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.
{"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}
G. S. Nijaguna, D. P. M. Kumar, B. N. Manjunath, T. J. S. Jain, N. Dayananda Lal
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.
{"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}
In resource‐constrained networks, IPv6 addresses are assigned to devices using SLAAC‐based EUI‐64, which generates unique addresses. However, the constant interface identifier (IID) across networks makes it vulnerable to reconnaissance attacks like location tracking, network activity correlation, address scanning, etc. This research work introduces a new addressing strategy that utilizes the Elegant Pairing function to guarantee the generation of nonpredictable unique IPv6 addresses, thereby mitigating different types of reconnaissance attacks. The proposed scheme achieves 100% address success rate (ASR) based on experimental evaluation while effectively thwarting reconnaissance attacks. Importantly, it achieves security enhancements without additional communication overhead and energy consumption.
{"title":"IPv6 addressing scheme to enhance the performance by mitigating reconnaissance attack","authors":"Pragya, Bijendra Kumar","doi":"10.1002/itl2.493","DOIUrl":"https://doi.org/10.1002/itl2.493","url":null,"abstract":"In resource‐constrained networks, IPv6 addresses are assigned to devices using SLAAC‐based EUI‐64, which generates unique addresses. However, the constant interface identifier (IID) across networks makes it vulnerable to reconnaissance attacks like location tracking, network activity correlation, address scanning, etc. This research work introduces a new addressing strategy that utilizes the Elegant Pairing function to guarantee the generation of nonpredictable unique IPv6 addresses, thereby mitigating different types of reconnaissance attacks. The proposed scheme achieves 100% address success rate (ASR) based on experimental evaluation while effectively thwarting reconnaissance attacks. Importantly, it achieves security enhancements without additional communication overhead and energy consumption.","PeriodicalId":509592,"journal":{"name":"Internet Technology Letters","volume":"60 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139258352","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}
With the development of the Internet of Things (IoT), the IoT technology is gradually applied to the sport training of athletes. According to the training feature of athletes collected by the IoT equipment, this paper proposes to use the method of feature image sequence analysis and feature extraction to automatically identify the training of athletes. The mathematical model of feature image recognition based on gray difference is established, and the pyramid iterative recognition algorithm is used to reduce the recognition error effectively. In addition, a mathematical model of image sequence feature extraction based on moment invariants is established, and the feature table for athlete matching is discussed in detail. Based on the concept of dynamic establishment of search area and the principle of two‐step template feature recognition and matching, through the analysis of the pictures of high jumpers, the change of the athlete angle of left knee in the process of high jump is obtained, which achieves the purpose of automatic identification of key actions. At the same time, the random error existing in manual recognition is completely eliminated.
{"title":"Efficient feature recognition and matching technology for IoT‐enabled sports training","authors":"Meng Du, Zhongliang Liu","doi":"10.1002/itl2.490","DOIUrl":"https://doi.org/10.1002/itl2.490","url":null,"abstract":"With the development of the Internet of Things (IoT), the IoT technology is gradually applied to the sport training of athletes. According to the training feature of athletes collected by the IoT equipment, this paper proposes to use the method of feature image sequence analysis and feature extraction to automatically identify the training of athletes. The mathematical model of feature image recognition based on gray difference is established, and the pyramid iterative recognition algorithm is used to reduce the recognition error effectively. In addition, a mathematical model of image sequence feature extraction based on moment invariants is established, and the feature table for athlete matching is discussed in detail. Based on the concept of dynamic establishment of search area and the principle of two‐step template feature recognition and matching, through the analysis of the pictures of high jumpers, the change of the athlete angle of left knee in the process of high jump is obtained, which achieves the purpose of automatic identification of key actions. At the same time, the random error existing in manual recognition is completely eliminated.","PeriodicalId":509592,"journal":{"name":"Internet Technology Letters","volume":"42 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139272994","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}
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