AIMC Topic: Neural Networks, Computer

Clear Filters Showing 6971 to 6980 of 31376 articles

EResNet-SVM: an overfitting-relieved deep learning model for recognition of plant diseases and pests.

Journal of the science of food and agriculture
BACKGROUND: The accurate recognition and early warning for plant diseases and pests are a prerequisite of intelligent prevention and control for plant diseases and pests. As a result of the phenotype similarity of the hazarded plant after plant disea...

High-performance deep spiking neural networks via at-most-two-spike exponential coding.

Neural networks : the official journal of the International Neural Network Society
Spiking neural networks (SNNs) provide necessary models and algorithms for neuromorphic computing. A popular way of building high-performance deep SNNs is to convert ANNs to SNNs, taking advantage of advanced and well-trained ANNs. Here we propose an...

Multimodal information bottleneck for deep reinforcement learning with multiple sensors.

Neural networks : the official journal of the International Neural Network Society
Reinforcement learning has achieved promising results on robotic control tasks but struggles to leverage information effectively from multiple sensory modalities that differ in many characteristics. Recent works construct auxiliary losses based on re...

Automatic Segmentation of Vestibular Schwannomas: A Systematic Review.

World neurosurgery
BACKGROUND: Vestibular schwannomas (VSs) are benign tumors often monitored over time, with measurement techniques for assessing growth rates subject to significant interobserver variability. Automatic segmentation of these tumors could provide a more...

Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India.

Environmental pollution (Barking, Essex : 1987)
This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such a...

Attentional decoder networks for chest X-ray image recognition on high-resolution features.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: This paper introduces an encoder-decoder-based attentional decoder network to recognize small-size lesions in chest X-ray images. In the encoder-only network, small-size lesions disappear during the down-sampling steps or ar...

Machine learning ensembles, neural network, hybrid and sparse regression approaches for weather based rainfed cotton yield forecast.

International journal of biometeorology
Cotton is a major economic crop predominantly cultivated under rainfed situations. The accurate prediction of cotton yield invariably helps farmers, industries, and policy makers. The final cotton yield is mostly determined by the weather patterns th...

Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling.

Journal of environmental management
A deep learning architecture, denoted as CNNsLSTM, is proposed for hourly rainfall-runoff modeling in this study. The architecture involves a serial coupling of the one-dimensional convolutional neural network (1D-CNN) and the long short-term memory ...

Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac leads.

BMC medical research methodology
One of the most common causes of death worldwide is heart disease, including arrhythmia. Today, sciences such as artificial intelligence and medical statistics are looking for methods and models for correct and automatic diagnosis of cardiac arrhythm...

Efficient diagnosis of psoriasis and lichen planus cutaneous diseases using deep learning approach.

Scientific reports
The tendency of skin diseases to manifest in a unique and yet similar appearance, absence of enough competent dermatologists, and urgency of diagnosis and classification on time and accurately, makes the need of machine aided diagnosis blatant. This ...