Analysis of brain imaging data for the detection of early age autism spectrum disorder using transfer learning approaches for internet of things

A Ashraf, Z Qingjie, WHK Bangyal… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In recent years, advanced magnetic resonance imaging (MRI) methods including as
functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging …

Using Machine Learning and the K-Nearest Neighbour Classification Method to Assess Autism Risk

M Singla, KS Gill, P Aggarwal… - 2024 4th International …, 2024 - ieeexplore.ieee.org
The K-Nearest Neighbour (KNN) classification approach, one of the machine learning
algorithms examined in this work, is used to determine a person's likelihood of developing …

Intelligent models for early Autism detection from MRI images

I Kadi, M Abbas, MMC Eddine - … in Engineering and …, 2024 - ojs.studiespublicacoes.com.br
Abstract Autism Spectrum Disorders (ASDs) are neurodevelopmental conditions that usually
manifest during childhood. It is a multi-symptom disorder, and its symptoms overlap with …

Applying Machine Learning for Autism Risk Evaluation Using a Decision Tree Classification Technique

K Mittal, KS Gill, D Upadhyay, V Singh… - 2024 2nd International …, 2024 - ieeexplore.ieee.org
The present work investigates the use of machine learning methods for evaluating the risk of
autism spectrum disorder (ASD) by using a Decision Tree Classifier. Autism, a multifaceted …

Applying Machine Learning and the Gradient Boosting Classification Method for Evaluating the Probability of Autism

K Mittal, KS Gill, D Upadhyay, S Dangi… - 2024 IEEE 9th …, 2024 - ieeexplore.ieee.org
This research investigates the use of machine learning, especially the Gradient Boosting
Classification (GBC) technique, to evaluate the likelihood of autism. Autism Spectrum …

Utilization of Naive Bayes Classifier for Autism Risk Assessment Using Machine Learning

KS Gill, D Upadhyay, S Dangi - 2024 3rd International …, 2024 - ieeexplore.ieee.org
The present work investigates the use of the Naive Bayes classifier for the purpose of autism
risk assessment via the implementation of machine learning methodologies. Autism …

Establishing a Machine Learning-Based Random Forest Classifier to Estimate Autism Risk

KS Gill, J Agrawal, R Chauhan… - 2024 3rd International …, 2024 - ieeexplore.ieee.org
The Random Forest model had strong performance in capturing complex patterns within the
dataset, with high levels of accuracy, sensitivity, and specificity during both the training and …

From Data to Diagnosis: Employing Machine Learning with LightGBM Classification to Evaluate Autism Probability

K Mittal, KS Gill, D Upadhyay… - … on Innovations and …, 2024 - ieeexplore.ieee.org
This study delves into the use of machine learning, namely the LightGBM Classification
algorithm, to evaluate the prevalence of autism. Timely and accurate diagnosis is crucial for …

Utilizing Cutting-Edge Deep Learning Strategies and Harnessing the Power of a Pre-Trained ResNet18 Convolutional Neural Network for Assessing the Risk of …

M Singla, KS Gill, P Aggarwal… - … Conference on E …, 2024 - ieeexplore.ieee.org
In this study, the ResNet18 CNN classification method, a machine learning algorithm
explored herein, is employed for the assessment of an individual's likelihood of developing …