A robot for artistic painting in authentic colors

A Karimov, E Kopets, S Leonov, L Scalera… - Journal of Intelligent & …, 2023 - Springer
Artistic robotic painting automates the process of creating an artwork. This complex and
challenging task includes several aspects: creating algorithms for rendering brushstrokes …

Predicting the accuracy of genomic predictions

JCM Dekkers, H Su, J Cheng - Genetics Selection Evolution, 2021 - Springer
Background Mathematical models are needed for the design of breeding programs using
genomic prediction. While deterministic models for selection on pedigree-based estimates of …

Developing a prediction model of demolition-waste generation-rate via principal component analysis

GW Cha, SH Choi, WH Hong, CW Park - International Journal of …, 2023 - mdpi.com
Construction and demolition waste accounts for a sizable proportion of global waste and is
harmful to the environment. Its management is therefore a key challenge in the construction …

Development of machine learning model for prediction of demolition waste generation rate of buildings in redevelopment areas

GW Cha, SH Choi, WH Hong, CW Park - International Journal of …, 2022 - mdpi.com
Owing to a rapid increase in waste, waste management has become essential, for which
waste generation (WG) information has been effectively utilized. Various studies have …

Performance improvement of machine learning model using autoencoder to predict demolition waste generation rate

GW Cha, WH Hong, YC Kim - Sustainability, 2023 - mdpi.com
Owing to the rapid increase in construction and demolition (C&D) waste, the information of
waste generation (WG) has been advantageously utilized as a strategy for C&D waste …

Developing an Optimal Ensemble Model to Estimate Building Demolition Waste Generation Rate

GW Cha, WH Hong, SH Choi, YC Kim - Sustainability, 2023 - mdpi.com
Smart management of construction and demolition (C&D) waste is imperative, and
researchers have implemented machine learning for estimating waste generation. In Korea …

[HTML][HTML] Supervised and unsupervised machine learning approaches for prediction and geographical discrimination of Iranian saffron ecotypes based on flower …

SM Alavi-Siney, J Saba, AF Siahpirani… - Information Processing in …, 2023 - Elsevier
A two-year field experiment (2014–2016; Zanjan, Iran) was conducted to monitor potential
diversity pattern and adaptability power among 18 Iranian saffron ecotypes under Zanjan …

Identification of early predictive biomarkers for severe cytokine release syndrome in pediatric patients with chimeric antigen receptor T-cell therapy

M Su, L Chen, L Xie, A Fleurie, R Jonquieres… - Frontiers in …, 2024 - frontiersin.org
CAR-T cell therapy is a revolutionary new treatment for hematological malignancies, but it
can also result in significant adverse effects, with cytokine release syndrome (CRS) being …

Using clinical and radiomic feature–based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma …

J Wang, X Zhu, J Zeng, C Liu, W Shen, X Sun, Q Lin… - European …, 2023 - Springer
Objective This study aimed to build radiomic feature-based machine learning models to
predict pathological clinical response (pCR) of neoadjuvant chemoradiation therapy (nCRT) …

Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach

Y Yimit, P Yasin, A Tuersun, A Abulizi, W Jia… - European Journal of …, 2023 - Springer
Background Cerebral alveolar echinococcosis (CAE) and brain metastases (BM) share
similar in locations and imaging appearance. However, they require distinct treatment …