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The Use of Expanding Monomers in Embedding and Related Processes
Published in Rajender K. Sadhir, Russell M. Luck, Expanding Monomers, 2020
Embedding and encapsulating of parts find wide use in industry because the processes offer many improvements in performance. These improvements include: mechanically and electrically stronger assemblies, modular construction, miniaturization, and protection of parts from the operating environment.
Deep Sentiments Analysis for Roman Urdu Dataset Using Faster Recurrent Convolutional Neural Network Model
Published in Applied Artificial Intelligence, 2022
Arfan Ali Nagra, Khalid Alissa, Taher M. Ghazal, Saigeeta Kukunuru, Muhammad Mugees Asif, Muhammad Fawad
The research of sentiment analysis has also been carried out in many other different languages like German, Italian, Korean and Indonesian. KOSAC is the Korean corpus based on 332 news articles. Parser1 is the German corpus created using Amazon reviews and it is a collection of 63,067 sentences (Safder and Hassan 2019). The Indonesian corpus was created using 5.3 Twitter tweets with the help of the twitter streaming API (Boland, Wira-Alam, and Messerschmidt 2013). Italian corpus is a collection of 2,648 sentences related to entertainment media like films and dramas. And the whole corpus was manually annotated and classified into five different sentiments like a negative, strong negative, neutral, positive, and strong positive (Zhang, Wang, and Liu 2018). Different methods have been used in previous work to process sentiment analysis. Both supervised (support vector machine (SVM)) and unsupervised (rule based) machine learning techniques have been applied to SemEval in 2014. It used the technique of determining the sentiment polarity of the sentence and based on that polarity score it classifies the data. If the polarity score is greater than 0, then it is positive; if it is smaller than 0, then it is negative; otherwise, it is neutral (Chen, Liu, and Chiu 2011). In version 2016 of SemEval, Gaussian Regression, Random Forest and Linear Regression techniques were used. To represent the influence of the positive emotion in a sentence, a supervised learning method is used to score between 0 and 1. Further, Spearman’s rank and Kendall’s rank were used to access the results (Attardi and Sartiano 2016). In the publication of SemEval 2017, two different classification methods were used long short-term memory RNN (LSTM_RNN). It can study long-term dependencies and uses word indexes as input chains and machine-based learning used for feature representation by embedding words. Embedding is an advanced NLP system that relates and represents the phrases or words in a real number vector. For separating the data-set into classes SVM is used. Further, random forest technique is used to produce decision trees (El-Beltagy, Kalamawy, and Soliman 2017).