id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
easat-11273	Choi, Daesoo	Disciplinary landscapes of deep learning: Cross-domain insights via LDA topic modeling	2025	15	.pdf	application/pdf	7409	345	27	By offering a comparative view across disciplines, this study highlights both shared and divergent trajectories in deep learning research. This study contributes to filling that gap by employing topic modeling as a lens to examine 69 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 12: 66-80, 2025 DOI: 10.55214/2576-8484.v9i12.11273 © 2025 by the author; licensee Learning Gate the thematic landscape of deep learning research across engineering, natural sciences, and social sciences, ultimately offering a more integrative view of how this technology is shaping knowledge production across the disciplinary spectrum. 3.	cache/easat-11273.pdf	txt/easat-11273.txt