scholarly journals Identifikasi Mutu Buah Pepaya California (Carica Papaya L.) Menggunakan Metode Jaringan Syaraf Tiruan

2021 ◽  
Vol 10 (1) ◽  
pp. 113-119
Author(s):  
Muhammad Ezar Al Rivan ◽  
Gabriela Repca Sung
Keyword(s):  
Neural Network ◽  
Carica Papaya ◽  
Training Data ◽  
Backpropagation Neural Network ◽  
Neural Network Method ◽  
Network Method ◽  
Hidden Layer ◽  
The Tropics

Papaya is one of the fruits that grows in the tropics area, one of the kinds that people’s love the most is papaya California. The quality identification of papaya California fruit can be measured using color, defect, and size. Color, defect and size extracted from image of papaya. The dataset that used in this research are 150 images papaya California. The dataset consist of 3 quality there are good, fair and low.  Identification of papaya using the backpropagation neural network method with 17 training function in each training data with 3 different neurons in the hidden layer. The best result of the test is using training function trainrp with 10 neurons is 81,33% for accuracy, 73,37% for precision, and 72% for recall, with 20 neurons is 82,67% for accuracy, 75,24% for precision, and 74% for recall, and with 25 neurons is 80,89% for accuracy, 74,42% for precision, and 71,33% for recall.

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.

Tumor diagnosis using the backpropagation neural network method

1998 ◽  
Author(s):  
Lixing Ma ◽  
Sydney Sukuta ◽  
Reinhard F. Bruch ◽  
Natalia I. Afanasyeva ◽  
Carl G. Looney
Keyword(s):  
Neural Network ◽  
Tumor Diagnosis ◽  
Backpropagation Neural Network ◽  
Neural Network Method ◽  
Network Method

GIẢI PHƯƠNG TRÌNH ĐẠO HÀM RIÊNG SỬ DỤNG MẠNG NEURAL NHÂN TẠO

2020 ◽  
Vol 45 (03) ◽  
Author(s):  
HO DAC QUAN ◽  
HUYNH TRUNG HIEU
Keyword(s):  
Neural Network ◽  
Differential Equation ◽  
Neural Networks ◽  
Partial Differential Equation ◽  
Partial Differential ◽  
Neural Network Method ◽  
Network Method ◽  
Hidden Layer

Phương trình đạo hàm riêng đã được ứng dụng rộng rãi trong các lĩnh vực khác nhau của đời sống như vật lý, hóa học, kinh tế, xử lý ảnh vv. Trong bài báo này chúng tôi trình bày một phương pháp giải phương trình đạo hàm riêng (partial differential equation - PDE) thoả điều kiện biên Dirichlete sửdụng mạng neural truyền thẳng một lớp ẩn (single-hidden layer feedfordward neural networks - SLFN) gọi là phương pháp mạng neural (neural network method – NNM). Các tham số của mạng neural được xác định dựa trên thuật toán huấn luyện mạng lan truyền ngược (backpropagation - BP). Kết quả nghiệm PDE thu được bằng phương pháp NNM chính xác hơn so với nghiệm PDE giải bằng phương pháp sai phân hữu hạn.

scholarly journals Smart guided missile using accelerometer and gyroscope based on backpropagation neural network method for optimal control output feedback

2020 ◽  
Vol 11 (2) ◽  
pp. 55
Author(s):  
Kamil Faqih ◽  
Sujito Sujito ◽  
Siti Sendari ◽  
Faiz Syaikhoni Aziz
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Data Processing ◽  
Output Feedback ◽  
Training System ◽  
Backpropagation Neural Network ◽  
Target Speed ◽  
Neural Network Method ◽  
Network Method ◽  
Control Output

As a maritime country with a large area, besides the need to defend itself with the military, it also needs to protect itself with aerospace technology that can be controlled automatically. This research aims to develop an air defense system that can control guided missiles automatically with high accuracy. The right method can provide a high level of accuracy in controlling missiles to the targeted object. With the backpropagation neural network method for optimal control output feedback, it can process information data from the radar to control missile’s movement with a high degree of accuracy. The controller uses optimal control output feedback, which is equipped with a lock system and utilizes an accelerometer that can detect the slope of the missile and a gyroscope that can detect the slope between the target direction of the missile to follow the target, control the position, and direction of the missile. The target speed of movement can be easily identified and followed by the missile through the lock system. Sampling data comes from signals generated by radars located in defense areas and from missiles. Each part’s data processing speed is calculated using a fast algorithm that is reliable and has a level of accuracy and fast processing. Data processing impacts on the accuracy of missile movements on any change in the position and motion of targets and target speed. Improved maneuvering accuracy in the first training system can detect 1000 files with a load of 273, while in the last training, the system can detect 1000 files without a load period. So the missile can be guided to hit the target without obstacles when maneuvering.

scholarly journals BRADYCARDIA AND TACHYCARDIA DETECTION SYSTEM WITH ARTIFICIAL NEURAL NETWORK METHOD

2016 ◽  
Vol 3 (2) ◽  
pp. 86
Author(s):  
Delima Ayu S ◽  
Franky Arisgraha ◽  
Retna Apsari
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Embedded System ◽  
Medical Devices ◽  
Detection System ◽  
Training Data ◽  
Backpropagation Neural Network ◽  
Neural Network Classification ◽  
Network Method ◽  
Artificial Neural Network Method

Heart disease is one disease with high mortality rate in the world. Based on WHO records from 112 countries at 2004, the rate is 29% of all deaths each year. Medical devices are necessary to diagnose one's health as an indication of a disease. Nowadays, Indonesia still imports medical devices, for the diagnosis of heart failure, from abroad. This research aims to assist the monitoring of cardiac patients with bradycardia and tachycardia appearances of message condition patient’s heart rate at the same time. The results were displayed with the output of bradycardia condition of the heart rate (heart rate less than 60 beats per minute) or tachycardia (heart rate over 100 beats per minute). The system displayed the data read from the heart to the PC embedded system to monitor the condition of the patients under decisions based on backpropagation neural network. Classification system could be performed quite well, training data and by testing the 10 pieces, the optimal weight gain was 1727 iteration, the learning rate was 0.1122, and the error was below 0.001 (0.0009997).

scholarly journals The application of backpropagation neural network method to estimate the sediment loads

2017 ◽  
Vol 101 ◽  
pp. 05016 ◽  
Author(s):  
Taufik Ari Gunawan ◽  
M. Syahril Badri Kusuma ◽  
M. Cahyono ◽  
Joko Nugroho
Keyword(s):  
Neural Network ◽  
Backpropagation Neural Network ◽  
Neural Network Method ◽  
Sediment Loads ◽  
Network Method

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

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