Results 91 to 100 of about 185,151 (349)
Multicluster Class-Balanced Ensemble
, 2021 Ensemble classifiers using clustering have significantly improved classification and prediction accuracies of many systems. These types of ensemble approaches create multiple clusters to train the base classifiers.
Jan, Zohaib, Verma, Brijesh
core +1 more source
Prediction and Analysis of Rice Production and Yields Using Ensemble Learning Techniques
Ilkom Jurnal IlmiahThis research focuses on predicting and analyzing rice production and yield throughout the world using ensemble learning techniques. The study applies and compares three methods: linear regression, ARIMA, and ensemble learning, to predict rice harvest ...
Yudha Islami Sulistya +6 more
doaj +1 more source
Phase Engineering of Nanomaterials (PEN): Evolution, Current Challenges, and Future Opportunities
Advanced Materials, EarlyView.This review summarizes the synthesis, phase transition, advanced characterization spanning ex situ to in situ and operando techniques, and diverse applications of phase engineering of nanomaterials (PEN). It further outlines key challenges and future opportunities, such as phase stability, architecture control, and artificial intelligence (AI)‐driven ...
Ye Chen +7 more
wiley +1 more source
Online ensemble learning in the presence of concept drift [PDF]
, 2011 In online learning, each training example is processed separately and then discarded. Environments that require online learning are often non-stationary and their underlying distributions may change over time (concept drift).
Minku, Leandro Lei
core
, 2020 Estimating the uniaxial compressive strength (UCS) of travertine rocks with an indirect modeling approach and machine learning algorithms is useful as models can reduce the cost and time required to obtain accurate measurements of UCS, which is important
Fijani, Elham +4 more
core +1 more source
Advanced Materials, EarlyView.This review summarizes the principles and challenges of nonaqueous lithium‐oxygen batteries and recent advances in cathode catalysts, including carbon‐based materials, metals, oxides, sulfides, nitrides, carbides, and redox mediators. It highlights emerging design strategies and artificial intelligence‐driven approaches, emphasizing data‐assisted ...
Yuqing Yao +8 more
wiley +1 more source
Distilling Diverse Knowledge for Deep Ensemble Learning
IEEE AccessBidirectional knowledge distillation improves network performance by sharing knowledge between networks during the training of multiple networks. Additionally, performance is further improved by using an ensemble of multiple networks during inference ...
Naoki Okamoto +3 more
doaj +1 more source
Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI‐Enabled Smart Textiles
Advanced Materials, EarlyView.Thermally drawn multimaterial fibers are rapidly advancing as intelligent structural units for next‐generation smart textiles. Integrating multimaterial architectures with neuromorphic and spiking‐neural‐network principles enables fabrics that can sense, compute, and adapt autonomously.
Vuong Dinh Trung +9 more
wiley +1 more source
Advanced Materials, EarlyView.We introduce a computational workflow that combines quantum chemical calculations and machine learning techniques to predict the catalytic performance of a wide range of catalysts in the nitrogen reduction reaction (NRR). The analysis of the trained models provides insights into the complex structure–activity relationship in experimental catalytic ...
Leonardo Di Ciano +5 more
wiley +1 more source