Results 111 to 120 of about 178,006 (285)
Quantum Algorithm for Spectral Regression for Regularized Subspace Learning
IEEE Access, 2019 In this paper, we propose an efficient quantum algorithm for spectral regression which is a dimensionality reduction framework based on the regression and spectral graph analysis. The quantum algorithm involves two core subroutines: the quantum principal
Fan-Xu Meng +3 more
doaj +1 more source
Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy
Advanced Science, EarlyView.Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu +4 more
wiley +1 more source
A Note on The Moments of Stochastic Shrinkage Parameters in Ridge Regression [PDF]
A common problem in econometric models and multiple regression in general is multicollinearity, which produces undesirable effects on the Least Squares estimators.
Hernán Rubio, Luis Firinguetti
core
Nonlinear Generalized Ridge Regression
, 2023 A Two-Stage approach is described that literally "straighten outs" any potentially nonlinear relationship between a y-outcome variable and each of p = 2 or more potential x-predictor variables. The y-outcome is then predicted from all p of these "linearized" spline-predictors using the form of Generalized Ridge Regression that is most likely to yield ...
openaire +2 more sources
Advanced Science, EarlyView.A Lattice Genome framework links geometric and process “genes” to lattice “phenotypes” via correction‐calibrated high‐throughput simulations and a growing performance database. Genome‐driven retrieval and recombination of unit cells enables component‐level, regionally tailored multi‐objective design: stress fields are programmed under constant relative
Haoyuan Deng +8 more
wiley +1 more source
Weighted Kernel Ridge Regression to Improve Genomic Prediction
AgricultureNonparametric models have recently been receiving increased attention due to their effectiveness in genomic prediction for complex traits. However, regular nonparametric models cannot effectively differentiate the relative importance of various SNPs ...
Chenguang Diao +6 more
doaj +1 more source
Advanced Science, EarlyView.A bioinspired piezoelectric sensor mimicking Pacinian corpuscles is developed to enable ultrasensitive and linear pressure sensing. A multilayer grooved architecture converts normal pressure into in‐plane strain, delivering high sensitivity, wide linear range, and efficient energy harvesting, enabling high‐fidelity wrist pulse monitoring and ...
Qi Yang +8 more
wiley +1 more source
ESTIMASI PARAMETER REGRESI RIDGE MENGGUNAKAN ITERASI HOERL, KENNARD, DAN BALDWIN (HKB) UNTUK PENANGANAN MULTIKOLINIERITAS (Studi Kasus Pengaruh BI Rate, Jumlah Uang Beredar, dan Nilai Tukar Rupiah terhadap Tingkat Inflasi di Indonesia) [PDF]
, 2015 Regression analysis is statistical method used to analyze the dependence of respond variables to predictor variable. In multiple linear regression analysis, there are assumptions that must be met, they are normality, homoscedasticity, absence of ...
Solekakh, Nur Aeniatus
core
Advanced Science, EarlyView.This review examines the evolution of bioprinting toward minimally invasive in situ strategies for internal organ regeneration. It defines the technological roadmap from handheld systems to advanced minimally invasive bioprinting platforms, positioning soft robotics as a core enabler.
Duc Tu Vu +9 more
wiley +1 more source
Advanced Science, EarlyView.This review offers a comprehensive comparison between perovskites and perovskite‐inspired materials (PIMs), focusing on their crystal structures, electronic properties, and chemical compositions. It evaluates the applicability of machine learning (ML) descriptors and models across both material classes.
Yangfan Zhang +6 more
wiley +1 more source