scholarly journals Identification the number of Mycobacterium tuberculosis based on sputum image using local linear estimator

2020 ◽  
Vol 9 (5) ◽  
pp. 2109-2116
Author(s):  
Nur Chamidah ◽  
Yolanda Swastika Yonani ◽  
Elly Ana ◽  
Budi Lestari
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Partial Least Square ◽  
Least Square ◽  
Linear Estimator ◽  
Discrete Wavelet ◽  
Linear Regression Method ◽  
Local Linear ◽  
Proposed Model ◽  
Local Linear Estimator ◽  
Long Time

Infectious disease caused by infection of Mycobacterium tuberculosis is called tuberculosis (TB). A common method in detecting TB is by identifying number of mycobacterium TB in sputum manually. Unfortunately, manually calculation by pathologists take a relatively long time. Previous researches on TB bacteria were still limited to detect the absence or presence of mycobacterium TB in images of sputum. This research aims are identifying number of mycobacterium TB and determining accuracy of classification TB severity by approaching nonparametric Poisson regression model and applying an estimator namely local linear. Steps include processing of image, reducing of dimension by applying partial least square and discrete wavelet transformation, and then identifying the number of mycobacterium TB by using the proposed model approach. In this research, we get deviance values of 28.410 for nonparametric and 93.029 for parametric approaches and the average of classification accuracy values for 4 iterations of 92.75% for nonparametric and 85.5% for parametric approaches. Thus, for identifying many of mycobacterium TB met in images of sputum and classifying of TB severity, the proposed identifying method gives higher accuracy and shorter time in identifying number of mycobacterium TB than parametric linear regression method.

Identification Model in Frying Oil Based on Interval Partial Least Squares Regression Analysis

2013 ◽  
Vol 718-720 ◽  
pp. 792-796
Author(s):  
Ming Fu Zhao ◽  
Zheng Wei Zhang ◽  
Nian Wang
Keyword(s):  
Partial Least Square ◽  
Frying Oil ◽  
Least Square ◽  
Absorption Peak ◽  
Least Squares Regression ◽  
Spectral Absorption ◽  
Characteristic Absorption Peak ◽  
Time Usage ◽  
Long Time ◽  
Optimum Interval

As we known frying oil belongs to waste oils when it has been excessive used, long time usage also cause serious effect. This paper chooses dragon fish oil which was fried 10 times excessively. We can extract the characteristic in absorption peak (323.391.443nm) of spectral absorption value as the dependent variable. Then build the interval partial least square model, Through the MATLAB, we can extract the optimum interval is 5 and the best factor of wavelength range is 7. Prediction of correlation coefficient for R is 0.998. By the cross validation verification Q22=-0.3461<0.0975, we can get the establishment of PLS equation as Y1, Y2, Y3. The model which we build can predict the content situation of characteristic absorption peak in frying oil effectively.

scholarly journals PENGARUH PERCEIVED VALUE, BRAND IMAGE TERHADAP CUSTOMER LOYALTY MELALUI CUSTOMER ENGAGEMENT SEBAGAI VARIABEL MEDIASI (STUDI PADA PELANGAN INDIHOME PT. TELKOM MANADO)

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Amriah Amir ◽  
Silvya L. Mandey ◽  
Hendra N. Tawas
Keyword(s):  
Customer Loyalty ◽  
Structural Equation ◽  
Brand Image ◽  
Perceived Value ◽  
Research Data ◽  
Partial Least Square ◽  
Least Square ◽  
Customer Engagement ◽  
Equation Modeling ◽  
Long Time

The study aims to analyze the effect of Perceived Value and Brand Image on Customer Loyalty with Customer Engagement as a Mediation Variable for Indihome Customers at PT. Telkom Manado. The population of this research were Indihome customers in Manado. Sampling was carried out based on Isaac Michael's table of 267 respondents. The research data were analyzed using PLS SEM (Partial Least Square - Structural Equation Modeling) with SmartPLS 3.0 software. The results showed that Perceived Value and Brand Image had an effect on Customer Engagement but did not directly influence Customer Loyalty. Perceived Value and Brand Image affect Customer Loyalty through Customer Engagement with full mediation. Customer Engagement affects Customer Loyalty. Indihome at Telkom Manado has already good at Perceived Value and Brand Image.  However, the product still couldn't drive the loyal customers to buy any add-on services or ensure to a long time subscription. The company need to find strategies that can improve the customer engagement and customer loyalty with Indihome as a product. Keywords : Perceived Value, Brand Image, Customer Engagement, Customer Loyalty

