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AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Rezumat articol ediţie STUDIA UNIVERSITATIS BABEŞ-BOLYAI În partea de jos este prezentat rezumatul articolului selectat. Pentru revenire la cuprinsul ediţiei din care face parte acest articol, se accesează linkul din titlu. Pentru vizualizarea tuturor articolelor din arhivă la care este autor/coautor unul din autorii de mai jos, se accesează linkul din numele autorului. |
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STUDIA INFORMATICA - Ediţia nr.2 din 2022 | |||||||
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EXTENDED MAMMOGRAM CLASSIFICATION FROM TEXTURAL FEATURES. Autori: ADÉL BAJCSI, CAMELIA CHIRA, ANCA ANDREICA. |
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Rezumat: DOI: 10.24193/subbi.2022.2.01 Published Online: 2023-02-06 pp. 5-20 VIEW PDF FULL PDF In the culture of the present emojis play an important role in written/typed communication, having a primary role of supplementing the words with emotional cues. While in different cultures emojis can be interpreted and thus used differently, a small set of emojis have clear meaning and strong sentiment polarity. In this work we study how to map natural language texts to emoji sequences, more precisely, we automatically assign emojis to movie subtitles/scripts. The pipeline of the proposed method is as follows: first the most relevant words are extracted from the movie subtitle, and then these are mapped to emojis. In order to perform the mapping, three methods are proposed: a lexical matching-based, a word embedding-based and a combined approach. To demonstrate the viability of the approach, we list some of the generated emojis for a randomly selected movie subset, showing also the deficiencies of the method in generating guessable sequences. Evaluation is performed via quizzes completed by human participants. Received by the editors: 20 September 2022. 2010 Mathematics Subject Classification. 68T35. 1998 CR Categories and Descriptors. I.2.1 [Artifical Intelligence]: Applications and Expert Systems – Medicine and science; I.2.6 [Artifical Intelligence]: Learning – Knowledge acquisition; I.4.7 [Image Processing and Computer Vision]: Feature Measurement – Feature representation; Key words and phrases. Breast cancer detection, Mammogram classification, GLRLM, Feature selection, Random Forests, MIAS, DDSM. |
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