Predicting Teenager's Future Stress Level from Micro-Blog

2015 ◽  
Author(s):  
Yiping Li ◽  
Jing Huang ◽  
Hao Wang ◽  
Ling Feng
Keyword(s):  
Stress Level

ABCA3 mutations influence the stress level and immunological function of lung epithelial A549 cells

Pneumologie ◽  
2010 ◽  
Vol 64 (01) ◽  
Author(s):  
E Kaltenborn ◽  
L Fragnet ◽  
KK Konzelmann ◽  
N Weichert ◽  
M Griese ◽  
...  
Keyword(s):  
Stress Level ◽  
A549 Cells ◽  
Immunological Function ◽  
Lung Epithelial

Development of the Express Method for Determining the Oxidative Stress Level of the Population

Food Industry ◽  
2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Ekaterina V. Pastushkova ◽  
Olga V. Chugunova ◽  
Leonid S. Volkanin
Keyword(s):  
Oxidative Stress ◽  
Stress Level ◽  
Express Method ◽  
Oxidative Stress Level

Conjectures, Hypotheses, and Theories of Drumlin Formation (In memory of Stanislaw Baranowski)

1981 ◽  
Vol 27 (97) ◽  
pp. 503-505 ◽  
Author(s):  
Ian J. Smalley
Keyword(s):  
Shear Strength ◽  
Pore Water ◽  
Stress Level ◽  
Critical Stress ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Glacial Till ◽  
Forming Process ◽  
Stress Levels

AbstractRecent investigations have shown that various factors may affect the shear strength of glacial till and that these factors may be involved in the drumlin-forming process. The presence of frozen till in the deforming zone, variation in pore-water pressure in the till, and the occurrence of random patches of dense stony-till texture have been considered. The occurrence of dense stony till may relate to the dilatancy hypothesis and can be considered a likely drumlin-forming factor within the region of critical stress levels. The up-glacier stress level now appears to be the more important, and to provide a sharper division between drumlin-forming and non-drumlin-forming conditions.

Rancang Bangun Sistem Pakar Prediksi Stres Belajar Dengan Neural Network Algoritma Backpropagation

2016 ◽  
Vol 7 (2) ◽  
pp. 105-112
Author(s):  
Adhi Kusnadi ◽  
Idul Putra
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Stress Level ◽  
Network Structure ◽  
Human Being ◽  
Accuracy Rate ◽  
Backpropagation Algorithm ◽  
Stress Prediction ◽  
Index Terms ◽  
Hidden Nodes

Stress will definitely be experienced by every human being and the level of stress experienced by each individual is different. Stress experienced by students certainly will disturb their study if it is not handled quickly and appropriately. Therefore we have created an expert system using a neural network backpropagation algorithm to help counselors to predict the stress level of students. The network structure of the experiment consists of 26 input nodes, 5 hidden nodes, and 2 the output nodes, learning rate of 0.1, momentum of 0.1, and epoch of 5000, with a 100% accuracy rate. Index Terms - Stress on study, expert system, neural network, Stress Prediction

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