Modification Of Regression Models To Solve Heterogeneity Problem Using Seaweed Drying Data

During the seaweed’s drying process, a lot of drying parameters are involved. One of the problems in regression analysis is the impact of heterogeneity parameters. The seaweed data was collected using sensor-smart farming technology attached to the v-Groove Hybrid Solar Drier. The proposed method...

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Main Author:	Joshua, Ibidoja Olayemi
Format:	Thesis
Language:	English
Published:	2023
Subjects:	QA1-939 Mathematics
Online Access:	http://eprints.usm.my/60406/1/IBIDOJA%20OLAYEMI%20JOSHUA%20-%20TESIS24.pdf
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spelling	my-usm-ep.604062024-04-22T08:24:52Z Modification Of Regression Models To Solve Heterogeneity Problem Using Seaweed Drying Data 2023-09 Joshua, Ibidoja Olayemi QA1-939 Mathematics During the seaweed’s drying process, a lot of drying parameters are involved. One of the problems in regression analysis is the impact of heterogeneity parameters. The seaweed data was collected using sensor-smart farming technology attached to the v-Groove Hybrid Solar Drier. The proposed method used the variance inflation factor to identify the heterogeneity parameters. To determine the 15, 25, 35, and 45 highranking important parameters for the seaweed, models such as ridge, random forest, support vector machine, bagging, boosting, LASSO, and elastic net are used before heterogeneity, after heterogeneity, and for the modified model. To reduce the outliers, robust regressions such as M Huber, M Hampel, M Bi Square, MM, and S estimators are used. Before the heterogeneity parameters were excluded from the model, the hybrid model of the ridge with the M Hampel estimator showed that better significant results were obtained with 2.14% outliers. After the heterogeneity parameters were excluded from the model, the support vector machine with the MM estimator showed that better significant results were obtained with 2.09% outliers. For the modified model, LASSO with M Bi square estimator showed that better significant results were obtained with 1.31% outliers. For future studies, the impact of heterogeneity using a hybrid model with imbalanced data or missing values can be investigated. Ensemble machine learning algorithms such as stacking, XGBoost, and AdaBoost can be used. 2023-09 Thesis http://eprints.usm.my/60406/ http://eprints.usm.my/60406/1/IBIDOJA%20OLAYEMI%20JOSHUA%20-%20TESIS24.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Matematik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA1-939 Mathematics
spellingShingle	QA1-939 Mathematics Joshua, Ibidoja Olayemi Modification Of Regression Models To Solve Heterogeneity Problem Using Seaweed Drying Data
description	During the seaweed’s drying process, a lot of drying parameters are involved. One of the problems in regression analysis is the impact of heterogeneity parameters. The seaweed data was collected using sensor-smart farming technology attached to the v-Groove Hybrid Solar Drier. The proposed method used the variance inflation factor to identify the heterogeneity parameters. To determine the 15, 25, 35, and 45 highranking important parameters for the seaweed, models such as ridge, random forest, support vector machine, bagging, boosting, LASSO, and elastic net are used before heterogeneity, after heterogeneity, and for the modified model. To reduce the outliers, robust regressions such as M Huber, M Hampel, M Bi Square, MM, and S estimators are used. Before the heterogeneity parameters were excluded from the model, the hybrid model of the ridge with the M Hampel estimator showed that better significant results were obtained with 2.14% outliers. After the heterogeneity parameters were excluded from the model, the support vector machine with the MM estimator showed that better significant results were obtained with 2.09% outliers. For the modified model, LASSO with M Bi square estimator showed that better significant results were obtained with 1.31% outliers. For future studies, the impact of heterogeneity using a hybrid model with imbalanced data or missing values can be investigated. Ensemble machine learning algorithms such as stacking, XGBoost, and AdaBoost can be used.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Joshua, Ibidoja Olayemi
author_facet	Joshua, Ibidoja Olayemi
author_sort	Joshua, Ibidoja Olayemi
title	Modification Of Regression Models To Solve Heterogeneity Problem Using Seaweed Drying Data
title_short	Modification Of Regression Models To Solve Heterogeneity Problem Using Seaweed Drying Data
title_full	Modification Of Regression Models To Solve Heterogeneity Problem Using Seaweed Drying Data
title_fullStr	Modification Of Regression Models To Solve Heterogeneity Problem Using Seaweed Drying Data
title_full_unstemmed	Modification Of Regression Models To Solve Heterogeneity Problem Using Seaweed Drying Data
title_sort	modification of regression models to solve heterogeneity problem using seaweed drying data
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Matematik
publishDate	2023
url	http://eprints.usm.my/60406/1/IBIDOJA%20OLAYEMI%20JOSHUA%20-%20TESIS24.pdf
_version_	1804888931152429056

Modification Of Regression Models To Solve Heterogeneity Problem Using Seaweed Drying Data

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