1,721,003 research outputs found
Multi-class machine learning detection of Edema, Vocal Paralysis and Vocal Nodules through voice
No full textThis paper aims to differentiate causes of dysphonia, namely Reinke's Edema, Vocal Cord Paralysis, and Vocal Nodules, also including healthy subjects. A proprietary dataset of 245 subjects underwent acoustic feature extraction and selection, and four classifiers were trained for multi-class classification. Loudness/Energy-related features were among the most effective, which is in line with the fact that the three diseases all cause different impairments in terms of voice volume. Cepstrum is also confirmed as an effective domain. The four classifiers obtained comparable performances, with Random Forest having the highest accuracy at 78.4% and Naïve Bayes offering the best compromise in terms of recall. Healthy subjects always lead to a higher recall, which is in line with the fact that identifying dysphonia is an easier task than differentiating among its causes
Osservazioni sulla presenza e persistenza del virus della PRRS in tessuti diversi di suini nati da scrofe infettate sperimentalmente
No full textGeneralità sul sistema immunitario e ruolo dell’immunità mucosale nelle infezioni virali nella specie suina.
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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