AIMC Topic: Neural Networks, Computer

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Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review.

Environmental science and pollution research international
Water resources are constantly threatened by pollution of potentially toxic elements (PTEs). In efforts to monitor and mitigate PTEs pollution in water resources, machine learning (ML) algorithms have been utilized to predict them. However, review st...

COVID-19 Hierarchical Classification Using a Deep Learning Multi-Modal.

Sensors (Basel, Switzerland)
Coronavirus disease 2019 (COVID-19), originating in China, has rapidly spread worldwide. Physicians must examine infected patients and make timely decisions to isolate them. However, completing these processes is difficult due to limited time and ava...

An analysis of information segregation in parallel streams of a multi-stream convolutional neural network.

Scientific reports
Visual information is processed in hierarchically organized parallel streams in the primate brain. In the present study, information segregation in parallel streams was examined by constructing a convolutional neural network with parallel architectur...

Sequence based model using deep neural network and hybrid features for identification of 5-hydroxymethylcytosine modification.

Scientific reports
RNA modifications are pivotal in the development of newly synthesized structures, showcasing a vast array of alterations across various RNA classes. Among these, 5-hydroxymethylcytosine (5HMC) stands out, playing a crucial role in gene regulation and...

TEC-miTarget: enhancing microRNA target prediction based on deep learning of ribonucleic acid sequences.

BMC bioinformatics
BACKGROUND: MicroRNAs play a critical role in regulating gene expression by binding to specific target sites within gene transcripts, making the identification of microRNA targets a prominent focus of research. Conventional experimental methods for i...

Multimodality Fusion Strategies in Eye Disease Diagnosis.

Journal of imaging informatics in medicine
Multimodality fusion has gained significance in medical applications, particularly in diagnosing challenging diseases like eye diseases, notably diabetic eye diseases that pose risks of vision loss and blindness. Mono-modality eye disease diagnosis p...

Application of Artificial Intelligence to Automate the Reconstruction of Muscle Cross-Sectional Area Obtained by Ultrasound.

Medicine and science in sports and exercise
PURPOSE: Manual reconstruction (MR) of the vastus lateralis (VL) muscle cross-sectional area (CSA) from sequential ultrasound (US) images is accessible, is reproducible, and has concurrent validity with magnetic resonance imaging. However, this techn...

The role of directed cycles in a directed neural network.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the dynamics of a directed acyclic neural network by edge adding control. We find that the local stability and Hopf bifurcation of the controlled network only depend on the size and intersection of directed cycles, instead of ...

Rapid 2D Na MRI of the calf using a denoising convolutional neural network.

Magnetic resonance imaging
PURPOSE: Na MRI can be used to quantify in-vivo tissue sodium concentration (TSC), but the inherently low Na signal leads to long scan times and/or noisy or low-resolution images. Reconstruction algorithms such as compressed sensing (CS) have been pr...

A deep-learning approach to predict bleeding risk over time in patients on extended anticoagulation therapy.

Journal of thrombosis and haemostasis : JTH
BACKGROUND: Thus far, all the clinical models developed to predict major bleeding in patients on extended anticoagulation therapy use the baseline predictors to stratify patients into different risk groups. Therefore, these models do not account for ...