This study investigates the impact of generative artificial intelligence (GenAI) on engineering students' creativity, examining the mediating roles of critical thinking and AI self-efficacy in this relationship. We analyze the data collected using SP...
BACKGROUND: Integrating engineering design processes into science education has become a significant priority in STEM instruction. However, many science teachers face difficulties incorporating these processes due to limited pedagogical expertise. Ge...
We develop an AI system that pairs engineering problems with biology-inspired solutions at a large scale, by analyzing over 101 million abstracts to identify thematic links between engineering and biology. We detect coherent themes in each domain wit...
With the rapid advancement of generative artificial intelligence (AI), its potential applications in higher education have attracted significant attention. This study investigated how 148 students from diverse engineering disciplines and regions acro...
Artificial intelligence (AI) is expected to transform many scientific disciplines, with the potential to significantly accelerate scientific discovery. This perspective calls for the development of data-centric water engineering to tackle water chall...
The present work proposes a comprehensive metaheuristic methodology for the development of a medical robot for the upper limb rehabilitation, which includes the topological optimization of the device, kinematic models (5 DOF), human-robot interface, ...
Frameworks for human activity recognition (HAR) can be applied in the clinical environment for monitoring patients' motor and functional abilities either remotely or within a rehabilitation program. Deep Learning (DL) models can be exploited to perfo...
Neural networks : the official journal of the International Neural Network Society
Feb 16, 2024
Spiking neural networks (SNNs) are brain-inspired models that utilize discrete and sparse spikes to transmit information, thus having the property of energy efficiency. Recent advances in learning algorithms have greatly improved SNN performance due ...
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