Results 81 to 90 of about 85,077 (292)

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

Advanced Multipurpose Spectroscopic Nanobio‐Device for Concurrent Lab‐on‐a‐Chip Label‐Free Separation and Detection of Extracellular Vesicles as Key‐Biomarkers for Point‐of‐Care Cardiovascular Disease Diagnostics

Advanced Healthcare Materials, EarlyView.
AIMSPec‐LoC is a novel lab‐on‐a‐chip platform integrating size‐based extracellular vesicle (EVs) separation with label‐free Raman spectroscopy and AI‐powered classification via SKiNET. This high‐throughput, portable system enables real‐time, multiplexed molecular fingerprinting of EVs from biofluids, offering transformative potential for early, non ...
Emma Buchan, Jonathan James Stanley Rickard, Mark Robert Thomas, Pola Goldberg Oppenheimer   +3 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

State‐of‐the‐Art, Insights, and Perspectives for MOFs‐Nanocomposites and MOF‐Derived (Nano)Materials

Advanced Materials, EarlyView.
Different approaches to MOF‐NP composite formation, such as ship‐in‐a‐bottle, bottle‐around‐the‐ship and in situ one‐step synthesis, are used. Owing to synergistic effects, the advantageous features of the components of the composites are beneficially combined, and their individual drawbacks are mitigated.
Stefanos Mourdikoudis, Subhajit Dutta, Saqib Kamal, Sergio Gómez‐Graña, Isabel Pastoriza‐Santos, Stefan Wuttke, Lakshminarayana Polavarapu   +6 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

Deepfake Image Classification Using Decision (Binary) Tree Deep Learning

Journal of Sensor and Actuator Networks
The unprecedented rise of deepfake technologies, leveraging sophisticated AI like Generative Adversarial Networks (GANs) and diffusion-based models, presents both opportunities and challenges in terms of digital media authenticity.
Mariam Alrajeh, Aida Al-Samawi
doaj   +1 more source

Machine‐Learning‐Aided Advanced Electrochemical Biosensors

Advanced Materials, EarlyView.
Electrochemical biosensors are highly sensitive, portable, and versatile. Advanced nanomaterials enhance their performance, while machine learning (ML) improves data analysis, minimizes interference, and optimizes sensor design. Despite progress in both fields, their combined potential in diagnostics remains underexplored.
Andrei Bocan, Roozbeh Siavash Moakhar, Carolina del Real Mata, Max Petkun, Tristan De Iure‐Grimmel, Sripadh Guptha Yedire, Hamed Shieh, Arash Khorrami Jahromi, Sahar Sadat Mahshid, Sara Mahshid   +9 more
wiley   +1 more source

CORR Synthesis: Can Decision Tree Learning Advance Orthopaedic Surgery Research? [PDF]

Clin Orthop Relat Res, 2023
Wilson A.
europepmc   +1 more source

Learning Multiple Tasks with Boosted Decision Trees [PDF]

, 2012
We address the problem of multi-task learning with no label correspondence among tasks. Learning multiple related tasks simultane- ously, by exploiting their shared knowledge can improve the predictive performance on every task. We develop the multi-task Adaboost en- vironment with Multi-Task Decision Trees as weak classifiers.
Faddoul, Jean Baptiste, Chidlovskii, Boris, Gilleron, Rémi, Torre, Fabien   +3 more
openaire   +3 more sources

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