Abstract Using specific deep learning models, we have introduced irony detection in Arabic language with the help of the IDAT 2019 Shared Task. We have tested a few available models and understand how the document content cleaning and pre-processing work. In the trials we have conducted we found that a higher F1 score is achieved and the RGGL ranks in a... Using specific deep learning models, we have introduced irony detection in Arabic language with the help of the IDAT 2019 Shared Task. We have tested a few available models and understand how the document content cleaning and pre-processing work. In the trials we have conducted we found that a higher F1 score is achieved and the RGGL ranks in a top level. We finally found that the introduced system can able to get competitive results. Read More Read Less

Abstract Irony detection is considered as an important system in opinion communication for a specific task in social networks. Thus, we studied and documented the results of IDAT 2019 Task for irony detection in the tweets of texts where we took Arabic to study. In this work we initially used the labelled data from the selected tweets. Finally we are able... Irony detection is considered as an important system in opinion communication for a specific task in social networks. Thus, we studied and documented the results of IDAT 2019 Task for irony detection in the tweets of texts where we took Arabic to study. In this work we initially used the labelled data from the selected tweets. Finally we are able to present the system, resources, and tools that we have deployed to achieve better results. Read More Read Less

Abstract The volume of data has been growing enormously so as the connections in the Internet have been increasing significantly. Thus data management of sensors data using IoT is significant. In the current study we have introduced a general design which is lightweight and can be customized in the IoT environment easily. We have stored the sensor data in the metadata... The volume of data has been growing enormously so as the connections in the Internet have been increasing significantly. Thus data management of sensors data using IoT is significant. In the current study we have introduced a general design which is lightweight and can be customized in the IoT environment easily. We have stored the sensor data in the metadata form in a relational database. Finally this data can be managed by framing queries in the nodes which is true in real-time, We have documented a framework for the weather monitoring and security domain using cloud and mobile platforms. Read More Read Less