Results 91 to 100 of about 6,303,129 (314)

Complementary Ensemble Learning

, 2021
To achieve high performance of a machine learning (ML) task, a deep learning-based model must implicitly capture the entire distribution from data. Thus, it requires a huge amount of training samples, and data are expected to fully present the real distribution, especially for high dimensional data, e.g., images, videos.
Nguyen, Hung, Chang, Morris
openaire   +2 more sources

Two‐Dimensional Materials as a Multiproperty Sensing Platform

Advanced Functional Materials, EarlyView.
Various sensing modalities enabled and/or enhanced by two‐dimensional (2D) materials are reviewed. The domains considered for sensing include: 1) optoelectronics, 2) quantum defects, 3) scanning probe microscopy, 4) nanomechanics, and 5) bio‐ and chemosensing.
Dipankar Jana, Shubhrasish Mukherjee, Dmitrii Litvinov, Magdalena Grzeszczyk, Sergey Grebenchuk, Makars Šiškins, Virgil Gavriliuc, Yihang Ouyang, Changyi Chen, Yuxuan Ye, Meng Yiming, Maciej Koperski   +11 more
wiley   +1 more source

‘Oxygen Bound to Magnesium’ as High Voltage Redox Center Causes Sloping of the Potential Profile in Mg‐Doped Layered Oxides for Na‐Ion Batteries

Advanced Functional Materials, EarlyView.
Na‐ion batteries ‐ Impact of doping on the oxygen redox: The sloping potential of NaMg0.1Ni0.4Mn0.5O2 above 4.0 V is caused by a new redox center (arising from the ‘O bound to Mg’), having a higher potential but being more irreversible compared to the ‘O bound to Ni’.
Yongchun Li, Konstantin Köster, Katherine A. Mazzio, Yanan Sun, Annica I. Freytag, Deniz Wong, Christian Schulz, Götz Schuck, Volodymyr Baran, Alba San Jose Mendez, Marcin Zając, Payam Kaghazchi, Philipp Adelhelm   +12 more
wiley   +1 more source

Digital Discovery of Synthesizable Metal−Organic Frameworks via Molecular Dynamics‑Informed, High‑Fidelity Deep Learning

Advanced Functional Materials, EarlyView.
Tabular foundation model interrogates the synthetic likelihood of metal−organic frameworks. Abstract Metal–organic frameworks (MOFs) are celebrated for their chemical and structural versatility, and in‑silico screening has significantly accelerated their discovery; yet most hypothetical MOFs (hMOFs) never reach the bench because their synthetic ...
Xiaoyu Wu, Rui Zheng, Quan Liu, Jianwen Jiang   +3 more
wiley   +1 more source

Pycobra: A Python Toolbox for Ensemble Learning and Visualisation [PDF]

, 2018
We introduce \texttt{pycobra}, a Python library devoted to ensemble learning (regression and classification) and visualisation. Its main assets are the implementation of several ensemble learning algorithms, a flexible and generic interface to compare ...
Desikan, Bhargav Srinivasa, Guedj, Benjamin   +1 more
core   +2 more sources

Integrative Approaches for DNA Sequence‐Controlled Functional Materials

Advanced Functional Materials, EarlyView.
DNA is emerging as a programmable building block for functional materials with applications in biomimicry, biochemical, and mechanical information processing. The integration of simulations, experiments, and machine learning is explored as a means to bridge DNA sequences with macroscopic material properties, highlighting current advances and providing ...
Aaron Gadzekpo, Ewa Anna Oprzeska‐Zingrebe, Mariana Kozlowska, Lennart Hilbert, Iliya D. Stoev   +4 more
wiley   +1 more source

A framework for detecting credit card fraud with cost-sensitive meta-learning ensemble approach

Scientific African, 2020
Electronic payment systems continue to seamlessly aid business transactions across the world, and credit cards have emerged as a means of making payments in E-payment systems. Fraud due to credit card usage has, however, remained a major global threat to
Toluwase Ayobami Olowookere, Olumide Sunday Adewale   +1 more
doaj   +1 more source

GENESIM : genetic extraction of a single, interpretable model [PDF]

, 2016
Models obtained by decision tree induction techniques excel in being interpretable.However, they can be prone to overfitting, which results in a low predictive performance. Ensemble techniques are able to achieve a higher accuracy. However, this comes at
De Turck, Filip, Janssens, Olivier, Ongenae, Femke, Van Hoecke, Sofie, Vandewiele, Gilles   +4 more
core   +2 more sources

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source

An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form

Future Internet
Phishing email assaults have been a prevalent cybercriminal tactic for many decades. Various detectors have been suggested over time that rely on textual information.
Zaher Salah, Hamza Abu Owida, Esraa Abu Elsoud, Esraa Alhenawi, Suhaila Abuowaida, Nawaf Alshdaifat   +5 more
doaj   +1 more source