky-on the Basis Of The Depression Scale 'Ces-d' Of Nationalmentioning

confidence: 99%

Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression

Baek

Chung

2020

IEEE Access

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“…Then, the eyes, eyebrows, nose, and mouth, which are the main factors for emotion extraction, are analyzed to extract characteristics data, and they are compared with feature data in a database provided for face analysis in facial recognition. The facial recognition technology analyzes facial expressions to determine emotional states, such as happiness, surprise, sadness, disgust, fear, confusion, etc., and is used for advertising-effect measurement, marketing, and education [16]. Founded by the Massachusetts Institute of Technology Media Lab, Affectiva released Affdex, a solution for recognizing facial expressions and identifying emotional states [17].…”

Section: Facial Recognition-based Application Servicesmentioning

confidence: 99%

Detection of Emotion Using Multi-Block Deep Learning in a Self-Management Interview App

2019

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Recently, domestic universities have constructed and operated online mock interview systems for students’ preparation for employment. Students can have a mock interview anywhere and at any time through the online mock interview system, and can improve any problems during the interviews via images stored in real time. For such practice, it is necessary to analyze the emotional state of the student based on the situation, and to provide coaching through accurate analysis of the interview. In this paper, we propose detection of user emotions using multi-block deep learning in a self-management interview application. Unlike the basic structure for learning about whole-face images, the multi-block deep learning method helps the user learn after sampling the core facial areas (eyes, nose, mouth, etc.), which are important factors for emotion analysis from face detection. Through the multi-block process, sampling is carried out using multiple AdaBoost learning. For optimal block image screening and verification, similarity measurement is also performed during this process. A performance evaluation of the proposed model compares the proposed system with AlexNet, which has mainly been used for facial recognition in the past. As comparison items, the recognition rate and extraction time of the specific area are compared. The extraction time of the specific area decreased by 2.61%, and the recognition rate increased by 3.75%, indicating that the proposed facial recognition method is excellent. It is expected to provide good-quality, customized interview education for job seekers by establishing a systematic interview system using the proposed deep learning method.

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“…The data of 1965 persons and the data of 4582 persons are used as a test set and a training set, respectively. The model learns with the use of the training set, and its performance is repeatedly evaluated 20 persons with the use of the test set [33].…”

Section: Performance Evaluationmentioning

confidence: 99%

Activity Recommendation Model Using Rank Correlation for Chronic Stress Management

et al. 2019

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Korean people are exposed to stress due to the constant competitive structure caused by rapid industrialization. As a result, there is a need for ways that can effectively manage stress and help improve quality of life. Therefore, this study proposes an activity recommendation model using rank correlation for chronic stress management. Using Spearman’s rank correlation coefficient, the proposed model finds the correlations between users’ Positive Activity for Stress Management (PASM), Negative Activity for Stress Management (NASM), and Perceived Stress Scale (PSS). Spearman’s rank correlation coefficient improves the accuracy of recommendations by putting a basic rank value in a missing value to solve the sparsity problem and cold-start problem. For the performance evaluation of the proposed model, F-measure is applied using the average precision and recall after five times of recommendations for 20 users. As a result, the proposed method has better performance than other models, since it recommends activities with the use of the correlation between PASM and NASM. The proposed activity recommendation model for stress management makes it possible to manage user’s stress effectively by lowering the user’s PSS using correlation.

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RETRACTED ARTICLE: Cloud based u-healthcare network with QoS guarantee for mobile health service

Cited by 26 publications

References 29 publications

Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression

Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression

Detection of Emotion Using Multi-Block Deep Learning in a Self-Management Interview App

Activity Recommendation Model Using Rank Correlation for Chronic Stress Management

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