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Found 16 papers, 2 papers with code
COVIDHealth: A Benchmark Twitter Dataset and Machine Learning based Web Application for Classifying COVID-19 Discussions
In this study, our primary objective is to develop a machine learning-based web application for automatically classifying COVID-19-related discussions on social media.
LEI2JSON: Schema-based Validation and Conversion of Livestock Event Information
Livestock producers often need help in standardising (i. e., converting and validating) their livestock event data.
MMTF-DES: A Fusion of Multimodal Transformer Models for Desire, Emotion, and Sentiment Analysis of Social Media Data
In this research, we have proposed a unified multimodal transformer-based framework with image-text pair settings to identify human desire, sentiment, and emotion.
COVIDFakeExplainer: An Explainable Machine Learning based Web Application for Detecting COVID-19 Fake News
This paper goes beyond by establishing BERT as the superior model for fake news detection and demonstrates its utility as a tool to empower the general populace.
AI-Driven Personalised Offloading Device Prescriptions: A Cutting-Edge Approach to Preventing Diabetes-Related Plantar Forefoot Ulcers and Complications
This chapter proposes an AI-powered Clinical Decision Support System (CDSS) to recommend personalised prescriptions of offloading devices (footwear and insoles) for patients with diabetes who are at risk of foot complications.
Machine Learning Techniques for Estimating Soil Moisture from Mobile Captured Images
Precise Soil Moisture (SM) assessment is essential in agriculture.
Automatic Cattle Identification using YOLOv5 and Mosaic Augmentation: A Comparative Analysis
This paper aims to present our recent research in utilizing five popular object detection models, looking at the architecture of YOLOv5, investigating the performance of eight backbones with the YOLOv5 model, and the influence of mosaic augmentation in YOLOv5 by experimental results on the available cattle muzzle images.
A Systematic Review of Machine Learning Techniques for Cattle Identification: Datasets, Methods and Future Directions
For the two main applications of cattle detection and cattle identification, all the ML based papers only solve cattle identification problems.
Understanding the Effect of Smartphone Cameras on Estimating Munsell Soil Colors from Imagery
The Munsell soil color chart (MSCC) is a in laboratories under controlled conditions.
An Ensemble-based Multi-Criteria Decision Making Method for COVID-19 Cough Classification
To address this issue, in this paper, we propose an ensemble-based multi-criteria decision making (MCDM) method for selecting top performance machine learning technique(s) for COVID-19 cough classification.
A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging
The purpose of this study is to systematically review, assess, and synthesize research articles that have used different machine learning techniques to detect and diagnose COVID-19 from chest X-ray and CT scan images.
Detecting Autism Spectrum Disorder using Machine Learning
Autism Spectrum Disorder (ASD), which is a neuro development disorder, is often accompanied by sensory issues such an over sensitivity or under sensitivity to sounds and smells or touch.
ECOVNet: An Ensemble of Deep Convolutional Neural Networks Based on EfficientNet to Detect COVID-19 From Chest X-rays
This paper proposed an ensemble of deep convolutional neural networks (CNN) based on EfficientNet, named ECOVNet, to detect COVID-19 using a large chest X-ray data set.
PDCOVIDNet: A Parallel-Dilated Convolutional Neural Network Architecture for Detecting COVID-19 from Chest X-Ray Images
The experimental results demonstrate that our proposed method significantly improves performance metrics: accuracy, precision, recall, and F1 scores reach 96. 58%, 96. 58%, 96. 59%, and 96. 58%, respectively, which is comparable or enhanced compared with the state-of-the-art methods.
Automatically Assessing Quality of Online Health Articles
The information ecosystem today is overwhelmed by an unprecedented quantity of data on versatile topics are with varied quality.
An Improved Naive Bayes Classifier-based Noise Detection Technique for Classifying User Phone Call Behavior
In order to improve the classification accuracy, we effectively identify noisy instances from the training dataset by analyzing the behavioral patterns of individuals.
