Results 251 to 260 of about 6,285,017 (364)

Techno‐Economic Analysis of Polylactic Acid from Corn Stover: Understanding Uncertainty and Variability via Artificial Intelligence Tools

Advanced Sustainable Systems, EarlyView.
This study analyses the uncertainty and variability existing in the economic performance of polylactic acid (PLA) production, via integrating techno‐economic analysis with process models and Monte Carlo simulation enabled by artificial intelligence. The results show that the minimum selling price varies from $2,107 to $3076/t PLA.
Yinqiao Wang, Jiawei Huang, Chuan Xu, Sipan Liu, Junwei Li, Kai Lan   +5 more
wiley   +1 more source

NSCLC EGFR Mutation Prediction via Random Forest Model: A Clinical-CT-Radiomics Integration Approach. [PDF]

Adv Respir Med
Benfares A, Alami B, Boukansa S, Qjidaa M, Benomar I, Serraj M, Lakhssassi A, Jamil MO, Maaroufi M, Qjidaa H.   +9 more
europepmc   +1 more source

Deep Learning Approach for Predicting Efficiency in Organic Photovoltaics from 2D Molecular Images of D/A Pairs

Advanced Theory and Simulations, EarlyView.
This study highlights the potential of deep learning, particularly Convolutional Neural Networks (CNNs), for predicting the photovoltaic performance of organic solar cells. By leveraging 2D images representing donor/acceptor molecular pairs, the model accurately estimates key performance indicators proving that this image‐based approach offers a fast ...
Khoukha Khoussa, Patrick Lévêque, Larbi Boubchir   +2 more
wiley   +1 more source

Intelligent resource allocation in internet of things using random forest and clustering techniques. [PDF]

Sci Rep
Derakhshanfard N, Hosseinzadeh L, Jafari FR, Ghaffari A.   +3 more
europepmc   +1 more source

First‐Principles Structure–Activity Relationship Insights Into Phenolic Scaffolds: QSAR Modeling and Drug‐Likeness Screening

Advanced Theory and Simulations, EarlyView.
Integrated machine learning framework for phenolic derivatives: classification (toxicity) and regression (logP) models identify top drug‐like compounds. Random Forest outperformed for toxicity, while Linear Regression best predicted logP. A weighted scoring approach prioritized five safe, lipophilicity‐optimized candidates, supporting rational ...
Houria Nacer, Sridhar Priyankha, Gopal Hema, Anouar Zerouali, Hamza Haddag, Muneerah Mogren Al Mogren, Muthuramalingam Prakash, Majdi Hochlaf   +7 more
wiley   +1 more source

Machine Learning Modeling of Hospital Length of Stay After Breast Cancer Surgery: Comparison of Random Forest and Linear Regression Approaches. [PDF]

Medicina (Kaunas)
Slavu I, Tulin R, Dogaru A, Dima I, Slavu CO, Mutu DG, Tulin A.   +6 more
europepmc   +1 more source

VDLIN: A Deep Learning‐Based Platform for Methylcobalamin‐Inspired Immunomodulatory Compound Screening

Advanced Science, EarlyView.
Using the convolutional neural network model VDLIN, Co7 is identified as a promising therapeutic candidate. Co7 demonstrates distinct advantages over MCB by effectively balancing anti‐inflammatory and immune‐stimulatory functions, making it a potential novel approach for immune modulation.
Xuefei Guo, Yang Zhao, Xianle Rong, Chenxi Niu, Shiyu Hu, Qiang Liu, Fuping You   +6 more
wiley   +1 more source

Optimized IoT protocol stack for seamless smart home communication using Random Forest-based interoperability analysis. [PDF]

Sci Rep
Sriram A, Manikandan D, Venketesan R, Ramaswamy K.   +3 more
europepmc   +1 more source

Genome‐Wide by Lifetime Environment Interaction Studies of Brain Imaging Phenotypes

Advanced Science, EarlyView.
This study explores genome‐wide by lifetime environment interactions on brain imaging phenotypes. Gene‐environment interactions explain more phenotypic variance than main effects, pinpoint regulatory variants, and reveal exposure‐specific biological pathways.
Sijia Wang, Meiyun Wang, Peng Zhang, Jingliang Cheng, Longjiang Zhang, Wenzhen Zhu, Shijun Qiu, Zuojun Geng, Guangbin Cui, Yongqiang Yu, Weihua Liao, Xi‐Nian Zuo, Hui Zhang, Bo Gao, Xiaojun Xu, Tong Han, Zhenwei Yao, Quan Zhang, Feng Liu, Qiang Xu, Jiayuan Xu, Jilian Fu, Nana Liu, Yuan Ji, Jie Tang, Lining Guo, Mengge Liu, Xiaoxiao Xiao, Xiaoxuan Liu, Wei Li, Caihong Wang, Wei Wei, Dapeng Shi, Su Lui, Zhihan Yan, Feng Chen, Jing Zhang, Wen Shen, Yanwei Miao, Dawei Wang, Jia‐Hong Gao, Yunjun Yang, Kai Xu, Junfang Xian, Bing Zhang, Xiaochu Zhang, Zhaoxiang Ye, Le Yu, Wen Qin, Meng Liang, Chunshui Yu, the CHIMGEN Consortium   +51 more
wiley   +1 more source

Satellite-based monitoring of forage quality in grasslands of the United Kingdom using sentinel-2 data and random forest regression. [PDF]

Front Vet Sci
Irisarri JGN, Texeira MA, Harris P, Pembleton KG.   +3 more
europepmc   +1 more source