HIGH AVAILABILITY IN SERVER CLUSTERS BY USING BACKPROPAGATION NEURAL NETWORK METHOD

Server is a host device applications to serve every request in finding information needs. The server must fully support the services used for the organization's digital needs 24 hours in a day, 7 days in a week, and 365 days in a year. The concept of High Availability is needed to maintain the quality of server services. The algorithm used to build HA can use both classical and modern algorithms. The algorithm used in this research is using backpropagation neural network. In this study, the parameter values to obtain optimal accuracy are learning rate 0.1, training data 80 and test data 20, the number of nodes in hidden layer 4, minimum error 0.0001, and the number of iterations 2500.The best accuracy value using these parameters is 93.79% .

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

Jurnal Sisfokom (Sistem Informasi dan Komputer) ◽

10.32736/sisfokom.v10i1.1105 ◽

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.

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Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach

Knowledge Engineering and Data Science ◽

10.17977/um018v2i12019p1-9 ◽

2019 ◽

Vol 2 (1) ◽

pp. 1

Author(s):

Hijratul Aini ◽

Haviluddin Haviluddin

Keyword(s):

Neural Network ◽

Palm Oil ◽

Good Accuracy ◽

Activation Function ◽

Crude Palm Oil ◽

Backpropagation Neural Network ◽

Neural Network Approach ◽

Learning Function ◽

Hidden Layer ◽

Accuracy Of Prediction

Crude palm oil (CPO) production at PT. Perkebunan Nusantara (PTPN) XIII from January 2015 to January 2018 have been treated. This paper aims to predict CPO production using intelligent algorithms called Backpropagation Neural Network (BPNN). The accuracy of prediction algorithms have been measured by mean square error (MSE). The experiment showed that the best hidden layer architecture (HLA) is 5-10-11-12-13-1 with learning function (LF) of trainlm, activation function (AF) of logsig and purelin, and learning rate (LR) of 0.5. This architecture has a good accuracy with MSE of 0.0643. The results showed that this model can predict CPO production in 2019.

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Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

Applied Sciences ◽

10.3390/app10051657 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1657 ◽

Cited By ~ 3

Author(s):

Jieun Baek ◽

Yosoon Choi

Keyword(s):

Neural Network ◽

Prediction Accuracy ◽

Deep Neural Network ◽

Training Data ◽

Open Pit ◽

Percentage Error ◽

Open Pit Mines ◽

Determination Coefficient ◽

Packet Data ◽

Hidden Layer

This paper proposes a deep neural network (DNN)-based method for predicting ore production by truck-haulage systems in open-pit mines. The proposed method utilizes two DNN models that are designed to predict ore production during the morning and afternoon haulage sessions, respectively. The configuration of the input nodes of the DNN models is based on truck-haulage conditions and corresponding operation times. To verify the efficacy of the proposed method, training data for the DNN models were generated by processing packet data collected over the two-month period December 2018 to January 2019. Subsequently, following training under different hidden-layer conditions, it was observed that the prediction accuracy of morning ore production was highest when the number of hidden layers and number of corresponding nodes were four and 50, respectively. The corresponding values of the determination coefficient and mean absolute percentage error (MAPE) were 0.99% and 4.78%, respectively. Further, the prediction accuracy of afternoon ore production was highest when the number of hidden layers was four and the corresponding number of nodes was 50. This yielded determination coefficient and MAPE values of 0.99% and 5.26%, respectively.

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RECOGNITION OF CONTACT STATE BY USING NEURAL NETWORK FOR MICROMACHINED ARRAY TYPE TACTILE SENSOR

International Journal of Information Acquisition ◽

10.1142/s0219878905000568 ◽

2005 ◽

Vol 02 (03) ◽

pp. 181-190 ◽

Cited By ~ 3

Author(s):

SEIJI AOYAGI ◽

TAKAAKI TANAKA ◽

KENJI MAKIHIRA

Keyword(s):

Neural Network ◽

Rotation Angle ◽

Simulated Data ◽

Tactile Sensor ◽

Training Data ◽

Sensing Element ◽

Array Type ◽

Tactile Pattern ◽

Hidden Layer ◽

Contact Position

In this paper, a force sensing element having a pillar and a diaphragm is proposed and thereafter fabricated by micromachining. Piezo resistors are fabricated on a silicon diaphragm for detecting distortions caused by a force input to a pillar on the diaphragm. Since a practical arrayed sensor consisting of many of this element is still under development, the output of an assumed arrayed type tactile sensor is simulated by FEM (finite element method). Using simulated data, the possibility of tactile pattern recognition using a neural network (NN) is investigated. The learning method of NN, the number of units of the input layer and the hidden layer, as well as the number of training data are investigated for realizing high probability of recognition. The 14 subjects having different shape and size are recognized. This recognition succeeded even if the contact position and the rotation angle of these objects are changed.

