Прегледај по Аутор "Junuz, Emina"

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    BGP Anomaly Prediction Using Ensemble Learning
    (2019) Cosovic, Marijana; Junuz, Emina
    This paper investigates anomalies such as worms, power outages, and routing table leak (RTL) events occurring in Border Gateway Protocol (BGP) that can cause connectivity and data loss issues. Ensemble learning is a machine learning model employing multiple classifiers in order to reliably identify network anomalies. We use bagging, boosting, and random forests ensemble models trained on network anomaly datasets for classification improvement. Models were compared with respect to the following performance metrics: F-measure, Matthews correlation coefficient (MCC), Receiver operating characteristic (ROC) curve, precision-recall (PR) curves and model execution time. We observed improvement in performance measures when ensemble classifiers realized in Python were used in comparison to our previously reported results on single classifiers. Further improvement in most performance measures was observed by using sampling techniques (oversampling and undersampling) on anomalous datasets. This approach increases model execution time which is not favorable for real-time anomaly detection models.
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    Classication Methods in Cultural Heritage
    (Research Area of ​​the CNR of Pisa Institute of Information Science and Technologies “A. Faedo” (ISTI), 2019) Ćosović, Marijana; Amelio, Alessia; Junuz, Emina
    This paper describes relevant classifi cation methods applied to the cultural heritage context. In particular, a categorisation of the classifi cation methods is provided according to tangible and intangible cultural heritage, where movable and immovable objects can be in the focus. A short description of each method is reported for each cultural heritage category in terms of feature representation, classifi cation approach and obtained results. The proposed survey can be useful in the research community of pattern recognition and visual computing for exploring the current literature about the topic. It will hopefully provide new insights for the advancement of knowledge discovery in cultural heritage.