AIMC Topic: Algorithms

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Accurate fall risk classification in elderly using one gait cycle data and machine learning.

Clinical biomechanics (Bristol, Avon)
BACKGROUND: Falls among the elderly are a major societal problem. While observations of medium-distance walking using inertial sensors identified potential fall predictors, classifying individuals at risk based on single gait cycles remains elusive. ...

A comprehensive analysis of key factors' impact on environmental performance: Evidence from Globe by novel super learner algorithm.

Journal of environmental management
This study aims to analyze comprehensively the impact of different economic and demographic factors, which affect economic development, on environmental performance. In this context, the study considers the Environmental Performance Index as the resp...

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus.

PloS one
Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worrying...

Factors influencing the development of artificial intelligence in orthodontics.

Orthodontics & craniofacial research
OBJECTIVES: Since developing AI procedures demands significant computing resources and time, the implementation of a careful experimental design is essential. The purpose of this study was to investigate factors influencing the development of AI in o...

FACNN: fuzzy-based adaptive convolution neural network for classifying COVID-19 in noisy CXR images.

Medical & biological engineering & computing
COVID-19 detection using chest X-rays (CXR) has evolved as a significant method for early diagnosis of the pandemic disease. Clinical trials and methods utilize X-ray images with computer and intelligent algorithms to improve detection and classifica...

Longitudinally consistent registration and parcellation of cortical surfaces using semi-supervised learning.

Medical image analysis
Temporally consistent and accurate registration and parcellation of longitudinal cortical surfaces is of great importance in studying longitudinal morphological and functional changes of human brains. However, most existing methods are developed for ...

Tackling the curse of dimensionality with physics-informed neural networks.

Neural networks : the official journal of the International Neural Network Society
The curse-of-dimensionality taxes computational resources heavily with exponentially increasing computational cost as the dimension increases. This poses great challenges in solving high-dimensional partial differential equations (PDEs), as Richard E...

Pain prediction model based on machine learning and SHAP values for elders with dementia in Taiwan.

International journal of medical informatics
INTRODUCTION: Pain conditions are common in elderly individuals, including those with dementia. However, symptoms associated with dementia may lead to poor recognition, assessment and management of pain. In this study, we incorporated the variables b...

DenseNet model incorporating hybrid attention mechanisms and clinical features for pancreatic cystic tumor classification.

Journal of applied clinical medical physics
PURPOSE: The aim of this study is to develop a deep learning model capable of discriminating between pancreatic plasma cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN) by leveraging patient-specific clinical features and imaging outcomes. T...

EEG classification model for virtual reality motion sickness based on multi-scale CNN feature correlation.

Computer methods and programs in biomedicine
BACKGROUND: Virtual reality motion sickness (VRMS) is a key issue hindering the development of virtual reality technology, and accurate detection of its occurrence is the first prerequisite for solving the issue.