Artificial Neural Network enabling Clinically Meaningful Biological Image Data Generation

Social Interaction ◽

Social Relations ◽

Image Data ◽

Training Phase ◽

Testing Phase ◽

Training Stage ◽

Artificial Neural ◽

Hand Writing

Humans are social beings who depend on social interaction. Social interaction that is often used is communication. Communication is one of the bridges to connect social relations between humans. Communication can be delivered in two ways, namely verbal or nonverbal. Handwriting is an example of nonverbal communication using paper and writing utensils. Each individual's writing has its own uniqueness so that handwriting often becomes the character or characteristic of the author. The handwriting pattern usually becomes a character for the writer so that people who recognize the writing will easily guess the ownership of the related handwriting. However, handwriting is often used by irresponsible people in the form of handwriting falsification. The acts of writing falcification often occur in the workplace or even in the field of education. This is one of the driving factors for creating a reliable system in tracking someone's handwriting based on their ownership. In this study, we will discuss the identification of a person's handwriting based on their ownership. The output of this research is in the form of ID from the author and accuracy in the form of percentage of system reliability in identifying. The results of this study are expected to have a good impact on all parties, in order to minimize plagiarism. Identification of handwriting to be built consists of two main processes, namely the training phase and the testing phase. At the training stage, the handwritten image is subjected to several processes, namely threshold, wavelet conversion, and then will be trained using the Backpropagation Artificial Neural Network. In the testing phase, the process is the same as in the training phase, but at the end of the process, a comparison will be made between the image data that has been stored during training with a comparison image. Backpropagation ANN can work optimally if it is trained using input data that has determined the size, learning rate, parameters, and the number of nodes on the network. It is expected that the offered method can work optimally so that it produces an accurate percentage in order to minimize handwriting falcification.

Prediction Data Processing Scheme using an Artificial Neural Network and Data Clustering for Big Data

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v6i1.pp330-336 ◽

2016 ◽

Vol 6 (1) ◽

pp. 330 ◽

Cited By ~ 1

Author(s):

Se-Hoon Jung ◽

Jong-Chan Kim ◽

Chun-Bo Sim

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Data Clustering ◽

Principal Component ◽

Component Analysis ◽

Learning Model ◽

Data Generation ◽

Processing Scheme ◽

Various types of derivative information have been increasing exponentially, based on mobile devices and social networking sites (SNSs), and the information technologies utilizing them have also been developing rapidly. Technologies to classify and analyze such information are as important as data generation. This study concentrates on data clustering through principal component analysis and K-means algorithms to analyze and classify user data efficiently. We propose a technique of changing the cluster choice before cluster processing in the existing K-means practice into a variable cluster choice through principal component analysis, and expanding the scope of data clustering. The technique also applies an artificial neural network learning model for user recommendation and prediction from the clustered data. The proposed processing model for predicted data generated results that improved the existing artificial neural network–based data clustering and learning model by approximately 9.25%.

Artificial Neural Network Potentials for Mechanics and Fracture Dynamics of Materials

10.21203/rs.3.rs-1178290/v1 ◽

2022 ◽

Author(s):

Gang Seob Jung ◽

Hoon Joo Myung ◽

Stephan Irle

Keyword(s):

Neural Network ◽

Crack Propagation ◽

Density Functional ◽

Tight Binding ◽

Training Data ◽

Data Generation ◽

Deformation And Failure ◽

Deformation And Fracture ◽

Abstract Atomistic understanding of mechanics and failure of materials is the key for engineering and applications. Modeling accurately brittle failure with crack propagation in covalent crystals requires a quantum mechanics-based description of individual bond-breaking events for large system sizes. Machine Learned (ML) potentials have emerged to overcome the traditional, physics-based modeling tradeoff between accuracy and accessible time and length scales. Previous studies have shown successful applications of ML potentials for describing the structure and dynamics of molecular systems and amorphous or liquid phases of materials. However, their application to deformation and failure processes in materials is yet uncommon. In this study, we discuss apparent limitations of ML potentials to describe deformation and fracture under loadings and propose a way to generate and select training data for their employment in simulations of deformation and fracture of crystals. We applied the proposed approach to 2D crystal graphene, utilizing the density-functional tight-binding (DFTB) method for more efficient and extensive data generation in place of density functional theory (DFT). Then, we explore how the data selection affects the accuracy of the developed artificial neural network potential (NNP), indicating that only the errors in total energies and atomic forces are insufficient to judge the NNP’s reliability. Therefore, we evaluate and select NNPs based on their performance in describing physical properties, e.g., stress-strain curves and geometric deformation. In sharp contrast to popular reactive bond order potentials, our optimized NNP predicts straight crack propagation in graphene along both armchair and zigzag lattice directions, as well as higher fracture toughness of zigzag edge direction. Our study provides significant insight into crack propagation mechanisms at atomic scales and highlights strategies for NNP developments of broader materials.

