Pub Date : 1900-01-01DOI: 10.32604/jiot.2020.010200
Xiaokan Wang, Qiong Wang
{"title":"Design and Research of Intelligent Alcohol Detector Based on Single Chip Microcomputer","authors":"Xiaokan Wang, Qiong Wang","doi":"10.32604/jiot.2020.010200","DOIUrl":"https://doi.org/10.32604/jiot.2020.010200","url":null,"abstract":"","PeriodicalId":345256,"journal":{"name":"Journal on Internet of Things","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131955155","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}
The Mimic Defense (MD) is an endogenous security technology with the core technique of Dynamic Heterogeneous Redundancy (DHR) architecture. It can effectively resist unknown vulnerabilities, backdoors, and other security threats by schedule strategy, negative feedback control, and other mechanisms. To solve the problem that Cyber Mimic Defense devices difficulty of supporting the TCP protocol. This paper proposes a TCP protocol normalization scheme for DHR architecture. Theoretical analysis and experimental results show that this scheme can realize the support of DHR-based network devices to TCP protocol without affecting the security of mimicry defense architecture.
{"title":"Research on the Key Techniques of TCP Protocol Normalization for Mimic Defense Architecture","authors":"Mingxing Zhu, Yansong Wang, Ruyun Zhang, Tianning Zhang, Heyuan Li, Hanguang Luo, Shunbin Li","doi":"10.32604/jiot.2021.014921","DOIUrl":"https://doi.org/10.32604/jiot.2021.014921","url":null,"abstract":"The Mimic Defense (MD) is an endogenous security technology with the core technique of Dynamic Heterogeneous Redundancy (DHR) architecture. It can effectively resist unknown vulnerabilities, backdoors, and other security threats by schedule strategy, negative feedback control, and other mechanisms. To solve the problem that Cyber Mimic Defense devices difficulty of supporting the TCP protocol. This paper proposes a TCP protocol normalization scheme for DHR architecture. Theoretical analysis and experimental results show that this scheme can realize the support of DHR-based network devices to TCP protocol without affecting the security of mimicry defense architecture.","PeriodicalId":345256,"journal":{"name":"Journal on Internet of Things","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121021142","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}
Pub Date : 1900-01-01DOI: 10.32604/JIOT.2021.013163
S. Kalyani, A. Sowjanya, K. V. Rao
{"title":"A Novel Integrated Machine & Business Intelligence Framework for Sensor Data Analysis","authors":"S. Kalyani, A. Sowjanya, K. V. Rao","doi":"10.32604/JIOT.2021.013163","DOIUrl":"https://doi.org/10.32604/JIOT.2021.013163","url":null,"abstract":"","PeriodicalId":345256,"journal":{"name":"Journal on Internet of Things","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128525989","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}
Pub Date : 1900-01-01DOI: 10.32604/jiot.2021.014877
Sajib Sarker, Ling Tan, Wenjie Ma, Shanshan Rong, Osibo Benjamin Kwapong, Oscar Famous Darteh
The novel coronavirus 2019 (COVID-19) rapidly spreading around the world and turns into a pandemic situation, consequently, detecting the coronavirus (COVID-19) affected patients are now the most critical task for medical specialists. The deficiency of medical testing kits leading to huge complexity in detecting COVID-19 patients worldwide, resulting in the number of infected cases is expanding. Therefore, a significant study is necessary about detecting COVID-19 patients using an automated diagnosis method, which hinders the spreading of coronavirus. In this paper, the study suggests a Deep Convolutional Neural Network-based multi-classification framework (COVMCNet) using eight different pre-trained architectures such as VGG16, VGG19, ResNet50V2, DenseNet201, InceptionV3, MobileNet, InceptionResNetV2, Xception which are trained and tested on the X-ray images of COVID-19, Normal, Viral Pneumonia, and Bacterial Pneumonia. The results from 4-class (Normal vs. COVID-19 vs. Viral Pneumonia vs. Bacterial Pneumonia) demonstrated that the pre-trained model DenseNet201 provides the highest classification performance (accuracy: 92.54%, precision: 93.05%, recall: 92.81%, F1-score: 92.83%, specificity: 97.47%). Notably, the DenseNet201 (4-class classification) pre-trained model in the proposed COV-MCNet framework showed higher accuracy compared to the rest seven models. Important to mention that the proposed COV-MCNet model showed comparatively higher classification accuracy based on the small number of pre-processed datasets that specifies the designed system can produce superior results when more data become available. The proposed multi-classification network (COV-MCNet) significantly speeds up the existing radiology based method which will be helpful for the medical community and clinical specialists to early diagnosis the COVID-19 cases during this pandemic.
