Open AccessRating Faculty for Foreign Student in Egypt by Bloom Filter Classifier and Collaborative Filtering
Author(s) -
Mahmood A. Mahmood,
Tarek A. Mohamed
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.a1267.059120
Subject(s) - collaborative filtering , classifier (uml) , nationality , bloom filter , computer science , filter (signal processing) , artificial intelligence , machine learning , data mining , recommender system , algorithm , computer vision , political science , immigration , law
This paper presents an approach of bloom filter classifier and collaborative filtering to help foreign student to choose the suitable faculty according to his nationality and number of years that need to study. Our approach consist of three phases are: input phase, classification phase, and recommendation phase. In Input phase, the student enters the nationality and number of suggested years study. In classification phase, the approach classifies the student according to input data based on bloom filter classifier. In recommendation phase, the approach recommended the top five faculty if exists based on collaborative filtering technique (CF). Our dataset collected from Misr University for Science and Technology (MUST) and the results of our approach suitable and has a good manner for the student with accuracy 90%.