Results 91 to 100 of about 112,373 (246)

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

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 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

Deep Contrastive Learning for High‐Throughput Prediction of Drug Resistance Mutations from Sequences

Advanced Science, EarlyView.
This study presents DeepMutDTA, a deep learning framework aimed at predicting mutation‐induced changes in protein‐drug interactions and prioritizing variants potentially linked to drug resistance. Trained on large‐scale data, it incorporates SimSiam‐MuTF, a label‐aware contrastive fine‐tuning strategy that encourages separation between WT and MT ...
Xiaowen Hu, Pan Zhang, Shangqian Wu, Hao Sun, Minwei Li, Sophia Tsoka, Zizhang Sheng, Lei Deng   +7 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

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  

A Perspective on Interactive Theorem Provers in Physics

Advanced Science, EarlyView.
Into an interactive theorem provers (ITPs), one can write mathematical definitions, theorems and proofs, and the correctness of those results is automatically checked. This perspective goes over the best usage of ITPs within physics and motivates the open‐source community run project PhysLean, the aim of which is to be a library for digitalized physics
Joseph Tooby‐Smith
wiley   +1 more source

NeuroSuite for Long‐Term Functional and Structural Studies of Air‐Liquid Interface Cerebral Organoids

Advanced Science, EarlyView.
NeuroSuite provides a modular hardware‐software platform integrating Neuroweb and NeuroMaps to enable long‐term, in situ electrophysiological interrogation of air–liquid interface organoid slices while preserving tissue architecture. Its components can be used together or independently to capture real‐time activity, spatial network dynamics, and ...
Belquis Haider, Sagnik Middya, Daniel J. Lloyd‐Davies‐Sánchez, Nino F. Läubli, Sulay Vora, Jakob Träuble, Rohan N. Krajeski, Yuqing Feng, Daniel Duncko, Josiah S. Riley, Rubén Ruiz‐Mateos Serrano, Ole Paulsen, Madeline A. Lancaster, George G. Malliaras, Gabriele S. Kaminski Schierle   +14 more
wiley   +1 more source

An Integrated NLP‐ML Framework for Property Prediction and Design of Steels

Advanced Science, EarlyView.
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju, Adithya Umanath Rai, Jose Thomson, Marzie Ghorbani, Shujing Zhao, Nick Birbilis   +5 more
wiley   +1 more source

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

Advanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan, Hengheng Zhao, Haotian Wang, Zhanjie Liu, Qian Li, Weifeng Zhang, Qingsong Zhao, Liqun Zhang, Jun Liu   +8 more
wiley   +1 more source