AIMC Topic: Neural Networks, Computer

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Cascaded Convolutional Neural Network Architecture for Speech Emotion Recognition in Noisy Conditions.

Sensors (Basel, Switzerland)
Convolutional neural networks (CNNs) are a state-of-the-art technique for speech emotion recognition. However, CNNs have mostly been applied to noise-free emotional speech data, and limited evidence is available for their applicability in emotional s...

A global neural network learning machine: Coupled integer and fractional calculus operator with an adaptive learning scheme.

Neural networks : the official journal of the International Neural Network Society
Find the global optimal solution of the model is one promising research topic in computational intelligent community. Dependent on analogies to natural processes, the evolutionary swarm intelligent algorithms are widely used for solving global optimi...

Self-paced and self-consistent co-training for semi-supervised image segmentation.

Medical image analysis
Deep co-training has recently been proposed as an effective approach for image segmentation when annotated data is scarce. In this paper, we improve existing approaches for semi-supervised segmentation with a self-paced and self-consistent co-trainin...

Learning exact enumeration and approximate estimation in deep neural network models.

Cognition
A system for approximate number discrimination has been shown to arise in at least two types of hierarchical neural network models-a generative Deep Belief Network (DBN) and a Hierarchical Convolutional Neural Network (HCNN) trained to classify natur...

Application of fuzzy neural network model and current-voltage analysis of biologically active points for prediction post-surgery risks.

Computer methods in biomechanics and biomedical engineering
The work investigates neural network model for prediction of post-surgical treatment risks. The descriptors of the risk classifiers are formed on the basis of the analysis of the current-voltage characteristics of one, two and three biologically acti...

Quantitative analysis of lithium in brine by laser-induced breakdown spectroscopy based on convolutional neural network.

Analytica chimica acta
In this study, a simple and effective method for accurate determination of lithium in brine samples was developed by the combination of laser induced breakdown spectroscopy (LIBS) and convolutional neural network (CNN). Our results clearly demonstrat...

WDA: An Improved Wasserstein Distance-Based Transfer Learning Fault Diagnosis Method.

Sensors (Basel, Switzerland)
With the growth of computing power, deep learning methods have recently been widely used in machine fault diagnosis. In order to realize highly efficient diagnosis accuracy, people need to know the detailed health condition of collected signals from ...

Egocentric-View Fingertip Detection for Air Writing Based on Convolutional Neural Networks.

Sensors (Basel, Switzerland)
This research investigated real-time fingertip detection in frames captured from the increasingly popular wearable device, smart glasses. The egocentric-view fingertip detection and character recognition can be used to create a novel way of inputting...

Deep LSTM-Based Transfer Learning Approach for Coherent Forecasts in Hierarchical Time Series.

Sensors (Basel, Switzerland)
Hierarchical time series is a set of data sequences organized by aggregation constraints to represent many real-world applications in research and the industry. Forecasting of hierarchical time series is a challenging and time-consuming problem owing...

Robust Multimodal Indirect Sensing for Soft Robots Via Neural Network-Aided Filter-Based Estimation.

Soft robotics
Sensory data are critical for soft robot perception. However, integrating sensors to soft robots remains challenging due to their inherent softness. An alternative approach is indirect sensing through an estimation scheme, which uses robot dynamics a...