Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Citation - Scopus: 1Türkçe Tweetler Üzerinden Yapay Sinir Ağları ile Cinsiyet Tahminlemesi(Institute of Electrical and Electronics Engineers Inc., 2019) Sezerer, Erhan; Tekir, Selma; Tekir, Selma; Sezerer, Erhan; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyYazar ayrımlaması, yazarı bilinmeyen bir metin üzerinden yazarına dair cinsiyet, yaş ve dil gibi bazı anahtar özniteliklerin belirlenmesidir. Özellikle güvenlik ve pazarlama alanında önem arz etmektedir. Bu çalışmada, kullanıcıların tweetleri kullanılarak cinsiyetleri tahminlenmektedir. Yinelemeli Sinir Ağı (YSA) ve ilgi mekanizmasının birleşiminden oluşan bir model önerilmiştir. Bildiğimiz kadarıyla bu çalışma Twitter veri kümesi ile Türkçe’de ilk defa yapılmıştır. Önerilen model Türkçe, İngilizce, İspanyolca ve Arapça dillerinde sınanmış ve sırasıyla 80.63, 81.73, 78.22, 78.5 doğruluk değerlerine ulaşılmıştır. Elde edilen doğruluk değerleri Türkçe’de en gelişkin, diğer dillerde ise rekabetçi bir başarım ortaya koymaktadır.Conference Object Citation - Scopus: 6Geodesic Distances for Web Document Clustering(Institute of Electrical and Electronics Engineers Inc., 2011) Tekir, Selma; Tekir, Selma; Keim, Daniel; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyWhile traditional distance measures are often capable of properly describing similarity between objects, in some application areas there is still potential to fine-tune these measures with additional information provided in the data sets. In this work we combine such traditional distance measures for document analysis with link information between documents to improve clustering results. In particular, we test the effectiveness of geodesic distances as similarity measures under the space assumption of spherical geometry in a 0-sphere. Our proposed distance measure is thus a combination of the cosine distance of the term-document matrix and some curvature values in the geodesic distance formula. To estimate these curvature values, we calculate clustering coefficient values for every document from the link graph of the data set and increase their distinctiveness by means of a heuristic as these clustering coefficient values are rough estimates of the curvatures. To evaluate our work, we perform clustering tests with the k-means algorithm on the English Wikipedia hyperlinked data set with both traditional cosine distance and our proposed geodesic distance. The effectiveness of our approach is measured by computing micro-precision values of the clusters based on the provided categorical information of each article. © 2011 IEEE.Conference Object Citation - WoS: 1Citation - Scopus: 7Intelligence analysis modeling(Institute of Electrical and Electronics Engineers Inc., 2006) Koltuksuz, Ahmet; Tekir, Selma; Tekir, Selma; Koltuksuz, Ahmet; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyIntelligence is the process of supporting the policymakers in making their decisions by providing them with the specific information they need. Intelligence analysis is the effort of extracting the nature of intelligence issue with the policy goal in mind. It is performed by intelligence analysts who form judgments that add value to the collected material. With the increased open source collection capabilities, there has emerged a need for a model of intelligence analysis that covers the basic elements of valuable information: relevancy, accuracy, and timeliness. There exist models such as vector space model of information retrieval which only addresses the relevancy aspect of information and cannot cope with nonlinear document spaces. In this paper, we discuss the requirements of an integrated model of intelligence analysis along with its peculiar characteristics.