AIMC Topic: Neural Networks, Computer

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Closed-loop transcranial ultrasound stimulation with a fuzzy controller for modulation of motor response and neural activity of mice.

Journal of neural engineering
. We propose a closed-loop transcranial ultrasound stimulation (TUS) with a fuzzy controller to realize real-time and precise control of the motor response and neural activity of mice.. The mean absolute value (MAV) of the electromyogram (EMG) and pe...

Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification.

Computational intelligence and neuroscience
Biomedical engineering is the application of the principles and problem-solving methods of engineering to biology along with medicine. Computation intelligence is the study of design of intelligent agents which are systems acting perceptively. The co...

Evaluating the Performance of Inclusive Growth Based on the BP Neural Network and Machine Learning Approach.

Computational intelligence and neuroscience
In this paper, we use the panel data of 281 cities in China from 2005 to 2020 for capturing the factors driving urban inclusive growth (IG). In doing this, we employ the BP neural network algorithm combined with the DEA model to measure the urban inc...

Machine learning-based classification of the movements of children with profound or severe intellectual or multiple disabilities using environment data features.

PloS one
Communication interventions have broadened from dialogical meaning-making, assessment approaches, to remote-controlled interactive objects. Yet, interpretation of the mostly pre-or protosymbolic, distinctive, and idiosyncratic movements of children w...

Neural Network Detection of Pacemakers for MRI Safety.

Journal of digital imaging
Flagging the presence of cardiac devices such as pacemakers before an MRI scan is essential to allow appropriate safety checks. We assess the accuracy with which a machine learning model can classify the presence or absence of a pacemaker on pre-exis...

Multimodal Medical Image Fusion Using Stacked Auto-encoder in NSCT Domain.

Journal of digital imaging
Medical image fusion is a process that aims to merge the important information from images with different modalities of the same organ of the human body to create a more informative fused image. In recent years, deep learning (DL) methods have achiev...

A Physics-Guided Neural Network for Predicting Protein-Ligand Binding Free Energy: From Host-Guest Systems to the PDBbind Database.

Biomolecules
Calculation of protein-ligand binding affinity is a cornerstone of drug discovery. Classic implicit solvent models, which have been widely used to accomplish this task, lack accuracy compared to experimental references. Emerging data-driven models, o...

Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving.

Sensors (Basel, Switzerland)
In the autonomous driving process, the decision-making system is mainly used to provide macro-control instructions based on the information captured by the sensing system. Learning-based algorithms have apparent advantages in information processing a...

DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites.

BMC bioinformatics
BACKGROUND: Addressing the laborious nature of traditional biological experiments by using an efficient computational approach to analyze RNA-binding proteins (RBPs) binding sites has always been a challenging task. RBPs play a vital role in post-tra...

Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction.

BMC bioinformatics
BACKGROUND: DNA N4-methylcytosine is part of the restrictive modification system, which works by regulating some biological processes, for example, the initiation of DNA replication, mismatch repair and inactivation of transposon. However, using expe...