onur karasoy

kongreara.com ulusal ve uluslararası kongre ve sempozyumlara ulaşabileceğiniz, bilimsel alan, konum ve tarihe göre filtreleme yapabileceğiniz bir arama motorudur. Bu sistem, yapılacak olan bilimsel etkinliklerinin duyurusunun yanı sıra geçmişte gerçekleştirilen kongre ve sempozyumların da belleği olmayı hedeflemektedir. Tamamlanan birçok etkinliğin web sayfaları aktif olarak hizmet vermediğinden eski verilere ulaşmak, araştırmacılar için sorun teşkil etmektedir. kongreara.com arşiv hizmetiyle geçmiş zamanlı etkinlik bilgilerinin (bildiri kitapçığı, çağrı metni, kongre tarihi ve yeri vb.) kaybolmasını engellemekte ve kolaylıkla ulaşabileceğiniz bir altyapı sunmaktadır.

In this study, it was aimed to develop SMS (Short Message) classification application based on deep learning. By examining messages collected from different age groups and regions, feature labels that could be effective in classification were included in the message data set. After that, the model to be used in the classification was created through Word2Vec library. With this model, new features were extracted, test messages were classified and analyzed, and the results were discussed.

While mobile instant messaging applications such as WhatsApp, Messenger, Viber offer benefits to phone users such as price, easy usage, stable, collective and direct communication, SMS (short message service) is still considered a more reliable privacy-preserving technology for mobile communication. This situation directs the institutions that want to perform the product promotion such as advertising, informing, promotion etc. to use SMS. However, spam messages sent from unknown sources constitute a serious problem for SMS recipients. In this study, a content-based classification model which uses the machine learning to filter out unwanted messages is proposed. From the selected dataset, the model to be used in the classification is created with the help of Word2Vec word embedding tool. Thanks to this model, two new features are revealed for calculating the distances of messages to spam and ham words. The performances of the classification algorithms are compared by taking these two new features into consideration. The random forest method succeeded with a correct accuracy rate of 99.64%. In comparison to other studies using the same dataset, more successful correct classification percentage is achieved.