2020
DOI: 10.37696/nkmj.660762
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Sağlık Alanında Veri Mahremiyetinin Korunmasına Yönelik Makine Öğrenmesi Uygulamalarına Yeni Bir Yaklaşım: Federe Öğrenme

Abstract: Aim: Today, data banks contain unpredictable data. Together with the advances in data science, large data offer the potential to better understand the causes of diseases. This potential results from the processing, analysis or modeling of machine learning algorithms. Various data sets stored in different institutions are not always shared directly due to privacy and legal concerns. This problem limits the full use of large data in health research. Federated learning is aimed at developing artificial intelligen… Show more

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Cited by 2 publications
(1 citation statement)
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“…Artificial Intelligence (AI) is considered to be one of the traditional method to generate the data throughout global IoT devices. AI advancement is started in the Deep learning (DL) and Machine Learning (ML) for the purpose of designing models to train data on growing demand of the applications on intelligent IoT in various fields like smart healthcare, smart vehicles and smart cities [1].…”
Section: Introductionmentioning
confidence: 99%
“…Artificial Intelligence (AI) is considered to be one of the traditional method to generate the data throughout global IoT devices. AI advancement is started in the Deep learning (DL) and Machine Learning (ML) for the purpose of designing models to train data on growing demand of the applications on intelligent IoT in various fields like smart healthcare, smart vehicles and smart cities [1].…”
Section: Introductionmentioning
confidence: 99%