Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractNovel Temporal Expression Patterns of EBF-Binding Proteins in Wing Morphs of The Grain Aphid Sitobion miscanthi    Next AbstractNovel Insights into Dimethylsulfoniopropionate Catabolism by Cultivable Bacteria in the Arctic Kongsfjorden »

Comput Intell Neurosci


Title:The Cognitive Transformation of Japanese Language Education by Artificial Intelligence Technology in the Wireless Network Environment
Author(s):Zhang S;
Address:"School of Foreign Languages and International Education, Dalian Ocean University, Dalian 116023, Liaoning, China"
Journal Title:Comput Intell Neurosci
Year:2022
Volume:20220707
Issue:
Page Number:7886369 -
DOI: 10.1155/2022/7886369
ISSN/ISBN:1687-5273 (Electronic) 1687-5265 (Print)
Abstract:"This study aims to solve the multiscale problems faced by the current classroom student behavior target detection based on the convolutional neural network (CNN) in the wireless network environment. Firstly, the recent reform of Japanese language education is introduced. Secondly, the multiscale problem research of classroom student behavior target detection is discussed. A CNN-based new extraction network is designed based on dilated convolution and pyramid features. An anchor reconstruction algorithm based on improved K-means clustering is presented for the self-made student behavior dataset. Finally, the performance of the designed algorithm is tested. The anchor reconstruction algorithm's mean average precision is 83.2%, and the average intersection over union is 73.7%. The experimental results of this scheme outperform the original single-shot multibox detector and K-means algorithms. Compared with other algorithms, the designed multiscale detection algorithm of classroom student behavior has the best detection effect on Pascal visual object classes (VOC) dataset. The detection accuracy of the entire dataset is 79.8%. Overall, the multiscale detection algorithm for classroom student behavior has a better detection effect on the Pascal VOC dataset and has good generalization ability and robustness. This research can guide students to recognize their class status and make corresponding adjustments to improve their learning efficiency, which has essential research significance and application value"
Keywords:Algorithms *Artificial Intelligence Cognition Humans Japan Language Technology *Volatile Organic Compounds;
Notes:"MedlineZhang, Su eng 2022/07/19 Comput Intell Neurosci. 2022 Jul 7; 2022:7886369. doi: 10.1155/2022/7886369. eCollection 2022"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 23-11-2024