Results 81 to 90 of about 4,847 (258)

LK-Index: A Learned Index for KNN Queries

IEEE Access
The k-Nearest Neighbor (kNN) search is a crucial problem in database and data mining, especially in high-dimensional space. However, traditional kNN algorithms based on distance metrics and brute-force search often have low search efficiency and accuracy, and high computational complexity when dealing with large-scale high-dimensional datasets.
openaire   +2 more sources

Machine Learning‐Based Correction of Reanalysis Surface Radiation Fluxes Using Ground‐Measured Data in the Southern Brazilian Pampa Biome

International Journal of Climatology, EarlyView.
This study demonstrates that ERA5 provides more accurate surface radiation flux estimates than MERRA2 across the Southern Brazilian Pampa. Machine learning models, particularly Random Forest, further improved the precision of reanalysis data for climate applications.
Olusola Samuel Ojo, Michel Baptistella Stefanello, Alecsander Mergen, Maria Eduarda Oliveira Pinheiro, Vanessa de Arruda Souza, Débora Regina Roberti   +5 more
wiley   +1 more source

Not All Missing Data are Equal: Choosing the Right Imputation Method for Binary Datasets

Quality and Reliability Engineering International, EarlyView.
ABSTRACT Missing binary predictors are common in reliability, quality control, and industrial decision systems, yet imputation methods are often chosen by convenience rather than evidence. We conduct a Monte Carlo study comparing mode substitution, sequential hot‐deck, missForest, MICE, and KNN with three neighbourhood sizes under MCAR, MAR, and MNAR ...
Manuel Delfino, Fabio Rapallo
wiley   +1 more source

Machine‐Learning‐Assisted Discovery and Accelerated Synthesis of Metal Phosphosulfides

Small, EarlyView.
The synthesizability and band gaps of 909 hypothetical ternary phosphosulfides are evaluated by a combination of density functional theory and multi‐fidelity machine learning. The accelerated material development workflow is extended to the experimental domain by demonstrating a route to high‐throughput synthesis and characterization of virtually any ...
Javier Sanz Rodrigo, Nicholas A. Kryger‐Nelson, Lena A. Mittmann, Eugène Bertin, Ivano E. Castelli, Andrea Crovetto   +5 more
wiley   +1 more source

Artificial intelligence empowers targeted protein degradation: Core technological innovations, multi‐scenario applications, and translational prospects

Smart Molecules, EarlyView.
AI is transforming TPD by improving the design, prediction, and optimization of degraders such as PROTACs, molecular glues, and LYTACs. This review summarizes key AI‐driven advances, highlights applications across drug discovery stages, and discusses remaining challenges and future directions for accelerating the development of therapies against ...
Shuanglin Qin, Rui Peng, Guangshuai Zhang, Si Yan, Jin Xiao, Zishu Liu, Ziyu Tan, Qingqing Xie, Shangjie Li, Wei Luo, Xiaohe Xiao   +10 more
wiley   +1 more source

Pickin' up good vibrations: a systematic review of footfall detection and analysis in the realm of wildlife surveying

Wildlife Biology, EarlyView.
Exploration of new wildlife surveying methodologies that leverage advances in sensor technology and machine learning has led to tentative research into the application of seismology techniques. This, most commonly, involves the deployment of a footfall trap – a seismic sensor and data logger customised for wildlife footfall.
Benjamin J. Blackledge, Luigi La Spada, Robert A. Briers, Niamh M. McHugh, Patrick J. C. White   +4 more
wiley   +1 more source

Review on enhancing clinical decision support system using machine learning

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Clinical decision‐making is a complex patient‐centred process. For an informed clinical decision, the input data is very thorough ranging from detailed family history, environmental history, social history, health‐risk assessments, and prior relevant medical cases.
Anum Masood, Usman Naseem, Junaid Rashid, Jungeun Kim, Imran Razzak   +4 more
wiley   +1 more source

Application of Intelligent Systems in CO2 Management: State of the Art and Future Prospects

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Machine learning (ML) integration is becoming increasingly popular in advancing the 4th industrial revolution, known as Industry 4.0. This review paper examines ML applications in CO2 management stages: emission, capture, and conversion. ML models, including multiple linear regression (MLR), multiple nonlinear regression (MNLR), and artificial
Muhammad Zulkefal, Iftikhar Ahmad, Asad Habib, Asad Ayub, Hamza Sethi, Ihsanullah Ihsanullah, Hakan Caliskan, Hiki Hong   +7 more
wiley   +1 more source

SFK: Shape‐ and Function‐Grounded Keypoint Representation for Sequential Manipulation

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Sequential manipulation is the process by which robots perform multiple interdependent steps to accomplish composite tasks, demanding tight integration of perception, planning and execution. Existing methods incorporate explicit features such as category, semantics, 6D pose or affordance to enhance consistency, yet single‐feature ...
Yaxin Liu, Yan Liu, Xinyu Song, Ruiqing Han, Haiyong Liu, Bo Huang, Yufeng Yao, Ming Zhong   +7 more
wiley   +1 more source

APTNet: A Condition‐Sensitive Modulation Framework for Surface Pressure Prediction on Supersonic Aircraft

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Accurate surface‐pressure prediction over broad operating envelopes is critical for supersonic aerodynamic analysis and design. To overcome the bottlenecks of traditional computational fluid dynamics (CFD) in real‐time performance and computational efficiency, data‐driven deep learning methods have emerged. However, existing data‐driven models
Hongbin Xu, Yin Long, Junlin Wu
wiley   +1 more source