新型冠状病毒2019 (COVID-19)在全球迅速蔓延并演变为大流行，因此，检测感染冠状病毒(COVID-19)的患者是目前医学专家最重要的任务。由于医疗检测工具的不足，在全球范围内检测COVID-19患者的工作非常复杂，导致感染人数不断增加。因此，有必要对阻止新冠病毒传播的自动诊断方法进行重要研究。本文采用VGG16、VGG19、ResNet50V2、DenseNet201、InceptionV3、MobileNet、InceptionResNetV2、Xception等8种不同的预训练架构，在COVID-19、正常肺炎、病毒性肺炎和细菌性肺炎的x射线图像上进行训练和测试，提出了一种基于深度卷积神经网络的多分类框架(COVMCNet)。4个类别(Normal vs. COVID-19 vs.病毒性肺炎vs.细菌性肺炎)的结果表明，预训练模型DenseNet201具有最高的分类性能(准确率:92.54%，准确率:93.05%，召回率:92.81%，f1评分:92.83%，特异性:97.47%)。值得注意的是，与其他7个模型相比，所提出的COV-MCNet框架中的DenseNet201(4类分类)预训练模型具有更高的准确性。值得一提的是，本文提出的COV-MCNet模型在少量预处理数据集的基础上显示出相对较高的分类精度，这说明当有更多数据可用时，设计的系统可以产生更好的结果。提出的多分类网络(COV-MCNet)大大加快了现有的基于放射学的方法，有助于医学界和临床专家在本次大流行期间对COVID-19病例进行早期诊断。
{"title":"Multi-Classification Network for Identifying COVID-19 Cases Using Deep Convolutional Neural Networks","authors":"Sajib Sarker, Ling Tan, Wenjie Ma, Shanshan Rong, Osibo Benjamin Kwapong, Oscar Famous Darteh","doi":"10.32604/jiot.2021.014877","DOIUrl":"https://doi.org/10.32604/jiot.2021.014877","url":null,"abstract":"The novel coronavirus 2019 (COVID-19) rapidly spreading around the world and turns into a pandemic situation, consequently, detecting the coronavirus (COVID-19) affected patients are now the most critical task for medical specialists. The deficiency of medical testing kits leading to huge complexity in detecting COVID-19 patients worldwide, resulting in the number of infected cases is expanding. Therefore, a significant study is necessary about detecting COVID-19 patients using an automated diagnosis method, which hinders the spreading of coronavirus. In this paper, the study suggests a Deep Convolutional Neural Network-based multi-classification framework (COVMCNet) using eight different pre-trained architectures such as VGG16, VGG19, ResNet50V2, DenseNet201, InceptionV3, MobileNet, InceptionResNetV2, Xception which are trained and tested on the X-ray images of COVID-19, Normal, Viral Pneumonia, and Bacterial Pneumonia. The results from 4-class (Normal vs. COVID-19 vs. Viral Pneumonia vs. Bacterial Pneumonia) demonstrated that the pre-trained model DenseNet201 provides the highest classification performance (accuracy: 92.54%, precision: 93.05%, recall: 92.81%, F1-score: 92.83%, specificity: 97.47%). Notably, the DenseNet201 (4-class classification) pre-trained model in the proposed COV-MCNet framework showed higher accuracy compared to the rest seven models. Important to mention that the proposed COV-MCNet model showed comparatively higher classification accuracy based on the small number of pre-processed datasets that specifies the designed system can produce superior results when more data become available. The proposed multi-classification network (COV-MCNet) significantly speeds up the existing radiology based method which will be helpful for the medical community and clinical specialists to early diagnosis the COVID-19 cases during this pandemic.","PeriodicalId":345256,"journal":{"name":"Journal on Internet of Things","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132607612","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}
Pub Date : 1900-01-01DOI: 10.32604/jiot.2022.042054
Jiale Cheng, Zilong Jin
{"title":"Evidence-Based Federated Learning for Set-Valued Classification of Industrial IoT DDos Attack Traffic","authors":"Jiale Cheng, Zilong Jin","doi":"10.32604/jiot.2022.042054","DOIUrl":"https://doi.org/10.32604/jiot.2022.042054","url":null,"abstract":"","PeriodicalId":345256,"journal":{"name":"Journal on Internet of Things","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129298363","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}
Pub Date : 1900-01-01DOI: 10.32604/jiot.2022.031043
Jinlin Wang, J. Teng, Yang He, Hongyu Yang, Yulong Ji, Zhikun Tang, Ningwei Bai
{"title":"Generation and Simulation of Basic Maneuver Action Library for 6-DOF Aircraft by Reinforcement Learning","authors":"Jinlin Wang, J. Teng, Yang He, Hongyu Yang, Yulong Ji, Zhikun Tang, Ningwei Bai","doi":"10.32604/jiot.2022.031043","DOIUrl":"https://doi.org/10.32604/jiot.2022.031043","url":null,"abstract":"","PeriodicalId":345256,"journal":{"name":"Journal on Internet of Things","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133381198","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}
Pub Date : 1900-01-01DOI: 10.32604/jiot.2022.040966
Rawan Sanyour, Manal A. Abdullah, S. Abdullah
{"title":"Quality of Experience in Internet of Things: A Systematic Literature Review","authors":"Rawan Sanyour, Manal A. Abdullah, S. Abdullah","doi":"10.32604/jiot.2022.040966","DOIUrl":"https://doi.org/10.32604/jiot.2022.040966","url":null,"abstract":"","PeriodicalId":345256,"journal":{"name":"Journal on Internet of Things","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124477013","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}
