scholarly journals Efficient Model Selection for Moisture Ratio Removal of Seaweed Using Hybrid Of Sparse And Robust Regression Analysis

2021 ◽  
pp. 669-681
Author(s):  
Anam Javaid ◽  
Mohd. Tahir Ismail ◽  
M.K.M. Ali
Keyword(s):  
Relative Humidity ◽  
Model Selection ◽  
Robust Regression ◽  
Real Life ◽  
Large Data ◽  
Moisture Ratio ◽  
Least Square ◽  
Percentage Error ◽  
Ambient Relative Humidity ◽  
Drying Behavior

The Internet of things ((IoT) consisted of physical devices networks such as sensors, home appliances, electronics, and software’s. It enables us to collect and exchange data in several fields. After data collection from IoT, variable selection is considered a major problem because many variables are involved in real life datasets. The current study focused on large data analysis of the problem of model selection, including interaction terms. The dataset used in this study is taken from solar drier with moisture ratio removal (%) as dependent variable while ambient temperature, chamber temperature, collector temperature, chamber relative humidity, ambient relative humidity, and solar radiation as independent variables. LASSO with Huber M, LASSO with Hampel M and LASSO with Bisquare M are proposed in this study. Comparison of proposed techniques are made with ridge regression and OLS (ordinary least square) after multicollinearity test and coefficient test. MAPE (mean absolute percentage error) is calculated for the efficient selected model to forecast. As a result, the model using LASSO with Bisquare-M provides a minimum MAPE value for the best efficient model. Thus, the resulting model with the selected variables can be used to predict Moisture Ratio Removal (%) to determine seaweed drying behavior.

scholarly journals Regression-in-Ratio Estimators for Population Mean by Using Robust Regression in Two Phase Sampling

2021 ◽  
Author(s):  
Aamir Raza ◽  
Muhammad Noor-ul-Amin
Keyword(s):  
Simulation Study ◽  
Robust Regression ◽  
Real Life ◽  
Least Square Method ◽  
Least Square ◽  
Two Phase ◽  
Extensive Simulation ◽  
Population Mean ◽  
Phase Sampling ◽  
Two Phase Sampling

The estimation of population mean is not meaningful using ordinary least square method when data contains some outliers. In the current study, we proposed efficient estimators of population mean using robust regression in two phase sampling. An extensive simulation study is conduct to examine the efficiency of proposed estimators in terms of mean square error (MSE). Real life example and extensive simulation study are cited to demonstrate the performance of the proposed estimators. Theoretical example and simulation studies showed that the suggested estimators are more efficient than the considered estimators in the presence of outliers.

scholarly journals A New Efficient Redescending M-Estimator for Robust Fitting of Linear Regression Models in the Presence of Outliers

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dost Muhammad Khan ◽  
Muhammad Ali ◽  
Zubair Ahmad ◽  
Sadaf Manzoor ◽  
Sundus Hussain
Keyword(s):  
Robust Regression ◽  
Real Life ◽  
Least Square Method ◽  
Least Square ◽  
Estimation Methods ◽  
Data Generation ◽  
Linear Regression Models ◽  
Robust Fitting ◽  
M Estimator ◽  
T Distribution

Robust regression is an important iterative procedure that seeks analyzing data sets that are contaminated with outliers and unusual observations and reducing their impact over regression coefficients. Robust estimation methods have been introduced to deal with the problem of outliers and provide efficient and stable estimates in their presence. Various robust estimators have been developed in the literature to restrict the unbounded influence of the outliers or leverage points on the model estimates. Here, a new redescending M-estimator is proposed using a novel objective function with the prime focus on getting highly robust and efficient estimates that give promising results. It is evident from the results that, for normal and clean data, the proposed estimator is almost as efficient as ordinary least square method and, however, becomes highly resistant to outliers when it is used for contaminated datasets. The simulation study is being carried out to assess the performance of the proposed redescending M-estimator over different data generation scenarios including normal, t-distribution, and double exponential distributions with different levels of outliers’ contamination, and the results are compared with the existing redescending M-estimators, e.g., Huber, Tukey Biweight, Hampel, and Andrew-Sign function. The performance of the proposed estimators was also checked using real-life data applications of the estimators and found that the proposed estimators give promising results as compared to the existing estimators.

