scholarly journals PREDIKSI PENYAKIT TUBERCULOSIS PARU (TB PARU) MENGGUNAKAN METODE LEARNING VEKTOR QUANTIZATION (LVQ)

2018 ◽  
Vol 15 (1) ◽  
pp. 20-27
Author(s):  
A W Rahmadani ◽  
A I Jaya ◽  
N Nacong
Keyword(s):  
Pulmonary Disease ◽  
Vector Quantization ◽  
Medical Records ◽  
Disease Risk ◽  
Health Centers ◽  
Training Data ◽  
Shortness Of Breath ◽  
Pulmonary Tb ◽  
Testing Data ◽  
Weight Classification

Tuberculosis pulmonary (TB pulmonary) is a contagious disease that attacks the lungs that can spread through the air when a person active TB cough, sneeze or talk. This study aims to predict Tuberculosis pulmonary disease  using Learning Vector quantization based on data from the medical records of the health centers kamonji, Palu city. The study was conducted using 8 TB pulmonary disease risk factors which are age, gender, fever, long cough, cough, chest pain, shortness of breath, and decreased body weight. Classification is done by using 100 data consisting of 80 training data and 20 testing data. Results of the study showed that tested all the data correctly with rank of accuracy is 100%.

scholarly journals Klasifikasi Multi Output pada Harga Smartphone Menggunakan Learning Vector Quantization (LVQ) dan Backpropagation (BP)

2021 ◽  
Vol 6 (2) ◽  
pp. 14-19
Author(s):  
Dinita Rahmalia ◽  
Mohammad Syaiful Pradana ◽  
Teguh Herlambang
Keyword(s):  
Vector Quantization ◽  
Error Rate ◽  
Gradient Descent ◽  
Euclidean Distance ◽  
Learning Vector Quantization ◽  
Training Data ◽  
Price Range ◽  
Testing Data ◽  
Internal Storage ◽  
Simulation Results

There are many smartphones with various price sold in market. The price of smartphone is affected by some components such as weight, internal storage, memory (RAM), rear camera, front camera and brands. There are two methods for classifying price class of smartphone in market such as Learning Vector Quantization (LVQ) and Backpropagation (BP). From classifying price class of smartphone in market using LVQ and BP, there are the differences on the both of them. LVQ classifies price range of smartphone by euclidean distance of weight and data on its iteration. BP classifies price range of smartphone by gradient descent of target and output on its iteration. In multi output classification, one object may have multi output. Based on simulation results, BP gives the better accuracy and error rate in training data and testing data than LVQ.  

scholarly journals Learning Vector Quantization 3 (LVQ3) and Spatial Fuzzy C-Means (SFCM) for Beef and Pork Image Classification

2018 ◽  
Vol 1 (2) ◽  
pp. 60 ◽  
Author(s):  
Jasril Jasril ◽  
Suwanto Sanjaya
Keyword(s):  
Image Segmentation ◽  
Vector Quantization ◽  
Learning Rate ◽  
Training Data ◽  
Muslim Country ◽  
Test Results ◽  
Fuzzy C Means ◽  
Testing Data ◽  
Total Data

Base on some cases in Indonesia, meat sellers often mix beef and pork. Indonesia is a predominantly Muslim country. Pork is forbidden in Islam. In this research, the classification of beef and pork image was performed. Spatial Fuzzy C-Means is used for image segmentation. GLCM and HSV are used as a feature of segmentation results. LVQ3 is used as a method of classification. LVQ3 parameters tested were the variety of learning rate values and window values. The learning rate values used is 0.0001; 0.01; 0.1; 0.4; 0.7; 0.9 and the window values used is 0.0001; 0.4; 0.7. The training data used is 90% of the total data, and the testing data used is 10%. Maximum epoch used is 1000 iterations. Based on the test results, the highest accuracy was 91.67%.

