Results 171 to 180 of about 113,531 (269)

An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning. [PDF]

Bioengineering (Basel), 2022
Fuadah YN, Pramudito MA, Lim KM.
europepmc   +1 more source

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

(Quasi) multitask support vector regression with heuristic hyperparameter optimization for whole-genome prediction of complex traits: a case study with carcass traits in broilers. [PDF]

G3 (Bethesda), 2023
Alves AAC, Fernandes AFA, Lopes FB, Breen V, Hawken R, Gianola D, Rosa GJM.   +6 more
europepmc   +1 more source

Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution

Advanced Intelligent Discovery, EarlyView.
Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren, Qian Jiang, Xin Jin, Puming Wang, Xing Chu, Shaowen Yao, Shengfa Miao   +6 more
wiley   +1 more source

MGWO-CNN: hyperparameter optimization of CNN classifier for cervical cancer detection using Modified Grey Wolf Optimizer. [PDF]

Sci Rep
Jain S, Jain A, Jangid M.
europepmc   +1 more source

Genetic CFL: Hyperparameter Optimization in Clustered Federated Learning. [PDF]

Comput Intell Neurosci, 2021
Agrawal S, Sarkar S, Alazab M, Maddikunta PKR, Gadekallu TR, Pham QV.   +5 more
europepmc   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

Combining K-fold cross validation with bayesian hyperparameter optimization for accuracy enhancement of land cover and land use classification. [PDF]

Sci Rep
Heidari P, Milan A.
europepmc   +1 more source

Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods. [PDF]

Expert Syst, 2022
Muzoğlu N, Halefoğlu AM, Avci MO, Kaya Karaaslan M, Yarman BSB.   +4 more
europepmc   +1 more source

Decoding Tattoo and Permanent Makeup Pigments: Linking Physicochemical Properties to Absorption, Distribution, Metabolism, and Elimination Profiles Using Quantitative Structure–Activity Relationship (QSAR)‐Based New Approach Methodologies (NAMs)

Advanced Intelligent Discovery, EarlyView.
This study applies QSAR‐based new approach methodologies to 90 synthetic tattoo and permanent makeup pigments, revealing systemic links between their physicochemical properties and absorption, distribution, metabolism, and elimination profiles. The correlation‐driven analysis using SwissADME, ChemBCPP, and principal component analysis uncovers insights
Girija Bansod, Ajay Vikram Singh, Preeti Bhardwaj, Tulika Rai, Sweta Vijay Nakhale, Amruta Shelar, Rajendra Patil, Peter Laux, Andreas Luch, Christopher J. Osgood, Michael W. Stacey   +10 more
wiley   +1 more source