Journal on Internet of Things最新文献_第5页

Design and Research of Intelligent Alcohol Detector Based on Single Chip Microcomputer 基于单片机的智能酒精检测仪设计与研究

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/jiot.2020.010200

Xiaokan Wang, Qiong Wang

引用次数: 0

Research on the Key Techniques of TCP Protocol Normalization for Mimic Defense Architecture 模拟防御体系中TCP协议规范化关键技术研究

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/jiot.2021.014921

Mingxing Zhu, Yansong Wang, Ruyun Zhang, Tianning Zhang, Heyuan Li, Hanguang Luo, Shunbin Li

引用次数: 0

A Novel Integrated Machine & Business Intelligence Framework for Sensor Data Analysis 一种用于传感器数据分析的新型集成机器与商业智能框架

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/JIOT.2021.013163

S. Kalyani, A. Sowjanya, K. V. Rao

引用次数: 0

Multi-Classification Network for Identifying COVID-19 Cases Using Deep Convolutional Neural Networks 基于深度卷积神经网络的COVID-19病例多分类网络识别

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/jiot.2021.014877

Sajib Sarker, Ling Tan, Wenjie Ma, Shanshan Rong, Osibo Benjamin Kwapong, Oscar Famous Darteh

{"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}

引用次数: 3

Evidence-Based Federated Learning for Set-Valued Classification of Industrial IoT DDos Attack Traffic 基于证据的工业物联网DDos攻击流量集值分类联邦学习

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/jiot.2022.042054

Jiale Cheng, Zilong Jin

引用次数: 0

Generation and Simulation of Basic Maneuver Action Library for 6-DOF Aircraft by Reinforcement Learning 基于强化学习的六自由度飞行器基本机动动作库生成与仿真

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/jiot.2022.031043

Jinlin Wang, J. Teng, Yang He, Hongyu Yang, Yulong Ji, Zhikun Tang, Ningwei Bai

引用次数: 0

Quality of Experience in Internet of Things: A Systematic Literature Review 物联网体验质量:系统文献综述

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/jiot.2022.040966

Rawan Sanyour, Manal A. Abdullah, S. Abdullah

引用次数: 0

Routing Protocol in Underwater Wireless Acoustic Communication Using Non Orthogonal Multiple Access 基于非正交多址的水下无线水声通信路由协议

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/jiot.2021.016747

J. V. Anand, R. Praveena, T. R. Ganesh Babu

引用次数: 0

Intrusion Detection Method Based on Active Incremental Learning in Industrial Internet of Things Environment 工业物联网环境下基于主动增量学习的入侵检测方法

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/jiot.2022.037416

Zeyong Sun, Guo Ran, Zilong Jin

引用次数: 0

New Solution Generation Strategy to Improve Brain Storm Optimization Algorithm for Classification 改进头脑风暴分类优化算法的新解生成策略

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/jiot.2021.014980

Yu Xue, Yan Zhao

{"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}

引用次数: 0