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MHDeep: Mental Health Disorder Detection System based on Body-Area and Deep Neural Networks
At the patient level, MHDeep DNNs achieve an accuracy of 100%, 100%, and 90. 0% for the three mental health disorders, respectively.
TUTOR: Training Neural Networks Using Decision Rules as Model Priors
The synthetic data generation module targets both the categorical and continuous features.
CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks
The novel coronavirus (SARS-CoV-2) has led to a pandemic.
STEERAGE: Synthesis of Neural Networks Using Architecture Search and Grow-and-Prune Methods
On MNIST dataset, our CNN architecture achieves an error rate of 0. 66%, with 8. 6x fewer parameters compared to the LeNet-5 baseline.
SCANN: Synthesis of Compact and Accurate Neural Networks
To address these challenges, we propose a two-step neural network synthesis methodology, called DR+SCANN, that combines two complementary approaches to design compact and accurate DNNs.
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