scholarly journals Functional Simulation of Human Blood Identification Device using Feed-Forward Artificial Neural Network for FPGA Implementation

2018 ◽  
Author(s):  
Denny Darlis ◽  
Heri Murwati ◽  
Rizki Ardianto Priramadhi ◽  
Mohamad Ramdhani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Success Rate ◽  
Human Blood ◽  
Fpga Implementation ◽  
Blood Type ◽  
Network Algorithms ◽  
Artifical Neural Network ◽  
Functional Simulation ◽  
Hardware Description

The identification of human blood type stillrequires a fast and accurate device considering the number ofblood samples that need to be distributed and transfusedimmediately. In this study we propose a hardwareimplementation of human blood type identification devices usingfeedforward neural network algorithms on grayscale images ofblood samples. The images to be used are 32x32 pixels, 48x48pixels, 64x64, 80x80, and 9x96 pixels. The algorithm wereimplemented using VHSIC Hardware Description Language.With artifical neural network implemented on Xilinx FPGASpartan 3S1000, the success rate of detection by grouping by themean and median ratios of the number of '1' bits is more than75%.

scholarly journals Prototyping Feed-Forward Artificial Neural Network on Spartan 3S1000 FPGA for Blood Type Classification

2022 ◽  
Vol 5 (01) ◽  
pp. 34
Author(s):  
Rizki Ardianto Priramadhi ◽  
Denny Darlis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Blood Type ◽  
Feed Forward ◽  
System Input ◽  
Neural Network Algorithm ◽  
Hardware Description ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Type Classification

In this research, a Feed-Forward Artificial Neural Network design was implemented on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board and prototyped blood type classification device. This research uses blood sample images as a system input. The system was built using VHSIC Hardware Description Language to describe the feed-forward propagation with a backpropagation neural network algorithm. We use three layers for the feed-forward ANN design with two hidden layers. The hidden layer designed has two neurons. In this study, the accuracy of detection obtained for four-type blood image resolutions results from 86%-92%, respectively.

Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

2018 ◽  
Author(s):  
Rizki Eka Putri ◽  
Denny Darlis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Image Resolution ◽  
Blood Type ◽  
Feed Forward ◽  
Testing Facility ◽  
System Input ◽  
Artificial Neural ◽  
Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

A study on the estimations of (n, t) reaction cross-sections at 14.5 MeV by using artificial neural network

2021 ◽  
pp. 2150168
Author(s):  
Hasan Özdoğan ◽  
Yiğit Ali Üncü ◽  
Mert Şekerci ◽  
Abdullah Kaplan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cross Sections ◽  
Reaction Cross ◽  
Forward Algorithm ◽  
Network Algorithms ◽  
Analytical Formulas ◽  
Artificial Neural ◽  
Semi Empirical ◽  
Reaction Cross Sections

In this paper, calculations of the [Formula: see text] reaction cross-sections at 14.5 MeV have been presented by utilizing artificial neural network algorithms (ANNs). The systematics are based on the account for the non-equilibrium reaction mechanism and the corresponding analytical formulas of the pre-equilibrium exciton model. Experimental results, obtained from the EXFOR database, have been used to train the ANN with the Levenberg–Marquardt (LM) algorithm which is a feed-forward algorithm and is considered one of the well-known and most effective methods in neural networks. The Regression [Formula: see text] values for the ANN estimation have been determined as 0.9998, 0.9927 and 0.9895 for training, testing and for all process. The [Formula: see text] reaction cross-sections have been reproduced with the TALYS 1.95 and the EMPIRE 3.2 codes. In summary, it has been demonstrated that the ANN algorithms can be used to calculate the [Formula: see text] reaction cross-section with the semi-empirical systematics.

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