scholarly journals Analysis on Machine Learning Techniques for Stress Detection among Employees

2021 ◽  
Vol 10 (1) ◽  
pp. 35-37
Author(s):  
B. K. Kiranashree ◽  
V. Ambika ◽  
A. D. Radhika
Keyword(s):  
Machine Learning ◽  
Mental Stress ◽  
Detection Method ◽  
Physiological Parameters ◽  
Machine Learning Techniques ◽  
Health Issues ◽  
Stress Detection ◽  
Learning Techniques ◽  
Skin Response ◽  
Management Techniques

Mental stress is a common and major issue nowadays especially among working professional, because employees have family commitments with their over workload, target, achievements, etc. Stress tends various health issues like heart attack, stroke, depression, and suicide. Mental stress is not only in employees even normal people also face this problem but the employees has so many stress management techniques to manage the stress like yoga, meditation etc., but still employees suffer from the stress. Stress calculated by the Traditional stress detection method has two types of physiological parameters one is questionnaire format and another one is physiological signals based on Heart rate variability, galvanic skin response, BP, and electrocardiography, etc., Machine learning techniques are applied to analyze and anticipate stress in employees. In this paper, we mainly focus on different machine learning techniques and physiological parameters for stress detection.

Explore the Use of Handwriting Information and Machine Learning Techniques in Evaluating Mental Workload

2020 ◽  
pp. 1423-1439
Author(s):  
Zhiming Wu ◽  
Tao Lin ◽  
Ningjiu Tang
Keyword(s):  
Machine Learning ◽  
Interaction Design ◽  
Mental Stress ◽  
Mental Workload ◽  
Low Cost ◽  
Research Question ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Psychological Evidence

Mental workload is considered one of the most important factors in interaction design and how to detect a user's mental workload during tasks is still an open research question. Psychological evidence has already attributed a certain amount of variability and “drift” in an individual's handwriting pattern to mental stress, but this phenomenon has not been explored adequately. The intention of this paper is to explore the possibility of evaluating mental workload with handwriting information by machine learning techniques. Machine learning techniques such as decision trees, support vector machine (SVM), and artificial neural network were used to predict mental workload levels in the authors' research. Results showed that it was possible to make prediction of mental workload levels automatically based on handwriting patterns with relatively high accuracy, especially on patterns of children. In addition, the proposed approach is attractive because it requires no additional hardware, is unobtrusive, is adaptable to individual users, and is of very low cost.

scholarly journals CorrAUC: a Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine Learning Techniques

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Muhammad Shafiq ◽  
Zhihong Tian ◽  
Ali Kashif Bashir ◽  
Xiaojiang Du ◽  
Mohsen Guizani
Keyword(s):  
Machine Learning ◽  
Detection Method ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Traffic Detection

Drought Stress Detection Using Low-Cost Computer Vision Systems and Machine Learning Techniques

2020 ◽  
Vol 22 (3) ◽  
pp. 27-29 ◽  
Author(s):  
Paula Ramos-Giraldo ◽  
Chris Reberg-Horton ◽  
Anna M. Locke ◽  
Steven Mirsky ◽  
Edgar Lobaton
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Drought Stress ◽  
Low Cost ◽  
Machine Learning Techniques ◽  
Stress Detection ◽  
Vision Systems ◽  
Learning Techniques ◽  
Computer Vision Systems

Prediction Models for Healthcare Using Machine Learning

2021 ◽  
pp. 70-91
Author(s):  
Ayushe Gangal ◽  
Peeyush Kumar ◽  
Sunita Kumari ◽  
Anu Saini
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Brain Diseases ◽  
Health Issues ◽  
Industry Study ◽  
Learning Techniques

Healthcare is always a sensitive issue for all of us, and it will always remain. Predicting various types of health issues in advance can lead us to a better life. Various types of health problems are there like cancer, heart diseases, diabetes, arthritis, pneumonia, lungs disease, liver disease, and brain disease, which all are at high risk. To reduce the risk of health issues, some suitable models are needed for prediction. Thus, it became as a motivational factor for the authors to survey the existing literature on this topic thoroughly and have consequently to identify suitable machine learning techniques so that improvement can be possible while selecting a prediction model. In this chapter, concept of survey is used to provide the prediction models for healthcare issues along with the challenges associated with each model. This chapter will broadly cover the following: machine learning algorithms used in health industry, study various prediction models for Cancer, Heart diseases, Diabetes and Brain diseases, comparative study of various machine learning algorithms used for prediction.

scholarly journals Predicting Mental Health Illness using Machine Learning Algorithms

2022 ◽  
Vol 2161 (1) ◽  
pp. 012021
Author(s):  
Konda Vaishnavi ◽  
U Nikhitha Kamath ◽  
B Ashwath Rao ◽  
N V Subba Reddy
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Childhood And Adolescence ◽  
Health Issues ◽  
Mental Health Issues ◽  
Learning Techniques ◽  
Tree Classifier

Abstract Early detection of mental health issues allows specialists to treat them more effectively and it improves patient’s quality of life. Mental health is about one’s psychological, emotional, and social well-being. It affects the way how one thinks, feels, and acts. Mental health is very important at every stage of life, from childhood and adolescence through adulthood. This study identified five machine learning techniques and assessed their accuracy in identifying mental health issues using several accuracy criteria. The five machine learning techniques are Logistic Regression, K-NN Classifier, Decision Tree Classifier, Random Forest, and Stacking. We have compared these techniques and implemented them and also obtained the most accurate one in Stacking technique based with an accuracy of prediction 81.75%.

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