scholarly journals Metode Back Propagation pada Jaringan Syaraf Tiruan Digunakan untuk Penilaian Kemampuan Bertahan pada Bayi dengan Berat Lahir Rendah

2017 ◽  
Vol 1 (1) ◽  
pp. 53
Author(s):  
Akik Hidayat ◽  
Juli Rejito ◽  
Sandy P. Kusuma
Keyword(s):  
Data Mining ◽  
Back Propagation

Bayi berat lahir rendah akan mengalami banyak masalah karena belum siapnya hidup diluar kandungan. Hal tersebut  menyebabkan angka kesakitan dan angka kematian pada bayi meningkat. Data mining dapat digunakan untuk mengetahui penilaian kemampuan bertahan lebih dini pada bayi berat lahir rendah. Jaringan syaraf tiruan backpropagation sebagai teknik dari data mining dapat digunakan untuk menganalisis data sehingga dapat memprediksi kemampuan bertahan pada bayi berat lahir rendah. Pada paper ini dibangun suatu sistem berbasis jaringan syaraf tiruan backpropagation untuk menilai tinggi atau rendahnya kemampuan bertahan pada bayi berat lahir rendah. Dari sistem yang dibuat memiliki tingkat keakuratan untuk menilai kemampuan bertahan bayi berat lahir rendah sebesar 83,33%.

scholarly journals Prediction of drilling leakage locations based on optimized neural networks and the standard random forest method

2021 ◽  
Vol 76 ◽  
pp. 24
Author(s):  
Junlin Su ◽  
Yang Zhao ◽  
Tao He ◽  
Pingya Luo
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Back Propagation ◽  
Optimal Solution ◽  
Southwestern China ◽  
Time Model ◽  
Actual Application ◽  
Network Training ◽  
Training Error ◽  
Parameter Values

Circulation loss is one of the most serious and complex hindrances for normal and safe drilling operations. Detecting the layer at which the circulation loss has occurred is important for formulating technical measures related to leakage prevention and plugging and reducing the wastage because of circulation loss as much as possible. Unfortunately, because of the lack of a general method for predicting the potential location of circulation loss during drilling, most current procedures depend on the plugging test. Therefore, the aim of this study was to use an Artificial Intelligence (AI)-based method to screen and process the historical data of 240 wells and 1029 original well loss cases in a localized area of southwestern China and to perform data mining. Using comparative analysis involving the Genetic Algorithm-Back Propagation (GA-BP) neural network and random forest optimization algorithms, we proposed an efficient real-time model for predicting leakage layer locations. For this purpose, data processing and correlation analysis were first performed using existing data to improve the effects of data mining. The well history data was then divided into training and testing sets in a 3:1 ratio. The parameter values of the BP were then corrected as per the network training error, resulting in the final output of a prediction value with a globally optimal solution. The standard random forest model is a particularly capable model that can deal with high-dimensional data without feature selection. To evaluate and confirm the generated model, the model is applied to eight oil wells in a well site in southwestern China. Empirical results demonstrate that the proposed method can satisfy the requirements of actual application to drilling and plugging operations and is able to accurately predict the locations of leakage layers.

scholarly journals Prediction of pile group scour in waves using support vector machines and ANN

2010 ◽  
Vol 13 (4) ◽  
pp. 609-620 ◽  
Author(s):  
Samaneh Ghazanfari-Hashemi ◽  
Amir Etemad-Shahidi ◽  
Mohammad H. Kazeminezhad ◽  
Amir Reza Mansoori
Keyword(s):  
Data Mining ◽  
Support Vector Machines ◽  
Back Propagation ◽  
Pile Group ◽  
Diameter Ratio ◽  
Support Vector ◽  
Scour Depth ◽  
Empirical Methods ◽  
Pile Groups ◽  
Vector Machines

Scour around pile groups is rather complicated and not yet fully understood due to the fact that it arises from the triple interaction of fluid–structure–seabed. In this study, two data mining approaches, i.e. Support Vector Machines (SVM) and Artificial Neural Networks (ANN), were applied to estimate the wave-induced scour depth around pile groups. To consider various arrangements of pile groups in the development of the models, datasets collected in the field and laboratory studies were used and arrangement parameters were considered in the models. Several non-dimensional controlling parameters, including the Keulegan–Carpenter number, pile Reynolds number, Shield's parameter, sediment number, gap to diameter ratio and number of piles were used as the inputs. Performances of the developed SVM and ANN models were compared with those of existing empirical methods. Results indicate that the data mining approaches used outperform empirical methods in terms of accuracy. They also indicate that SVM will provide a better estimation of scour depth than ANN (back-propagation/multi-layer perceptron). Sensitivity analysis was also carried out to investigate the relative importance of non-dimensional parameters. It was found that the Keulegan–Carpenter number and gap to diameter ratio have the greatest effect on the equilibrium scour depth around pile groups.