Pub Date : 1900-01-01DOI: 10.32604/jiot.2021.016747
J. V. Anand, R. Praveena, T. R. Ganesh Babu
{"title":"Routing Protocol in Underwater Wireless Acoustic Communication Using Non Orthogonal Multiple Access","authors":"J. V. Anand, R. Praveena, T. R. Ganesh Babu","doi":"10.32604/jiot.2021.016747","DOIUrl":"https://doi.org/10.32604/jiot.2021.016747","url":null,"abstract":"","PeriodicalId":345256,"journal":{"name":"Journal on Internet of Things","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125630987","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}
Pub Date : 1900-01-01DOI: 10.32604/jiot.2022.037416
Zeyong Sun, Guo Ran, Zilong Jin
{"title":"Intrusion Detection Method Based on Active Incremental Learning in Industrial Internet of Things Environment","authors":"Zeyong Sun, Guo Ran, Zilong Jin","doi":"10.32604/jiot.2022.037416","DOIUrl":"https://doi.org/10.32604/jiot.2022.037416","url":null,"abstract":"","PeriodicalId":345256,"journal":{"name":"Journal on Internet of Things","volume":"2001 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131362850","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}
Pub Date : 1900-01-01DOI: 10.32604/jiot.2021.014980
Yu Xue, Yan Zhao
: As a new intelligent optimization method, brain storm optimization (BSO) algorithm has been widely concerned for its advantages in solving classical optimization problems. Recently, an evolutionary classification optimization model based on BSO algorithm has been proposed, which proves its effectiveness in solving the classification problem. However, BSO algorithm also has defects. For example, large-scale datasets make the structure of the model complex, which affects its classification performance. In addition, in the process of optimization, the information of the dominant solution cannot be well preserved in BSO, which leads to its limitations in classification performance. Moreover, its generation strategy is inefficient in solving a variety of complex practical problems. Therefore, we briefly introduce the optimization model structure by feature selection. Besides, this paper retains the brainstorming process of BSO algorithm, and embeds the new generation strategy into BSO algorithm. Through the three generation methods of global optimal, local optimal and nearest neighbor, we can better retain the information of the dominant solution and improve the search efficiency. To verify the performance of the proposed generation strategy in solving the classification problem, twelve datasets are used in experiment. Experimental results show that the new generation strategy can improve the performance of BSO algorithm in solving classification problems.
{"title":"New Solution Generation Strategy to Improve Brain Storm Optimization Algorithm for Classification","authors":"Yu Xue, Yan Zhao","doi":"10.32604/jiot.2021.014980","DOIUrl":"https://doi.org/10.32604/jiot.2021.014980","url":null,"abstract":": As a new intelligent optimization method, brain storm optimization (BSO) algorithm has been widely concerned for its advantages in solving classical optimization problems. Recently, an evolutionary classification optimization model based on BSO algorithm has been proposed, which proves its effectiveness in solving the classification problem. However, BSO algorithm also has defects. For example, large-scale datasets make the structure of the model complex, which affects its classification performance. In addition, in the process of optimization, the information of the dominant solution cannot be well preserved in BSO, which leads to its limitations in classification performance. Moreover, its generation strategy is inefficient in solving a variety of complex practical problems. Therefore, we briefly introduce the optimization model structure by feature selection. Besides, this paper retains the brainstorming process of BSO algorithm, and embeds the new generation strategy into BSO algorithm. Through the three generation methods of global optimal, local optimal and nearest neighbor, we can better retain the information of the dominant solution and improve the search efficiency. To verify the performance of the proposed generation strategy in solving the classification problem, twelve datasets are used in experiment. Experimental results show that the new generation strategy can improve the performance of BSO algorithm in solving classification problems.","PeriodicalId":345256,"journal":{"name":"Journal on Internet of Things","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130017502","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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