Comparison of sparse and robust regression techniques in efficient model selection for moisture ratio removal of seaweed using solar drier

Javaid Anam, . and Mohd. Tahir Ismail, . and Majahar Ali Majid Khan, . Comparison of sparse and robust regression techniques in efficient model selection for moisture ratio removal of seaweed using solar drier. pp. 609-625. ISSN 2231-8526

Solar drier is considered to be an important product used in the internet of things (IoT). It is used to dry different kinds of products used in agriculture or aquaculture. There are many factors that have different effects on the drying of items in the solar drier. The current study focused on the removal of the moisture ratio in the drying process for seaweed using solar drier. For this purpose a dataset containing 1924 observations was used to study the effect of six different independent variables on the dependent variable. Moisture ratio removal () was considered to be dependent variable with ambient temperature chamber temperature collector temperature chamber relative humidity ambient relative humidity and solar radiation as independent variables. All possible models were used in the analysis till fifth order interaction terms. Hybrid model of LASSO with bisquare M was proposed for efficient selection of the model. The procedure based on four phases was used for efficient model selection and a comparison was made with other existing sparse and robust regression techniques. The result indicates that the proposed technique is better than other existing techniques in terms of mean squared error (MSE) and mean absolute percentage error (MAPE).

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Abstract

Solar drier is considered to be an important product used in the internet of things (IoT). It is used to dry different kinds of products used in agriculture or aquaculture. There are many factors that have different effects on the drying of items in the solar drier. The current study focused on the removal of the moisture ratio in the drying process for seaweed using solar drier. For this purpose a dataset containing 1924 observations was used to study the effect of six different independent variables on the dependent variable. Moisture ratio removal () was considered to be dependent variable with ambient temperature chamber temperature collector temperature chamber relative humidity ambient relative humidity and solar radiation as independent variables. All possible models were used in the analysis till fifth order interaction terms. Hybrid model of LASSO with bisquare M was proposed for efficient selection of the model. The procedure based on four phases was used for efficient model selection and a comparison was made with other existing sparse and robust regression techniques. The result indicates that the proposed technique is better than other existing techniques in terms of mean squared error (MSE) and mean absolute percentage error (MAPE).

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Item Type:	Article
AGROVOC Term:	Solar drying
AGROVOC Term:	Application of technology
AGROVOC Term:	Techniques
AGROVOC Term:	Seaweeds
AGROVOC Term:	Drying
AGROVOC Term:	Monitoring
AGROVOC Term:	Moisture content
AGROVOC Term:	Solar radiation
AGROVOC Term:	Ambient conditions
AGROVOC Term:	Temperature
Depositing User:	Mr. AFANDI ABDUL MALEK
Last Modified:	24 Apr 2025 00:55
URI:	http://webagris.upm.edu.my/id/eprint/9371

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