Principles and Applications of Data Science

• Autore: Liu Chuan-Ming
• Pubblicazione: Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
• Descrizione fisica: 1 online resource (168 p.)
• Soggetto topico: History of engineering and technology; Technology: general issues
• Soggetto non controllato: air pollution; behaviour network; behaviour sequence pattern; classification; combined classification model; convolutional neural network; corpus data; crop production prediction; data mining; deep learning; deep transfer learning; dictionary; digitization; electronic health records; fair exchange; feature fusion; fine-tuning; focal-segmental; forward secrecy; graph neural network; image classification; Internet of Things; kidney disease; kidney glomeruli; lexical data; linguistic linked open data; medical image; mining; mobile application; neuro-fuzzy; peasant farming; PM10; PM2.5; prediction model; predictive analytics; raw material; sclerosed glomeruli; self-attention mechanism; similar user recommendation; smart farming system; supervised; terminology; terminology application; user preference learning; water crystal
• Persona (resp. second.): LiuChuan-Ming
• Sommario/riassunto: Data science is an emerging multidisciplinary field which lies at the intersection of computer science, statistics, and mathematics, with different applications and related to data mining, deep learning, and big data. This Special Issue on "Principles and Applications of Data Science" focuses on the latest developments in the theories, techniques, and applications of data science. The topics include data cleansing, data mining, machine learning, deep learning, and the applications of medical and healthcare, as well as social media.
• Titolo autorizzato: Principles and Applications of Data Science
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910580211103321
• Lo trovi qui: Univ. Federico II