scholarly journals Application of artificial neural networks in the design of biomedical materials

2021 ◽  
Vol 233 ◽  
pp. 02003
Author(s):  
Yu Chen
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Raw Materials ◽  
Material Design ◽  
Research Field ◽  
The Body ◽  
Biomedical Science ◽  
Biomedical Materials ◽  
Normal Human ◽  
Artificial Neural

Biomedical science is a scientific field that includes the intersection of multiple technologies, combining the theoretical methods of biology, medicine and engineering. Biomedical materials are now a branch of the body that studies materials that are adapted to the body’s functioning to ensure normal human activity. Because of its closely related to human activities, it has become an important research field in our time. Therefore, the purpose of this paper is to explore the application of artificial intelligence in the design of biological science materials. Therefore, in the case of using high-quality materials, the material design is improved and optimized by using artificial neural networks under the basis of adverse rejection reactions to the properties of raw materials. The experimental results show that artificial neural network can be better connected and reaction, which is beneficial to improve sensitivity and use emergency measures to deal with it.

Artificial Neural Networks in Materials Science Application

2010 ◽  
Vol 20-23 ◽  
pp. 1211-1216 ◽  
Author(s):  
Wen Yu Zhang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Materials Science ◽  
Material Design ◽  
Widespread Application ◽  
Plastic Processing ◽  
Artificial Neural ◽  
Science Application ◽  
Compound Materials ◽  
Material Preparation

Because but the artificial neural networks has the strong non-linear problem handling ability also the fault tolerance strong obtains the widespread application in the materials science.This article to its material design, the material preparation craft optimizes, the plastic processing, the heat treatment, the compound materials, corrode, domain and so on casting applications have carried on the discussion.

scholarly journals Reconstruction of Depolarization Pattern in Myocardial Infarction Cases Using Two Cascaded Stages of Artificial Neural Networks

2021 ◽  
Vol 2128 (1) ◽  
pp. 012016
Author(s):  
Nihal A. Mabrouk ◽  
Abdelreheem M. Khalifa ◽  
Abdelmenem A. Nasser ◽  
Moustafa H. Aly
Keyword(s):  
Myocardial Infarction ◽  
Neural Networks ◽  
Inverse Problem ◽  
Artificial Neural Networks ◽  
Electrical Activity ◽  
Heart Diseases ◽  
Three Dimensional ◽  
The Body ◽  
Forward Model ◽  
Artificial Neural

Abstract Our paper introduces a new technique for diagnosis of various heart diseases without the need of highly experts to investigate the electrocardiogram (ECG). Using the same electrodes of the ECG machine, it will be able to transmit directly the electrical activity inside the heart to a moving picture. Our technique is based on artificial intelligence algorithm using artificial neural networks (ANN). Finding the trans-membrane potential (TMP) inside the heart from the body surface potential (BSP) is known as the inverse problem of ECG. To have a unique solution for the inverse problem the data used should be obtained from a forward model. A three dimensional (3-D) model of cellular activation whole heart embedded in torso is simulated and solved using COMSOL Multiphysics software. In our previous paper, one ANN succeeded in displaying the wave propagation on the surface of a normal heart. In this paper, we used a configuration of ANNs to display different cases of heart with myocardial infarction (MI). To check the system accuracy, eight MI cases with different sizes and locations in the heart are simulated in the forward model. This configuration proved to be highly accurate in displaying each MI case -size and location- presenting the infarction as an area with no electrical activity.

Meta-heuristic Techniques to train Artificial Neural Networks for Medical Image Classification: A Review

2020 ◽  
Vol 13 ◽  
Author(s):  
Priyanka Jindal ◽  
Dharmender Kumar
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Medical Imaging ◽  
Image Classification ◽  
Medical Image ◽  
The Body ◽  
Clinical Practices ◽  
Heuristic Techniques ◽  
Medical Image Classification ◽  
Artificial Neural

: Medical imaging has been utilized in various forms in clinical applications for better diagnostic and treatment of diseases. These imaging technologies help in recognizing body's ailing region with ease. In addition, it causes no pain to patient as the interior part of the body can be seen without opening too much of the body. Nowadays, various image processing techniques such as segmentation, registration, classification, restoration, contrast enhancement and many more exists to enhance image quality. Among all these techniques, classification plays an important role in computer-aided diagnosis for easy analysis and interpretation of these images. Image classification not only classifies diseases with high accuracy but also finds out which part of the body is infected by the disease. The usage of Neural networks classifier in medical imaging applications opened new doors or opportunities to researchers stirring them to excel in this domain. Moreover, accuracy in clinical practices and development of more sophisticated equipment is necessary in medical field for more accurate and quicker decisions. Therefore, keeping this in mind, researchers started focusing on adding intelligence by using meta-heuristic techniques to classification methods. This paper provides a brief survey on role of artificial neural networks in medical image classification, various types of meta-heuristic algorithms applied for optimization purpose, their hybridization. A comparative analysis showing the effect of applying these algorithms on some classification parameters such as accuracy, sensitivity, specificity is also provided. From the comparison, it can be observed that the usage of these methods significantly optimizes these parameters leading us to diagnosis and treatment of a number of diseases in their early stage.

