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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

Computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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利用羊水量对分娩时进行评估的一项随机临床试验:羊水指数与单一最大羊水暗区检测技术比较

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世界核心医学期刊文摘（妇产科学分册） 2005年第1期1卷 9-9页

作者： moses J. Doherty D.A. Magann E.F. S.P.Chauhan 程洪燕 Depts. of Obstetric s and Gynecology Univ. of Mississippi Medical Center Jackson Miss Univ. W. A.Spartanburg SC United States Dr.

Objective The purpose of this study was to determine whether an intra partum as sessment of amniotic fluid identifies a pregnancy that is at risk for an adverse outcome. Study design Parturients who were admitted for delivery were assigned randomly to have the amniotic fluid assessed either by amniotic fluid index or b y the presence of a 2 ×1 pocket. Results The amniotic fluid index was obtained in 499 pregnancies, and the 2 ×1 technique was performed in 501. Oligohydramnio s was diagnosed in 25%of amniotic fluid index pregnancies versus 8%with the us e of the 2 ×1 pocket technique (P me.

关键词：羊水暗区羊水指数羊水量随机临床试验晚期减速胎儿窘迫变异减速评估组

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