Comparison of Pronuclear (Pn) Number Observations Based on Embryologist’s Experience and Detection by Artifical Intelligence (Ai) Trained With Deep Learning Technology

Watanabe, Hiroyuki; Fukunaga, Noritaka; Sanami, Sho; Kitasaka, Hiroya; Tsuzuki, Yuji; Kida, Yutaka; Takeda, Seiji; Kondo, Fumiya; Takeda, Sho; Asada, Yoshimasa

doi:10.1016/j.fertnstert.2020.08.232

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“…Currently, with the development of information technology, artificial intelligence (AI) technology has gradually entered the public vision and has been widely used in the field of water treatment (Bian & Wang 2020;Bowen et al 2020;Dasgupta et al 2020;Watanabe et al 2020;Wu et al 2020). Therefore, many AI techniques have been applied to turbidity prediction (Khairi et al 2016;Baghalian & Ghodsian 2017;Daghbandan et al 2019;Song & Zhang 2020;Zounemat-Kermani et al 2020).…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Estimating effluent turbidity in the drinking water flocculation process with an improved random forest model

Wang

Chang

et al. 2021

Water Supply

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During the drinking water treatment, the uncertain changes of raw water quality bring great difficulties to the control of flocculants dosage, especially because the feedback information based on the effluent turbidimeter of sedimentation tank can only be obtained after a long time when the influent water quality changes for the large lag characteristics of flocculation process. The prediction of effluent turbidity of sedimentation tank can effectively solve aforementioned problem. Given that it is difficult for the ordinary random forest (RF) model to accurately predict the effluent turbidity of sedimentation tank for the complicated changes of raw water quality, an improved random forest (IRF) model composed of long-term and short-term parts is proposed, which can capture the periodicity and time-varying characteristics of influent water quality data. The experimental results show that, the root mean square error and mean absolute percentage error of IRF model in Baiyangwan waterworks are improved 67.52% and 67.91% respectively, compared with those of ordinary RF model. The proposed effluent turbidity predictions are also successfully developed in Xujiang waterworks and Xiangcheng waterworks of Suzhou, China. This research provides an effective method for real-time predicting the effluent turbidity of sedimentation tank according to the influent water quality data.

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Section: Graphical Abstract Introductionmentioning

confidence: 99%

Estimating effluent turbidity in the drinking water flocculation process with an improved random forest model

Wang

Chang

et al. 2021

Water Supply

View full text Add to dashboard Cite

show abstract

Comparison of Pronuclear (Pn) Number Observations Based on Embryologist’s Experience and Detection by Artifical Intelligence (Ai) Trained With Deep Learning Technology

Abstract: A revised protocol for in vitro development of mouse oocytes from primordial follicles dramatically improves their developmental competence.

Cited by 1 publication

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Estimating effluent turbidity in the drinking water flocculation process with an improved random forest model

Estimating effluent turbidity in the drinking water flocculation process with an improved random forest model

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