AIMC Topic: Neural Networks, Computer

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MBANet: Multi-branch aware network for kidney ultrasound images segmentation.

Computers in biology and medicine
Due to the influence of kidney morphology, heterogeneous structure and image quality, segmenting kidney in ultrasound images is challenging. To alleviate this challenge, we proposed a novel deep neural network architecture, namely Multi-branch Aware ...

Application of machine learning to the monitoring and prediction of food safety: A review.

Comprehensive reviews in food science and food safety
Machine learning (ML) has proven to be a useful technology for data analysis and modeling in a wide variety of domains, including food science and engineering. The use of ML models for the monitoring and prediction of food safety is growing in recent...

RBFNN Design Based on Modified Nearest Neighbor Clustering Algorithm for Path Tracking Control.

Sensors (Basel, Switzerland)
Radial basis function neural networks are a widely used type of artificial neural network. The number and centers of basis functions directly affect the accuracy and speed of radial basis function neural networks. Many studies use supervised learning...

Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception.

Nature communications
Perception is thought to be shaped by the environments for which organisms are optimized. These influences are difficult to test in biological organisms but may be revealed by machine perceptual systems optimized under different conditions. We invest...

Ensemble of EfficientNets for the Diagnosis of Tuberculosis.

Computational intelligence and neuroscience
Tuberculosis (TB) remains a life-threatening disease and is one of the leading causes of mortality in developing regions due to poverty and inadequate medical resources. Tuberculosis is medicable, but it necessitates early diagnosis through reliable ...

An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks.

Computational intelligence and neuroscience
With rapid advancement in artificial intelligence (AI) and machine learning (ML), automatic modulation classification (AMC) using deep learning (DL) techniques has become very popular. This is even more relevant for Internet of things (IoT)-assisted ...

Comprehensive Evaluation of Tourism Resources Based on Multispecies Evolutionary Genetic Algorithm-Enabled Neural Networks.

Computational intelligence and neuroscience
With the development of neural network technology and the rapid growth of China's tourism economic income at this stage, the research on the comprehensive evaluation of tourism resources has gradually emerged. Based on this, this paper studies the ne...

Malicious Code Variant Identification Based on Multiscale Feature Fusion CNNs.

Computational intelligence and neuroscience
The increasing volume and types of malwares bring a great threat to network security. The malware binary detection with deep convolutional neural networks (CNNs) has been proved to be an effective method. However, the existing malware classification ...

Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes.

Methods (San Diego, Calif.)
As one of the most common post-transcriptional epigenetic modifications, N6-methyladenine (6 mA), plays an essential role in various cellular processes and disease pathogenesis. Therefore, accurately identifying 6 mA modifications is necessary for a ...

Runoff forecasting model based on variational mode decomposition and artificial neural networks.

Mathematical biosciences and engineering : MBE
Accurate runoff forecasting plays a vital role in water resource management. Therefore, various forecasting models have been proposed in the literature. Among them, the decomposition-based models have proved their superiority in runoff series forecas...