scholarly journals Implementation of the Perceptron Method for Recognizing the Patterns of Types of Intestinal Nematode Worms

2020 ◽  
Vol 4 (1) ◽  
pp. 180-186
Author(s):  
Erni Rouza ◽  
Jufri ◽  
Luth Fimawahib
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Training Data ◽  
Perceptron Algorithm ◽  
Intestinal Nematode ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
The Right ◽  
Nematode Worm

The purpose of pattern recognition is do the process of classifying an object into one particular class based on the pattern it has, so it can be used to recognize patterns of intestinal nematode worm types. One of the methods used in pattern recognition is by utilizing the artificial neural network method, the artificial neural network is able to represent a complex Input-Output relationship. For that the algorithm used is the perceptron algorithm. Perceptron is one method of Artificial Neural Networks. In the introduction of types of intestinal nematode worms, a computer must be trained in advance using training data and test data, this study discusses how a computer can recognize a pattern of types of intestinal nematode worms using the perceptron method. Based on the results of testing trials with input in the form of worm image scan results, based on the results of the perceptron method testing is able to recognize the pattern recognition of the types of intestinal nematode worms and be able to analyze with the right results of 100% for pinworms patterns, hookworm patterns, and 40- 50% for roundworms, by comparing the output value and the target value entered first.

scholarly journals Automation of (Macro)molecular Properties Using a Bootstrapping Swarm Artificial Neural Network Method: Databases for Machine Learning

2019 ◽  
Author(s):  
Blerta Rahmani ◽  
Hiqmet Kamberaj
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Quantum Mechanics ◽  
Molecular Properties ◽  
Training Data ◽  
Neural Network Method ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural

AbstractIn this study, we employed a novel method for prediction of (macro)molecular properties using a swarm artificial neural network method as a machine learning approach. In this method, a (macro)molecular structure is represented by a so-called description vector, which then is the input in a so-called bootstrapping swarm artificial neural network (BSANN) for training the neural network. In this study, we aim to develop an efficient approach for performing the training of an artificial neural network using either experimental or quantum mechanics data. In particular, we aim to create different user-friendly online accessible databases of well-selected experimental (or quantum mechanics) results that can be used as proof of the concepts. Furthermore, with the optimized artificial neural network using the training data served as input for BSANN, we can predict properties and their statistical errors of new molecules using the plugins provided from that web-service. There are four databases accessible using the web-based service. That includes a database of 642 small organic molecules with known experimental hydration free energies, the database of 1475 experimental pKa values of ionizable groups in 192 proteins, the database of 2693 mutants in 14 proteins with given values of experimental values of changes in the Gibbs free energy, and a database of 7101 quantum mechanics heat of formation calculations.All the data are prepared and optimized in advance using the AMBER force field in CHARMM macromolecular computer simulation program. The BSANN is code for performing the optimization and prediction written in Python computer programming language. The descriptor vectors of the small molecules are based on the Coulomb matrix and sum over bonds properties, and for the macromolecular systems, they take into account the chemical-physical fingerprints of the region in the vicinity of each amino acid.Graphical TOC Entry

Design of an Artificial Neural Network Pattern Recognition Scheme Using Full Factorial Experiment

2013 ◽  
Vol 465-466 ◽  
pp. 1149-1154
Author(s):  
Ibrahim Masood ◽  
Nadia Zulikha Zainal Abidin ◽  
Nur Rashida Roshidi ◽  
Noor Azlina Rejab ◽  
Mohd Faizal Johari
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Recognition Performance ◽  
Training Data ◽  
Design Parameters ◽  
Ann Model ◽  
Multivariate Quality Control ◽  
Artificial Neural ◽  
Full Factorial

