Browsing by Subject "Artificial neural network"
Now showing items 1-20 of 31
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Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning
( Elsevier , 2023 , Article)This study focuses on predicting the dynamic viscosity of nanofluids, specifically Polyalpha-Olefin-hexagonal boron nitride (PAO-hBN) using machine learning models. The primary goal of this research is to assess and contrast ... -
Air quality monitoring and prediction system using machine-to-machine platform
(2012 , Conference Paper)This paper presents an ambient air quality monitoring and prediction system. The system consists of several distributed monitoring stations that communicate wirelessly to a backend server using machine-to-machine communication ... -
An ensemble neural network framework for improving the detection ability of a base control chart in non-parametric profile monitoring
( Elsevier , 2022 , Article)Profile monitoring is a challenging issue in statistical process control (SPC). It aims to use a functional relationship between a response variable and one or more explanatory variable(s) to summarize the quality of a ... -
Artificial neural networks for predicting hydrogen production in catalytic dry reforming: A systematic review
( MDPI AG , 2021 , Article Review)Dry reforming of hydrocarbons, alcohols, and biological compounds is one of the most promising and effective avenues to increase hydrogen (H2 ) production. Catalytic dry reforming is used to facilitate the reforming process. ... -
Automated Detection of Brain Tumor through Magnetic Resonance Images Using Convolutional Neural Network
( Hindawi , 2021 , Article)Brain tumor is a fatal disease, caused by the growth of abnormal cells in the brain tissues. Therefore, early and accurate detection of this disease can save patient's life. This paper proposes a novel framework for the ... -
Bioinspired modeling and biogeography-based optimization of electrocoagulation parameters for enhanced heavy metal removal
( Elsevier , 2022 , Article)Electrocoagulation is an effective wastewater treatment process for the removal of heavy metals. This study focuses on deriving optimal conditions for removing heavy metals, viz. Lead (Pb), Cobalt (Co), and Manganese (Mn) ... -
A comprehensive optimization study of personal cooling radiant desks integrated to HVAC system for energy efficiency and thermal comfort in office buildings
( Elsevier , 2023 , Article)Personal comfort systems (PCS) maintain the occupant's preferred thermal environment and expand his thermal comfort experience under varying environmental conditions. Among PCSs, radiant-based systems are popular due to ... -
Computational studies for the effective electrical conductivity of copper powder filled LDPE/LLDPE composites
( National Institute of Science Communication and Information Resources , 2020 , Article)The effective electrical conductivity (EEC) of low density polyethylene (LDPE) and linear low density polyethylene (LLDPE) polymer composites filled with copper has been studied. The nonlinear behavior has been observed ... -
Constitutive models for the prediction of the hot deformation behavior of the 10%Cr steel alloy
( MDPI AG , 2019 , Article)The aim of this paper is to establish a reliable model that provides the best fit to the specific behavior of the flow stresses of the 10%Cr steel alloy at the time of hot deformation. Modified Johnson-Cook and strain-compensated ... -
Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network
( Elsevier , 2024 , Article)This research aims to enhance the safety level and crash resiliency of targeted woven roving glass/epoxy composite material for various industry 4.0 applications. Advanced machine learning algorithms are used in this study ... -
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 ... -
Design of composite rectangular tubes for optimum crashworthiness performance via experimental and ANN techniques
( Elsevier Ltd , 2022 , Article)This paper examines the crashworthiness performance of composite rectangular tubes using experimental and artificial neural network (ANN) techniques. Based on experimentally obtained values of different crashworthiness ... -
Developing ANN-Kriging hybrid model based on process parameters for prediction of mean residence time distribution in twin-screw wet granulation
( Elsevier B.V. , 2019 , Article)Artificial neural network (ANN) modelling is applied to predict the mean residence time of pharmaceutical formulation in a twin-screw granulator. Process parameters including feed flow rate, screw speed, and liquid to solid ... -
Direct Torque Control Based on Artificial Neural Network of a Five-Phase PMSM Drive
( Springer , 2018 , Book chapter)Direct torque control (DTC) based on artificial neural network (ANN) of a five-phase permanent magnet synchronous motor drive (PMSM) is presented in this paper. Using the mathematical model of the five-phase motor, DTC ... -
Employing machine learning techniques in monitoring autocorrelated profiles
( Springer Science and Business Media Deutschland GmbH , 2023 , Article)In profile monitoring, it is usually assumed that the observations between or within each profile are independent of each other. However, this assumption is often violated in manufacturing practice, and it is of utmost ... -
Estimation of Mechanical Properties of Copper Powder Filled Linear Low-Density Polyethylene Composites
( Springer Science and Business Media B.V. , 2022 , Article)Purpose: The complex geometry of many composites is in a loose multi-phase and the large difference in the mechanical and electrical properties of the different components makes it difficult to predict the effective ... -
Experimental Investigation and Uncertainty Prediction of the Load-Carrying Capacity of Composite Double Hat for Lattice Core Sandwich Panels Using Artificial Neural Network
( Institute of Electrical and Electronics Engineers Inc. , 2020 , Conference Paper)Carbon fiber reinforced composites are promising candidates for building advanced multifunctional structures with superior properties that are suitable for the next generation of automotive and aircraft applications. This ... -
Forecasting the impact of environmental stresses on the frequent waves of COVID19
( Springer , 2021 , Article)A novel approach to link the environmental stresses with the COVID-19 cases is adopted during this research. The time-dependent data are extracted from the online repositories that are freely available for knowledge and ... -
Locating leaks in water mains using noise loggers
( American Society of Civil Engineers (ASCE) , 2016 , Article)Because of their potential danger to public health, economic loss, environmental damage, and energy waste, underground water pipelines leaks have received more attention globally. Researchers have proposed active leakage ... -
Machine learning based photovoltaics (PV) power prediction using different environmental parameters of Qatar
( MDPI AG , 2019 , Article)Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 ...