Results 141 to 150 of about 155,735 (298)

Ensemble Learning for the Land Cover Classification of Loess Hills in the Eastern Qinghai–Tibet Plateau Using GF-7 Multitemporal Imagery

Remote Sensing
The unique geographic environment, diverse ecosystems, and complex landforms of the Qinghai–Tibet Plateau make accurate land cover classification a significant challenge in plateau earth sciences.
Feifei Shi, Xiaohong Gao, Runxiang Li, Hao Zhang   +3 more
doaj   +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

Ensembling Supervised and Unsupervised Machine Learning Algorithms for Detecting Distributed Denial of Service Attacks

Algorithms
The distributed denial of service (DDoS) attack is one of the most pernicious threats in cyberspace. Catastrophic failures over the past two decades have resulted in catastrophic and costly disruption of services across all sectors and critical ...
Saikat Das, Mohammad Ashrafuzzaman, Frederick T. Sheldon, Sajjan Shiva   +3 more
doaj   +1 more source

A Computational Strategy for Identifying Self‐Assembling Food‐Derived Molecules for Antiparasitic Nanotherapy

Advanced Science, EarlyView.
An integrated computational screening strategy identified ursolic acid (UA) and 18β‐glycyrrhetinic acid (18βGA) as a self‐assembling food‐derived molecular pair. The resulting carrier‐free nanoparticles (UA‐18βGA) showed synergistic antiparasitic activity, reduced combined toxicity, and host‐protective anti‐inflammatory effects in zebrafish and murine ...
Shenye Qu, Ting Wang, Jietao Liu, Jiacheng Qin, Bin Yang, Yihang Liu, Pengfei Li, Gaoxue Wang, Fei Ling   +8 more
wiley   +1 more source

Optimal weighted parameters of ensemble convolutional neural networks based on a differential evolution algorithm for enhancing pornographic image classification

Engineering and Applied Science Research, 2021
Use of ensemble convolutional neural networks (CNNs) has become a more robust strategy to improve image classification performance. However, the success of the ensemble method depends on appropriately selecting the optimal weighted parameters. This paper
Sarayut Gonwirat, Olarik Surinta
doaj  

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

Ensemble learning for predicting subsurface bearing layer depths in Tokyo

Results in Engineering
In order to improve the accuracy of geotechnical investigations, this study developed an ensemble learning method for predicting the depth of the bearing layer in Tokyo. Due to the limitations of traditional geotechnical surveys and the need for detailed
Yuxin Cong, Shinya Inazumi
doaj   +1 more source

Improving Hoeffding Trees [PDF]

, 2007
Modern information technology allows information to be collected at a far greater rate than ever before. So fast, in fact, that the main problem is making sense of it all.
Richard Kirkby, Kirkby, Richard Brendon
core  

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

An adaptive ensemble feature selection technique for model-agnostic diabetes prediction

Scientific Reports
Ensemble learning aggregates several models’ outputs to improve the overall model’s performance. Ensemble feature selection separating the appropriate features from the extra and non-essential features. In this paper, the main focus will be to expand the
K. Natarajan, Dhanalakshmi Baskaran, Selvakumar Kamalanathan   +2 more
doaj   +1 more source