Registered Designation of Origin Areas of Fermented Food Products Defined by Microbial Phenotypes and Artificial Neural Networks

1999 ◽  
Vol 65 (10) ◽  
pp. 4484-4489 ◽  
Author(s):  
M. F. S. Lopes ◽  
C. I. Pereira ◽  
F. M. S. Rodrigues ◽  
M. P. Martins ◽  
M. C. Mimoso ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Raw Materials ◽  
Food Products ◽  
Fermented Food ◽  
Fermented Foods ◽  
Fermented Sausage ◽  
Local Environments ◽  
Artificial Neural ◽  
Counterfeit Products

ABSTRACT Cheese produced from raw ewes’ milk andchouriço, a Portuguese dry fermented sausage, are still produced in a traditional way in certain regions of Portugal by relying on colonization by microbial populations associated with the raw materials, equipment, and local environments. For the purpose of describing the product origins and types of these fermented foods, metabolic phenotypes can be used as descriptors of the product as well as to determine the presence of compounds with organoleptic value. The application of artificial neural networks to the metabolic profiles of bacterial isolates was assayed and allowed the separation of products from different regions. This method could then be used for the Registered Designation of Origin certification process of food products. Therefore, besides test panel results for these traditionally produced food products, another tool for validating products for the marketplace is available to the producers. The method can be improved for the detection of counterfeit products.

scholarly journals The Influence of Composite Laminate Stacking Sequence on Failure Load of Bonding Joints Using Experimental and Artificial Neural Networks Methods

2018 ◽  
Author(s):  
Bahadir Birecikli ◽  
Omer Ali Karaman ◽  
Selahattin Baris Celebi ◽  
Aydin Turgut
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Composite Laminate ◽  
Failure Load ◽  
Learning Algorithm ◽  
Fiber Composite ◽  
Material Design ◽  
Experimental Results ◽  
Stacking Sequence ◽  
Artificial Neural

The objective of this article was to forecast the ultimate failure load laminate stacking sequence combination on bonding joints which are exposed to tensile strength by using artificial neural networks. We have glass fiber composite materials with three different sequence combinations ([0°/90°], [±45°], [0°/90°/±45°]). Various adherend thicknesses and also ductile type adhesive was used in the experiment. The bonding geometry is a single lap and has four types of overlap angles 30°, 45°, 60°, 75° respectively. The experimental results demonstrate that composite laminate stacking sequence profoundly affects the bonding joints of failure load. Taking experimental results into account, Levenberg–Marquardt learning algorithm model was used by preferring a three layer forward on ANN so as to discipline network. In order to procure a precise ANN tool, an integrate methodology of experimental method has been used. The outcomes are used to ensure the experimental data’s to the ANN. The method of ANN permits surveying much adequately the probabilities of composite laminate stacking sequence combination using the prevalent ones which are [0°/90°], [±45°] and [0°/90°/±45°]. Testing data and training results were quite well 0.998, 0.997 and 0.998 in turn. Consequences acquired can be used by engineers who are interested in the composite material design to enhance failure load.

A method for optical imaging and monitoring of the excretion of fluorescent nanocomposites from the body using artificial neural networks

2018 ◽  
Vol 14 (4) ◽  
pp. 1371-1380 ◽  
Author(s):  
Olga E. Sarmanova ◽  
Sergey A. Burikov ◽  
Sergey A. Dolenko ◽  
Igor V. Isaev ◽  
Kirill A. Laptinskiy ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optical Imaging ◽  
The Body ◽  
Artificial Neural

Fabrication and Wear Behavior Investigation of the Carbon/Epoxy Composites Based on Wood Using Artificial Neural Networks

2011 ◽  
Vol 413 ◽  
pp. 95-102 ◽  
Author(s):  
Hossein Vafaeenezhad ◽  
Seyed Mojtaba Zebarjad ◽  
Jalil Vahdati Khaki
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wear Behavior ◽  
Raw Materials ◽  
Normal Load ◽  
Carbon Matrix ◽  
Cost Effective ◽  
Quercus Suber ◽  
Wear Properties ◽  
Artificial Neural

Since wood is the main component of the applied raw materials, it can be used as matrix in carbon composites, also it can be taken into consideration as a cost effective advanced application and have this potential to suppress many expensive fabrication and finishing procedures. Wood samples from Oak tree (Quercus suber) were heated at different temperatures to produce porous carbon templates. Subsequently, the Carbonized wood was infiltrated with an epoxy in order to fabricate the final carbon/epoxy composite. Scanning electron microscopy was used to elucidate parameters affecting on microstructure and wear properties of products. In this context, artificial neural networks (ANN) and design of experiments method (DOE) was implemented to analyze the wear performance of a new class of cellulose based composites. This work indicates that epoxy shows good reinforcement characteristics as it improves the sliding wear resistance of the carbon matrix and that factors like carbonization temperature, sliding distance and normal load are the important factors affecting the wear behaviors.

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