CATFISH DRYING (Clarias Batraclus) USING ‘TEKO BERSAYAP’ SOLAR DRYER

2012 ◽  
Vol 2 (1) ◽  
pp. 14-20
Author(s):  
Yuwana Yuwana
Keyword(s):  
Relative Humidity ◽  
Moisture Content ◽  
Ambient Temperature ◽  
Drying Rate ◽  
Drying Process ◽  
Drying Time ◽  
Ambient Relative Humidity ◽  
Solar Dryer

Experiment on catfish drying employing ‘Teko Bersayap’ solar dryer was conducted. The result of the experiment indicated that the dryer was able to increase ambient temperature up to 44% and decrease ambient relative humidity up to 103%. Fish drying process followed equations : KAu = 74,94 e-0,03t for unsplitted fish and KAb = 79,25 e-0,09t for splitted fish, where KAu = moisture content of unsplitted fish (%), KAb = moisture content of splitted fish (%), t = drying time. Drying of unsplitted fish finished in 43.995 hours while drying of split fish completed in 15.29 hours. Splitting the fish increased 2,877 times drying rate.

Risk assessment for health insurance using equation modeling and machine learning

2021 ◽  
Vol 25 (2) ◽  
pp. 201-225
Author(s):  
Amrik Singh ◽  
K.R. Ramkumar
Keyword(s):  
Machine Learning ◽  
Health Insurance ◽  
Risk Analysis ◽  
Real Life ◽  
Equation Model ◽  
Least Square ◽  
Parameter Analysis ◽  
Risk Indicators ◽  
Equation Modeling ◽  
Pairwise Correlation

Due to the advancement of medical sensor technologies new vectors can be added to the health insurance packages. Such medical sensors can help the health as well as the insurance sector to construct mathematical risk equation models with parameters that can map the real-life risk conditions. In this paper parameter analysis in terms of medical relevancy as well in terms of correlation has been done. Considering it as ‘inverse problem’ the mathematical relationship has been found and are tested against the ground truth between the risk indicators. The pairwise correlation analysis gives a stable mathematical equation model can be used for health risk analysis. The equation gives coefficient values from which classification regarding health insurance risk can be derived and quantified. The Logistic Regression equation model gives the maximum accuracy (86.32%) among the Ridge Bayesian and Ordinary Least Square algorithms. Machine learning algorithm based risk analysis approach was formulated and the series of experiments show that K-Nearest Neighbor classifier has the highest accuracy of 93.21% to do risk classification.

scholarly journals Formation of Conidial Pseudochains by Tomato Powdery Mildew Oidium neolycopersici

Plant Disease ◽  
2006 ◽  
Vol 90 (7) ◽  
pp. 915-919 ◽  
Author(s):  
W. Oichi ◽  
Y. Matsuda ◽  
T. Nonomura ◽  
H. Toyoda ◽  
L. Xu ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Powdery Mildew ◽  
Critical Level ◽  
Oidium Neolycopersici ◽  
Ambient Relative Humidity ◽  
Digital Microscope ◽  
Tomato Powdery Mildew ◽  
A Chain ◽  
Weak Wind ◽  
Generative Cells

The formation of conidial pseudochains by the tomato powdery mildew Oidium neolycopersici on tomato leaves was monitored using a high-fidelity digital microscope. Individual living conidiophores that formed mature conidial cells at their apex were selected for observation. The conidial cells were produced during repeated division and elongation by the generative cells of the conidiophores. Under weak wind conditions (0.1 m/s), these conidial cells did not separate from each other to produce a chain of conidial cells (pseudochain). The pseudochains dropped from the conidiophores once four conidial cells were connected. The conidiophores resumed conidium production, followed by another cycle of pseudochain formation. The formation of pseudochains by tomato powdery mildew was not influenced by the ambient relative humidity. On the other hand, the conidial cells produced were easily wind dispersed without forming pseudochains when conidiophores were exposed to stronger winds (1.0 m/s). The present study successfully demonstrated that the pathogen required wind to disperse progeny conidia from the conidiophores and produced conidial pseudochains when the wind was below a critical level, independent of high relative humidity as reported previously.

scholarly journals Influence of the ambient humidity on the concentration of natural deposition-mode ice-nucleating particles

2016 ◽  
Vol 16 (2) ◽  
pp. 927-932 ◽  
Author(s):  
M. L. López ◽  
E. E. Ávila
Keyword(s):  
Relative Humidity ◽  
Linear Trend ◽  
Ground Level ◽  
Ambient Air ◽  
Air Masses ◽  
Ambient Relative Humidity ◽  
Thermodynamic Conditions ◽  
Rainy Days ◽  
Deposition Mode

