AIMC Topic: Neural Networks, Computer

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Fusing satellite imagery and ground-based observations for PM air pollution modeling in Iran using a deep learning approach.

Scientific reports
With the rapid advancement of urbanization and industrialization in cities, air pollution has become one of the significant environmental challenges and issues in many countries. The concentration of particulate matter with an aerodynamic diameter of...

An adaptive deep learning approach based on InBNFus and CNNDen-GRU networks for breast cancer and maternal fetal classification using ultrasound images.

Scientific reports
Convolutional Neural Networks (CNNs), a sophisticated deep learning technique, have proven highly effective in identifying and classifying abnormalities related to various diseases. The manual classification of these is a hectic and time-consuming pr...

Gradual poisoning of a chest x-ray convolutional neural network with an adversarial attack and AI explainability methods.

Scientific reports
Given artificial intelligence's transformative effects, studying safety is important to ensure it is implemented in a beneficial way. Convolutional neural networks are used in radiology research for prediction but can be corrupted through adversarial...

Uncovering memorization effect in the presence of spurious correlations.

Nature communications
Machine learning models often rely on simple spurious features - patterns in training data that correlate with targets but are not causally related to them, like image backgrounds in foreground classification. This reliance typically leads to imbalan...

Deep learning model for grading carcinoma with Gini-based feature selection and linear production-inspired feature fusion.

Scientific reports
The most common types of kidneys and liver cancer are renal cell carcinoma (RCC) and hepatic cell carcinoma (HCC), respectively. Accurate grading of these carcinomas is essential for determining the most appropriate treatment strategies, including su...

A simplified minimodel of visual cortical neurons.

Nature communications
Artificial neural networks (ANNs) have been shown to predict neural responses in primary visual cortex (V1) better than classical models. However, this performance often comes at the expense of simplicity and interpretability. Here we introduce a new...

FPGA implementation of deep learning architecture for ankylosing spondylitis detection from MRI.

Scientific reports
Ankylosing Spondylitis (AS), commonly known as Bechterew's disease, is a complex, potentially disabling disease that develops slowly over time and progresses to radiographic sacroiliitis. The etiology of this disease is poorly understood, making it d...

A FPGA based recurrent neural networks-based impedance spectroscopy system for detection of YAKE in tuna.

Scientific reports
This paper evaluates the use of impedance spectroscopy combined with artificial intelligence. Both technologies have been widely used in food classification and it is proposed a way to improve classifications using recurrent neural networks that trea...

Cross-language dissemination of Chinese classical literature using multimodal deep learning and artificial intelligence.

Scientific reports
Against the backdrop of rapid advancements in artificial intelligence (AI), multimodal deep learning (DL) technologies offer new possibilities for cross-language translation. This work proposes a multimodal DL-based translation model, the Transformer...

Cuff-less blood pressure monitoring via PPG signals using a hybrid CNN-BiLSTM deep learning model with attention mechanism.

Scientific reports
Blood pressure (BP) serves as a fundamental indicator of cardiovascular health, measuring the force exerted by circulating blood against arterial walls during each heartbeat. This paper introduces an advanced deep learning framework for precise, non-...