Deep learning for micro-expression recognition: A survey
Micro-expressions (MEs) are involuntary facial movements revealing people's hidden
feelings in high-stake situations and have practical importance in various fields. Early …
feelings in high-stake situations and have practical importance in various fields. Early …
Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems
The use of artificial neural networks (ANN) in fault detection analysis is widespread. This
paper aims to provide an overview on its application in the field of fault identification and …
paper aims to provide an overview on its application in the field of fault identification and …
Deep learning-enabled anomaly detection for IoT systems
Abstract Internet of Things (IoT) systems have become an intrinsic technology in various
industries and government services. Unfortunately, IoT devices and networks are known to …
industries and government services. Unfortunately, IoT devices and networks are known to …
[PDF][PDF] Applied predictive modeling
M Kuhn - 2013 - mathematics.foi.hr
This is a book on data analysis with a specific focus on the practice of predictive modeling.
The term predictive modeling may stir associations such as machine learning, pattern …
The term predictive modeling may stir associations such as machine learning, pattern …
An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction
Heart failure (HF) has been considered as one of the deadliest human diseases worldwide
and the accurate prediction of HF risks would be vital for HF prevention and treatment. To …
and the accurate prediction of HF risks would be vital for HF prevention and treatment. To …
Learning from imbalanced data sets with weighted cross-entropy function
This paper presents a novel approach to deal with the imbalanced data set problem in
neural networks by incorporating prior probabilities into a cost-sensitive cross-entropy error …
neural networks by incorporating prior probabilities into a cost-sensitive cross-entropy error …
Evaluation of neural architectures trained with square loss vs cross-entropy in classification tasks
Modern neural architectures for classification tasks are trained using the cross-entropy loss,
which is widely believed to be empirically superior to the square loss. In this work we …
which is widely believed to be empirically superior to the square loss. In this work we …
Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings
Faulty structural design may cause multistory reinforced concrete (RC) buildings to collapse
suddenly. All attempts are directed to avoid structural failure as it leads to human life danger …
suddenly. All attempts are directed to avoid structural failure as it leads to human life danger …
A channel-fused dense convolutional network for EEG-based emotion recognition
Human emotion recognition could greatly contribute to human–computer interaction with
promising applications in artificial intelligence. One of the challenges in recognition tasks is …
promising applications in artificial intelligence. One of the challenges in recognition tasks is …
[PDF][PDF] Cross-entropy vs. squared error training: a theoretical and experimental comparison.
In this paper we investigate the error criteria that are optimized during the training of artificial
neural networks (ANN). We compare the bounds of the squared error (SE) and the …
neural networks (ANN). We compare the bounds of the squared error (SE) and the …