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Editorial: Machine Learning, Advances in Computing, Renewable Energy and Communication (MARC)
(
Springer Science and Business Media Deutschland GmbH
, 2022 , Conference Paper)
Machine learning (ML) is the subcategory of artificial intelligence (AI), which has the capability to imitate human behavior intelligently as per the task performed by the human. In the modern time, any organization ...
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 ...
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 ...
Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges
(
Elsevier
, 2022 , Article Review)
As the globally increasing population drives rapid urbanization in various parts of the world, there is a great need to deliberate on the future of the cities worth living. In particular, as modern smart cities embrace ...
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 ...
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 ...