The impact of ecological civilization construction on the resilience of green technology innovation: evidence based on double dual machine learning by Cunlin Li, Cunlin Li, Shiyu Zhang, Shiyu Zhang, Baojun Yang, Baojun Yang

Subjects: Get full text

Machine Learning Classification Model to Label Sources Derived from Factor Analysis Receptor Models for Source Apportionment by Vikas Kumar, Vasudev Malyan, Manoranjan Sahu, Basudev Biswal

Subjects: Get full text

Enhancing the estimation of cadmium content in rice leaves by integrating vegetation indices and color indices using machine learning by Xiaoyun Huang, Shengxi Chen, Tianling Fu, Chengwu Fan, Hongxing Chen, Song Zhang, Hui Chen, Song Qin, Zhenran Gao

Subjects: Get full text

Utilizing Machine Learning-based Classification Models for Tracking Air Pollution Sources: A Case Study in Korea by Yelim Choi, Bogyeong Kang, Daekeun Kim

Subjects: “…Machine learning…”
Get full text

Multi-omics and machine learning-driven CD8+ T cell heterogeneity score for head and neck squamous cell carcinoma by Di He, Zhan Yang, Tian Zhang, Yaxian Luo, Lianjie Peng, Jiatao Yan, Tao Qiu, Jingyu Zhang, Luying Qin, Zhichao Liu, Mouyuan Sun

Subjects: Get full text

Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients. by Yulin Lai, Peiyuan Huang

“…<h4>Methods</h4>Machine learning components, including ridge regression, XGBoost, k-nearest neighbor, light gradient boosting machine, logistic regression, support vector machine, neural network, and random forest, were used to construct a predictive model and identify the risk factors for SPMs with data from the Surveillance, Epidemiology and End Results. …”
Get full text

Determination of 5-fluorouracil anticancer drug solubility in supercritical CO 2 using semi-empirical and machine learning models by Gholamhossein Sodeifian, Ratna Surya Alwi, Reza Derakhsheshpour, Nedasadat Saadati Ardestani

Subjects: Get full text

Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning by Min Liang, Zhiwen Zhang, Langming Wu, Mafeng Chen, Shifan Tan, Jian Huang

Subjects: “…Machine learning…”
Get full text

Estimation of the Compressive Strength of Self-Compacting Concrete (SCC) by a Machine Learning Technique Coupling with Novel Optimization Algorithms by Ling Chen, Wengang Jiang

“…This research aims to model the CS of SCC via a machine learning technique of Support Vector Regression (SVR). …”
Get full text

Author Correction: Integrating fault detection and classification in microgrids using supervised machine learning considering fault resistance uncertainty by Morteza Barkhi, Javad Pourhossein, Seyed Ali Hosseini

Get full text

Developing clinical prognostic models to predict graft survival after renal transplantation: comparison of statistical and machine learning models by Getahun Mulugeta, Temesgen Zewotir, Awoke Seyoum Tegegne, Mahteme Bekele Muleta, Leja Hamza Juhar

Subjects: Get full text

FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron microscopy images. by Nicholas T Gigliotti, Justin Lee, Emily H Mang, Giancarlo R Zambrano, Mitra L Taheri

“…Current imaging techniques allow us to visualize these critical remodeling events and developments in image analysis have employed a combination of analysis software and machine learning techniques to improve the efficiency and accuracy with which features are measured. …”
Get full text

A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction by Md. Alamin Talukder, Majdi Khalid, Nasrin Sultana

Subjects: Get full text

Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact by A. Canberk Ulusoy, Tuğçe Toprak, M. Alper Selver, Pelin Güneri, Betül İlhan

Subjects: “…Machine learning…”
Get full text

Machine learning reveals sex differences in distinguishing between conduct-disordered and neurotypical youth based on emotion processing dysfunction by Gregor Kohls, Erik M. Elster, Peter Tino, Graeme Fairchild, Christina Stadler, Arne Popma, Christine M. Freitag, Stephane A. De Brito, Kerstin Konrad, Ruth Pauli

Subjects: Get full text

Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients by Yulin Lai, Peiyuan Huang

Get full text

Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer by Xiaolong Liu, Pengxian Tao, He Su, Yulan Li

Subjects: Get full text

ChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training data by Rebecca K. Lindsey, Sorin Bastea, Sebastien Hamel, Yanjun Lyu, Nir Goldman, Vincenzo Lordi

“…Abstract We present new parameterizations of the ChIMES physics informed machine-learned interatomic model for simulating carbon under conditions ranging from 300 K and 0 GPa to 10,000 K and 100 GPa, along with a new multi-fidelity active learning strategy. …”
Get full text

Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers by Hui Shang, Tao Feng, Dong Han, Fengying Liang, Bin Zhao, Lihang Xu, Zhendong Cao

“…The clinical features, radiomic features, and deep learning features were organized and integrated, and five machine learning methods were employed to develop 15 predictive models. …”
Get full text

Circadian rhythm related genes signature in glioma for drug resistance prediction: a comprehensive analysis integrating transcriptomics and machine learning by Junbo Liao, Yingxing Duan, Xiangwang Xu, Yaxue Liu, Chaohong Zhan, Gelei Xiao

Get full text