Results 101 to 110 of about 19,913 (253)

Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling. [PDF]

Educ Inf Technol (Dordr), 2023
Guleria P, Sood M.
europepmc   +1 more source

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Advanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi, Yu Kumagai, Arata Takamatsu, Fumiyasu Oba   +3 more
wiley   +1 more source

Corrigendum: Educational Data Mining Techniques for Student Performance Prediction: Method Review and Comparison Analysis. [PDF]

Front Psychol, 2021
Zhang Y, Yun Y, An R, Cui J, Dai H, Shang X.   +5 more
europepmc   +1 more source

Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy

Advanced Intelligent Discovery, EarlyView.
Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang, Andrew Bai, Alexandra Bermudez, Yunqi Hong, Zoe Latham, Iris Sloan, Michael Liu, Vishrut Goyal, Cho‐Jui Hsieh, Neil Y. C. Lin   +9 more
wiley   +1 more source

Educational Data Mining Techniques for Student Performance Prediction: Method Review and Comparison Analysis. [PDF]

Front Psychol, 2021
Zhang Y, Yun Y, An R, Cui J, Dai H, Shang X.   +5 more
europepmc   +1 more source

Causal Inference in Educational Data Mining

The domain of causal inference, encompassing fields of statistics, philosophy, economics, and computer science, has seen rapid advancements. This emerging science addresses the challenges involved in estimating effects amidst complex situations in which confounding variables can obscure results.
Anthony F. Botelho, Avery Harrison Closser, Adam C. Sales, Neil T. Heffernan, Kirk P. Vanacore   +4 more
openaire   +2 more sources

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Data‐ and Theory‐Guided Design of Dual‐Role V‐Doped RuO2 for High‐Performance Acidic Oxygen Evolution

Angewandte Chemie, EarlyView.
A data‐ and theory‐guided paradigm, leveraging large‐scale data mining of 718 catalysts and microkinetic modeling, identifies V‐doped RuO2 as optimal for acidic OER. Vanadium doping drives electron withdrawal from Ru centers, generating Lewis acidic sites that polarize O–H bonds and accelerate deprotonation kinetics. Experimental validation achieves an
Zhongliang Liu, Heng Liu, Kai Zhou, Miaomiao Liu, Tianrui Xue, Jian Zhang, Yiting Song, Jialin Cui, Hao Li, Huihui Li, Chunzhong Li   +10 more
wiley   +2 more sources

Predicting learner's performance through video sequences viewing behavior analysis using educational data-mining. [PDF]

Educ Inf Technol (Dordr), 2021
El Aouifi H, El Hajji M, Es-Saady Y, Douzi H.   +3 more
europepmc   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source