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Boosting algorithms

Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties

Yuliang Pan, Diwei Liu, Lei Deng

Altered kinetics of circulating progenitor cells in cardiopulmonary bypass (CPB) associated vasoplegic patients: A pilot study

Sanhita Nandi, Uma Rani Potunuru, [ ... ], Paul Ramesh Thangaraj

Machine learning with textural analysis of longitudinal multiparametric MRI and molecular subtypes accurately predicts pathologic complete response in patients with invasive breast cancer

Aaquib Syed, Richard Adam, [ ... ], Tim Q. Duong

Advancements in intrusion detection: A lightweight hybrid RNN-RF model

Nasrullah Khan, Muhammad Ismail Mohmand, [ ... ], Wadii Boulila

Water level prediction using soft computing techniques: A case study in the Malwathu Oya, Sri Lanka

Namal Rathnayake, Upaka Rathnayake, Tuan Linh Dang, Yukinobu Hoshino

Predicting learning achievement using ensemble learning with result explanation

Tingting Tong, Zhen Li

3-Dimensional convolutional neural networks for predicting StarCraft Ⅱ results and extracting key game situations

Insung Baek, Seoung Bum Kim

Developing interpretable machine learning-Shapley additive explanations model for unconfined compressive strength of cohesive soils stabilized with geopolymer

Anh Quan Ngo, Linh Quy Nguyen, Van Quan Tran

Hollow-tree super: A directional and scalable approach for feature importance in boosted tree models

Stephane Doyen, Hugh Taylor, [ ... ], Michael E. Sughrue

An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG)

Satria Mandala, Ardian Rizal, [ ... ], Abdul Hanan Abdullah

Machine learning-based models to predict the conversion of normal blood pressure to hypertension within 5-year follow-up

Aref Andishgar, Sina Bazmi, [ ... ], Fariba Ahmadizar

Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques

Colin Griesbach, Andreas Groll, Elisabeth Bergherr

An intelligent framework to measure the effects of COVID-19 on the mental health of medical staff

Muhammad Irfan, Ahmad Shaf, [ ... ], Bahran Taghreed Ali M.

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