Qi Zhuang^a,* , Dong Liu^b, Bo Liu^c, Mei Liu^a

Frontiers in Heat and Mass Transfer, Vol.20, pp. 1-6, 2023, DOI:10.5098/hmt.20.13

Abstract In the actual operation of wet gas pipeline, liquid accumulation is easy to form in the low-lying and uphill sections of the pipeline, which leads to a series of problems such as reduced pipeline transportation efficiency, increased pipeline pressure drop, hydrate formation, slug flow and intensified corrosion in the pipeline. Accurate calculation of liquid holdup is of great significance to the research of flow pattern identification, pipeline corrosion evaluation and prediction, and gas pipeline transportation efficiency calculation. Based on the experimental data of liquid holdup in horizontal pipeline, a commonly used BP neural network (BPNN) model is established in this… More >

Hanan M. Alghamdi¹, Saadia H.A. Hamza², Aisha M. Mashraqi³, Sayed Abdel-Khalek^4,5,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5985-5999, 2022, DOI:10.32604/cmc.2022.031732

Abstract World Wide Web enables its users to connect among themselves through social networks, forums, review sites, and blogs and these interactions produce huge volumes of data in various forms such as emotions, sentiments, views, etc. Sentiment Analysis (SA) is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive, negative, and neutral. However, Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing (NLP). Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array… More >