The rectified Barkhausen noise envelope, induction rate dB/dt signal, and hysteresis curve have been measured simultaneously for an annealed and plastically deformed mild steel specimen. The two peaks in the Barkhausen noise envelope of the annealed specimen occurring in the knee regions of the hysteresis curve are due to domain nucleation and annihilation, while the single-peak in the Barkhausen noise envelope of the deformed specimen at the coercive field is due to the dislocation pinning occurring during irreversible wall displacement. The increase of the coercive field with the plastic strain is attributed to the increase in the dislocation density. The strain dependence of the Barkhausen noise energy is explained in terms of the energy released during domain nucleation and annihilation, and the hysteresis loss in terms of pinning effects of a moving wall. The variations of the frequency spectrum with the plastic deformation and magnetising frequency are characterised by the Barkhausen noise exhibiting a clustering and overlapping nature.
The increase in the volume of user-generated content on Twitter has resulted in tweet sentiment analysis becoming an essential tool for the extraction of information about Twitter users' emotional state. Consequently, there has been a rapid growth of tweet sentiment analysis in the area of natural language processing. Tweet sentiment analysis is increasingly applied in many areas, such as decision support systems and recommendation systems. Therefore, improving the accuracy of tweet sentiment analysis has become practical and an area of interest for many researchers. Many approaches have tried to improve the performance of tweet sentiment analysis methods by using the feature ensemble method. However, most of the previous methods attempted to model the syntactic information of words without considering the sentiment context of these words. Besides, the positioning of words and the impact of phrases containing fuzzy sentiment have not been mentioned in many studies. This study proposed a new approach based on a feature ensemble model related to tweets containing fuzzy sentiment by taking into account elements such as lexical, word-type, semantic, position, and sentiment polarity of words. The proposed method has been experimented on with real data, and the result proves effective in improving the performance of tweet sentiment analysis in terms of the F 1 score. INDEX TERMS Feature ensemble model, fuzzy sentiment, tweet embeddings, tweet sentiment analysis.
cial media following its introduction has witnessed a lot of scholarly attention in recent years due to its growing popularity. These various social media sites have become the mecca of information because of their less costly and easy accessibility. Although these sites were developed to enhance our lives, they are seen as both angelic and vicious. Growing misinformation and fake content by malicious users have not only plagued our online social media ecosystem into chaos, but it also meted untold suffering to humankind. Recently, social media has witnessed a reverberation amid the proliferation of fake news which has made people reluctant to engage in genuine news sharing for fear that such information is false. Consequently, there is a dire need for these fake content to be detected and removed from social media. This study explores the various methods of combating fake news on social media such as Natural Language Processing, Hybrid model. We surmised that detecting fake news is a challenging and complex issue, however, it remains a workable task. Revelation in this study holds that the application of hybrid-machine learning techniques and the collective effort of humans could stand a higher chance of fighting misinformation on social media.
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