Identify student stress level detection with backpropagation method
نویسندگان
چکیده
منابع مشابه
How to Identify and Prioritize Psychosocial Factors Impacting Stress Level
We develop a methodological approach to identify and prioritize psychosocial factors (stressors) requiring priority action to reduce stress levels. Data analysis was carried out on a random sample of 10 000 French employees who completed, during a routine interview with the occupational physician, a 25-item questionnaire about stress levels, as well as a questionnaire about 58 stressors grouped...
متن کاملComparison Results Between Usual Backpropagation and Modified Backpropagation with Weighting: Application to Radar Detection
This paper presents some relevant results of a novel variant of the Backpropagation Algorithm to be applied during the Multilayer Perceptrons learning phase. The novelty consists in a weighting operation when the MLP learns the weights. The purpose is to modify the Mean Square Error objective giving more relevance to less frequent training patterns and resting relevance to the frequent ones. Th...
متن کاملMinimum stress optimal design with the level set method
This paper is devoted to minimum stress design in structural optimization. We propose a simple and efficient numerical algorithm for shape and topology optimization based on the level set method coupled with the topological derivative. We compute a shape derivative, as well as a topological derivative, for a stress-based objective function. Using an adjoint equation we implement a gradient algo...
متن کاملAn Improved Backpropagation Method with Adaptive Learning Rate
A method improving the convergence rate of the backpropagation algorithm is proposed. This method adapts the learning rate using the Barzilai and Borwein [IMA J.Numer. Anal., 8, 141–148, 1988] steplength update for gradient descent methods. The determined learning rate is different for each epoch and depends on the weights and gradient values of the previous one. Experimental results show that ...
متن کاملHigh-Level Student Modeling with Machine Learning
We have constructed a learning agent that models student behavior at a high level of granularity for a mathematics tutor. Rather than focusing on whether the student knows a particular piece of knowledge, the learning agent determines how likely the student is to answer a problem correctly and how long he will take to generate this response. To construct this model, we used traces from previous...
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: Journal of Physics: Conference Series
سال: 2019
ISSN: 1742-6588,1742-6596
DOI: 10.1088/1742-6596/1175/1/012030