Internet Technology Letters最新文献_第3页

Intelligent internet of things induced preschool education assistance system 智能物联网诱导学前教育辅助系统

Internet Technology Letters Pub Date : 2023-11-23 DOI: 10.1002/itl2.494

Hong Zhao

{"title":"Intelligent internet of things induced preschool education assistance system","authors":"Hong Zhao","doi":"10.1002/itl2.494","DOIUrl":"https://doi.org/10.1002/itl2.494","url":null,"abstract":"With the rapid development of AI technology, how to construct smart learning environment has become a hot topic in education. However, most of existing studies focus on the higher education. It is still an open issue to establish smart learning environment in kindergartens for preschool education. In order to solve this issue, this paper designs an intelligent classroom education perception system to assist preschool education. The system end‐edge‐cloud collaboration structure. The end node captures videos of children in the classroom and send the collected videos to edge node. The edge node contains a NVIDIA® Jetson™ TX2 in which an AI module is deployed. The AI module adopts end‐to‐end architecture, which contains four parts: human detector, symmetric spatial transformation network module, non‐maximum suppression module, and a spatial temporal graph convolutional network module. Compared with previous works, the proposed scheme considers both spatial information and temporal information of skeletal key points. The experimental results show that the proposed smart preschool education assistance system can help teachers to recognize most of common preschool children activities during classroom.","PeriodicalId":509592,"journal":{"name":"Internet Technology Letters","volume":"11 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139245016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantum squirrel search algorithm based support vector machine algorithm for brain tumor classification 基于量子松鼠搜索算法的脑肿瘤分类支持向量机算法

Internet Technology Letters Pub Date : 2023-11-22 DOI: 10.1002/itl2.484

G. S. Nijaguna, D. P. M. Kumar, B. N. Manjunath, T. J. S. Jain, N. Dayananda Lal

{"title":"Quantum squirrel search algorithm based support vector machine algorithm for brain tumor classification","authors":"G. S. Nijaguna, D. P. M. Kumar, B. N. Manjunath, T. J. S. Jain, N. Dayananda Lal","doi":"10.1002/itl2.484","DOIUrl":"https://doi.org/10.1002/itl2.484","url":null,"abstract":"A Brain tumor is growth or mass of irregular cells inside your brain, several various kinds of brain tumors survive. A few brain tumors are cancerous (malignant), also various brain tumors are noncancerous (benign). The existing approach faces problem related to local optima issues, complexity in computational time, less convergence speed and less exploration ability. The stimulated quality Selection of Quantum Squirrel Search Algorithm (QSSA) is based on equally appearance with methylation information of prostate cancer. Issues with multiple models, multiple dimensions, and unimodal optimization are all addressed by this QSSA concept. The input image of the CE‐MRI dataset consists of 3064 segments with comprise (708 slices) meningiomas, (1426 slices) gliomas and (930 slices) pituitary tumors. In order to extract appropriate data from an image, a convolutional neural network (CNN) executes a number of mathematical processes, including convolutions and pooling. The CNN model's benefits include a large number of important features that can be extracted and good accuracy. Then, Support Vector Machine (SVM), a machine learning technique used for supervised learning, is typically associated within the double classification. The SVM model benefits from a large effective dimensional space and adequate memory. The proposed QSSA has obtained high Accuracy 98.3%, Sensitivity 95.4% and Specificity 97.9% than existing Correlation Learning Mechanism (CLM) which has 90.4% accuracy, 86% sensitivity and 91.5% specificity respectively.","PeriodicalId":509592,"journal":{"name":"Internet Technology Letters","volume":"2 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139249598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IPv6 addressing scheme to enhance the performance by mitigating reconnaissance attack IPv6 寻址方案，通过减少侦查攻击提高性能

Internet Technology Letters Pub Date : 2023-11-20 DOI: 10.1002/itl2.493

Pragya, Bijendra Kumar

引用次数: 0

Efficient feature recognition and matching technology for IoT‐enabled sports training 用于物联网体育训练的高效特征识别和匹配技术

Internet Technology Letters Pub Date : 2023-11-15 DOI: 10.1002/itl2.490

Meng Du, Zhongliang Liu

引用次数: 0