<abstract> <p>Sarcasm means the opposite of what you desire to express, particularly to insult a person. Sarcasm detection in social networks SNs such as Twitter is a significant task as it has assisted in studying tweets using NLP. Many existing study-related methods have always focused only on the content-based on features in sarcastic words, leaving out the lexical-based features and context-based features knowledge in isolation. This shows a loss of the semantics of terms in a sarcastic expression. This study proposes an improved model to detect sarcasm from SNs. We used three feature set engineering: context-based on features set, Sarcastic based on features, and lexical based on features. Two Novel Algorithms for an effective model to detect sarcasm are divided into two stages. The first used two algorithms one with preprocessing, and the second algorithm with feature sets. To deal with data from SNs. We applied various supervised machine learning (ML) such as k-nearest neighbor classifier (KNN), na?ve Bayes (NB), support vector machine (SVM), and Random Forest (RF) classifiers with TF-IDF feature extraction representation data. To model evaluation metrics, evaluate sarcasm detection model performance in precision, accuracy, recall, and F1 score by 100%. We achieved higher results in Lexical features with KNN 89.19 % accuracy campers to other classifiers. Combining two feature sets (Sarcastic and Lexical) has shown slight improvement with the same classifier KNN; we achieved 90.00% accuracy. When combining three feature sets (Sarcastic, Lexical, and context), the accuracy is shown slight improvement. Also, the same classifier we achieved is a 90.51% KNN classifier. We perform the model differently to see the effect of three feature sets through the experiment individual, combining two feature sets and gradually combining three feature sets. When combining all features set together, achieve the best accuracy with the KNN classifier.</p> </abstract>
The widespread use of online social networks has culminated in across-the-board social communication among users, resulting in a considerable amount of user-generated contact data. Cybercrime has become a significant issue in recent years with the rise of online communication and social network. Cybercrime has lately been identified as a severe national psychological concern among platform users, and building a reliable detection model is crucial. Cyberbullying is the phrase used to describe such online harassment, insults, and attacks. It has become challenging to identify such unauthorized content due to the massive number of user-generated content. Because deep neural networks have various advantages over conventional machine learning approaches, researchers are turning to them more frequently to identify cyberbullying. Deep learning and machine learning have several uses in text classification. This article suggested the novel neural network model through parameters of an algorithmic and optimization comparative analysis of nine category approaches, four neural networks, and five machine learning, in two scenarios with real-world datasets of cyberbullying. Moreover, this work also analyzes the impact of word embedding and feature extraction techniques based on text mining and NLP on algorithms' performances. We performed extensive experiments on the two scenarios with a split dataset to demonstrate the merit of this research, comparing nine classification approaches through five feature extraction techniques. Our proposed cybercriminal detection model using neural networks, deep learning, and machine learning outperforms the existing state-of-the-art method of cybercriminal detection in terms of accuracy achieving higher performance.
In this study, physical models were designed and fabricated to investigate the hydraulic behaviour of dead-end and looped PVC manifolds. The physical models consisted of a water supply tank with overflow, PVC manifolds, steel supports, collection tank, pump, pressure sensors and valves to allow flow control. Throughout the study, the water level in the supply tank was kept constant. The hydraulic behaviour of dead-end manifolds was investigated using different spacing, S between outlets (S= 3m, S=2.5m, S=2m, S=1.5m, and S=0.75m). The hydraulic behaviour of looped manifolds was investigated using a single outlet spacing of 1.5m. The comparison between the hydraulic behaviour of looped and dead-end manifolds was carried out using the data of the 1.5m outlet spacing. The value of uniformity, U for dead-end and looped manifolds was 82% and 92%, respectively. The value of friction ratio, fn/f1, was found to be 33 and 0.18 for dead-end and looped manifolds, respectively. The experimental data of this study were used to validate selected formulae for estimation of the friction correction factor (G Factor). The results showed that the equation proposed by Alazba et al. (2012) yielded the most satisfactory estimation. The performance of the selected formulae was tested using two statistical indices.
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