Results 91 to 100 of about 148,752 (225)

The National Clinical Database Risk Calculator and the 5‐Item Modified Frailty Index Predict the Development of Postoperative Delirium After Surgery for Hepatocellular Carcinoma

Annals of Gastroenterological Surgery, EarlyView.
This study demonstrates that combining the NCD Risk Calculator with the mFI‐5 enables more precise stratification of postoperative delirium risk in patients undergoing surgery for HCC. Patients classified as high risk by the combined model showed a markedly higher incidence of delirium than those in the intermediate‐ and low‐risk groups.
Kiyotaka Hosoda, Akira Shimizu, Koji Kubota, Tsuyoshi Notake, Hiroki Sakai, Kentaro Yoshikawa, Tomohiko Ikehara, Hikaru Hayashi, Koya Yasukawa, Yuji Soejima   +9 more
wiley   +1 more source

Predicting High‐Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion

Advanced Intelligent Discovery, EarlyView.
A multimodal fusion pipeline predicts high‐resolution ion distributions in imaging mass spectrometry by integrating Fourier transform ion cyclotron resonance, time‐of‐flight matrix‐assisted laser desorption/ionization, and time‐of‐flight secondary ion mass spectrometry data.
Md Inzamam Ul Haque, Ramakrishnan Kannan, Jacob D. Hinkle, Sylwia A. Stopka, Anton V. Ievlev, Nathalie Y. R. Agar, Olga S. Ovchinnikova, Debangshu Mukherjee   +7 more
wiley   +1 more source

BertSent: Transformer-Based Model for Sentiment Analysis of Penta-Class Tweet Classification

IEEE Access
Sentiment analysis (SA) is a popular method for obtaining relevant and subjective information from textual content. Sentiment analysis of multimedia material is helpful for various reasons but it is seen as challenging since the messages are often brief,
Maram Fahaad Almufareh, NZ Jhanjhi, Navid Ali Khan, Saleh Naif Almuayqil, Mamoona Humayun, Danish Javed   +5 more
doaj   +1 more source

Resampling fMRI time series

NeuroImage, 2005
The problem of selecting a threshold for the statistical parameter maps in functional MRI (fMRI) is a delicate issue. The use of advanced test statistics and/or the complex dependence structure of fMRI noise may preclude parametric statistical methods for finding appropriate thresholds.
Ola, Friman, Carl-Fredrik, Westin
openaire   +2 more sources

Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review

Advanced Intelligent Discovery, EarlyView.
Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang, Jiayi Tan, Qizhen Song, Tian Du, Jens Hauch, Christoph J. Brabec   +5 more
wiley   +1 more source

A Software Framework for the Integration of Infrastructure Simulation Models

Journal of Open Research Software, 2019
Infrastructure systems, such as those that generate and transmit energy, process waste water and enable the transportation of people and goods, provide fundamental services that underpin modern society.
Will Usher, Tom Russell
doaj   +1 more source

Multispectral data restoration study [PDF]

A digital resampling technique for LANDSAT data is reported that incorporates a deconvolution concept to minimize spatial and radiometric degradation of data during resampling for geometric correction.
Shah, N. J., Wilson, C. L.
core   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Prediction of TBM Performance Under Imbalanced Geological Conditions Using Resampling and Data Synthesis Techniques

Applied Sciences
Predicting Tunnel Boring Machine (TBM) working parameters, such as cutterhead torque, is crucial for ensuring safe and efficient tunneling. However, complex and changing geological conditions pose a significant challenge to this study, leading to ...
Qianli Zhang, Shiyu Li, Jian Ling, Zhenyu Liu   +3 more
doaj   +1 more source

Novel Resampling Improves Statistical Power for Multiple-Trait QTL Mapping

G3: Genes, Genomes, Genetics, 2017
Multiple-trait analysis typically employs models that associate a quantitative trait locus (QTL) with all of the traits. As a result, statistical power for QTL detection may not be optimal if the QTL contributes to the phenotypic variation in only a ...
Riyan Cheng, R. W. Doerge, Justin Borevitz   +2 more
doaj   +1 more source