AIMC Topic: Neural Networks, Computer

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Comparing likelihood-based and likelihood-free approaches to fitting and comparing models of intertemporal choice.

Behavior research methods
Machine learning methods have recently begun to be used for fitting and comparing cognitive models, yet they have mainly focused on methods for dealing with models that lack tractable likelihoods. Evaluating how these approaches compare to traditiona...

Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis.

Scientific reports
Recent innovations in medical imaging have markedly improved brain tumor identification, surpassing conventional diagnostic approaches that suffer from low resolution, radiation exposure, and limited contrast. Magnetic Resonance Imaging (MRI) is pivo...

Ethical considerations and robustness of artificial neural networks in medical image analysis under data corruption.

Scientific reports
Medicine is one of the most sensitive fields in which artificial intelligence (AI) is extensively used, spanning from medical image analysis to clinical support. Specifically, in medicine, where every decision may severely affect human lives, the iss...

MEGDTA: multi-modal drug-target affinity prediction based on protein three-dimensional structure and ensemble graph neural network.

BMC genomics
BACKGROUND: Drug development is a time-consuming and costly endeavor, and utilizing computer-aided methods to predict drug-target affinity (DTA) can significantly accelerate this process. The key to accurate DTA prediction lies in selecting appropria...

MANIT: a multilayer ANN integrated framework using biometrics and historical features for online examination proctoring.

Scientific reports
Online education has become a globally accepted norm, bringing benefits and challenges. One of the most debated aspects is the academic integrity of online examinations. Without physical proctoring, the authenticity of the candidates' scores can alwa...

MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models.

BMC biology
BACKGROUND: Protein-protein interactions (PPIs) play a critical role in essential biological processes such as signal transduction, enzyme activity regulation, cytoskeletal structure, immune responses, and gene regulation. However, current methods ma...

Enhancing synthetic pelvic CT generation from CBCT using vision transformer with adaptive fourier neural operators.

Biomedical physics & engineering express
This study introduces a novel approach to improve Cone Beam CT (CBCT) image quality by developing a synthetic CT (sCT) generation method using CycleGAN with a Vision Transformer (ViT) and an Adaptive Fourier Neural Operator (AFNO).A dataset of 20 pro...

Colour segmentation of printed fabrics by integrating adaptive neural network and density peak clustering algorithm.

PloS one
With the development of computer vision and image processing technology, color segmentation of printed fabrics has gradually become a key task in the textile industry. However, the existing methods often face the problems of low segmentation accuracy...

A rolling bearing fault diagnosis method based on an improved parallel one-dimensional convolutional neural network.

PloS one
As a critical component of industrial equipment, the fault diagnosis of rolling bearings is essential for reducing unplanned downtime and improving equipment reliability. Existing methods achieve an accuracy of no more than 92% in low signal-to-noise...

HSPC-Net: A hierarchical shape-preserving completion network for machine part point cloud completion.

PloS one
With the continuous advancement of 3D scanning technology, point cloud data of mechanical components has found widespread applications in industrial design, manufacturing, and repair. However, due to limitations in scanning precision and acquisition ...