Results 111 to 120 of about 524,166 (313)

Active Learning‐Driven Discovery of Sub‐2 Nm High‐Entropy Nanocatalysts for Alkaline Water Splitting

Advanced Functional Materials, EarlyView.
High‐entropy nanoparticles (HENPs) hold great promise for electrocatalysis, yet optimizing their compositions remains challenging. This study employs active learning and Bayesian Optimization to accelerate the discovery of octonary HENPs for hydrogen and oxygen evolution reactions.
Sakthivel Perumal, Da Bean Han, Thandapani Marimuthu, Taewaen Lim, Hyun Woo Kim, Junhyeok Seo   +5 more
wiley   +1 more source

Impact of The Covid-19 Pandemic on Student Learning Styles: Naïve Bayes and Decision Tree Classification in Education

Jurnal Sisfokom
The Covid-19 pandemic significantly changed education with social distancing and changes in the learning environment. In this study, one strong reason for the significance of the research is the urgency of changes in students' learning styles during the ...
Zaqi Kurniawan, Rizka Tiaharyadini
doaj   +1 more source

Software Defect Prediction Using Extreme Gradient Boosting(XGBoost) with Tune Hyperparameter [PDF]

Al-Rafidain Journal of Computer Sciences and Mathematics
Software applications have become widely spread in an unprecedented manner in our daily lives, controlling some of the most sensitive and critical aspects within institutions. Examples include automated systems such as traffic control, aviation, and self-
Tariq AL-Hadidi, Safwan Omar Hasoon
doaj   +1 more source

Decision tree learning in Neo4j on homogeneous and unconnected graph nodes from biological and clinical datasets. [PDF]

BMC Med Inform Decis Mak, 2023
Mondal R, Do MD, Ahmed NU, Walke D, Micheel D, Broneske D, Saake G, Heyer R.   +7 more
europepmc   +1 more source

Predicting Aggregation Behavior of Nanoparticles in Liquid Crystals via Automated Data‐Driven Workflows

Advanced Functional Materials, EarlyView.
Herein, a comprehensive framework that enabled the optimization of colloidal solubility within a high‐dimensional parameter space and study of reversible assembly processes is developed. This data‐driven workflow integrated innovations including the robotic platform for automated AuNPs functionalization, machine learning for predicting and revealing ...
Yueyang Gao, Niamh Mac Fhionnlaoich, Max Besenhard, Arun Pankajakshan, Federico Galvanin, Stefan Guldin   +5 more
wiley   +1 more source

Evaluation of four machine learning methods in predicting orthodontic extraction decision from clinical examination data and analysis of feature contribution

Frontiers in Bioengineering and Biotechnology
IntroductionThe study aims to predict tooth extraction decision based on four machine learning methods and analyze the feature contribution, so as to shed light on the important basis for experts of tooth extraction planning, providing reference for ...
Jialiang Huang, Jialiang Huang, Ian-Tong Chan, Zhixian Wang, Xiaoyi Ding, Ying Jin, Congchong Yang, Congchong Yang, Congchong Yang, Yichen Pan, Yichen Pan, Yichen Pan   +11 more
doaj   +1 more source

Exclusive Biosynthesis of Pullulan Using Taguchi's Approach and Decision Tree Learning Algorithm by a Novel Endophytic Aureobasidium pullulans Strain. [PDF]

Polymers (Basel), 2023
Saber WIA, Al-Askar AA, Ghoneem KM.
europepmc   +1 more source

The New Approach on Fuzzy Decision Trees [PDF]

Jooyeol Yun, Jun won Seo, and Taeseon Yoon (2014) THE NEW APPROACH ON FUZZY DECISION TREES International Journal of Fuzzy Logic Systems (IJFLS) Vol.4, No.3, July 2014, 2014
Decision trees have been widely used in machine learning. However, due to some reasons, data collecting in real world contains a fuzzy and uncertain form. The decision tree should be able to handle such fuzzy data. This paper presents a method to construct fuzzy decision tree.
arxiv  

Flux‐Regulated Crystallization of Perovskites Using Machine Learning‐Predicted Solvent Evaporation Rates for X‐Ray Detectors

Advanced Functional Materials, EarlyView.
By integrating machine learning into flux‐regulated crystallization (FRC), accurate prediction of solvent evaporation rates in real time, improving crystallization control and reducing crystal growth variability by over threefold, is achieved. This enhances the reproducibility and quality of perovskite single crystals, leading to reproducible ...
Tatiane Pretto, Sergey Dayneko, Antoine Pavesic Junior, Nicholas Sandor, Alexander Hart, Yuki Haruta, Magdalena Bazalova‐Carter, Marcos J. L. Santos, Makhsud I. Saidaminov   +8 more
wiley   +1 more source

Utilizing decision tree machine learning model to map dental students’ preferred learning styles with suitable instructional strategies

BMC Medical Education
Background Growing demand for student-centered learning (SCL) has been observed in higher education settings including dentistry. However, application of SCL in dental education is limited.
Lily Azura Shoaib, Syarida Hasnur Safii, Norisma Idris, Ruhaya Hussin, Muhamad Amin Hakim Sazali   +4 more
doaj   +1 more source