Biological image classification using rough-fuzzy artificial neural network

Expert Systems with Applications ◽

10.1016/j.eswa.2015.07.075 ◽

2015 ◽

Vol 42 (24) ◽

pp. 9482-9488 ◽

Cited By ~ 20

Author(s):

Carlos Affonso ◽

Renato Jose Sassi ◽

Ricardo Marques Barreiros

Keyword(s):

Neural Network ◽

Image Classification ◽

Artificial Neural ◽

Biological Image

An artificial neural network interpreter for transient thermography image data

NDT & E International ◽

10.1016/s0963-8695(96)00071-0 ◽

1997 ◽

Vol 30 (5) ◽

pp. 291-295 ◽

Cited By ~ 30

Author(s):

M.B. Saintey ◽

D.P. Almond

Keyword(s):

Neural Network ◽

Image Data ◽

Transient Thermography ◽

Jaringan Syaraf Tiruan Perambatan Balik Untuk Pengenalan Wajah

JURNAL SISTEM INFORMASI BISNIS ◽

10.21456/vol6iss1pp30-37 ◽

2016 ◽

Vol 6 (1) ◽

pp. 30

Author(s):

Nahdi Sabuari ◽

Rizal Isnanto ◽

Kusworo Adi

Keyword(s):

Neural Network ◽

Face Recognition ◽

Face Detection ◽

Image Data ◽

Training Algorithm ◽

Accuracy Rate ◽

Average Accuracy ◽

The Face ◽

This research discusses about face detection and face recognition in an image. Face detection has only two classifications, i.e face and not face. Face recognition is compatible with some classifications of a number individuals who want to be recognized. Face detection and face recognition in thi study using Haar-Like Feature method and Artificial Neural Network Backpropagation. A method Haar-Like Feature used for detection and extraction in an image, because the clasification on this method showed success at used to detect image of the face. Artificial Neural Network Backpropagation is a training algorithm that is used to do training simulated on facial image data training stored in a database. This study uses Ms. Excel 2007 as database with 10 individual sample image, every image in each individuals having three distance with every range has four defferent light intensities, so that the data training stored in the database reached 120 data training. The results shows that the face detection and face recognition which is developed can recognize a face image with an average accuracy rate reaches 80,8% for each distance.

ANALYSIS OF BIOLO ARTIFICIAL NEURAL NETWORK IN PREDICTION OF AEROBIC EXERCISE INDEX BASED ON ALGORITHM

Revista Brasileira de Medicina do Esporte ◽

10.1590/1517-8692202127042021_0126 ◽

2021 ◽

Vol 27 (4) ◽

pp. 367-371

Author(s):

Lei Ru ◽

Bin Zhang ◽

Jing Duan ◽

Guo Ru

Keyword(s):

Neural Network ◽

Heart Rate ◽

Body Weight ◽

Body Fat ◽

Experimental Period ◽

Ecg Signal ◽

Resting Heart Rate ◽

Artificial Neural ◽

Biological Image

ABSTRACT Objective: To study the relationship between aerobic activity and cardiac autonomic nerve activity by artificial neural network algorithm and biological image fusion; because of the artificial neural network model (ANN) problems, biological image processing technology is introduced based on ANN. Methods: An Ann under biological image intelligence algorithm is proposed, a classifier suitable for electrocardiograph (ECG) screening is designed, and an ECG signal screening system is successfully established. Moreover, the data set of normal recovered ECG signals of the subjects during the experimental period is constructed, and a classifier is used to extract the characteristic data of a normal ECG signal during the experimental period. Results: The changes in resting heart rate and other physical health indicators are analyzed by combining resting physiological indicators, namely heart rate, body weight, body mass index and body fat rate. The results show that the self-designed classifier can efficiently process the ECG images, and long-term regular activities can improve the physical conditions of most people. Most subjects’ body weight and body fat rate decrease with the extension of experiment time, and the resting heart rate decreases relatively. Conclusions: Certain indicators can be used to predict a person's dynamic physical health, which indicates that the experimental research of index prediction in this research has a good effect, which not only extends the application of artificial neural network but also lays a foundation for the research and implementation of ECG intelligent testing wearable devices. Level of evidence II; Therapeutic studies - investigation of treatment results.

