UNT Scholarly Works - 6 Matching Results

Search Results

Results: 1 - 6
open access

Predicting psoriasis using routine laboratory tests with random forest

Description: Article describes how psoriasis is a chronic inflammatory skin disease that affects approximately 125 million people worldwide. The goal of the authors' study is to derive a powerful predictive model for psoriasis disease based on only routine hospital tests.
Date: October 19, 2021
Creator: Zhou, Jing; Li, Yuzhen & Guo, Xuan
Item Type: Refine your search to only Article
Partner: UNT College of Engineering
open access

FlexiChain 3.0: Distributed Ledger Technology-Based Intelligent Transportation for Vehicular Digital Asset Exchange in Smart Cities

Description: Article describes how, due to the enormous amounts of data being generated between users, Intelligent Transportation Systems (ITS) are complex Cyber-Physical Systems that necessitate a reliable and safe infrastructure. In this work, the authors explore Distributed Ledger Technology (DLT) and collect data about consensus algorithms and their applicability to be used in the IoV as the backbone of ITS.
Date: April 19, 2023
Creator: Alkhodair, Ahmad; Mohanty, Saraju P. & Kougianos, Elias
Item Type: Refine your search to only Article
Partner: UNT College of Engineering
open access

Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model

Description: Article is a study proposing an approach for blood cancer disease prediction using the supervised machine learning approach to perform blood cancer prediction with high accuracy using microarray gene data.
Date: January 19, 2022
Creator: Rupapara, Vaibhav; Rustam, Furqan; Aljedaani, Wajdi; Shahzad, Hina Fatima; Lee, Ernesto & Ashraf, Imran
Item Type: Refine your search to only Article
Partner: UNT College of Engineering
open access

Classifying Abdominal Fat Distribution Patterns by Using Body Measurement Data

Description: This article aims to explore new categorization that characterizes the distribution clusters of visceral and subcutaneous adipose tissues (VAT and SAT) measured by magnetic resonance imaging (MRI), to analyze the relationship between the VAT-SAT distribution patterns and the novel body shape descriptors (BSDs), and to develop a classifier to predict the fat distribution clusters using the BSDs.
Date: February 19, 2021
Creator: Sun, Jingjing; Xu, Bugao; Lee, Jane & Freeland-Graves, Jeanne H.
Item Type: Refine your search to only Article
Partner: UNT College of Engineering
Back to Top of Screen