The study aimes to analyze the discourse of Arab tweeters regarding the normalization of Arab-Israeli relations on the social media site (Twitter).
Study Methodology: The study relay on the survey method, using the comprehensive survey method, and this research is one of the descriptive types that aim to describe the phenomenon in a qualitative way, and discuss it through the method of in-depth analysis, in addition to using the tools of (discourse analysis).
Study sample: A sample of (8686) tweets for (7425) tweeters, which was posted on Twitter during the period from 15/9 to 30/9/2020, on the issue of normalizing Arab-Israeli relations.
Study tools: The data study was collected using the data mining tool from social networks (NodeXL), along with a discourse analysis sheet.
The most prominent results of the study:
The thesis (popular rejection of normalization with Israel) rank first for the theses addressed in the tweets. The negative features attributed to the active forces in the Arab countries focused on their official position in support and defender of normalization. As for the positive features, the role and popular position rejecting normalization were shared. The frameworks of the case focused on the framework of Palestinian reconciliation, the framework of the US-Iranian conflict in the Middle East, and the framework of the conflict within the ruling regimes regarding normalization. While the personality framework focused on Palestinian martyrs, Arab presidents and rulers who support normalization, as well as clerics. The causes and consequences were varied in their content.
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The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices
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