scholarly journals Correlation Feature Selection (CFS) and Probabilistic Neural Network (PNN) for Diabetes Disease Prediction

2018 ◽  
Vol 7 (3.27) ◽  
pp. 325
Author(s):  
K Kalaiselvi ◽  
P Sujarani
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Feature Selection ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Healthcare Sector ◽  
Disease Prediction ◽  
Two Stages ◽  
Day By Day

The healthcare sector is a broad area with the abundance of patient information, which creates enormously large records day by day. Though the scientific industry is rich in information but it is poor in knowledge. Diabetics are considered as a primary health issue of the world. As per the WHO 2014 survey According to WHO 2014 report, over 422 million people are affected from the diabetics globally. In the minimization of massive investigations implied on the patients, the data mining uses many mechanisms and strategies to diagnose the diabetic problem. The main objective of this proposal is to introduce assemble Data Mining based Diabetes Disease Prediction System which provides a detailed analysis of diabetics using the database of diabetics patient. The formulated work comprises of two stages such as feature selection ad prediction methods which are made known to maximize the outputs of diabetes disease prediction. Initially Correlation Feature Selection (CFS) is formulated to identify the salient features for the diabetic repository. The identified features are fed into the classifier named Probabilistic Neural Network (PNN) classifier. As the diabetic of the patient is classified using PNN meanwhile the accuracy can be fine – tuned when using the identified features. Depending on the category of data, the diabetic information is gathered from the learning repository. The outputs are correlated with the current algorithms namely Back Propagation Neural Network (BPNN), Multilayer Perceptron, Neural Network (MLPNN) were used to fetch the outputs.  

Application of Data Mining Technology in Structural Strength Design

2007 ◽  
Vol 353-358 ◽  
pp. 2770-2773
Author(s):  
Lei Wang ◽  
Jia Li ◽  
Chen Gen Wang
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Bp Neural Network ◽  
Structural Strength ◽  
Back Propagation ◽  
Engineering Application ◽  
The Finite Element Method ◽  
Weld Region ◽  
Strength Design ◽  
Engine Rotor

Based on neural networks, the present paper gives an engineering application of data mining. The back propagation (BP) neural network is used as the algorithm of data mining. Then the effects of structural technologic parameters on stress in the weld region of the shield engine rotor in a submarine are analyzed. The mined data come from the numerical simulations of the finite element method. The effects of different parameters on the stress in the weld region are achieved from the results of the data mining. The discovered knowledge is beneficial to the security improvement of structural strength design for the engine rotor.

scholarly journals A Novel Classification Approach through Integration of Rough Sets and Back-Propagation Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Lei Si ◽  
Xin-hua Liu ◽  
Chao Tan ◽  
Zhong-bin Wang
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Bp Neural Network ◽  
Rough Sets ◽  
Back Propagation ◽  
Pso Algorithm ◽  
Back Propagation Neural Network ◽  
Prototype System ◽  
Useful Knowledge ◽  
Rules Extraction

Classification is an important theme in data mining. Rough sets and neural networks are the most common techniques applied in data mining problems. In order to extract useful knowledge and classify ambiguous patterns effectively, this paper presented a hybrid algorithm based on the integration of rough sets and BP neural network to construct a novel classification system. The attribution values were discretized through PSO algorithm firstly to establish a decision table. The attribution reduction algorithm and rules extraction method based on rough sets were proposed, and the flowchart of proposed approach was designed. Finally, a prototype system was developed and some simulation examples were carried out. Simulation results indicated that the proposed approach was feasible and accurate and was outperforming others.

Predicting Forest Fire Hazards Using Data Mining Techniques: Decision Tree and Neural Networks

2014 ◽  
Vol 1051 ◽  
pp. 466-470 ◽  
Author(s):  
Nizar Hamadeh ◽  
Ali Karouni ◽  
Bassam Daya
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Relative Humidity ◽  
Forest Fires ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Training Algorithms ◽  
Fire Hazards ◽  
Data Mining Techniques ◽  
Using Data

