Results 161 to 170 of about 251,718 (265)

Immune cell profiling supports early prediction of sepsis-associated acute kidney disease using a decision tree algorithm. [PDF]

Biomark Res
Wu MY, Lai CH, Chiu YL, Tseng PC, Nanda JD, Lin CF, Wu MS.   +6 more
europepmc   +1 more source

Classification of biomedical lung cancer images using optimized binary bat technique by constructing oblique decision trees [PDF]

Shobha Aswal, Neelu Jyothi Ahuja, Ritika Mehra   +2 more
openalex   +1 more source

Digital Discovery of Synthesizable Metal−Organic Frameworks via Molecular Dynamics‑Informed, High‑Fidelity Deep Learning

Advanced Functional Materials, EarlyView.
Tabular foundation model interrogates the synthetic likelihood of metal−organic frameworks. Abstract Metal–organic frameworks (MOFs) are celebrated for their chemical and structural versatility, and in‑silico screening has significantly accelerated their discovery; yet most hypothetical MOFs (hMOFs) never reach the bench because their synthetic ...
Xiaoyu Wu, Rui Zheng, Quan Liu, Jianwen Jiang   +3 more
wiley   +1 more source

Predictive accuracy of multiple regression analysis stratified into four groups of admission motor FIM based on decision tree analysis. [PDF]

Jpn J Compr Rehabil Sci
Tokunaga M, Sannomiya K.
europepmc   +1 more source

Causal Sensitivity Analysis for Decision Trees

, 2015
Chengbo Li
openalex   +1 more source

Implementation of the Adaboost Method to Increase the Accuracy of Early Diabetes Predictions to Prevent Death Decision Tree-Based [PDF]

Laskar Alam
openalex   +1 more source

RSL19BD at DBDC4: Ensemble of Decision Tree-based and LSTM-based Models [PDF]

, 2019
Chih‐Hao Wang, Sosuke Kato, Tetsuya Sakai   +2 more
openalex   +1 more source

Classification of Lactuca Sativa Seed Varieties Using Spectral- Textural-Spatial Features and Decision Tree Classifier

, 2023
M. Simons, Seth Dayao, Meo Vincent, C Caya, S Fabiyi, C Mendigoria, R Concepcion, I, S Lauguico, K Siphengphet, J Alejandrino, E Dadios, A Bandala, X Yi, M Eramian, R Wang, E Neufeld, E Gne, S Aygn, M Krc, A Kalateh, Y akr, K Kiratiratanapruk, W Sinthupinyo, C Hortinela, A Yumang, M Chloe, M Sta, R Juana, Diloy, C Hortinela, K Tupas, M Caya, R Maramba, J Mendoza, P Suman, V Quiones, M Macawile, A Ballado, J Cruz, M Caya, K Bejerano, C Hortinela, J Balbin, V Luis, M Quiones, A Yumang, C Hortinela, Z Mabunga, J Cruz, G Magwili, A Samortin, D Cruz   +52 more
openalex   +1 more source

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source

A Study on the Decision Tree Approach for Predicting EO/EG Facility Leakage and Diffusion Hazards. [PDF]

ACS Omega
Cao Q, Zhang Z, Mu Y, Wang Y, Lin Y, Zhou J, Yu A, Liu Y.   +7 more
europepmc   +1 more source