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A Clustering Method for Pruning Fully Connected Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.204-210.600 ◽

2011 ◽

Vol 204-210 ◽

pp. 600-603

Author(s):

Gang Li ◽

Xing San Qian ◽

Chun Ming Ye ◽

Lin Zhao

Keyword(s):

Neural Network ◽

Data Mining ◽

Submersible Pump ◽

Clustering Method ◽

Backpropagation Neural Network ◽

Data Set ◽

Initial Network ◽

Sample Data ◽

Hidden Layer ◽

Fully Connected

This paper focuses mainly on a clustering method for pruning Fully Connected Backpropagation Neural Network (FCBP). The initial neural network is fully connected, after training with sample data, a clustering method is employed to cluster weights between input to hidden layer and from hidden to output layer, and connections that are relatively unnecessary are deleted, thus the initial network becomes a PCBP (Partially Connected Backpropagation) Neural Network. PCBP can be used in prediction or data mining more efficiently than FCBP. At the end of this paper, An experiment is conducted to illustrate the effects of PCBP using the submersible pump repair data set.

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MODELS OF GENERATION OF INPUT DATA OF TRAINING OF NEURAL NETWORK MODULES FOR DIAGNOSTIC OF DISEASES IN UROLOGY

RADIOELECTRONIC AND COMPUTER SYSTEMS ◽

10.32620/reks.2019.4.13 ◽

2019 ◽

pp. 116-122

Author(s):

Mykola Ivanovych Fedorenko

Keyword(s):

Neural Network ◽

Mathematical Model ◽

Input Data ◽

Probabilistic Approach ◽

Training Data ◽

Digital Data ◽

Training Methods ◽

Training Procedure ◽

Network Modules ◽

Parameter Values

The subject of the research presented in the article is neural network modules (NNMs), which are used to solve problems in the practice of diagnosing diseases in urology. This work aims to develop a mathematical model for generating a multitude of uroflowmetric parameters, in particular, graphs of uroflowrograms of the required volume, used as input data for NNM training. Objective: to develop a mathematical model for the formation of uroflowmetric parameters using a probabilistic approach based on a uniform "white noise". To develop an effective algorithm for the procedure for generating new parameter values and tools for its implementation. Methods used: NNM training methods, mathematical modeling methods, digital signal processing methods, tools for generating and processing random numerical sequences, digital data filtering methods. The following results were obtained: when creating and implementing a mathematical model for generating a large amount of training data, the requirements of randomness are taken into account when obtaining new values of uroflowmetric parameters. And at the same time, the obtained noise values are filtered to values of a given range, which are percentage-wise comparable to the amplitude value of the uroflowmetric parameter. Conclusions. The scientific novelty of the results is as follows: the NNM training method for recognizing diseases in urology has been improved by developing a mathematical model to generate uroflowmetric parameters for NNM training. The presented model allows you to create the necessary amount of data for training neural network modules in the course of experimental research on the recognition of diseases. The generation of uroflowmetric parameters is based on adding noise to the parameter values. This allows you to change the input data of the NNM training in a given range. This ensures the creation of the required input volume of the NNM training procedure. In the future, this contributes to the testing process of trained neural network modules with reliable information on the diagnosis of diseases in urology.

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Texture Feature On Determining Quantity of Soil Organic Matter For Patchouli Plant Using Backpropagation Neural Network

Journal of Information Technology and Computer Science ◽

10.25126/jitecs.20194168 ◽

2019 ◽

Vol 4 (1) ◽

pp. 1

Author(s):

Candra Dewi ◽

Suci Sundari ◽

Mardji Mardji

Keyword(s):

Neural Network ◽

Organic Matter ◽

Soil Organic Matter ◽

Texture Feature ◽

Texture Features ◽

Training Data ◽

Backpropagation Neural Network ◽

Color Features ◽

Accuracy Result ◽

Occurrence Matrix

Patchouli (Pogostemon Cablin Bent) has higher PA (Patchouli Alcohol) and oil production if grown in soil containing 75% organic matter. One way that can be used to detect the content of organic matter is to use soil images. The problem in the use of soil images is the color of the soil that is almost similar, namely the gradation between dark brown to black. Therefore, color features are not enough to be used as input in the recognition process. For this purposes, texture features are added in this study in addition to color features. The color features are extracted using color moment and the texture features are extracted using Gray Level Co-occurrence Matrix (GLCM). These feature was then chosen to get the best combination as input in the identification process using the Backpropagation Neural Network (BPNN). The system identifies the quantity of soil organic matter into five classes, namely very low, low, medium, high, and very high. The highest accuracy result obtained was 73% and MSE value 0.5122 by using five GLCM features (Angular Second Moment, contrast, correlation, Inverse Difference Moment, and entropy). This result was obtained by using the BPNN parameter, namely learning rate values 0.5, maximum iteration values of 1000, number training data 210, and total test data 12.

