Temat: metadata - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Access Logs : Underestimated Privacy Risks
Autorzy:: Glet, Michał
Kaczyński, Kamil
Powiązania:: https://bibliotekanauki.pl/articles/227087.pdf
Data publikacji:: 2020
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: metadata
privacy
cybersecurity
instant messaging
access log
Opis:: Access logs may offer service providers a lot of information about specific users. Depending on the type of the service offers, the operator is capable of obtaining the user’s IP, location, communication habits, device information and so on. In this paper, we analyze a sample instant messenger service that is operating for a certain period of time. In our sandbox, we gathered enough data to correlate user communication habits with their localization, and even contacts. We show how seriously metadata may impact the user’s privacy and make some recommendations about mitigating the quantity of data collected in connection with this type of services.
Źródło:: International Journal of Electronics and Telecommunications; 2020, 66, 3; 405-410
2300-1933
Pojawia się w:: International Journal of Electronics and Telecommunications
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: An Ensemble of Statistical Metadata and CNN Classification of Class Imbalanced Skin Lesion Data
Autorzy:: Nayak, Sachin
Vincent, Shweta
Sumathi, K.
Kumar, Om Prakash
Pathan, Sameena
Powiązania:: https://bibliotekanauki.pl/articles/2055258.pdf
Data publikacji:: 2022
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: classification
Convolutional Neural Networks
Ensemble Learning
machine learning
metadata
Opis:: Skin Cancer is one of the most widely present forms of cancer. The correct classification of skin lesions as malignant or benign is a complex process that has to be undertaken by experienced specialists. Another major issue of the class imbalance of data causes a bias in the results of classification. This article presents a novel approach to the usage of metadata of skin lesions images to classify them. The usage of techniques addresses the problem of class imbalance to nullify the imbalances. Further, the use of a convolutional neural network (CNN) is proposed to finetune the skin lesion data classification. Ultimately, it is proven that an ensemble of statistical metadata analysis and CNN usage would result in the highest accuracy of skin color classification instead of using the two techniques separately.
Źródło:: International Journal of Electronics and Telecommunications; 2022, 68, 2; 251--257
2300-1933
Pojawia się w:: International Journal of Electronics and Telecommunications
Dostawca treści:: Biblioteka Nauki

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