Automated recognition of process variation patterns using an artificial neural network (ANN) model classifier is a useful technique for multivariate quality control. Proper design of the classifier is critical for achieving effective recognition performance (RP). The existing classifiers were mainly designed empirically. In this research, full factorial design of experiment was utilized for investigating the effect of four design parameters, i.e., recognition window size, training data amount, training data quality and hidden neuron amount. The pattern recognition study focuses on bivariate correlated process mean shifts for cross correlation function, ρ = 0.1 ~ 0.9 and mean shifts, μ = ± 0.75 ~ 3.00 standard deviations. Raw data was used as input representation for a generalized model ANN classifier. The findings suggested that: (i) the best performance for each pattern could be achieved by setting different design parameters through specific classifiers, which (ii) gave superior result (average RP = 98.85%) compared to an empirical design (average RP = 96.5%). This research has provided a new perspective in designing ANN pattern recognition scheme in the field of statistical process control.

scholarly journals IMPLEMENTASI JARINGAN SYARAF TIRUAN DALAM MEMPREDIKSI FREKUENSI RESONANSI ANTENA MIKROSTRIP

2019 ◽  
Vol 12 (1) ◽  
pp. 33-40
Author(s):  
Khairi Budayawan ◽  
Yuhandri Yuhandri ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Resonant Frequency ◽  
Microstrip Antennas ◽  
Training Data ◽  
Backpropagation Algorithm ◽  
Neural Network Learning ◽  
Artificial Neural Network Method ◽  
Dimensional Parameters ◽  
Artificial Neural

The resonant frequency of an antenna is determined by the dimensional parameters and permittivity of the antenna substrate. Generally, to get the resonant frequency, a complex mathematical formula is needed to solve. For this reason, an intelligent method is offered to determine the resonant frequency more easily. In this study, an artificial neural network method with Backpropagation algorithm is used to overcome the problem. The data used were consisting of 80 training data and 15 testing data. The results have shown that the artificial neural network learning method with the backpropagation algorithm was successfully utilized to calculate the resonant frequency of microstrip antennas, where the precision of the resonant frequency obtained of 93.33% at an error of = 1%, and 100% at an error of = 2%.

scholarly journals Prediksi Volume Penggunaan Air PDAM Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation

2018 ◽  
Vol 2 (3) ◽  
Author(s):  
Budy Satria
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Consumption ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Training Data ◽  
Clean Water ◽  
Customer Data ◽  
Artificial Neural Network Method ◽  
Artificial Neural

As the population growth rate in Duri increases, the need for clean water also increases as needed. In Indonesia, PDAM is an institution that regulates and manages the provision of clean water for the community. So the amount of water produced and distributed should be adjusted to the demand for water. However, the problem arises in the form of waste of water at PT. PDAM Duri. Purpose of this study is to predict the amount of water consumption at PT. PDAM Duri by implementing Backpropagation  Artificial Neural Network method. Variables of data taken from customer data were social, general social, household 1, household 2, household 3, commerce 1, commerce 2 and commerce 3. Data used in the prediction process was training data in 2016 and data testing in 2017. Actual amount of data at PT. PDAM Duri City 2016 until 2017 was 2.840.165 when the prediction result using artificial neural network back propagation method was 2.843.388. The number of training epochs was 4595 and the achievement of MSE (Mean Squared Error) on the test was 0,001 and the result of accuracy was 99,99900000%. Final result of this research was artificial neural network using back propagation method could predict the using of water consumption at PT. PDAM  Duri for next year.  

scholarly journals Mengidentifikasi Tanaman Beracun pada Pola Daun dengan Jaringan Syaraf Tiruan Learning Vector Quantification

2019 ◽  
Vol 3 (1) ◽  
pp. 7
Author(s):  
Muhammad Jurnalies Habibie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Poisonous Plants ◽  
Toxic Plants ◽  
Network Method ◽  
Different Types ◽  
Artificial Neural Network Method ◽  
Artificial Neural

Technology nowadays is starting to go very fast, so that all people can use it. Toxic plants are very dangerous if consumed. Therefore to avoid undesirable events, an introduction to the community is needed to find out which plants are poisonous. Plants have many different types to recognize poisonous plants can be seen from the recognition of leaf patterns in these plants. For this reason, in order to determine the use of Learning Vector Quantification artificial neural networks. In this study, the use of input photos obtained from the camera. Photos will be processed later to extract the characteristics. Next, the process of pattern recognition can get the features in the photo. So that later it gets its characteristics. then the classification process uses the Learning Vector Quantification artificial neural network method. This research was conducted to be able to distinguish poisonous plants from those that are not. Which later the data is collected for grouping in accordance with the same data, so that information can be set about the plant.

