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Showing 6 of 26 academic articles

Machine learning algorithms review

Qingyang Ling • Applied and Computational Engineering (2023)

Machine learning is a field of study where the computer can learn for itself without a human explicitly hardcoding the knowledge for it. These algorithms make up the backbone of machine learning. This paper aims to study the field of machin...

OPENALEX cc-by DOI: https://doi.org/10.54254/2755-2721/4/20230355

Machine learning applications in epilepsy

Bardia Abbasi, Daniel M. Goldenholz • Epilepsia (2019)

Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recogn...

OPENALEX Open Access DOI: https://doi.org/10.1111/epi.16333 PMC: 9897263

Explainable artificial intelligence: A survey

Filip Karlo Došilović, Mario Brčić, Nikica Hlupić • International Convention on Information and Communication Technology, Electronics and Microelectronics (2018)

In the last decade, with availability of large datasets and more computing power, machine learning systems have achieved (super)human performance in a wide variety of tasks. Examples of this rapid development can be seen in image recognitio...

OPENALEX DOI: https://doi.org/10.23919/mipro.2018.8400040

Artificial Intelligence and Deep Learning in Ophthalmology

Daniel Shu Wei Ting, Louis Robert Pasquale, Lily Peng, John Peter Campbell et al. • Springer eBooks (2018)

Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to i...

OPENALEX Open Access DOI: https://doi.org/10.1007/978-3-030-64573-1_200 PMC: 6362807

Identification of Aspergillus at section and species levels by artificial intelligence-based microscopic morphology image recognition

Meng Tan, Zhe Guo, Yanyi Wang, Xinyi Xu et al. • Journal of Clinical Microbiology (2026)

ABSTRACT Rapid and accurate identification of Aspergillus species in clinical microbiology laboratories is crucial for aspergillosis diagnosis and antifungal therapy. However, traditional methods still face challenges in distinguishing phyl...

DOAJ Open Access (DOAJ) DOI: 10.1128/jcm.00012-26