{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:29:21Z","timestamp":1777703361153,"version":"3.51.4"},"reference-count":22,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2019,6,19]],"date-time":"2019-06-19T00:00:00Z","timestamp":1560902400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2019,7,9]]},"abstract":"<jats:p>\u00a0In 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\u2019s 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\u2019s 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\u2019s features taking into consideration the misclassification rates.<\/jats:p>","DOI":"10.3233\/jifs-181324","type":"journal-article","created":{"date-parts":[[2019,6,21]],"date-time":"2019-06-21T11:48:38Z","timestamp":1561117718000},"page":"783-795","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Telecommunication Fuzzy Rules for Multi Services Access Nodes Locations using Artificial Bee Colony"],"prefix":"10.1177","volume":"37","author":[{"given":"Walaa","family":"Elsanabary","sequence":"first","affiliation":[{"name":"Assistant Lecturer, Information System Department, Sadat Academy For Management Sciences, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mona","family":"Gamal","sequence":"additional","affiliation":[{"name":"Lecturer, Information System Department, Faculty of Computers and 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