Results 111 to 120 of about 5,416,756 (285)

Analisis Model Prediksi Penyakit Jantung Menggunakan Adaptive Boosting, Gradient Boosting, dan Extreme Gradient Boosting

Deteksi dini penyakit jantung merupakan langkah penting untuk meningkatkan kualitas diagnosis dan perawatan pasien. Namun, metode prediksi manual yang sering digunakan tenaga medis memiliki keterbatasan dalam efisiensi waktu, akurasi, dan kemampuan ...
Sah, A. (Andrian), Jafar, R. R. (Rhaishudin), Muharrom, M. (Muhammad), Niesa, C. (Chaeroen)   +3 more
core   +3 more sources

Optimization by gradient boosting

, 2017
Gradient boosting is a state-of-the-art prediction technique that sequentially produces a model in the form of linear combinations of simple predictors---typically decision trees---by solving an infinite-dimensional convex optimization problem.
Biau, Gérard, Cadre, Benoît, Gérard Biau, Benoît Cadre   +3 more
core   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Mathematical and machine learning analysis of high-density polyethylene thermal degradation

Scientific Reports
Thermogravimetric analysis (TGA) is widely used to characterize polymer degradation, but repeated experiments and conventional single-step kinetic models can become inefficient or inadequate when degradation is nonlinear, multistage, atmosphere-dependent,
Sonia Sakthi S, Arulmozhi M
doaj   +1 more source

Integrating Machine Learning With Constant‐Potential Simulation to Unravel Charge‐Transfer Mechanisms in Electrochemical Nitrogen Fixation

Advanced Science, EarlyView.
Integrating interpretable machine learning with the fixed‐potential method reveals a novel mechanism: the catalytic activity of the electrochemical nitrogen reduction reaction is governed by partial charge transfer, induced by variations in the intermediate potential of zero charge under constant potential.
Yufei Xue, Dushuo Feng, Yuefei Zhang, Yang Zhang, Yalong Jiao, Aijun Du, Guoping Gao   +6 more
wiley   +1 more source

Hybrid metaheuristic (WOA-HHO) optimized machine learning for compressive strength prediction of nano silica modified self compacting recycled aggregate concrete

Ain Shams Engineering Journal
This study presents a hybrid experimental machine learning (ML) framework to assess and predict the compressive strength of nano silica (NS) modified self-compacting concrete (SCC) incorporating recycled coarse aggregates (RCA).
Ahmed A. Alawi Al-Naghi, Muhammad Sarmad Mahmood, Tariq Ali, Muhammad Zeeshan Qureshi, Nejib Ghazouani, Mohamed Hechmi El Ouni, D. Manikandan, Krishnaraj Ramaswamy   +7 more
doaj   +1 more source

Top ten variable importance using gradient boosting models.

, 2015
Top ten variable importance using gradient boosting models.
Ryan Chang-Ho Choi (769460), Anish Thillanathan (769461), Lee Min-Ho (769462), Abigail Nanapragasam (769459), Giovanni Montana (488247), Petros-Pavlos Ypsilantis (769458), Lakshmana Ayaru (521462)   +6 more
core   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

Prediction of oxidation resistance of Ti-V-Cr burn resistant titanium alloy based on machine learning

npj Materials Degradation
A machine learning model was developed to predict the oxidation resistance of Ti-V-Cr burn-resistant titanium alloy, and the natural logarithm of the parabolic oxidation rate constant (lnk p ) was utilized as the model output.
Yuanzhi Sun, Guangbao Mi, Peijie Li, Liangju He   +3 more
doaj   +1 more source