Teknik Bilimler Meslek Yüksekokulu / Vocational School of Technical Sciences

Permanent URI for this collectionhttps://hdl.handle.net/11727/2031

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    Correlations between problem and solution domain measures of open source software
    (2017) Ayyidiz, Tulin Ercelebi; Kocyigit, Altan; 0000-0002-7372-0223; AAE-1726-2021
    Software size measurement and effort estimation methodologies in use today usually take the detailed requirements of software to be developed as the primary input and a certain amount of time and expertise is needed for size measurement. This paper analyzes the open source projects' correlations between the problem domain measures (the number of nouns and verbs) and solution domain measures (the number of software classes and methods). In this paper, 27 open source software projects are analyzed. Linear regression and cross validation techniques are applied to investigate the relation between the sizes of problem domain (i.e., conceptual) and solution domain (i.e., design) measures. The results reveal a strong correlation between the problem domain measures and the solution domain measures constituting the corresponding software. The results suggest that it is possible to use problem domain descriptions in the early stages of software development projects to make plausible predictions for the size and effort of the software.
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    Comparison of SVM and ANFIS for Snore Related Sounds Classification by Using the Largest Lyapunov Exponent and Entropy
    (,2013, 2013) Ankışhan, Haydar; Yılmaz, Derya
    Snoring, which may be decisive for many diseases, is an important indicator especially for sleep disorders. In recent years, many studies have been performed on the snore related sounds (SRSs) due to producing useful results for detection of sleep apnea/hypopnea syndrome (SAHS). The first important step of these studies is the detection of snore from SRSs by using different time and frequency domain features. The SRSs have a complex nature that is originated from several physiological and physical conditions. The nonlinear characteristics of SRSs can be examined with chaos theory methods which are widely used to evaluate the biomedical signals and systems, recently. The aim of this study is to classify the SRSs as snore/breathing/silence by using the largest Lyapunov exponent (LLE) and entropy with multiclass support vector machines (SVMs) and adaptive network fuzzy inference system (ANFIS). Two different experiments were performed for different training and test data sets. Experimental results show that the multiclass SVMs can produce the better classification results than ANFIS with used nonlinear quantities. Additionally, these nonlinear features are carrying meaningful information for classifying SRSs and are able to be used for diagnosis of sleep disorders such as SAHS.