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Application of Neural Network Artificial for Monitoring Aroma of Coffe Blending Process

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.2.3.86 ◽

2018 ◽

Vol 2 (3) ◽

pp. 147

Author(s):

Susanti Roza ◽

Zas Ressy Aidha ◽

Milda Yuliza ◽

Suryadi ◽

Surfa Yondri

Keyword(s):

Neural Network ◽

Training Data ◽

Neural Cells ◽

Arabica Coffee ◽

Labview Software ◽

Coffee Powder ◽

Artificial Neural Network Ann ◽

Average Success Rate ◽

Hidden Layer ◽

The One

This study aims to identify the type of coffee powder aroma from the coffee beans blending using backpropagation artificial neural network (ANN). Backpropagation is a controlled training implementing a weight adjustment pattern to achieve a minimum error value between the the predicted and the actual output. Within this study, the coffee aroma testing utilized electronic tasting sensor system consisted of 4 sensors namely TGS 2611, TGS 2620, TGS 2610 and TGS 2602. The coffee aroma monitoring and data collection in this system applied LabVIEW software as a virtual instrumentation. The testing result of this ANN was able to distinguish the coffee variety of Robusta, Arabica coffee powder and the one without any coffee aroma. The backpropagation architecture was formed by 3 layers consisting of 1 input layer with 4 input nerve cells, 1 hidden layer with 8 neural cells, and 2 output layers by applying the backpropagation training algorithm. The training data was taken from 70 data samples of each circumstance of coffee with 5 testing times. The results of the training and testing showed that the established backpropagation was capable to identify and differenciate the coffee powder in accordance with the given input with different average success rate; 91.96% for Robusta coffee, 100 % for Arabica coffee, and no 84.24% for without coffee aroma.

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BRADYCARDIA AND TACHYCARDIA DETECTION SYSTEM WITH ARTIFICIAL NEURAL NETWORK METHOD

Indonesian Journal of Tropical and Infectious Disease ◽

10.20473/ijtid.v3i2.206 ◽

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).

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Evaluasi Performasi Ruang Warna pada Klasifikasi Diabetic Retinophaty Menggunakan Convolution Neural Network

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021834459 ◽

2021 ◽

Vol 8 (3) ◽

pp. 619

Author(s):

Candra Dewi ◽

Andri Santoso ◽

Indriati Indriati ◽

Nadia Artha Dewi ◽

Yoke Kusuma Arbawa

Keyword(s):

Neural Network ◽

Diabetic Retinopathy ◽

Deep Learning ◽

Convolutional Neural Network ◽

Learning Algorithm ◽

Color Space ◽

Training Data ◽

Color Spaces ◽

Deep Learning Algorithm ◽

Optimal Accuracy

Semakin meningkatnya jumlah penderita diabetes menjadi salah satu faktor penyebab semakin tingginya penderita penyakit diabetic retinophaty. Salah satu citra yang digunakan oleh dokter mata untuk mengidentifikasi diabetic retinophaty adalah foto retina. Dalam penelitian ini dilakukan pengenalan penyakit diabetic retinophaty secara otomatis menggunakan citra fundus retina dan algoritme Convolutional Neural Network (CNN) yang merupakan variasi dari algoritme Deep Learning. Kendala yang ditemukan dalam proses pengenalan adalah warna retina yang cenderung merah kekuningan sehingga ruang warna RGB tidak menghasilkan akurasi yang optimal. Oleh karena itu, dalam penelitian ini dilakukan pengujian pada berbagai ruang warna untuk mendapatkan hasil yang lebih baik. Dari hasil uji coba menggunakan 1000 data pada ruang warna RGB, HSI, YUV dan L*a*b* memberikan hasil yang kurang optimal pada data seimbang dimana akurasi terbaik masih dibawah 50%. Namun pada data tidak seimbang menghasilkan akurasi yang cukup tinggi yaitu 83,53% pada ruang warna YUV dengan pengujian pada data latih dan akurasi 74,40% dengan data uji pada semua ruang warna. AbstractIncreasing the number of people with diabetes is one of the factors causing the high number of people with diabetic retinopathy. One of the images used by ophthalmologists to identify diabetic retinopathy is a retinal photo. In this research, the identification of diabetic retinopathy is done automatically using retinal fundus images and the Convolutional Neural Network (CNN) algorithm, which is a variation of the Deep Learning algorithm. The obstacle found in the recognition process is the color of the retina which tends to be yellowish red so that the RGB color space does not produce optimal accuracy. Therefore, in this research, various color spaces were tested to get better results. From the results of trials using 1000 images data in the color space of RGB, HSI, YUV and L * a * b * give suboptimal results on balanced data where the best accuracy is still below 50%. However, the unbalanced data gives a fairly high accuracy of 83.53% with training data on the YUV color space and 74,40% with testing data on all color spaces.

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