Search
Now showing items 51-60 of 63
An integrated framework of data-driven, metaheuristic, and mechanistic modeling approach for biomass pyrolysis
(
Elsevier
, 2022 , Article)
This study presents an integrated hybrid framework of data-driven (cascade forward neural network (CFNN)), metaheuristic (artificial bee colony (ABC)), and a mechanistic modeling (Aspen simulation) approach for the biomass ...
Urban resilience and livability performance of European smart cities: A novel machine learning approach
(
Elsevier
, 2022 , Article)
Smart cities are centres of economic opulence and hope for standardized living. Understanding the shades of urban resilience and livability in smart city models is of paramount importance. This study presents a novel ...
State of charge estimation for a group of lithium-ion batteries using long short-term memory neural network
(
Elsevier
, 2022 , Article)
The present paper estimates for the first time the State of Charge (SoC) of a high capacity grid-scale lithium-ion battery storage system used to improve the power profile in a distribution network. The proposed long ...
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. ...
The impact of COVID-19 pandemic on electricity consumption and electricity demand forecasting accuracy: Empirical evidence from the state of Qatar
(
Elsevier Ltd
, 2022 , Article)
The goal of this study is to use machine-learning (ML) techniques and empirical big data to examine the influence of the COVID-19 pandemic on electricity usage and electricity demand forecasting accuracy in buildings in ...
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 ...