Lebanon is known as a tourist destination for its scenic green mountains but the fires have been threatening this green forestry all over the world. The consequences of forest fires are disastrous on the natural environment and ecological systems, not to mention the population, by worsening poverty and lowering the quality of life. Two data mining techniques are used for the purpose of prediction and decision-making: Decision trees and back propagation forward neural networks. Four meteorological attributes are utilized: temperature, relative humidity, wind speed and daily precipitation. The obtained tree drawn from applying the first algorithm could classify these attributes from the most significant to the least significant and better foretell fire incidences. Adopting neural networks with different training algorithms shows that networks with 2 inputs only (temperature and relative humidity) retrieve better results than 4-inputs networks with less mean squared error. Feed forward and Cascade forward networks are under scope, with the use of different training algorithms.

scholarly journals PREDICTION OF HUMAN HEART DISEASE

2021 ◽  
Vol 6 (5) ◽  
Author(s):  
Mohith N Raate ◽  
Dr. Kiran V
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Heart Disease ◽  
Support Systems ◽  
Heart Diseases ◽  
Back Propagation ◽  
Clinical Decision Support Systems ◽  
Principal Component ◽  
Clinical Decision ◽  
Support Vector

Data mining techniques have been widely used in clinical decision support systems for prediction and diagnosis of various diseases with good accuracy. These techniques have been very effective in designing clinical support systems because of their ability to discover hidden patterns and relationships in medical data. One of the most important applications of such systems is in diagnosis of heart diseases because it is one of the leading causes of deaths all over the world. Almost all systems that predict heart diseases use clinical dataset having parameters and inputs from complex tests conducted in labs. None of the system predicts heart diseases based on risk factors such as age, blood pressure, fasting blood sugar, chest pain etc. Heart disease patients have lot of these visible risk factors in common which can be used very effectively for diagnosis. System based on such risk factors would not only help medical professionals but it would give patients a warning about the probable presence of heart disease even before he visits a hospital or goes for costly medical check-ups. Hence this paper presents a technique for prediction of heart disease using major risk factors. This technique involves two most successful data mining tools, Support vector machine and Principal component analysis. The hybrid system implemented uses the global optimization advantage PCA for initialization of neural network weights. The learning is fast, more stable and accurate as compared to back propagation.

scholarly journals Prediction Model for Financial Distress Using Proposed Data Mining Approach

2019 ◽  
Vol 11 (2) ◽  
pp. 37-44 ◽  
Author(s):  
Raghad Mohammed Hadi ◽  
Shatha H. Jafer Al-khalisy ◽  
Najlaa Abd Hamza3
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Financial Distress ◽  
Financial Analysis ◽  
Stock Exchange ◽  
Back Propagation ◽  
Data Mining Approach ◽  
Financial Failure ◽  
Great Discovery

The problem of financial distress researches are the lack of awareness of banks about the risks of financial failure and its impact on the continuity of its activity in the future, as the traditional methods used to predict financial failure through financial analysis based on financial ratios in a single result gives misleading results cannot be relied upon to judge the continuity of the activity of banks, With an increase in the number of failed banks and their inability to continue. Which requires the discovery of modern techniques that serve as an early warning of the possibility of failure and lack of continuity. The research aims to apply data mining technology to predict the financial failure of banks, and how it can provide information that helps to judge the extent to which banks continue to operate. This effort suggested founded back propagation artificial neural network to build predict system. The proposed module evaluated with banks fromFree Iraq Stock Exchange dataset the investigational outcomes displays capable method to identify failure banks with great discovery rate and small wrong terror rate.

scholarly journals Energy Efficient Data Mining Approach for Estimating the Diabetes

2021 ◽  
Vol 3 (2) ◽  
pp. 110-123
Author(s):  
Rajesh Sharma R
Keyword(s):  
Data Mining ◽  
Organ Failure ◽  
Human Body ◽  
Healthy Lifestyle ◽  
Back Propagation ◽  
Primary Data ◽  
Diabetic Patients ◽  
Medical Practitioners ◽  
Infected People ◽  
Balanced Diet

Diabetes is a major cause of organ failure in the human body, and it is one of the leading causes of organ failure. As of now, there is no preventive medicine or vaccine for diabetes. As a result, people all around the world are accustomed to living with diabetes for the rest of their lives. Medical practitioners advise diabetic patients to have a healthy lifestyle that includes regular exercise and a well-balanced diet in order to prevent the effects of diabetes from spreading to other organs of the human body. In most cases, the diabetes is spreading like a heredity disease to the infected people and even to children and it can’t be estimated priory. In recent days, the deep learning algorithms are widely used to estimate the forthcoming effects of several problems by using the data mining process. In the proposed work, the performance of deep ANN and back propagation ANN is considered for estimating diabetes from several primary data factors obtained from a publicly available dataset called Pima Indian diabetes dataset.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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