A hybrid machine learning approach in modeling the impact of chromium concentration in blood and gonads on the concentration of the reproductive hormones of Urva auropunctatus. (April 2021)

• Record Type: Journal Article
• Authors: Andleeb, Shaista; Bao, Shenxu; Ahmad, Zulfiqar; Kumar Jha, Sunil
• Is Part Of: Measurement. Volume 174(2021)
• Publication Date: 2021-04
• Subjects: Chromium toxicity -- Hormone imbalance -- Urva auropunctatus -- Hybrid machine learning -- Adaptive neuro-fuzzy inference systems; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109055
• Languages: English
• ISSNs: 0263-2241

Abstract

Graphical abstract: Highlights: Chromium uptake analysis of Urva auropunctatus living in contaminated terrain. Assessment of Cr bioaccumulation in the blood, and kidney and liver tissues. Chromium distresses concentrations of reproductive hormones of Urva auropunctatus. ANFIS modeling of the effect of chromium concentration on reproductive hormones. The optimum performance of ANFIS in the estimation of concentration of hormones. Abstract: The toxic impact of chromium on hormonal concentrations of the Urva auropuctatus in a tannery industrial area has been analyzed through exposure to a sub-lethal contaminated environment. Chromium concentration in the body tissues (blood, testes, and ovaries) of the exposed Urva auropunctatus was found significantly higher than the control. Moreover, the concentrations of reproductive hormones (follicle-stimulating hormone (FSH), luteinizing hormone (LH), progesterone, estradiol, and testosterone) were reduced in chromium exposed animals. A hybrid approach of artificial neural network and fuzzy methods (adaptive neuro-fuzzy inference system (ANFIS)) has been designed in the modeling of hormonal concentration of Urva auropunctatus of control and experimental groups using the concentration of chromium in the body tissues. The ANFIS results in satisfactory estimation accuracy (minimum root mean square error (RMSE) = 0.01) in the estimation of the concentration of reproductive hormones.