AIMC Topic: Neural Networks, Computer

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Cefixime removal via WO/Co-ZIF nanocomposite using machine learning methods.

Scientific reports
In this research, an upgraded and environmentally friendly process involving WO/Co-ZIF nanocomposite was used for the removal of Cefixime from the aqueous solutions. Intelligent decision-making was employed using various models including Support Vect...

A versatile automated pipeline for quantifying virus infectivity by label-free light microscopy and artificial intelligence.

Nature communications
Virus infectivity is traditionally determined by endpoint titration in cell cultures, and requires complex processing steps and human annotation. Here we developed an artificial intelligence (AI)-powered automated framework for ready detection of vir...

Detection of Parkinson disease using multiclass machine learning approach.

Scientific reports
Parkinson's Disease (PD) is a prevalent neurological condition characterized by motor and cognitive impairments, typically manifesting around the age of 50 and presenting symptoms such as gait difficulties and speech impairments. Although a cure rema...

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration.

Environmental science and pollution research international
Accurately predicting potential evapotranspiration (PET) is crucial in water resource management, agricultural planning, and climate change studies. This research aims to investigate the performance of two machine learning methods, the adaptive netwo...

Analysis of convolutional neural networks for fronto-temporal dementia biomarker discovery.

International journal of computer assisted radiology and surgery
PURPOSE: Frontotemporal lobe dementia (FTD) results from the degeneration of the frontal and temporal lobes. It can manifest in several different ways, leading to the definition of variants characterised by their distinctive symptomatologies. As thes...

Multi-label classification of retinal diseases based on fundus images using Resnet and Transformer.

Medical & biological engineering & computing
Retinal disorders are a major cause of irreversible vision loss, which can be mitigated through accurate and early diagnosis. Conventionally, fundus images are used as the gold diagnosis standard in detecting retinal diseases. In recent years, more a...

A topological description of loss surfaces based on Betti Numbers.

Neural networks : the official journal of the International Neural Network Society
In the context of deep learning models, attention has recently been paid to studying the surface of the loss function in order to better understand training with methods based on gradient descent. This search for an appropriate description, both anal...

FDAA: A feature distribution-aware transferable adversarial attack method.

Neural networks : the official journal of the International Neural Network Society
In recent years, the research on transferable feature-level adversarial attack has become a hot spot due to attacking unknown deep neural networks successfully. But the following problems limit its transferability. Existing feature disruption methods...

GRAM: An interpretable approach for graph anomaly detection using gradient attention maps.

Neural networks : the official journal of the International Neural Network Society
Detecting unusual patterns in graph data is a crucial task in data mining. However, existing methods face challenges in consistently achieving satisfactory performance and often lack interpretability, which hinders our understanding of anomaly detect...

Multi-output neural network model for predicting biochar yield and composition.

The Science of the total environment
In biomass pyrolysis for biochar production, existing prediction models face computational challenges and limited accuracy. This study curated a comprehensive dataset, revealing pyrolysis parameters' dominance in biochar yield (54.8 % importance). Py...