<Record><identifier xmlns="http://purl.org/dc/elements/1.1/">URN:NBN:SI:doc-019C6I7P</identifier><date>2023</date><creator>Albasri, Safaa</creator><creator>Oraibi, Zakariya A.</creator><relation>documents/doc/0/URN_NBN_SI_doc-019C6I7P_001.pdf</relation><relation>documents/doc/0/URN_NBN_SI_doc-019C6I7P_001.txt</relation><format format_type="volume">47</format><format format_type="issue">7</format><format format_type="type">article</format><format format_type="extent">str. 115-125</format><identifier identifier_type="DOI">10.31449/inf.v47i7.4790</identifier><identifier identifier_type="ISSN">1854-3871</identifier><identifier identifier_type="COBISSID">208417283</identifier><identifier identifier_type="URN">URN:NBN:SI:doc-019C6I7P</identifier><language>eng</language><publisher publisher_location="Ljubljana">Informatika</publisher><source>Informatica (Ljubljana)</source><rights>BY</rights><subject language_type_id="slv">COVID-19</subject><subject language_type_id="slv">globoko učenje</subject><subject language_type_id="slv">napovedovanje</subject><subject language_type_id="slv">pandemije</subject><subject language_type_id="slv">rentgenske slike</subject><subject language_type_id="slv">umetna inteligenca</subject><title>A robust end-to-end CNN architecture for efficient COVID-19 prediction form X-ray images with imbalanced data</title></Record>