scholarly journals LOCAL LINEAR FITTING UNDER NEAR EPOCH DEPENDENCE: UNIFORM CONSISTENCY WITH CONVERGENCE RATES

2012 ◽  
Vol 28 (5) ◽  
pp. 935-958 ◽  
Author(s):  
Degui Li ◽  
Zudi Lu ◽  
Oliver Linton
Keyword(s):  
Convergence Rates ◽  
Regression Function ◽  
Linear Estimator ◽  
Uniform Consistency ◽  
Linear Fitting ◽  
Semiparametric Modeling ◽  
Local Linear Fitting ◽  
Local Linear ◽  
Local Linear Estimator ◽  
Nonparametric Kernel

Local linear fitting is a popular nonparametric method in statistical and econometric modeling. Lu and Linton (2007, Econometric Theory23, 37–70) established the pointwise asymptotic distribution for the local linear estimator of a nonparametric regression function under the condition of near epoch dependence. In this paper, we further investigate the uniform consistency of this estimator. The uniform strong and weak consistencies with convergence rates for the local linear fitting are established under mild conditions. Furthermore, general results regarding uniform convergence rates for nonparametric kernel-based estimators are provided. The results of this paper will be of wide potential interest in time series semiparametric modeling.

A COMPARITIVE ANALYSIS AND TIME SERIES FORECASTING OF MONTHLY STREAM FLOW DATA USING HYBRID MODEL

2015 ◽  
Vol 76 (13) ◽  
Author(s):  
Siraj Muhammed Pandhiani ◽  
Ani Shabri
Keyword(s):  
Hybrid Model ◽  
Stream Flow ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Least Square ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Flow Data ◽  
Indus River ◽  
Proposed Model

In this study, new hybrid model is developed by integrating two models, the discrete wavelet transform and least square support vector machine (WLSSVM) model. The hybrid model is then used to measure for monthly stream flow forecasting for two major rivers in Pakistan. The monthly stream flow forecasting results are obtained by applying this model individually to forecast the rivers flow data of the Indus River and Neelum Rivers. The root mean square error (RMSE), mean absolute error (MAE) and the correlation (R) statistics are used for evaluating the accuracy of the WLSSVM, the proposed model. The results are compared with the results obtained through LSSVM. The outcome of such comparison shows that WLSSVM model is more accurate and efficient than LSSVM.

Spatiotemporal forecasting for groundwater level using a WT-LSTM model

2020 ◽  
Author(s):  
wei qin ◽  
Chengpeng Lu ◽  
Long Sun ◽  
Jiayun Lu
Keyword(s):  
Neural Network ◽  
Groundwater Level ◽  
Wavelet Transforms ◽  
Groundwater Resources ◽  
Arima Model ◽  
Discrete Wavelet ◽  
Linear Regression Method ◽  
Ann Model ◽  
Periodic Term ◽  
Proposed Model

&lt;p&gt;Accurate groundwater level forecasting models is essential to ensure the sustainable utilization and efficient protection of groundwater resources. In this paper, a novel method for groundwater level forecasting is proposed on the basis of coupling discrete wavelet transforms (WT) and long and short term memory neural network (LSTM) . In this model, the wavelet transform is used to decompose the cumulative displacement into the term of trend and term of periodicity . The trend term reflects the long-term tendency of groundwater level variation, which is simulated by a linear regression method. The periodic term driven by external factors such as rainfall, the river stage and the distance from river, is modelled using a LSTM method. The distance from river and the distance from observation wells are used for spatiotemporal model interpretation. Finally, the trend term and periodic term are superposed to achieve the cumulative spatiotemporal prediction of groundwater level. A typical study area located in Haihe basin is taken as an example to validate the performance of the proposed model. The proposed mode (WT-LSTM) is compared with the regular artificial neural network (ANN) model and autoregressive integrated moving average (ARIMA) model. The results show that the prediction accuracy of WT-LSTM model is higher than ANN model and ARIMA model, especially during the flood period. Furthermore, the spatiotemporal groundwater level forecasting is not only included the observation of groundwater and precipitation, but should also take the influence factors of surface water into consideration. The proposed model gives a new sight in the prediction of groundwater level.&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document