AIMC Topic: Neural Networks, Computer

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A Review of CNN Applications in Smart Agriculture Using Multimodal Data.

Sensors (Basel, Switzerland)
This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and y...

Predicting drug combination side effects based on a metapath-based heterogeneous graph neural network.

BMC bioinformatics
In recent years, combined drug screening has played a very important role in modern drug discovery. Generally, synergistic drug combinations are crucial in treatment for many diseases. However, the toxic side effects of drug combinations are probably...

Fast and accurate deep learning scans for signatures of natural selection in genomes using FASTER-NN.

Communications biology
Deep learning classification models based on Convolutional Neural Networks (CNNs) are increasingly used in population genetic inference for detecting signatures of natural selection. Prevailing detection methods treat the design of the classifier as ...

Extracting organs of interest from medical images based on convolutional neural network with auxiliary and refined constraints.

Scientific reports
Accurately extracting organs from medical images provides radiologist with more comprehensive evidences to clinical diagnose, which offers up a higher accuracy and efficiency. However, the key to achieving accurate segmentation lies in abundant clues...

Developing a brain inspired multilobar neural networks architecture for rapidly and accurately estimating concrete compressive strength.

Scientific reports
Concrete compressive strength is a critical parameter in construction and structural engineering. Destructive experimental methods that offer a reliable approach to obtaining this property involve time-consuming procedures. Recent advancements in art...

Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach.

Scientific reports
A dynamics informed neural networks (DINNs) incorporating the susceptible-exposed-infectious-recovered-vaccinated (SEIRV) model was developed to enhance the understanding of the temporal evolution dynamics of infectious diseases. This work integrates...

Assessment of using transfer learning with different classifiers in hypodontia diagnosis.

BMC oral health
BACKGROUND: Hypodontia is the absence of one or more teeth in the primary or permanent dentition during development, and radiographic imaging is the most common method of diagnosis. However, in recent years, artificial intelligence-based decision sup...

An efficient deep unrolling network for sparse-view CT reconstruction via alternating optimization of dense-view sinograms and images.

Physics in medicine and biology
. Recently, there have been many advancements in deep unrolling methods for sparse-view computed tomography (SVCT) reconstruction. These methods combine model-based and deep learning-based reconstruction techniques, improving the interpretability and...

A novel multi-user collaborative cognitive radio spectrum sensing model: Based on a CNN-LSTM model.

PloS one
Cognitive Radio (CR) technology enables wireless devices to learn about their surrounding spectrum environment through sensing capabilities, thereby facilitating efficient spectrum utilization without interfering with the normal operation of licensed...

Dorsoventral comparison of intraspecific variation in the butterfly wing pattern using a convolutional neural network.

Biology letters
Butterfly wing patterns exhibit notable differences between the dorsal and ventral surfaces, and morphological analyses of them have provided insights into the ecological and behavioural characteristics of wing patterns. Conventional methods for dors...