AIMC Topic: Neural Networks, Computer

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Predicting sensory evaluation of spinach freshness using machine learning model and digital images.

PloS one
The visual perception of freshness is an important factor considered by consumers in the purchase of fruits and vegetables. However, panel testing when evaluating food products is time consuming and expensive. Herein, the ability of an image processi...

Keyword spotting techniques to improve the recognition accuracy of user-defined keywords.

Neural networks : the official journal of the International Neural Network Society
The existing keyword spotting (KWS) techniques can recognize pre-defined keywords well but have a poor recognition accuracy for user-defined keywords. In real use cases, there is a high demand for users to define their keywords for various reasons. T...

Stochastic quasi-synchronization of heterogeneous delayed impulsive dynamical networks via single impulsive control.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the quasi-synchronization problem of the stochastic heterogeneous complex dynamical networks with impulsive couplings and multiple time-varying delays. It is shown that this kind of dynamical networks can achieve exponential q...

D-MONA: A dilated mixed-order non-local attention network for speaker and language recognition.

Neural networks : the official journal of the International Neural Network Society
Attention-based convolutional neural network (CNN) models are increasingly being adopted for speaker and language recognition (SR/LR) tasks. These include time, frequency, spatial and channel attention, which can focus on useful time frames, frequenc...

Predicting Readmission After Anterior, Posterior, and Posterior Interbody Lumbar Spinal Fusion: A Neural Network Machine Learning Approach.

World neurosurgery
BACKGROUND: Readmission after spine surgery is costly and a relatively common occurrence. Previous research identified several risk factors for readmission; however, the conclusions remain equivocal. Machine learning algorithms offer a unique perspec...

AIDeveloper: Deep Learning Image Classification in Life Science and Beyond.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Artificial intelligence (AI)-based image analysis has increased drastically in recent years. However, all applications use individual solutions, highly specialized for a particular task. Here, an easy-to-use, adaptable, and open source software, call...

GanDTI: A multi-task neural network for drug-target interaction prediction.

Computational biology and chemistry
Drug discovery processes require drug-target interaction (DTI) prediction by virtual screenings with high accuracy. Compared with traditional methods, the deep learning method requires less time and domain expertise, while achieving higher accuracy. ...

Energy-efficient Mott activation neuron for full-hardware implementation of neural networks.

Nature nanotechnology
To circumvent the von Neumann bottleneck, substantial progress has been made towards in-memory computing with synaptic devices. However, compact nanodevices implementing non-linear activation functions are required for efficient full-hardware impleme...

Sensor-Based Human Activity Recognition with Spatio-Temporal Deep Learning.

Sensors (Basel, Switzerland)
Human activity recognition (HAR) remains a challenging yet crucial problem to address in computer vision. HAR is primarily intended to be used with other technologies, such as the Internet of Things, to assist in healthcare and eldercare. With the de...

A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images.

Scientific reports
The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates automation. This work proposes deep CNN based multi-phase mitosis detection framework "M...