AIMC Topic: Neural Networks, Computer

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Robustness of Deep Learning models in electrocardiogram noise detection and classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automatic electrocardiogram (ECG) signal analysis for heart disease detection has gained significant attention due to busy lifestyles. However, ECG signals are susceptible to noise, which adversely affects the performance of...

Impact of quantum and neuromorphic computing on biomolecular simulations: Current status and perspectives.

Current opinion in structural biology
New high-performance computing architectures are becoming operative, in addition to exascale computers. Quantum computers (QC) solve optimization problems with unprecedented efficiency and speed, while neuromorphic hardware (NMH) simulates neural net...

DeepFace: Deep-learning-based framework to contextualize orofacial-cleft-related variants during human embryonic craniofacial development.

HGG advances
Orofacial clefts (OFCs) are among the most common human congenital birth defects. Previous multiethnic studies have identified dozens of associated loci for both cleft lip with or without cleft palate (CL/P) and cleft palate alone (CP). Although seve...

Investigating quantitative approach for microalgal biomass using deep convolutional neural networks and image recognition.

Bioresource technology
The effective monitoring of microalgae cultivation is crucial for optimizing their energy utilization efficiency. In this paper, a quantitative analysis method, using microalgae images based on two convolutional neural networks, EfficientNet (EFF) an...

Position-Aware Indoor Human Activity Recognition Using Multisensors Embedded in Smartphones.

Sensors (Basel, Switzerland)
Composite indoor human activity recognition is very important in elderly health monitoring and is more difficult than identifying individual human movements. This article proposes a sensor-based human indoor activity recognition method that integrate...

Detection Method of Epileptic Seizures Using a Neural Network Model Based on Multimodal Dual-Stream Networks.

Sensors (Basel, Switzerland)
Epilepsy is a common neurological disorder, and its diagnosis mainly relies on the analysis of electroencephalogram (EEG) signals. However, the raw EEG signals contain limited recognizable features, and in order to increase the recognizable features ...

Automated discovery of symbolic laws governing skill acquisition from naturally occurring data.

Nature computational science
Skill acquisition is a key area of research in cognitive psychology as it encompasses multiple psychological processes. The laws discovered under experimental paradigms are controversial and lack generalizability. This paper aims to unearth the laws ...

Prediction of essential oil content in C. sintoc Leaves based on the direction of vegetation slope in Mount Ciremai National Park using ANFIS Artificial Neural Network.

Brazilian journal of biology = Revista brasleira de biologia
C. sintoc is a plant that has a high essential oil content. Essential oils have many health benefits. Mount Ciremai National Park is an area that has abundant vegetation, especially C. sintoc. The purpose of this study was to predict the volume of oi...

A Retinex-based network for image enhancement in low-light environments.

PloS one
Most of the existing low-light image enhancement methods suffer from the problems of detail loss, color distortion and excessive noise. To address the above-mentioned issues, this paper proposes a neural network-based low-light image enhancement netw...

Heteroscedasticity effects as component to future stock market predictions using RNN-based models.

PloS one
Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility ...