Wheway, V.: Using boosting to detect noisy data. In: Kowalczyk, R., Loke, S.W., Reed, N.E., Graham, G. (eds.) PRICAI-WS 2000. LNCS (LNAI), vol. 2112, pp. 123-130. Springer, Heidelberg (2001)V. Wheway, "Using boosting to detect noisy data," in Advances in Artificial ...
Ashish [27] applied SVM and the extreme gradient boosting method to detect ischemic heart disease using the Z-Alizadeh Sani dataset. Among various ML algorithms, SVM has proven to be one of the most outstanding methods [28]. The main idea of SVM [29] is to establish an optimal decision ...
Random Forest and Gradient Boosting Tree (GBT), to detect the network intrusions by training the model over UNSW NB15 and CICIDS2017 dataset [25]. Khanet al.proposed a novel approach based on a two-stage deep learning model and tested the model KDD99...
the unavailability of labelled data in the domain poses another challenge. Therefore, there is a need to design and develop effective unsupervised learning-based technique that can help detect and prevent health insurance fraud, provide actionable insights to relevant stakeholders...
Sleep stage classification DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal CNN J. Neurosci. Methods 2019 Sleep stage classification Driving fatigue detection from EEG using a modified PCANet method PCANet, SVM Comput. Intell. Neurosci. 2019 Sleep stage classifica...
In this section, the trained machine learning algorithms, which are Multi-Layer Perception, K-Nearest Neighbour, Support Vector Machine, Random Forest, and Adaptive Boosting, are discussed along with the key information of the collected data. Machine learning algorithms Multi-layer perception (MLP) ...
(e.g., eye blinks and eye movements) across the scalp and make it harder to detect and remove them using the preprocessing methods37. During data acquisition, the participant viewed a screen, located roughly 1 m away. A grating pattern of black and white bars was displayed at the ...
Disease Detection Deep learning to detect atrial fibrillation from short noisy ECG segments measured with wireless sensors CNN Circulation 2018 Disease Detection Ecg classification using three-level fusion of different feature descriptors CNN Expert Syst. Appl. 2018 Disease Detection A robust deep convolutio...
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