No abstract

Abstract-Java Agent Development Framework (JADE) is a software framework to make easy the development of MultiAgent applications in compliance with the Foundation for Intelligent Physical Agents (FIPA) specifications. JADE propose new infrastructure solutions to support the development of useful and convenient distributed applications. Security is one of the most important issues in implementing and deploying such applications. JADE-S security add-ons are one of the most popular security solutions in JADE platform. It provides several security services including authentication, authorization, signature and encryption services. Authorization service will give authorities to perform an action based on a set of permission objects attached to every authenticated user. This service has several drawbacks when implemented in a scalable distributed context aware applications. In this paper, an ontology-based access control model called (OJADEAC) is proposed to be applied in JADE platform by combining Semantic Web technologies with context-aware policy mechanism to overcome the shortcoming of this service. The access control model is represented by a semantic ontology, and a set of two level semantic rules representing platform and application specific policy rules. OJADEAC model is distributed, intelligent, dynamic, context-aware and use reasoning engine to infer access decisions based on ontology knowledge.

The massive spread of COVID-19 made it one of the biggest current pandemics in the world. Predicting the extent of the virus' spread is critical to containing the threat, because it helps to take appropriate measures and decisions at the state level as well as at the personal level, where it is possible to avoid travel to the places of spread and take the necessary measures to limit the spread of the virus. In this research, an intelligent model has been built to predict the extent of the spread of Covid-19 disease in the Iraqi governorates. COVID-19 data for Iraq's governorates was obtained from a website affiliated with the Iraqi Ministry of Health. The data was reconstructed according to a certain structure to be used in training the prediction model. The LSTM deep learning algorithm was adopted for its effective performance in predicting the direction of the recorded cases in the future. The results showed a high accuracy in the performance of the model.

Nowadays, data from different real-time data streams are coming in. The classical relational database systems cannot manage such big data. Big data should be managed in a way that keeps the semantic relations between different concepts. Ontology is a powerful tool that comes from the concept of the semantic web, can formulate data schema as semantically connected objects. Ontology can be shared and reused across different domains and sites. In this paper, an ontology that captures the main concepts and their relationships in Mosul university is proposed. The main steps for OMU (ontology for Mosul University) development are showen using protégé ontology editor. Also, several queries are implemented to show how we can use inference engine to infer new and implicit knowledge from ontologies. visualization tools are used to visualize OMU ontology.