Abstract. This study reports measurements of deposition-mode ice-nucleating particle (INP) concentrations at ground level during the period July–December 2014 in Córdoba, Argentina. Ambient air was sampled into a cloud chamber where the INP concentration was measured at a temperature of −25 °C and a 15 % supersaturation over ice. Measurements were performed on days with different thermodynamic conditions, including rainy days. The effect of the relative humidity at ground level (RHamb) on the INP concentration was analyzed. The number of INPs activated varied from 1 L−1 at RHamb of 25 % to 30 L−1 at RHamb of 90 %. In general, a linear trend between the INP concentration and the RHamb was found, suggesting that this variability must be related to the effectiveness of the aerosols acting as INPs. From the backward trajectories analysis, it was found that the link between INP concentration and RHamb is independent of the origin of the air masses. The role of biological INPs and nucleation occurring in pores and cavities was discussed as a possible mechanism to explain the increase of the INP concentration during high ambient relative humidity events. This work provides valuable measurements of deposition-mode INP concentrations from the Southern Hemisphere where INP data are sparse so far.

scholarly journals ANALYSIS OF HEAT AND MASS TRANSFER ON COOLING TOWER FILL

2020 ◽  
Vol 14 (1) ◽  
pp. 25
Author(s):  
Abdul Aziz Rohman Hakim ◽  
Engkos Achmad Kosasih
Keyword(s):  
Heat Transfer ◽  
Mass Transfer ◽  
Relative Humidity ◽  
Heat And Mass Transfer ◽  
Cooling Tower ◽  
Stream Velocity ◽  
Design Data ◽  
Analogy Method ◽  
Ambient Relative Humidity ◽  
Air Stream

This paper discusses heat and mass transfer in cooling tower fill. In this research, dry bulb temperature at the bottom fill, ambient relative humidity, air stream velocity entering fill, dry bulb temperature leaving the fill, relative humidity of air leaving the fill, inlet and outlet water temperature of cooling tower were measured. Those data used in heat and mass transfer calculation in cooling tower fill. Then, do the heat and mass transfer calculation based on proposed approch. The results are compared with design data. The design and analogy method showed different  result. The parameter which influence the heat transfer at cooling tower are represented by coefficient of heat transfer hl and coefficient of mass transfer k­l. The differencies result between design and analogy method shows that there is important parameter which different. Deeply study needed for it.

scholarly journals A robust vector autoregressive model for forecasting economic growth in Malaysia

2018 ◽  
Vol 14 (3) ◽  
pp. 382-385
Author(s):  
Azme Khamis ◽  
Nur Azreen Abdul Razak ◽  
Mohd Asrul Affendi Abdullah
Keyword(s):  
Economic Growth ◽  
Least Square Method ◽  
Economic Indicator ◽  
Performance Measure ◽  
Least Square ◽  
Economic Indicators ◽  
Percentage Error ◽  
Monthly Basis ◽  
Vector Autoregressive ◽  
Auto Regressive

Economic indicator measures how solid or strong an economy of a country is. Basically, economic growth can be measured by using the economic indicators as they give an account of the quality or shortcoming of an economy. Vector Auto-regressive (VAR) method is commonly useful in forecasting the economic growth involving a bounteous of economic indicators. However, problems arise when its parameters are estimated using least square method which is very sensitive to the outliers existence. Thus, the aim of this study is to propose the best method in dealing with the outliers data so that the forecasting result is not biased. Data used in this study are the economic indicators monthly basis starting from January 1998 to January 2016. Two methods are considered, which are filtering technique via least median square (LMS), least trimmed square (LTS), least quartile difference (LQD) and imputation technique via mean and median. Using the mean absolute percentage error (MAPE) as the forecasting performance measure, this study concludes that Robust VAR with LQD filtering is a more appropriate model compare to others model. 

scholarly journals Big Data Affluence in Statistics Application: A Comparison of Real Life and Simulated Open Data

2020 ◽  
Author(s):  
Nureni Adeboye ◽  
◽  
Oyedunsi Olayiwola ◽  
Keyword(s):  
Big Data ◽  
Real Life ◽  
Open Data ◽  
Large Data ◽  
Learning Ability ◽  
Sufficient Data ◽  
Data Repositories ◽  
Classroom Activities ◽  
Life Data ◽  
Real Life Data

Large data repositories or database management still remain a mirage and tough challenge to accomplish by most developing countries and establishments around the globe. This necessitates the need to improvise on the gathering of suitable data with a good spread to serve as a complement, in the absence of sufficient real-life data. Statisticians are increasingly posed with thought-provoking and even paradoxical questions, challenging our qualifications for entering the statistical paradises created by Big Data. Through classroom activities that involved both sourced real-life and simulated big data in R-environment, models were built and estimates obtained from the adopted techniques revealed the robustness of simulated datasets in a unified observation with improved significant values as reflected in the results. Students appreciated the use of such big data as it enhances their machine learning ability and the availability of sufficient data without delay.

Sign in / Sign up

Export Citation Format

Share Document