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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 ...
Optimal filler content for cotton fiber/PP composite based on mechanical properties using artificial neural network
(
Elsevier Ltd
, 2020 , Other)
In this paper, a machine learning-based approach has been proposed to integrate artificial intelligence during the designing of fiber-reinforced polymeric composites. With the help of the proposed approach, an artificial ...
Machine learning for prediction of the uniaxial compressive strength within carbonate rocks
(
Springer Science and Business Media Deutschland GmbH
, 2023 , Article)
The Uniaxial Compressive Strength (UCS) is an essential parameter in various fields (e.g., civil engineering, geotechnical engineering, mechanical engineering, and material sciences). Indeed, the determination of UCS in ...
Dairy Cow Rumination Detection: A Deep Learning Approach
(
Springer Science and Business Media Deutschland GmbH
, 2020 , Conference Paper)
Cattle activity is an essential index for monitoring health and welfare of the ruminants. Thus, changes in the livestock behavior are a critical indicator for early detection and prevention of several diseases. Rumination ...
Factors Affecting Student Satisfaction Towards Online Teaching: A Machine Learning Approach
(
Springer Science and Business Media Deutschland GmbH
, 2022 , Conference Paper)
During the outbreak of the Covid-19 pandemic, universities were forced to adopt technology and collaboration tools to reinforce online teaching and sustain their operations. This radical change pushes universities, ...
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 ...
Machine Learning Methods for Dysgraphia Screening with Online Handwriting Features
(
Institute of Electrical and Electronics Engineers Inc.
, 2022 , Conference Paper)
Dysgraphia, a major learning disorder that primarily interferes with writing skills can hinder the academic track of children unless recognized in the early stage. The diversity in the symptoms, as well as the emergence ...
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. ...
Machine Learning Approach to Predict Metro Ridership based on Land Use Densities
(
Qatar University Press
, 2021 , Poster)
Predicting metro ridership is an essential requirement for efficient metro operation and management. The dependence of metro ridership on the land use densities entails a need for an accurate predictive model. To this end, ...