Results 21 to 30 of about 75,250 (263)
Smart Learning Services Based on Smart Cloud Computing
Sensors, 2011 Context-aware technologies can make e-learning services smarter and more efficient since context-aware services are based on the user’s behavior. To add those technologies into existing e-learning services, a service architecture model is needed to ...
Yong-Ik Yoon, Su-Mi Song, Svetlana Kim
doaj +1 more source
The Improving Effect of Intelligent Speech Recognition System on English Learning
Advances in Multimedia, 2022 To improve the effect of English learning in the context of smart education, this study combines speech coding to improve the intelligent speech recognition algorithm, builds an intelligent English learning system, combines the characteristics of human ...
Qi Luo
doaj +1 more source
Smart Learning Environments, 2021 This paper discusses the uses and applications of the Pedagogy of Experience Complexity for Smart Learning (PECSL), a four-tier model of considerations for the design and development of smart learning activities.
Pen Lister
doaj +1 more source
Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller
Sensors, 2022 The Internet of Things (IoT) era is mainly dependent on the word “Smart”, such as smart cities, smart homes, and smart cars. This aspect can be achieved through the merging of machine learning algorithms with IoT computing models.
Mahmoud Hussein +4 more
doaj +1 more source
Learning Setting-Generalized Activity Models for Smart Spaces [PDF]
IEEE Intelligent Systems, 2012 The data mining and pervasive computing technologies found in smart homes offer unprecedented opportunities for providing context-aware services, including health monitoring and assistance to individuals experiencing difficulties living independently at home.
openaire +3 more sources
Decentralized Smart Grid Stability Modeling with Machine Learning
Energies, 2023 Predicting the stability of a Decentralized Smart Grid is key to the control of such systems. One of the key aspects that is necessary when observing the control of DSG systems is the need for rapid control. Due to this, the application of AI-based machine learning (ML) algorithms may be key to achieving a quick and precise stability prediction.
Borna Franović +3 more
openaire +2 more sources
Review on Interpretable Machine Learning in Smart Grid
Energies, 2022 In recent years, machine learning, especially deep learning, has developed rapidly and has shown remarkable performance in many tasks of the smart grid field.
Chongchong Xu +4 more
doaj +1 more source
Frontiers in Psychology, 2022 The rapid improvement of technologies such as artificial intelligence in recent years has resulted in the development of smart technologies (ST) that can influence learning performance in different fields.
Fei Jiang +5 more
doaj +1 more source
Development of Smart Content Model-based Augmented Reality to Support Smart Learning [PDF]
Journal of Science Learning, 2019 Augmented Reality (AR) is an optical technology that combines virtual objects or worlds into real worlds like in real time and increases user perceptions and interactions with the real world. Information conveyed by virtual objects helps users carry out activities (tasks) in the real world.
Fatimah, Siti +3 more
openaire +2 more sources
The Journal of Engineering, 2023 This article proposes a deep learning (DL) model made of Long Short Term Memory (LSTM) and Adaptive Neuro Fuzzy Inference System (ANFIS) to detect fault in smart distribution grid assisted by communication systems using smart meter data.
Camille Franklin Mbey +3 more
doaj +1 more source