Results 91 to 100 of about 35,702 (279)

SE-stacking: Improving user purchase behavior prediction by information fusion and ensemble learning.

PLoS ONE, 2020
Online shopping behavior has the characteristics of rich granularity dimension and data sparsity and presents a challenging task in e-commerce. Previous studies on user behavior prediction did not seriously discuss feature selection and ensemble design ...
Jing Xu, Jie Wang, Ye Tian, Jiangpeng Yan, Xiu Li, Xin Gao   +5 more
doaj   +1 more source

Machine Learning for Designing Perovskites and Perovskite‐Inspired Solar Materials: Emerging Opportunities and Challenges

Advanced Science, EarlyView.
This review offers a comprehensive comparison between perovskites and perovskite‐inspired materials (PIMs), focusing on their crystal structures, electronic properties, and chemical compositions. It evaluates the applicability of machine learning (ML) descriptors and models across both material classes.
Yangfan Zhang, Yiming Xia, Ali Shakiba, Hongrui Zhang, Xiaojing Hao, Priyank V. Kumar, Mahesh P. Suryawanshi   +6 more
wiley   +1 more source

A New Framework for Classifying University-Industry Collaboration Using Synthetic Minority Oversampling Technique and Stacking Ensemble

IEEE Access
University-industry collaboration has emerged as a critical driver of innovation and economic growth. However, predicting the outcomes of these collaborations remains methodologically challenging.
Uzapi Hange, Ezenwa Chike Nwanesi, Monhesea Obrey Patrick Bah   +2 more
doaj   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

Effective classifiers for detecting objects [PDF]

, 2007
Several state-of-the-art machine learning classifiers are compared for the purposes of object detection in complex images, using global image features derived from the Ohta color space and Local Binary Patterns.
Mayo, Michael
core   +1 more source

Engineering Na‐Rich P2‐Type Layered Oxides Through Li/Ti Dual Doping for Oxygen Redox Activation and Superior Structural Stability

Advanced Energy Materials, EarlyView.
P2‐type sodium layered oxides have potential for high‐voltage operation but suffer from structural instability and capacity fading. This work demonstrates that synergistic Li and Ti co‐doping enhances sodium inventory, suppresses detrimental phase transitions, and activates reversible lattice oxygen redox.
Rishika Jakhar, Shrestha Ghosh, Adesh Rohan Mishra, Shristi Pradhan, Debalina Sarkar, Yuanlong Bill Zheng, Zengqing Zhuo, Tianyi Li, Lu Ma, Minghao Zhang, Shyue Ping Ong, Matthew Li, Leeann Sun, Prabhat Thapliyal, Jing Wang, Abhik Banerjee, Ying Shirley Meng   +16 more
wiley   +1 more source

Application of bagging, boosting and stacking to intrusion detection

, 2012
This paper investigates the possibility of using ensemble algorithms to improve the performance of network intrusion detection systems. We use an ensemble of three different methods, bagging, boosting and stacking, in order to improve the accuracy and ...
Prugel-Bennett, Adam, Syarif, Iwan, Wills, Gary, Zaluska, Ed   +3 more
core  

Inversion of the Impedance Response Towards Physical Parameter Extraction Using Interpretable Machine Learning

Advanced Energy Materials, EarlyView.
ABSTRACT Interpreting the impedance response of perovskite solar cells (PSCs) is challenging due to the complex coupling of ionic and electronic motion. While drift‐diffusion (DD) modelling is a reliable method, its mathematical complexity makes directly extracting physical parameters from experimental data infeasible.
Mahmoud Nabil, Isel Grau, Ricardo Grau‐Crespo, Said Hamad, Juan A. Anta   +4 more
wiley   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Active Learning for Stacking and AdaBoost-Related Models

Stats
Ensemble learning (EL) has become an essential technique in machine learning that can significantly enhance the predictive performance of basic models, but it also comes with an increased cost of computation.
Qun Sui, Sujit K. Ghosh
doaj   +1 more source