scholarly journals Implementation of Backpropagation Artificial Network Methods for Early Children’s Intelligence Prediction

2021 ◽  
Vol 328 ◽  
pp. 04033
Author(s):  
I Budiman ◽  
A Mubarak ◽  
S Kapita ◽  
S Do. Abdullah ◽  
M Salmin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Childhood ◽  
Training Data ◽  
Backpropagation Neural Network ◽  
Neural Network Method ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
Backpropagation Artificial Neural Network

Intelligence is the ability to process certain types of information derived from human biological and psychological factors. This study aims to implement a Backpropagation artificial neural network for prediction of early childhood intelligence and how to calculate system accuracy on children's intelligence using the backpropagation artificial neural network method. The Backpropagation Neural Network method is one of the best methods in dealing with the problem of recognizing complex patterns. Backpropagation Neural Networks have advantages because the learning is done repeatedly so that it can create a system that is resistant to damage and consistently works well. The application of the Backpropagation Neural Network method is able to predict the intelligence of early childhood. The results of the calculation of the Backpropagation Artificial Neural Network method from 42 children's intelligence data being tested, with 27 training data and 15 test data, the results obtained 100% accuracy percentage results.

scholarly journals Prediksi Jenis Cacing Nematoda Usus Yang Menginfeksi Siswa Dengan Menggunakan Metoda LVQ

2017 ◽  
Vol 8 (2) ◽  
pp. 170-184
Author(s):  
Erni Rouza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Training Data ◽  
Neural Network Learning ◽  
Intestinal Nematode ◽  
Artificial Neural ◽  
Level Of Training ◽  
Artificial Neural Network Ann

Abstrak-Pada saat ini, Jaringan Syaraf Tiruan (JST) telah banyak menjadi objek penelitian yang menarik, karena penerapannya sangat potensial dalam berbagai bidang sains, salah satu penerapannya didalam memprediksi penyakit. Penelitian ini bertujuan untuk mencoba menerapkan metode Learning vector Quantization (LVQ) dalam memprediksi jenis cacing Nematoda usus yang menginfeksi siswa dari nilai akurasi yang dihasilkan, karena beberapa penelitian menunjukkan bahwa anak usia sekolah dasar merupakan golongan yang sering terkena infeksi cacing usus. Dari hasil pelatihan dan pengujian menggunakan metode Learning Vector Quantization (LVQ) diketahui bahwa tingkat akurasi sesuai dengan hasil sebenarnya dan nilainya konstan, proses cepat hanya membutuhkan waktu paling lama 3 menit dan memberikan hasil yang optimal yaitu tingkat akurasi data latih sebesar 78,6885%, serta 80% untuk data uji. Hal ini menunjukkan bahwa jaringan yang terbentuk sudah cukup baik, akurat dan cepat dalam melakukan pembelajaran terhadap data input yang diberikan dalam memprediksi jenis cacing Nematoda Usus yang menginfeksi siswa. Kata kunci : Cacing Nematoda Usus, Jaringan Syaraf Tiruan, Learning Vector Quantization Abstract- At this time, an Artificial Neural Network (ANN) has been an interesting objects of research, because of application has potential in various fields of science, one application was used to predict diseases. This study aims to try to implement methods Learning vector quantization (LVQ) in predicting the type of Nematode worms that infect the intestines of students from the resulting accuracy value, because some studies show that children of primary school age are often exposed to a class of intestinal worm infections. From the results of the training and testing using methods Learning Vector Quantization (LVQ) note that the level of accuracy in accordance with the actual results and the value of the constant, quick process only takes a maximum of 3 minutes and provide optimal results is the level of training data accuracy of 78.6885%, and 80% for the test data. This indicates that the network is formed is quite good, accurate and fast in doing the learning on the input data given in predicting Intestinal Nematode worm species that infect students. Keywords: Intestinal Netamoda Worms, Artificial Neural Network, Learning Vector Quantization

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

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