Results 161 to 170 of about 167,051 (298)

A SURVEY ON STUDENT\'S OPINION ABOUT EFFECTIVE FACTORS FOR ACADEMIC DROPOUT IN SHAHID SADOUGHI UNIVERSITY

Journal of Medical Education and Development, 2007
Introduction: University's students are faced with a lot of problems such as dropout. There are many factors to happen and by destroyed. Aim of this study was to determine the student's opinions about most effective factors for academic dropout in Shahid
T Salimi, L Shahbazi, S.H Seyed Hassani, M.H Ahmadieh, H Karimi, M.H Dehghanpour   +5 more
doaj  

A general equilibrium theory of college with education subsidies, in-school labor supply, and borrowing constraints [PDF]

This paper analyzes the effectiveness of three different types of education policies: tuition subsidies (broad based, merit based, and flat tuition), grant subsidies (broad based and merit based), and loan limit restrictions.
Carlos Garriga, Mark P. Keightley
core  

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology

Advanced Intelligent Discovery, EarlyView.
Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh, Raiyan Siddique, Sarah S. Sharif, Yaser Mike Banad   +3 more
wiley   +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

Model interpretability on private-safe oriented student dropout prediction. [PDF]

PLoS One
Liu H, Mao M, Li X, Gao J.
europepmc   +1 more source

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

Advanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta, Madison Dautle, Shaoqiang Zhang, Yong Chen   +3 more
wiley   +1 more source

A PSO weighted ensemble framework with SMOTE balancing for student dropout prediction in smart education systems. [PDF]

Sci Rep
Jain A, Dubey AK, Khan S, Panwar A, Alkhatib M, Alshahrani AM.   +5 more
europepmc   +1 more source

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

Advanced Intelligent Discovery, EarlyView.
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary, Nicholas Ballard   +1 more
wiley   +1 more source

Student dropout prediction through machine learning optimization: insights from moodle log data. [PDF]

Sci Rep
Rebelo Marcolino M, Reis Porto T, Thompsen Primo T, Targino R, Ramos V, Marques Queiroga E, Munoz R, Cechinel C.   +7 more
europepmc   +1 more source