Affiliations: [a] Assistant Lecturer, Information System Department, Sadat Academy For Management Sciences, Egypt
| [b] Lecturer, Information System Department, Faculty of Computers and Information, Kafrelsheikh University, Egypt
| [c] Professor, Information System Department, Faculty of Computers and Information, Mansoura University, Egypt
| [d] Professor, Information System Department, Sadat Academy For Management Sciences, Egypt
Correspondence:
[*]
Corresponding author. Walaa Elsanabary, Assistant Lecturer, Information System Department, Sadat Academy For Management Sciences, Egypt. E-mail: [email protected].
Abstract: In the presence of high competition market, planning the infrastructures of Telecommunication Access Network (TAN) is one of the most important tasks facing telecommunication companies especially after the trend of using optical fiber cables. This infrastructure is controlled by a list of barriers which affect selecting the locations of the most widely used technology Multi Services Access Nodes (MSAN). Therefore, the importance of determining the appropriate location of MSANs is appeared. This paper presents the capabilities of the Artificial Bee Colony (ABC) to find the fuzzy classifications rules for the telecommunication MSANs locations based on a set of MSAN’s planning barriers. This system starts by preparing the training data set using the benefits of Geographic Information System (GIS) for generating digital maps. The system helps in analyzing spatial data of existing TAN and the barriers which affect planning TAN. Afterwards, the system fuzzifies the MSAN’s planning barriers using Particle Swarm Optimization and Total Entropy as fitness function (PSO-TE). Then, the ABC capabilities, correlation function and confidence rate as a fitness function and the mamdani inference system are utilized to find the appropriate telecommunication fuzzy rules with respect of training data. The system ends by evaluating the generated telecommunication fuzzy rules for MSAN locations via comparing the result of proposed model with a number of classification algorithms found in literature based on the test data set. The total classification accuracy of the TFRML-ABC model is 97.8%. Hence, the proposed TFRML-ABC model is concluded to be efficient in classifying the MSAN’s features taking into consideration the misclassification rates.
Keywords: Artificial bee colony (ABC), correlation, Min-Max mamdani inference system, particle swarm optimization-total entropy (PSO-TE), telecommunication access network (TAN), geographic information system (GIS), multi services access node (MSAN)
