Epidemics control is a continues struggle. In this paper is an attempt to model and then simulate an epidemiological disease known as Cutaneous Leishmaniasis (CL), which is currently affecting large communities in Libya. The model is developed to facilitate the Agent Based Models (ABM) as one of the many tools applied for epidemiological management. Validation of the model is considered by comparing the model's behavior with a trend of field data used by Libyan authorities. The methodology used for describing and designing CL model is derived from nature of the disease mechanisms. The ABM model involves three types of agents: Human, Rodent and Sand-fly. Each agent has its own properties. Additionally, global model parameters are used for following the human infection processes. Several experiments are given for illustrating the model performance, and monitor the number of people infected. Simulation results show that active human agents are more vulnerable to sand-fly bites, and infection rate is increasing or decreasing dependent on number of sand-fly vectors, number of host rodents, and human population awareness level. arabic 9 English 68
Rudwan A. Husain, Hala Shaari, Marwa Solla, Hassan A. H. Ebrahem(3-2019)

Abstract: High speed communication and data movement is the key for mobile technologies and systems evolutions, which requires broadband communications and intelligent networking. The 5G mobile infrastructure enables development of many mobile technologies to fully use new technologies such as AI, mobile edge computing, M2M and data analytics. 5G/IMT-2020 will provide new applications and services for both developed and developed countries. Applications such as smart transport systems, e-health, education, smart grid, agriculture and disaster relief. In this paper we simulate and evaluate the mobile system related to its mobile application by using suitable queuing models to provide best performance based on its QoS.
Abdussalam Nuri Baryun, Khaled BenHamed(11-2021)

: Deoxyribonucleic acid (DNA) micro-arrays present a powerful means of observing thousands of gene terms levels at the same time. They consist of high dimensional datasets, which challenge conventional clustering methods. The data’s high dimensionality calls for Self Organizing Maps (SOMs) to cluster DNA micro-array data. The DNA micro-array dataset are stored in huge biological databases for several purposes . The proposed methods are based on the idea of selecting a gene subset to distinguish all classes, it will be more effective to solve a multi-class problem, and we will propose a genetic programming (GP) based approach to analyze multi-class micro-array datasets. This biological dataset will be derived from multiple biological databases. The procedure responsible for extracting datasets called DNA-Aggregator. We will design a biological aggregator, which aggregates various datasets via DNA micro-array community-developed ontology based upon the concept of semantic Web for integrating and exchanging biological data. Our aggregator is composed of modules that retrieve the data from various biological databases. It will also enable queries by other applications to recognize the genes. The genes will be categorized in groups based on a classification method, which collects similar expression patterns. Using a clustering method such as k-mean is required either to discover the groups of similar objects from the biological database to characterize the underlying data distribution. arabic 9 English 55
Mariam Abojela Msaad, Zakaria Suliman Zubi(1-2011)