Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
Browse
3 results
Search Results
Conference Object Citation - Scopus: 4An Aspect-Lexicon Creation and Evaluation Tool for Sentiment Analysis Researchers(Springer Verlag, 2012) Husaini, Mus'ab; Koçyiğit, Ahmet; Tapucu, Dilek; Yanıkoğlu, Berrin; Saygın, YücelIn this demo paper, we present SARE, a modular and extendable semi-automatic system that 1) assists researchers in building gold-standard lexicons and evaluating their lexicon extraction algorithms; and 2) provides a general and extendable sentiment analysis environment to help researchers analyze the behavior and errors of a core sentiment analysis engine using a particular lexicon.Conference Object A Data Coding and Screening System for Accident Risk Patterns: A Learning System(WITPress, 2011) Geçer Sargın, Feral; Geçer Sargın, Feral; Duvarcı, Yavuz; Duvarcı, Yavuz; İnan, E.; İnan, E.; Kumova, Bora İsmail; Kumova, Bora İsmail; Atay Kaya, İlgi; Atay Kaya, İlgiAccidents on urban roads can occur for many reasons, and the contributing factors together pose some complexity in the analysis of the casualties. In order to simplify the analysis and track changes from one accident to another for comparability, an authentic data coding and category analysis methods are developed, leading to data mining rules. To deal with a huge number of parameters, first, most qualitative data are converted into categorical codes (alpha-numeric), so that computing capacity would also be increased. Second, the whole data entry per accident are turned into ID codes, meaning each crash is possibly unique in attributes, called 'accident combination', reducing the large number of similar value accident records into smaller sets of data. This genetical code technique allows us to learn accident types with its solid attributes. The learning (output averages) provides a decision support mechanism for taking necessary cautions for similar combinations. The results can be analyzed by inputs, outputs (attributes), time (years) and the space (streets). According to Izmir's case results; sampled data and its accident combinations are obtained for 3 years (2005 - 2007) and their attributes are learned. © 2011 WIT Press.Conference Object Citation - WoS: 2Citation - Scopus: 5Machine Learning Based Learner Modeling for Adaptive Web-Based Learning(Springer Verlag, 2007) Aslan, Burak Galip; İnceoğlu, Mustafa MuratEspecially in the first decade of this century, learner adapted interaction and learner modeling are becoming more important in the area of web-based learning systems. The complicated nature of the problem is a serious challenge with vast amount of data available about the learners. Machine learning approaches have been used effectively in both user modeling, and learner modeling implementations. Recent studies on the challenges and solutions about learner modeling are explained in this paper with the proposal of a learner modeling framework to be used in a web-based learning system. The proposed system adopts a hybrid approach combining three machine learning techniques in three stages.