Search
Now showing items 31-40 of 42
Performance analysis of conventional machine learning algorithms for diabetic sensorimotor polyneuropathy severity classification
(
MDPI
, 2021 , Article)
Background: Diabetic peripheral neuropathy (DSPN), a major form of diabetic neuropathy, is a complication that arises in long-term diabetic patients. Even though the application of machine learning (ML) in disease diagnosis ...
Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux
(
Elsevier
, 2020 , Article)
Artificial neural networks (ANN) are black box models that are becoming more popular than transport-based models due to their high accuracy and less computational time in predictions. The literature shows a lack of ANN ...
Netizens' behavior towards a blockchain-based esports framework: a TPB and machine learning integrated approach
(
Emerald Publishing
, 2021 , Article)
Purpose: Based on the concepts confined in Ajzen's theory of planned behavior (TPB), this study investigates users' attitudes towards adoption of a blockchain-based framework in the esports industry that proposes a scheme ...
Protein glycation – biomarkers of metabolic dysfunction and early-stage decline in health in the era of precision medicine
(
Elsevier
, 2021 , Article)
Protein glycation provides a biomarker in widespread clinical use, glycated hemoglobin HbA1c (A1C). It is a biomarker for diagnosis of diabetes and prediabetes and of medium-term glycemic control in patients with established ...
Methodological considerations for identifying multiple plasma proteins associated with all-cause mortality in a population-based prospective cohort
(
Nature Research
, 2021 , Article)
Novel methods to characterize the plasma proteome has made it possible to examine a wide range of proteins in large longitudinal cohort studies, but the complexity of the human proteome makes it difficult to identify robust ...
Predictive ANN models for varying filler content for cotton fiber/PVC composites based on experimental load displacement curves
(
Elsevier Ltd
, 2020 , Article)
In this paper, artificial neural network (ANN) models are developed to predict the load-displacement curves for better understanding the behavior of cotton fiber/polyvinyl chloride (PVC) composites. Series of experiments ...
Data-driven modeling to predict the load vs. displacement curves of targeted composite materials for industry 4.0 and smart manufacturing
(
Elsevier Ltd
, 2021 , Article)
This work presents an approach for smart manufacturing focusing on Industry 4.0 to predict the load vs. displacement curve of targeted cotton fiber/Polypropylene (PP) composite materials while complying with the required ...
Characterizing fracture toughness using machine learning
(
Elsevier B.V.
, 2021 , Article)
The existing models for fracture toughness characterization based on nanoindentations that account for the fracture length are limited to simple (ideal) geometries that are absent in shales. The present study proposes two ...
On the Investigation of Monthly River Flow Generation Complexity Using the Applicability of Machine Learning Models
(
Hindawi Limited
, 2021 , Article)
Streamflow is associated with several sources on nonstationaries and hence developing machine learning (ML) models is always the motive to provide a reliable methodology to understand the actual mechanism of streamflow. ...
A new approach to predicting cryptocurrency returns based on the gold prices with support vector machines during the COVID-19 pandemic using sensor-related data
(
MDPI
, 2021 , Article)
In a real-world situation produced under COVID-19 scenarios, predicting cryptocurrency returns accurately can be challenging. Such a prediction may be helpful to the daily economic and financial market. Unlike forecasting ...