Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Article
    Citation - WoS: 4
    Citation - Scopus: 4
    A New Shapley-Based Feature Selection Method in a Clinical Decision Support System for the Identification of Lung Diseases
    (MDPI, 2023) Kababulut, Fevzi Yasin; Kuntalp, Damla Gurkan; Düzyel, Okan; Özcan, Nermin; Kuntalp, Mehmet
    The aim of this study is to propose a new feature selection method based on the class-based contribution of Shapley values. For this purpose, a clinical decision support system was developed to assist doctors in their diagnosis of lung diseases from lung sounds. The developed systems, which are based on the Decision Tree Algorithm (DTA), create a classification for five different cases: healthy and disease (URTI, COPD, Pneumonia, and Bronchiolitis) states. The most important reason for using a Decision Tree Classifier instead of other high-performance classifiers such as CNN and RNN is that the class contributions of Shapley values can be seen with this classifier. The systems developed consist of either a single DTA classifier or five parallel DTA classifiers each of which is optimized to make a binary classification such as healthy vs. others, COPD vs. Others, etc. Feature sets based on Power Spectral Density (PSD), Mel Frequency Cepstral Coefficients (MFCC), and statistical characteristics extracted from lung sound recordings were used in these classifications. The results indicate that employing features selected based on the class-based contribution of Shapley values, along with utilizing an ensemble (parallel) system, leads to improved classification performance compared to performances using either raw features alone or traditional use of Shapley values.
  • Article
    Citation - WoS: 11
    Citation - Scopus: 11
    The Utilization of Supervised Classification Sampling for Environmental Monitoring in Turin (italy)
    (MDPI, 2021) Salata, Stefano
    In a world threatened by climate change, the need to observe the land transformation is crucial to set environmental policies. One of the most prominent issues of environmental monitoring is the availability of updated and reliable land use data. The last land-use release in Piedmont Region (Italy) is in 2010, while the most updated Normalized Difference Vegetation Index is in 2016. To overcome this limit, in this study, a supervised classification sampling has been applied on a Sentinel-2 image produced by the Copernicus Program on 29 September 2020, using Esri ArcGIS (ver.10.8 Redlands, California, US) by accessing via ONDA-DIAS services to L2A products. After land classification, three maps were generated-the Habitat Quality, the Habitat Decay, and the Normalized Difference Vegetation Index. This study aimed at classifying the environmental status in five classes ranging from critical to health with a double perspective-(i) to make a comparative metropolitan assessment between municipalities and (ii) to evaluate the quality of urban public green areas in the city of Turin while defining a different kind of intervention. Results indicate that products derived from supervised classification sampling can be applied in a wide range of applications while reaching seasonal monitoring of the environmental status and delivering just-in-time solutions.