An Analog Circuit Fault Diagnosis Approach Based on Wavelet-based fractal analysis and Multiple Kernel SVM

2020 ◽  
Vol 13 ◽  
Author(s):  
Jianfeng Jiang
Keyword(s):  
Fault Diagnosis ◽  
Fractal Analysis ◽  
Analog Circuit ◽  
Training Data ◽  
Support Vector ◽  
Pass Filter ◽  
Multiple Kernel ◽  
Testing Data ◽  
Circuit Fault Diagnosis ◽  
Diagnosis Approach

Objective: In order to diagnose the analog circuit fault correctly, an analog circuit fault diagnosis approach on basis of wavelet-based fractal analysis and multiple kernel support vector machine (MKSVM) is presented in the paper. Methods: Time responses of the circuit under different faults are measured, and then wavelet-based fractal analysis is used to process the collected time responses for the purpose of generating features for the signals. Kernel principal component analysis (KPCA) is applied to reduce the features’ dimensionality. Afterwards, features are divided into training data and testing data. MKSVM with its multiple parameters optimized by chaos particle swarm optimization (CPSO) algorithm is utilized to construct an analog circuit fault diagnosis model based on the testing data. Results: The proposed analog diagnosis approach is revealed by a four opamp biquad high-pass filter fault diagnosis simulation. Conclusion: The approach outperforms other commonly used methods in the comparisons.

scholarly journals Clinical characteristics of inpatients with coronavirus disease 2019 (COVID-19) in Sichuan province

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wen Wang ◽  
Lei Chen ◽  
Qiao He ◽  
Mingqi Wang ◽  
Mei Liu ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Clinical Features ◽  
Medical Records ◽  
Clinical Characteristics ◽  
Sichuan Province ◽  
Chest Ct ◽  
Shortness Of Breath ◽  
Course Of Disease ◽  
West China ◽  
Radiologic Abnormality

Abstract Background The outbreak of COVID-19 has resulted in serious concerns in China and abroad. To investigate clinical features of confirmed and suspected patients with COVID-19 in west China, and to examine differences between severe versus non-severe patients. Methods Patients admitted for COVID-19 between January 21 and February 11 from fifteen hospitals in Sichuan Province, China were included. Experienced clinicians trained with methods abstracted data from medical records using pre-defined, pilot-tested forms. Clinical characteristics between severe and non-severe patients were compared. Results Of the 169 patients included, 147 were laboratory-confirmed, 22 were suspected. For confirmed cases, the most common symptoms from onset to admission were cough (70·7%), fever (70·5%) and sputum (33·3%), and the most common chest CT patterns were patchy or stripes shadowing (78·0%); throughout the course of disease, 19·0% had no fever, and 12·4% had no radiologic abnormality; twelve (8·2%) received mechanical ventilation, four (2·7%) were transferred to ICU, and no death occurred. Compared to non-severe cases, severe ones were more likely to have underlying comorbidities (62·5% vs 26·2%, P = 0·001), to present with cough (92·0% vs 66·4%, P = 0·02), sputum (60·0% vs 27·9%, P = 0·004) and shortness of breath (40·0% vs 8·2%, P <  0·0001), and to have more frequent lymphopenia (79·2% vs 43·7%, P = 0·003) and eosinopenia (84·2% vs 57·0%, P = 0·046). Conclusions The symptoms of patients in west China were relatively mild, and an appreciable proportion of infected cases had no fever, warranting special attention.

Improving Pattern Recognition Rate by Gaussian Hopfield Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 2042-2045 ◽  
Author(s):  
Shang Jen Chuang ◽  
Chiung Hsing Chen ◽  
Chien Chih Kao ◽  
Fang Tsung Liu
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Gaussian Distribution ◽  
Test Pattern ◽  
Recognition Rate ◽  
Hopfield Neural Network ◽  
Training Data ◽  
New Method ◽  
Gaussian Filter ◽  
Testing Data

English letters cannot be recognized by the Hopfield Neural Network if it contains noise over 50%. This paper proposes a new method to improve recognition rate of the Hopfield Neural Network. To advance it, we add the Gaussian distribution feature to the Hopfield Neural Network. The Gaussian filter was added to eliminate noise and improve Hopfield Neural Network’s recognition rate. We use English letters from ‘A’ to ‘Z’ as training data. The noises from 0% to 100% were generated randomly for testing data. Initially, we use the Gaussian filter to eliminate noise and then to recognize test pattern by Hopfield Neural Network. The results are we found that if letters contain noise between 50% and 53% will become reverse phenomenon or unable recognition [6]. In this paper, we propose to uses multiple filters to improve recognition rate when letters contain noise between 50% and 53%.

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