Results 111 to 120 of about 6,303,129 (314)

Automatic Detection of Students’ Engagement During Online Learning: A Bagging Ensemble Deep Learning Approach

IEEE Access
The COVID-19 pandemic has reshaped education and shifted learning from in-person to online. While this shift offers advantages such as liberating the learning process from time and space constraints and enabling education to occur anywhere and anytime, a
Mayanda Mega Santoni, T. Basaruddin, Kasiyah Junus, Oenardi Lawanto   +3 more
doaj   +1 more source

On Ensemble Learning

, 2020
In this paper, we consider ensemble classifiers, that is, machine learning based classifiers that utilize a combination of scoring functions. We provide a framework for categorizing such classifiers, and we outline several ensemble techniques, discussing how each fits into our framework.
Stamp, Mark, Chandak, Aniket, Wong, Gavin, Ye, Allen   +3 more
openaire   +2 more sources

Electrocatalytic Reduction of CO2 to Ethylene: Catalyst Design and Synchrotron‐Based Characterizations

Advanced Functional Materials, EarlyView.
This review evaluates strategies for electrochemical CO2 reduction to ethylene, focusing on copper‐based catalyst design and microenvironment modulation to achieve industrial‐grade performance. By leveraging operando synchrotron‐based characterizations, we provide a multiscale understanding of dynamic structural transformations and key reaction ...
Meng Zhang, Zuolong Chen, Yimin A. Wu
wiley   +1 more source

Self-poised Ensemble Learning

, 2005
This paper proposes a new approach to train ensembles of learning machines in a regression context. At each iteration a new learner is added to compensate the error made by the previous learner in the prediction of its training patterns. The algorithm operates directly over values to be predicted by the next machine to retain the ensemble in the target
Nanculef, R, Valle, C, Allende, H, Moraga, C   +3 more
openaire   +3 more sources

Artificial Intelligence as the Next Visionary in Liquid Crystal Research

Advanced Functional Materials, EarlyView.
The functions of AI in the research laboratory are becoming increasingly sophisticated, allowing the entire process of hypothesis formulation, material design, synthesis, experimental design, and reiterative testing to be automated. In our work, we conceive how the incorporation of AI in the laboratory environment will transform the role and ...
Mert O. Astam, Ouassim L'Karkouri, Danqing Liu   +2 more
wiley   +1 more source

Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning [PDF]

Expert systems with applications, 2022
Md. Alamin Talukder, Md. Manowarul Islam, Md. Ashraf Uddin, Arnisha Akhter, Khondokar Fida Hasan, M. A. Moni   +5 more
semanticscholar   +1 more source

Amyloidogenic Peptide Fragments Designed From Bacterial Collagen‐like Proteins Form Hydrogel

Advanced Functional Materials, EarlyView.
This study identified amyloidogenic sequence motifs in bacterial collagen‐like proteins and exploited these to design peptides that self‐assemble into β‐sheet fibers and form hydrogels. One hydrogel supported healthy fibroblast growth, showing promise for biocompatible materials. Our work demonstrates that bacterial sequences can be harnessed to create
Vamika Sagar, Xuen J Ng, Lukas Lechel, Ashish Chand Rajwar, Hendrik Bargel, Abhishek A. Jalan   +5 more
wiley   +1 more source

Bioinspired Adaptive Sensors: A Review on Current Developments in Theory and Application

Advanced Materials, EarlyView.
This review comprehensively summarizes the recent progress in the design and fabrication of sensory‐adaptation‐inspired devices and highlights their valuable applications in electronic skin, wearable electronics, and machine vision. The existing challenges and future directions are addressed in aspects such as device performance optimization ...
Guodong Gong, You Zhou, Qingxiu Li, Wenyu Zhao, Sunyingyue Geng, Hangfei Li, Yan‐Bing Leng, Shirui Zhu, Guanglong Ding, Yongbiao Zhai, Ziyu Lv, Ye Zhou, Su‐Ting Han   +12 more
wiley   +1 more source

Predicting Residential Energy Consumption in South Africa Using Ensemble Models

Applied Computational Intelligence and Soft Computing
This study presents ensemble machine learning (ML) models for predicting residential energy consumption in South Africa. By combining the best features of individual ML models, ensemble models reduce the drawbacks of each model and improve prediction ...
David Attipoe, Donatien Koulla Moulla, Ernest Mnkandla, Alain Abran   +3 more
doaj   +1 more source

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source