Machine learning based human interacted robotic intelligence to detect the different categories of speech DOI

Ravinjit Singh,

Yasmeen Yasmeen,

Aditi Srivastava

и другие.

2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), Год журнала: 2023, Номер unknown, С. 1 - 8

Опубликована: Май 25, 2023

Realizing artificial intelligence depends heavily on human-robot interaction (HRI) technology. Here, we present dual-function sound actuators built graphene for exchanges that use machine learning. (GHRI). The GHRI's triboelectric acoustic detection mechanism and thermoacoustic output allow it to serve as both a prosthetic ear tongue. Triboelectric materials, electrodes, sources made possible by laser-induced contributed the overall success of combined device. GHRI can recognize speech identities, feelings, substance, other information in human optimizing structure parameters produce high sensitivity working longevity. With help learning, convolutional neural network is used teach 30 voice categories, with an accuracy 99.72% 97.83% training test datasets, respectively. In addition, AIs communicate through recognized characteristics. implications our research future robots are vast.

Язык: Английский

Envisioning the future of learning and teaching engineering in the artificial intelligence era: Opportunities and challenges DOI
Muhsin Menekşe

Journal of Engineering Education, Год журнала: 2023, Номер 112(3), С. 578 - 582

Опубликована: Июнь 20, 2023

Язык: Английский

Процитировано

27

Investigating the interrelationship among academic emotions, classroom engagement, and self-efficacy in the context of EFL learning in smart classrooms in China DOI Creative Commons
Yeting Hu, Chuanzhi Fang, Jinhua Wu

и другие.

Australian Journal of Psychology, Год журнала: 2024, Номер 76(1)

Опубликована: Янв. 29, 2024

Objective This study aims to fill in the gap by exploring interrelationship among academic emotions, classroom engagement and self-efficacy EFL learning Chinese non-English majors smart classrooms.

Язык: Английский

Процитировано

6

AI-Enabled Learning Ecosystems DOI

S. Santhana Hari,

R. Harine Rajashree,

J. Dharani

и другие.

IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 165 - 192

Опубликована: Фев. 13, 2025

Artificial intelligence (AI) has rapidly evolved from a speculative concept into transformative force impacting multiple domains, with education being one of its most promising areas influence. The potential AI lies in creating dynamic, adaptive, and student-centered learning ecosystems that enhance the teaching process. In this chapter, we will delve how can transform education, starting AI-integrated smart classrooms foster personalized efficient learning. We explore use for asset tracking to ensure safety support inclusive students special needs. Furthermore, chapter discuss integration design thinking, enhancing creativity problem-solving skills. also examine generative AI's impact on learning, revolutionize assessment methods. These discussions, supported by scholarly references, aim provide comprehensive understanding role shaping future while addressing associated challenges.

Язык: Английский

Процитировано

0

ARGUS: Visualization of AI and Machine Learning-Assisted Task Guidance in Augmented Reality DOI

Ahmad Sajadi,

Maryam Alaeifard,

Majid Modaberi

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Online peer mediation versus teacher mediation in dynamic second language pragmatics assessment DOI
Zahra Fakher Ajabshir

Education and Information Technologies, Год журнала: 2024, Номер 29(16), С. 21195 - 21215

Опубликована: Апрель 29, 2024

Язык: Английский

Процитировано

1

The Effect of Danmaku Font Size on Online Learning Outcomes for Learners with Different Cognitive Styles: Evidence from Eye Movements DOI

Fengqiang Gao,

Chunze Xu, Qing Lv

и другие.

International Journal of Human-Computer Interaction, Год журнала: 2024, Номер unknown, С. 1 - 15

Опубликована: Июнь 7, 2024

Danmaku is increasingly used during online interactions - such as learner communication in learning. As an emerging interaction method, not only enhances the viewer experience, but also allows learners to achieve better learning outcomes. Therefore, this study a 2 (Danmaku font size: large, small) × (cognitive style: field-independent, field-dependent) between-participants design investigate effect of size on through eye-tracking studies and reveal mechanism outcomes field-independent field-dependent learners. The results show that affects perception with different cognitive styles learning, i.e., under style, small can improve perceptual experience. Under focus more area, so large In summary, found impacts styles. sizes should be selected according teaching. This provide research basis optimization suggestions for how use teaching design, which will help deepen understanding role

Язык: Английский

Процитировано

0

Establishing a user demand hierarchy model driven by a mental model for complex operating systems DOI
Wenyu Wu, X. SUN,

Ziwei He

и другие.

International Journal of Industrial Ergonomics, Год журнала: 2024, Номер 103, С. 103634 - 103634

Опубликована: Авг. 31, 2024

Язык: Английский

Процитировано

0

Machine learning based human interacted robotic intelligence to detect the different categories of speech DOI

Ravinjit Singh,

Yasmeen Yasmeen,

Aditi Srivastava

и другие.

2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), Год журнала: 2023, Номер unknown, С. 1 - 8

Опубликована: Май 25, 2023

Realizing artificial intelligence depends heavily on human-robot interaction (HRI) technology. Here, we present dual-function sound actuators built graphene for exchanges that use machine learning. (GHRI). The GHRI's triboelectric acoustic detection mechanism and thermoacoustic output allow it to serve as both a prosthetic ear tongue. Triboelectric materials, electrodes, sources made possible by laser-induced contributed the overall success of combined device. GHRI can recognize speech identities, feelings, substance, other information in human optimizing structure parameters produce high sensitivity working longevity. With help learning, convolutional neural network is used teach 30 voice categories, with an accuracy 99.72% 97.83% training test datasets, respectively. In addition, AIs communicate through recognized characteristics. implications our research future robots are vast.

Язык: Английский

Процитировано

0