Prediction Data Processing Scheme using an Artificial Neural Network and Data Clustering for Big Data

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v6i1.9334 ◽

2016 ◽

Vol 6 (1) ◽

pp. 330 ◽

Cited By ~ 3

Author(s):

Se-Hoon Jung ◽

Jong-Chan Kim ◽

Chun-Bo Sim

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Data Clustering ◽

Principal Component ◽

Component Analysis ◽

Learning Model ◽

Data Generation ◽

Processing Scheme ◽

Various types of derivative information have been increasing exponentially, based on mobile devices and social networking sites (SNSs), and the information technologies utilizing them have also been developing rapidly. Technologies to classify and analyze such information are as important as data generation. This study concentrates on data clustering through principal component analysis and K-means algorithms to analyze and classify user data efficiently. We propose a technique of changing the cluster choice before cluster processing in the existing K-means practice into a variable cluster choice through principal component analysis, and expanding the scope of data clustering. The technique also applies an artificial neural network learning model for user recommendation and prediction from the clustered data. The proposed processing model for predicted data generated results that improved the existing artificial neural network–based data clustering and learning model by approximately 9.25%.

A Model for Hourly Solar Radiation Data Generation from Daily Solar Radiation Data Using a Generalized Regression Artificial Neural Network

International Journal of Photoenergy ◽

10.1155/2015/968024 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13 ◽

Cited By ~ 15

Author(s):

Tamer Khatib ◽

Wilfried Elmenreich

Keyword(s):

Neural Network ◽

Solar Radiation ◽

Percentage Error ◽

Data Generation ◽

Error Statistics ◽

Radiation Data ◽

Proposed Model ◽

Artificial Neural ◽

Generalized Regression

This paper presents a model for predicting hourly solar radiation data using daily solar radiation averages. The proposed model is a generalized regression artificial neural network. This model has three inputs, namely, mean daily solar radiation, hour angle, and sunset hour angle. The output layer has one node which is mean hourly solar radiation. The training and development of the proposed model are done using MATLAB and 43800 records of hourly global solar radiation. The results show that the proposed model has better prediction accuracy compared to some empirical and statistical models. Two error statistics are used in this research to evaluate the proposed model, namely, mean absolute percentage error and root mean square error. These values for the proposed model are 11.8% and −3.1%, respectively. Finally, the proposed model shows better ability in overcoming the sophistic nature of the solar radiation data.

Environment Technology Resources Proceedings of the International Scientific and Practical Conference ◽

LAND COVER CLASSIFICATION BASED ON MODIS IMAGERY DATA USING ARTIFICIAL NEURAL NETWORKS

10.17770/etr2017vol2.2545 ◽

2017 ◽

Vol 2 ◽

pp. 159

Author(s):

Arthur Stepchenko

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Neural Networks ◽

Artificial Neural Networks ◽

Land Cover ◽

Classification System ◽

Image Data ◽

Automated Classification ◽

Remote sensing has been widely used to obtain land cover information using automated classification. Land cover is a measure of what is overlaying the surface of the earth. Accurate mapping of land cover on a regional scale is useful in such fields as precision agriculture or forest management and is one of the most important applications in remote sensing. In this study, multispectral MODIS Terra NDVI images and an artificial neural network (ANN) were used in land cover classification. Artificial neural network is a computing tool that is designed to simulate the way the human brain analyzes and process information. Artificial neural networks are one of the commonly applied machine learning algorithm, and they have become popular in the analysis of remotely sensed data, particularly in classification or feature extraction from image data more accurately than conventional method. This paper focuses on an automated classification system based on a pattern recognition neural network. Variational mode decomposition method is used as an image data pre-processing tool in this classification system. The result of this study will be land cover map.