The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets.

A medical diagnosis system (DRCAD), which consists of two sub-modules Bayesian and rule-based inference models, is presented in this study. Three types of tests are conducted to assess the performances of the models producing synthetic data based on the ALARM network. The results indicate that the linear combination of the aforementioned models leads to a 5% and a 30% improvement in medical diagnosis when compared to the "Rule Based Method" and the "Bayesian Network Based Method", respectively.

Abstract-Story Link Detection (SLD) is known as a sub-task of Topic Detection and Tracking (TDT). SLD aims to specify whether two randomly selected stories discuss the same topic or not. This sub-task drew special attention within the TDT research community as many tasks in TDT are thought to be solved automatically once SLD performs as expected. In this study, performance tests were carried out on the BilCol-2005 Turkish news corpus composed of approximately 209,000 news items using vector space model (VSM) and relevance model (RM) methods with respect to varied index term counts. Accordingly, best results obtained were as follows: the VSM method performed best with 30 terms (F-measure=0.2970) while RM method did with 4 terms (F-measure=0.1910). Furthermore, the combination of two methods using the AND and OR functions increased the precision ratio by 7.9% and recall ratio by 1.2%, respectively, indicating that retrieval performance of SLD algorithms can be increased to some extent by employing both VSM and RM models.

Arama motorları, hem belgelerin yazarları hem de bilgi ihtiyaçlarını açıklayan kullanıcılar tarafından kullanılan sözlükler arasındaki farklar yüzünden ortaya çıkan uçurumlarla başa çıkmada yetersiz hale gelmişlerdir. Bu problemi azaltmanın bir yolu kavram tabanlı bilgi erişimini ortaya koymaktır. Bu çalışmada, bazı perspektiflerde RUBRIC sisteminin bir uzantısı olarak değerlendirilen ancak betimsel düzeyde erişime uygun farklı özellikler içeren bir erişim modeli önerilmekte ve elde edilen sonuçlar değerlendirilmektedir.

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