Sarcasm is conceders one patterns of undesirable communication, which causes social problems in humans relationships because of the contradictory verbal and nonverbal expressions is known sarcasm may defined , statements with intent to belittle others in different situations, dealing with the individual deliberately, to diminish the will or overwhelmed by the recipe stupid, which generate a reaction when a person listener, raising the intentions of ridicule and undermine it, and this may happen is different from the irony interest increased in sarcasm since the development of scientists "Watzlawick '" and Bevin "Jackson's" in 1967 theory interactive and considered the sarcasm one interaction patterns in the social relationship, which used by peers and adults in our society, so the current research aims to:-Introduce the sarcasm for University of Baghdad staff which achieved when researcher build scale of sarcasm
- identify the level degree of sarcasm among the staff for University of Baghdad
The comparison in sarcasm between males and females of Baghdad University staff-
The current research is determined by University of Baghdad staff in colleges , institutes and centers to achieve the current research goals, sarcasm scale applied on sample consist of (158) employees ,(77)females and (81)males who selected randomly from colleges and centers of researches for University of Baghdad
After collecting and manipulate data statistically by using: T- test for two independent samples, Pearson's correlation coefficient , standard-degree amended and the percentage, the research found the following results
- low level degree of sarcasm in (68%)of the staff were level degree (40 and under), while the percentage accounted the highest level (15%) of employees who were level degree (60 and higher), while the percentage of the middle group was (17 %) the level of scores between (40-60)
- Differences was found in staff, indicates males use sarcasm more than female
As a complement to related aspects of this research, the researchers has put a number of recommendations and suggestions.
Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an
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