Dina M. Ibrahim

The term "clickbait" refers to content specifically designed to capture readers' attention, often through misleading headlines, leading to frustration among social media users. In this study, titled "Mushakkal," which translates to "variety" in Arabic, we utilized a Convolutional Neural Network (CNN)—a deep learning approach—to detect clickbait within an Arabic dataset. We compared three optimizers: RMSprop, Adam, and Adadelta, evaluating various parameter settings to determine the most effective combination for detecting clickbait in Arabic content. Our findings revealed that the CNN model performed best when both pre-processing and Word2Vec techniques were applied. The Adam optimizer outperformed the others, achieving a Macro-F1 score of 77%. The RMSprop optimizer closely followed, attaining a Macro-F1 score of 76%. In contrast, Adadelta proved to be the least effective for classifying Arabic text.

Social media allows people interact to express their thoughts or feelings about different subjects. However, some of users may write offensive twits to other via social media which known as cyber bullying. Successful prevention depends on automatically detecting malicious messages. Automatic detection of bullying in the text of social media by analyzing the text "twits" via one of the machine learning algorithms. In this paper, we have reviewed algorithms for automatic cyberbullying detection in Arabic of machine learning, and after comparing the highest accuracy of these classifications we will propose the techniques Ridge Regression (RR) and Logistic Regression (LR), which achieved the highest accuracy between the various techniques applied in the automatic cyberbullying detection in English and between the techniques that was used in the sentiment analysis in Arabic text, The purpose of this work is applying these techniques for detecting cyberbullying in Arabic.

LoRaWAN is one of the new and updated standards for IoT applications. However, the expected high density of peripheral devices for each gateway, and the absence of an operative synchronization mechanism between the gateway and peripherals, all of which challenges the networks scalability. In this paper, we propose to normalize the communication of LoRaWAN networks using a Reservation-ALOHA (R-ALOHA) instead of the standard ALOHA approach used by LoRa. The implementation is a library package placed on top of the standard LoRaWAN; thus, no modification in pre-existing LoRaWAN structure and libraries is required. Our proposed approach is based on a distributed synchronization service that is suitable for low-cost IoT end-nodes. R-ALOHA LoRaWAN gives better performance in comparison with the previous models; Pure-ALOHA LoRaWAN, Slotted-ALOHA LoRaWAN, and TDMA LoRaWAN. It significantly improves the performance of network regarding the probability of collision, the maximum throughput, and the maximum duty cycle.