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Now showing items 11-20 of 36
Machine learning-based multi-target regression to effectively predict turning movements at signalized intersections
(
Elsevier
, 2022 , Article)
Effective prediction of turning movement counts at intersections through efficient and accurate methods is essential and needed for various applications. Commonly predictive methods require extensive data collection, ...
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 ...
Modeling of Surface Roughness in Turning Operation Using Extreme Learning Machine
(
Springer Verlag
, 2015 , Article)
Prediction model allows the machinist to determine the values of the cutting performance before machining. According to the literature, various modeling techniques have been investigated and applied to predict the cutting ...
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 ...
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 ...
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 ...
Modeling and sensitivity analysis of the forward osmosis process to predict membrane flux using a novel combination of neural network and response surface methodology techniques
(
MDPI AG
, 2021 , Article)
The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) ...
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 ...
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 ...
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) ...