Results 121 to 130 of about 346,286 (280)

Data Mining in Higher Education

Periodicals of Engineering and Natural Sciences (PEN), 2013
Data mining is slowly but surely making its way into the educational field after dominating the business fields.  This paper focuses on the research completed in the area of data mining in the higher education sector: colleges and universities.  We will look at the different implementation of data mining and to what extent was it utilized and benefited
Dina A. Aziz AlHammadi, Mehmet Sabih Aksoy   +1 more
openaire   +2 more sources

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

Advanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang, Yongqing Zhang, Xinyu Deng, Hyeon Jeong Lee, Delong Zhang   +4 more
wiley   +1 more source

Predicting student specializations: a Machine Learning Approach based on Academic Performance

Je-LKS: Journal of E-Learning and Knowledge Society
Education is a cornerstone of societal progress, equipping people with essential skills and knowledge. In today’s dynamic global society, personalized learning experiences are crucial. Data-driven methodologies, especially Educational Data Mining (EDM),
Athanasios Angeioplastis, Nikolaos Papaioannou, Alkiviadis Tsimpiris, Angeliki Kamilali, Dimitrios Varsamis   +4 more
doaj   +1 more source

The use of video clickstream data to predict university students' test performance: A comprehensive educational data mining approach. [PDF]

Educ Inf Technol (Dordr), 2023
Yürüm OR, Taşkaya-Temizel T, Yıldırım S.   +2 more
europepmc   +1 more source

When Biology Meets Medicine: A Perspective on Foundation Models

Advanced Intelligent Discovery, EarlyView.
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu, Yuxing Wang, Limin Zou, Jing Huang   +3 more
wiley   +1 more source

Corrigendum to ``Academic data derived from a university e-government analytic platform: An educational data mining approach'' [Data in Brief, Volume 49 (2023) /109357]. [PDF]

Data Brief, 2023
Chytas K, Tsolakidis A, Triperina E, Karanikolas NN, Skourlas C.   +4 more
europepmc   +1 more source

Machine‐Learning‐Assisted Onset‐Time Determination in Transient Luminescence Thermometry

Advanced Intelligent Discovery, EarlyView.
Artificial neural networks enable autonomous extraction of onset times from transient heating curves in luminescence thermometry. Using Ln3+‐doped upconverting nanoparticles as luminescent thermometers, we combine experimental transients with physically motivated synthetic curves to enhance data diversity and improve generalization.
David J. Sousa, Erving Ximendes, Luís D. Carlos, Carlos D. S. Brites   +3 more
wiley   +1 more source

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

Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing

Advanced Intelligent Systems, EarlyView.
This Roadmap outlines the transformative role of artificial intelligence‐augmented additive manufacturing, highlighting advances in design, monitoring, and product development. By integrating tools such as generative design, computer vision, digital twins, and closed‐loop control, it presents pathways toward smart, scalable, and autonomous additive ...
Ali Zolfagharian, Liuchao Jin, Qi Ge, Wei‐Hsin Liao, Andrés Díaz Lantada, Francisco Franco Martínez, Tianyu Zhang, Tao Liu, Charlie C. L. Wang, Mohammad Hossein Mosallanejad, Reza Ghanavati, Abdollah Saboori, Alejandro De Blas De Miguel, William Solórzano‐Requejo, Yi Cai, Xiangyang Dong, Huangyi Qu, Najmeh Samadiani, Guangyan Huang, Austin Downey, Yanzhou Fu, Lang Yuan, Tsz‐Kwan (Glory) Lee, Arbind Agrahari Baniya, Eisha Waseem, Abdul Rahman Sani, Abbas Z. Kouzani, Yijia Wu, Markus P. Nemitz, Masoud Shirzad, Dageon Oh, Seung Yun Nam, Amedeo Franco Bonatti, Irene Chiesa, Gabriele Maria Fortunato, Giovanni Vozzi, Carmelo De Maria, Mahdi Bodaghi   +37 more
wiley   +1 more source

Catalysis AI Agent Guides Discovering the Universal Design Principle of Cu‐Based Single‐Atom Alloy Catalysts for CO2 Electroreduction

Angewandte Chemie, EarlyView.
Artificial intelligence (AI) enables the systematic analysis and comparative evaluation of experimental and theoretical data, optimizes the catalytic reaction research workflow, and accelerates the discovery of high‐performance electrocatalysts. ABSTRACT Copper (Cu)‐based single‐atom alloys (SAAs) represent a promising strategy for optimizing the ...
Xuning Wang, Zhong Li, Di Zhang, Hao Li, Haoxiang Xu, Daojian Cheng   +5 more
wiley   +2 more sources