Ridge Regression as Efficient Model Selection and Forecasting of Fish Drying Using V-Groove Hybrid Solar Drier

Lim, Hui Yin; Fam, Pei Shan; Javaid, Anam; Ali, Majid Khan Majahar

doi:10.47836/pjst.28.4.04

Cited by 9 publications

(11 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Econometrics issue such as multicollinearity and outliers are observed inthis study. For this purpose, correlation matrix is used to check the multicollinearity between various variables (Lim et al, 2020). The values greater than 0.95 for the correlation will indicate the multicollinearity between variables (Javaid et al; 2019).…”

Section: Methodsmentioning

confidence: 99%

Exploring the Factors Associated with Unemployment Rate

2022

Central European Management Journal

View full text Add to dashboard Cite

Unemployment is a major problem in the world. It is very consistent problem all over the countries. The main purpose of this study is to know which factor is the main reason of unemployment. The analysis is based on the secondary data collected through authentic internet resources. The dataset is taken on annually basis from 1991 to 2020. The main demographic factors involved in this research are Gross Domestic Product (GDP), Foreign Direct Investment (FDI), Population Growth Rate (PGR) and Inflation. Multicollinearity analysis and outlier analysis is performed on all the factors included in the analysis. No multicollinearity issue is found in the analysis, while some outliers are observed in FDI. Due to outlier issue, the research is moved on to the robust regression analysis. Two strong factors are found for reduction of the unemployment rate such as PGR and FDI. The findings showed that the unemployment rate can be decreased by the increase in FDI and PGR. The results can also be generalized as well as on the other countries economy.

show abstract

Section: Methodsmentioning

confidence: 99%

Exploring the Factors Associated with Unemployment Rate

2022

Central European Management Journal

View full text Add to dashboard Cite

show abstract

“…Observations that deviate from the distribution's general shape or pattern are called outliers [3]. The relationship between the observed and the dependent variable can be estimated by OLS regression, by minimizing the sum of squares [4]. OLS also has limitations when the assumptions are violated [5].…”

Section: Introductionmentioning

confidence: 99%

“…The presence of outliers in the data makes the LS estimator unstable, inefficient, and unreliable [7]. Agricultural data has outliers because of factors that cannot be regulated, and these outliers will increase the standard errors [4,8]. The presence of outliers affects the performance of OLS, and a robust regression is used [9].…”

Section: Introductionmentioning

confidence: 99%

Robust M-estimators and Machine Learning Algorithms for Improving the Predictive Accuracy of Seaweed Contaminated Big Data

Ibidoja

Shan

Mukhtar

et al. 2023

J. Nig. Soc. Phys. Sci.

View full text Add to dashboard Cite

A common problem in regression analysis using ordinary least squares (OLS) is the effect of outliers or contaminated data on the estimates of the parameters. A robust method that is not sensitive to outliers and can handle contaminated data is needed. In this study, the objective is to determine the significant parameters that determine the moisture content of the seaweed after drying and develop a hybrid model to reduce the outliers. The data were collected with sensors from the v-Groove Hybrid Solar Drier (v-GHSD) at Semporna, South-Eastern Coast of Sabah, Malaysia. After the second order interaction, we have 435 drying parameters, each parameter has 1914 observations. First, we used four machine learning algorithms, such as random forest, support vector machine, bagging and boosting to determine the significant parameters by selecting 15, 25, 35 and 45 parameters. Second, we developed the hybrid model using robust methods such as M. Bi-Square, M. Hampel and M. Huber. The results show that there is a significant improvement in the reduction of the number of outliers and better prediction using hybrid model for the contaminated seaweed big data. For the highest variable importance of 45 significant drying parameters of seaweed, the hybrid model bagging M Bi-square performs better because it has the lowest percentage of outliers of 4.08 %.

show abstract

“…Application of the important variables will improve accuracy, reduce overfitting, and ensure robustness. Lim et al (2020) used ridge regression to determine the drying parameters of fish and included the interaction terms.…”

Section: Introductionmentioning

confidence: 99%

Intelligence System via Machine Learning Algorithms in Detecting the Moisture Content Removal Parameters of Seaweed Big Data

Joshua Ibidoja,

Pei Shan,

Eri Suheri

et al. 2023

JST

View full text Add to dashboard Cite

The parameters that determine the removal of moisture content have become necessary in seaweed research as they can reduce cost and improve the quality and quantity of the seaweed. During the seaweed’s drying process, many drying parameters are involved, so it is hard to find a model that can determine the drying parameters. This study compares seaweed big data performance using machine learning algorithms. To achieve the objectives, four machine learning algorithms, such as bagging, boosting, support vector machine, and random forest, were used to determine the significant parameters from the data obtained from v-GHSD (v-Groove Hybrid Solar Drier). The mean absolute percentage error (MAPE) and coefficient of determination (R2) were used to assess the model. The importance of variable selection cannot be overstated in big data due to the large number of variables and parameters that exceed the number of observations. It will reduce the complexity of the model, avoid the curse of dimensionality, reduce cost, remove irrelevant variables, and increase precision. A total of 435 drying parameters determined the moisture content removal, and each algorithm was used to select 15, 25, 35 and 45 significant parameters. The MAPE and R-Square for the 45 highest variable importance for random forest are 2.13 and 0.9732, respectively. It performed best, with the lowest error and the highest R-square. These results show that random forest is the best algorithm to decide the vital drying parameters for removing moisture content.

show abstract

Ridge Regression as Efficient Model Selection and Forecasting of Fish Drying Using V-Groove Hybrid Solar Drier

Cited by 9 publications

References 38 publications

Exploring the Factors Associated with Unemployment Rate

Exploring the Factors Associated with Unemployment Rate

Robust M-estimators and Machine Learning Algorithms for Improving the Predictive Accuracy of Seaweed Contaminated Big Data

Intelligence System via Machine Learning Algorithms in Detecting the Moisture Content Removal Parameters of Seaweed Big Data

Contact Info

Product

Resources

About