Differential privacy for deep and federated learning: A survey
Users' privacy is vulnerable at all stages of the deep learning process. Sensitive information
of users may be disclosed during data collection, during training, or even after releasing the …
of users may be disclosed during data collection, during training, or even after releasing the …
Machine learning for reliability engineering and safety applications: Review of current status and future opportunities
Abstract Machine learning (ML) pervades an increasing number of academic disciplines and
industries. Its impact is profound, and several fields have been fundamentally altered by it …
industries. Its impact is profound, and several fields have been fundamentally altered by it …
Data‐Driven Materials Innovation and Applications
Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …
experimental and computational investigative methodologies, the massive amounts of data …
Prediction of in‐hospital mortality in emergency department patients with sepsis: a local big data–driven, machine learning approach
Objectives Predictive analytics in emergency care has mostly been limited to the use of
clinical decision rules (CDR s) in the form of simple heuristics and scoring systems. In the …
clinical decision rules (CDR s) in the form of simple heuristics and scoring systems. In the …
Forecasting fine-grained air quality based on big data
In this paper, we forecast the reading of an air quality monitoring station over the next 48
hours, using a data-driven method that considers current meteorological data, weather …
hours, using a data-driven method that considers current meteorological data, weather …
[HTML][HTML] Machine learning-based classification of rock discontinuity trace: SMOTE oversampling integrated with GBT ensemble learning
This paper presents a hybrid ensemble classifier combined synthetic minority oversampling
technique (SMOTE), random search (RS) hyper-parameters optimization algorithm and …
technique (SMOTE), random search (RS) hyper-parameters optimization algorithm and …
Machine learning models for the identification of prognostic and predictive cancer biomarkers: a systematic review
The identification of biomarkers plays a crucial role in personalized medicine, both in the
clinical and research settings. However, the contrast between predictive and prognostic …
clinical and research settings. However, the contrast between predictive and prognostic …
A systematic review of applications of machine learning techniques for wildfire management decision support
Wildfires threaten and kill people, destroy urban and rural property, degrade air quality,
ravage forest ecosystems, and contribute to global warming. Wildfire management decision …
ravage forest ecosystems, and contribute to global warming. Wildfire management decision …
Comparison of decision tree algorithms for EMG signal classification using DWT
E Gokgoz, A Subasi - Biomedical signal processing and control, 2015 - Elsevier
Decision tree algorithms are extensively used in machine learning field to classify
biomedical signals. De-noising and feature extraction methods are also utilized to get higher …
biomedical signals. De-noising and feature extraction methods are also utilized to get higher …
Serum drug concentrations predictive of pulmonary tuberculosis outcomes
Background. Based on a hollow-fiber system model of tuberculosis, we hypothesize that
microbiologic failure and acquired drug resistance are primarily driven by low drug …
microbiologic failure and acquired drug resistance are primarily driven by low drug …