FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313646899> ?p ?o ?g. }

• W4313646899 endingPage "177" @default.
• W4313646899 startingPage "177" @default.
• W4313646899 abstract "Gas–liquid flow is a significant phenomenon in various engineering applications, such as in nuclear reactors, power plants, chemical industries, and petroleum industries. The prediction of the flow patterns is of great importance for designing and analyzing the operations of two-phase pipeline systems. The traditional numerical and empirical methods that have been used for the prediction are known to result in a high inaccuracy for scale-up processes. That is why various artificial intelligence-based (AI-based) methodologies are being applied, at present, to predict the gas–liquid flow regimes. We focused in the current study on a thorough comparative analysis of machine learning (ML) and deep learning (DL) in predicting the flow regimes with the application of a diverse set of ML and DL frameworks to a database comprising 11,837 data points, which were collected from thirteen independent experiments. During the pre-processing, the big data analysis was performed to analyze the correlations among the parameters and extract important features. The comparative analysis of the AI-based models’ performances was conducted using precision, recall, F1-score, accuracy, Cohen’s kappa, and receiver operating characteristics curves. The extreme gradient boosting method was identified as the optimum model for predicting the two-phase flow regimes in inclined or horizontal pipelines." @default.
• W4313646899 created "2023-01-07" @default.
• W4313646899 creator A5009356597 @default.
• W4313646899 creator A5040735440 @default.
• W4313646899 creator A5077540771 @default.
• W4313646899 date "2023-01-06" @default.
• W4313646899 modified "2023-10-01" @default.
• W4313646899 title "Comparative Performance of Machine-Learning and Deep-Learning Algorithms in Predicting Gas–Liquid Flow Regimes" @default.
• W4313646899 cites W1170214759 @default.
• W4313646899 cites W1968969471 @default.
• W4313646899 cites W1975375657 @default.
• W4313646899 cites W1976883852 @default.
• W4313646899 cites W1978541339 @default.
• W4313646899 cites W1981278230 @default.
• W4313646899 cites W1989170328 @default.
• W4313646899 cites W1998477104 @default.
• W4313646899 cites W2004363204 @default.
• W4313646899 cites W2006191565 @default.
• W4313646899 cites W2010258271 @default.
• W4313646899 cites W2012735095 @default.
• W4313646899 cites W2015855404 @default.
• W4313646899 cites W2017040119 @default.
• W4313646899 cites W2022238671 @default.
• W4313646899 cites W2022578046 @default.
• W4313646899 cites W2024972524 @default.
• W4313646899 cites W2025702300 @default.
• W4313646899 cites W2032293007 @default.
• W4313646899 cites W2033414439 @default.
• W4313646899 cites W2035082772 @default.
• W4313646899 cites W2038664241 @default.
• W4313646899 cites W2040877251 @default.
• W4313646899 cites W2045371192 @default.
• W4313646899 cites W2049122463 @default.
• W4313646899 cites W2052387186 @default.
• W4313646899 cites W2056132907 @default.
• W4313646899 cites W2057201640 @default.
• W4313646899 cites W2070493638 @default.
• W4313646899 cites W2083038992 @default.
• W4313646899 cites W2087913253 @default.
• W4313646899 cites W2090811425 @default.
• W4313646899 cites W2090812699 @default.
• W4313646899 cites W2093123783 @default.
• W4313646899 cites W2100537585 @default.
• W4313646899 cites W2138718980 @default.
• W4313646899 cites W2160523498 @default.
• W4313646899 cites W2342603028 @default.
• W4313646899 cites W2472739243 @default.
• W4313646899 cites W2525911586 @default.
• W4313646899 cites W2536580267 @default.
• W4313646899 cites W2588524044 @default.
• W4313646899 cites W2768514818 @default.
• W4313646899 cites W2789289184 @default.
• W4313646899 cites W2803556351 @default.
• W4313646899 cites W2885195348 @default.
• W4313646899 cites W2908454195 @default.
• W4313646899 cites W2911964244 @default.
• W4313646899 cites W2914167033 @default.
• W4313646899 cites W2917287736 @default.
• W4313646899 cites W2945399219 @default.
• W4313646899 cites W2967002257 @default.
• W4313646899 cites W2973129203 @default.
• W4313646899 cites W2990039883 @default.
• W4313646899 cites W2994980192 @default.
• W4313646899 cites W2996135944 @default.
• W4313646899 cites W3006689658 @default.
• W4313646899 cites W3008460667 @default.
• W4313646899 cites W3010496653 @default.
• W4313646899 cites W3049349475 @default.
• W4313646899 cites W3049752713 @default.
• W4313646899 cites W3086569303 @default.
• W4313646899 cites W3110767883 @default.
• W4313646899 cites W3116401024 @default.
• W4313646899 cites W3151171962 @default.
• W4313646899 cites W3207294759 @default.
• W4313646899 cites W3216625121 @default.
• W4313646899 cites W4239510810 @default.
• W4313646899 cites W4283367807 @default.
• W4313646899 doi "https://doi.org/10.3390/pr11010177" @default.
• W4313646899 hasPublicationYear "2023" @default.
• W4313646899 type Work @default.
• W4313646899 citedByCount "1" @default.
• W4313646899 countsByYear W43136468992023 @default.
• W4313646899 crossrefType "journal-article" @default.
• W4313646899 hasAuthorship W4313646899A5009356597 @default.
• W4313646899 hasAuthorship W4313646899A5040735440 @default.
• W4313646899 hasAuthorship W4313646899A5077540771 @default.
• W4313646899 hasBestOaLocation W43136468991 @default.
• W4313646899 hasConcept C11413529 @default.
• W4313646899 hasConcept C119857082 @default.
• W4313646899 hasConcept C154945302 @default.
• W4313646899 hasConcept C169258074 @default.
• W4313646899 hasConcept C178790620 @default.
• W4313646899 hasConcept C185592680 @default.
• W4313646899 hasConcept C199360897 @default.
• W4313646899 hasConcept C2524010 @default.
• W4313646899 hasConcept C2777955874 @default.
• W4313646899 hasConcept C2781060337 @default.
• W4313646899 hasConcept C33923547 @default.

• next