The advancement of technology has paved the way for a new type of bullying, which often leads to negative stigma in the social setting. Cyberbullying is a cybercrime wherein one individual becomes the target of harassment and hatred. It has recently become more prevalent due to a rise in the usage of social media platforms, and, in some severe situations, it has even led to victims’ suicides. In the literature, several cyberbullying detection methods are proposed, but they are mainly focused on word-based data and user account attributes. Furthermore, most of them are related to the English language. Meanwhile, only a few papers have studied cyberbullying detection in Arabic social media platforms. This paper, therefore, aims to use machine learning in the Arabic language for automatic cyberbullying detection. The proposed mechanism identifies cyberbullying using the Support Vector Machine (SVM) classifier algorithm by using a real dataset obtained from YouTube and Twitter to train and test the classifier. Moreover, we include the Farasa tool to overcome text limitations and improve the detection of bullying attacks.

Scheduling algorithms are of utmost importance in WiMAX for efficient use of radio resources. A scheduling algorithm should take into account the WiMAX QoS classes and service requirements. It should also provide high throughput. In this paper, we propose a review of scheduling algorithms proposed for WiMAX. We focus on the real-time Polling Service (rtPS) QoS class. NS-2 simulations show interesting results. We highlight a problem that may exist with the WiMAX rtPS QoS class and we provide solutions for it. Simulation results concerning proposed WiMAX schedulers are discussed. We propose an enhancement of the maximum Signal-to-Interference Ratio (mSIR) scheduler, called modified maximum Signal-to-Interference Ratio (mmSIR). We show through extensive simulations that this enhancement provides better mean sojourn time in addition to an improvement in throughput.

Nowadays, waste management faces the challenge of providing effective and efficient solutions for waste collection, disposal, and recycling while respecting health and environmental standards. This challenge also includes the lack of understanding of the diverse factors that influence the various stages of waste management, inefficient route planning, insufficient resources, etc. Faster collection, management, and processing of waste are possible with smart containers and IoT technologies allowing waste real-time data provision. Thus, this research proposes a waste management system based on generic and comprehensive generic context ontology and smart containers. The context ontology is conceived to solve the limits and the insufficiencies of waste management by covering all the waste facets for all the stakeholders, optimizing, analyzing, and reusing the waste data and conditions. For given smart container and waste management context, we need to have a global view of the relevant contextual data according to a unified model such as the waste environment data, the waste activity context, the waste computing context, the user context, the collaboration context, etc. One significant advantage of our system is that it provides a unified model for waste management contextual data that could be reused for other waste management systems covering all the properties of this domain. The proposed solution implements an intelligent and adaptive IoT system for waste management according to different waste contexts, waste objectives, and waste activities. The proposed system has been successfully tested under different scenarios in Jeddah City Municipality.

This paper introduces a new call admission control (CAC) mechanism for Long Term Evolution (LTE) networks supporting multimedia services with different classes of traffic. Our CAC mechanism classifies calls into real time and non-real time users, then estimates the channel quality based upon the received signal strength (RSS) value, and finally identifies the call as either new call (NC) or handoff call (HC) request before performing admission control decision. We also use a simple preemption technique in order to allocate the resources to high priority bearer requests. We show through extensive simulation analysis that our CAC mechanism provides high number of accepted users with higher priorities while providing high system throughput.