A survey on federated learning in data mining
Sign Up to like & getrecommendations! Published in 2022 at "Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"
DOI: 10.1002/widm.1443
Abstract: Data mining is a process to extract unknown, hidden, and potentially useful information from data. But the problem of data island makes it arduous for people to collect and analyze scattered data, and there is… read more here.
Keywords: learning data; mining; data mining; survey federated ... See more keywords
Machine Learning and Data Analytics in Pervasive Health.
Sign Up to like & getrecommendations! Published in 2018 at "Methods of information in medicine"
DOI: 10.1055/s-0038-1673243
Abstract: INTRODUCTION This accompanying editorial provides a brief introduction to this focus theme, focused on "Machine Learning and Data Analytics in Pervasive Health". OBJECTIVE The innovative use of machine learning technologies combining small and big data… read more here.
Keywords: medicine; machine learning; data analytics; pervasive health ... See more keywords
Learning from data streams and class imbalance
Sign Up to like & getrecommendations! Published in 2019 at "Connection Science"
DOI: 10.1080/09540091.2019.1572975
Abstract: With the wide application of machine learning algorithms to the real world, class imbalanceandconceptdrift havebecomecrucial learning issues. Applications in variousdomains such as riskmanagement, anomaly detection, fraud detection, software engineering, social media mining, and recommender systems… read more here.
Keywords: concept drift; data streams; class; class imbalance ... See more keywords
Learning and Data-Driven Beam Selection for mmWave Communications: An Angle of Arrival-Based Approach
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2895594
Abstract: This paper investigates how angle-of-arrival (AoA) information can be exploited by deep-/machine-learning approaches to perform beam selection in the uplink of a mmWave communication system. Specifically, we consider a hybrid beamforming setup comprising an analog… read more here.
Keywords: data driven; angle arrival; beam selection; learning data ... See more keywords
Neural Networks and Imbalanced Learning for Data-Driven Scientific Computing With Uncertainties
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3052680
Abstract: Uncertainty quantification in complex engineering problems is challenging because of necessitating large numbers of expensive model evaluations. This paper proposes a two-stage framework for developing accurate machine learning-based surrogate models in structural engineering. The studied… read more here.
Keywords: data driven; imbalanced learning; networks imbalanced; neural networks ... See more keywords
A Survey on Deep Learning for Data-Driven Soft Sensors
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2021.3053128
Abstract: Soft sensors are widely constructed in process industry to realize process monitoring, quality prediction, and many other important applications. With the development of hardware and software, industrial processes have embraced new characteristics, which lead to… read more here.
Keywords: soft sensors; survey deep; learning data; data driven ... See more keywords
Studying Up Machine Learning Data
Sign Up to like & getrecommendations! Published in 2022 at "Proceedings of the ACM on Human-Computer Interaction"
DOI: 10.1145/3492853
Abstract: Research in machine learning (ML) has argued that models trained on incomplete or biased datasets can lead to discriminatory outputs. In this commentary, we propose moving the research focus beyond bias-oriented framings by adopting a… read more here.
Keywords: learning data; machine learning; studying machine; research ... See more keywords
Intelligently learning from data
Sign Up to like & getrecommendations! Published in 2019 at "Critical Care"
DOI: 10.1186/s13054-019-2424-7
Abstract: Main text Methods from the fields of artificial intelligence (AI) and machine learning (ML) are entering the medical literature at an unprecedented rate. A PubMed search using the keyword “Machine Learning” has shown an accelerating… read more here.
Keywords: medicine; intelligently learning; causal; learning data ... See more keywords
Deep Learning With Data Enhancement for the Differentiation of Solitary and Multiple Cerebral Glioblastoma, Lymphoma, and Tumefactive Demyelinating Lesion
Sign Up to like & getrecommendations! Published in 2021 at "Frontiers in Oncology"
DOI: 10.3389/fonc.2021.665891
Abstract: Objectives To explore the MRI-based differential diagnosis of deep learning with data enhancement for cerebral glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and tumefactive demyelinating lesion (TDL). Materials and Methods This retrospective study analyzed… read more here.
Keywords: cerebral glioblastoma; deep learning; enhancement; data enhancement ... See more keywords