Estimation of chemical resistance of dental ceramics by neural network

Živko-Babić, Jasenka; Lisjak, Dragutin; Ćurković, Lidija; Jakovac, Marko

doi:10.1016/j.dental.2007.01.008

Cited by 16 publications

(6 citation statements)

References 5 publications

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“…In order to improve the model, we decided to construct hybrid models combining the ARIMA model and ANNs. ANNs are based on present understanding of the biological nervous system the ability to learn through example [ 24 ] and have been widely used as efficient methods in modeling nonlinear and dynamic systems [ 25 ]. GRNN is one of the widely used static neural networks.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China

Guo

et al. 2015

PLoS ONE

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BackgroundCases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS.MethodsTwo hybrid models, one composed of nonlinear autoregressive neural network (NARNN) and autoregressive integrated moving average (ARIMA) the other composed of generalized regression neural network (GRNN) and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model.ResultsThe ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve.ConclusionDeveloping and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS.

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Section: Discussionmentioning

confidence: 99%

Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China

Guo

et al. 2015

PLoS ONE

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“…It is based on present understanding of the biological nervous system and its ability to learn through example [15, 16]. Neural networks are used to solve the problems which cannot be modeled, in particular.…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Network for Prediction of Dissolution Kinetics

Elçiçek

Akdoğan

Karagöz

2014

The Scientific World Journal

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Colemanite is a preferred boron mineral in industry, such as boric acid production, fabrication of heat resistant glass, and cleaning agents. Dissolution of the mineral is one of the most important processes for these industries. In this study, dissolution of colemanite was examined in water saturated with carbon dioxide solutions. Also, prediction of dissolution rate was determined using artificial neural networks (ANNs) which are based on the multilayered perceptron. Reaction temperature, total pressure, stirring speed, solid/liquid ratio, particle size, and reaction time were selected as input parameters to predict the dissolution rate. Experimental dataset was used to train multilayer perceptron (MLP) networks to allow for prediction of dissolution kinetics. Developing ANNs has provided highly accurate predictions in comparison with an obtained mathematical model used through regression method. We conclude that ANNs may be a preferred alternative approach instead of conventional statistical methods for prediction of boron minerals.

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“…CAD/CAM technique helps in scanning prepared tooth and designing of restoration with milling. Thus, fabrication of prosthetic restoration has become easy as there are no casting steps involved [12].…”

Section: Role Of Artificial Intelligence In Orthodonticsmentioning

confidence: 99%

Artificial Intelligence: An Advancing Front of Dentistry

Bhatia¹,

Tiwari²

2019

Act Scie Dental

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Advances in digital technology have greatly transformed our everyday lives bringing everything within the reach of our fingertips. Over the years newer technologies have developed based on principles that try to simulate the functioning of human brain. The vast search in this field has given rise to what is known as artificial intelligence (AI). AI based diagnosis is gaining importance due to its ability to detect and diagnose lesions which may go unnoticed to the human eye, thereby paving way for a holistic practice. Artificial intelligence can be a useful modality in diagnosis and treatment of lesions of oral cavity and can be employed in screening and classifying suspicious altered mucosa undergoing premalignant and malignant changes. Its various applications in different areas of dentistry has been explained thoroughly in this paper. As we know, the world is rapidly progressing towards digitalisation in every sphere of life. This digital technology has further simplified life. This digital revolution has revolutionised dentistry in every aspect and improved the quality of dental care. The human brain is incomparable structure which is composed of networks of interlinked neurons which transmit signals throughout the body [1]. Human brain has a complex neural structure. Over the years man has tried to build technologies which can simulate functioning of human brain accurately which has led to development of technology what is known as artificial intelligence (AI). AI is defined as a field of science and engineering concerned with the computational understanding of what is commonly called intelligent behaviour and with the creation of artefacts that exhibit such behaviour [2]. The applications of AI in the field of telecommunication and aerospace like expert systems, game playing, theorem-proving, natural language processing, image recognition and robotics have grown manifold. Technology has also 18. Bijio Alexzender and Sunil J. "Artificial intelligence in dentistry: current concepts and a peep into the future".

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Estimation of chemical resistance of dental ceramics by neural network

Cited by 16 publications

References 5 publications

Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China

Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China

The Use of Artificial Neural Network for Prediction of Dissolution Kinetics

Artificial Intelligence: An Advancing Front of Dentistry

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