Journal of Intelligent Systems and Internet of Things最新文献_第3页

A Dockers Storage Performance Evaluation: Impact of Backing File Systems Dockers存储性能评估:备份文件系统的影响

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.030101

A. Ramadan

引用次数: 2

Skin Cancer Detection using Neutrosophic c-means and Fuzzy c-means Clustering Algorithms 基于中性c均值和模糊c均值聚类算法的皮肤癌检测

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.080103

A. Abdelhafeez, Hoda K. Mohamed

{"title":"Skin Cancer Detection using Neutrosophic c-means and Fuzzy c-means Clustering Algorithms","authors":"A. Abdelhafeez, Hoda K. Mohamed","doi":"10.54216/jisiot.080103","DOIUrl":"https://doi.org/10.54216/jisiot.080103","url":null,"abstract":"Melanoma is the kind of skin cancer that poses the greatest risk to one's life and has the maximum mortality rate within the group of skin cancer disorders. Even so, the automated placement and classification of skin lesions at initial phases remains a complicated task due to the lack of contrast melanoma molarity and skin fraction and a greater level of color similarity among melanoma-affected and -nonaffected areas. Contemporary technological improvements and research methods enabled it to recognize and distinguish this type of skin cancer more successfully. A clustering technique called neutrosophic c-means clustering (NCMC) is presented in this research to group ambiguous data in the detection of skin cancer. This algorithm takes its cues from both fuzzy c-means and the neutrosophic set structure. To arrive at such a structure, an appropriate objective function must first be created and then minimized. The clustering issue must then be stated as a restricted minimization problem, the solution of which is determined by the objective function. This paper made a comparison between NCMC and fuzzy c-means clustering (FCMC). The results show that the NCMC is more suitable than the FCMC.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"295 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":"114131451","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: 2

Automated Deep Learning based Video Summarization Approach for Forest Fire Detection 基于深度学习的森林火灾探测视频自动摘要方法

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.050201

A. N. Al-Masri

{"title":"Automated Deep Learning based Video Summarization Approach for Forest Fire Detection","authors":"A. N. Al-Masri","doi":"10.54216/jisiot.050201","DOIUrl":"https://doi.org/10.54216/jisiot.050201","url":null,"abstract":"Due to the exponential increase in video data, an automated examination of videos has become essential. A significant requirement is the capability of the automated video summarization process, which is helpful in vast application areas from surveillance to security. It assists in monitoring the user application with reduced memory and time. Therefore, this paper designs an automated deep learning-based video summarization approach for forest fire detection (ADLVS-FFD). The ADLVS-FFD technique aims to summarize the captured videos and detects the existence of forest fire in it. In addition, the ADLVS-FFD technique involves different subprocesses such as frame splitting, feature extraction, and classification. Besides, a merged Gaussian mixture model (MGMM) is used to extract keyframes and features. Moreover, the long short-term memory (LSTM) model is employed to detect and classify input images into normal and forest fire images. To ensure the better performance of the ADLVS-FFD technique, a comprehensive experimental validation process takes place on a benchmark video dataset. The resultant experimental validation process highlighted the supremacy of the ADLVS-FFD technique over the recent methods.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"52 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":"127997706","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: 1

Smart Learning in the Ecosystem: Examines Smart Learning Structural Design Features Considering IoT and IoB 生态系统中的智能学习:考察考虑物联网和IoB的智能学习结构设计特征

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.070102

Ossama H. Embarak, Maryam J. Almesmari, Fatima R. Aldarmaki

引用次数: 0

Several Factors to Analyze the Issues Concerned with the Internet of Things: Methods and analysis 分析物联网相关问题的几个因素:方法与分析

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.070202

A. Abdelaziz, A. N. Mahmoud

{"title":"Several Factors to Analyze the Issues Concerned with the Internet of Things: Methods and analysis","authors":"A. Abdelaziz, A. N. Mahmoud","doi":"10.54216/jisiot.070202","DOIUrl":"https://doi.org/10.54216/jisiot.070202","url":null,"abstract":"The fast advancement of technology has contributed to a rise in everyone's demand to be connected to the internet. The Internet of Things is a notion that emerged with the fourth industrial revolution as a result of the finding that things that were born of the Internet can connect without the need for external causes (IoT). The communication of items with one another ensures that firms will spend as little time and money as possible on labor. Businesses that want to make the switch to the Internet of Things are going to run across a lot of challenges. The process of identifying and fixing these issues may result in both time and financial waste. Within the scope of this research, we looked at the challenges that are associated with the IoT. As a consequence of the research, the level of relevance of the elements that are generating these challenges was figured out using multi-criteria decision-making (MCDM) procedures, and the findings were given to the businesses. We decided on the primary criteria, as well as the secondary criteria that are connected to these primary criteria. The communication of different objects with one another is the primary goal of businesses that are making the shift to Industry 4.0. The purpose of this research was to identify the factors that contributed to the challenges encountered throughout the evolution to Industry 4.0. In the next step of the MCDM process, the DEMATEL approach was used to evaluate the level of significance associated with each of the factors. As a result of the research, we were able to establish which factors should be regarded as important for businesses that are interested in making the shift to the IoT. In this approach, businesses will be able to speed up the transition while limiting the amount of time and money lost in the process.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"463 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":"123027322","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

Patient Health Monitoring Using Feed Forward Neural Network With Cloud Based Internet of Things 基于云的物联网的前馈神经网络患者健康监测

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.000203

P. Shukla, P. Shukla

{"title":"Patient Health Monitoring Using Feed Forward Neural Network With Cloud Based Internet of Things","authors":"P. Shukla, P. Shukla","doi":"10.54216/jisiot.000203","DOIUrl":"https://doi.org/10.54216/jisiot.000203","url":null,"abstract":"The healthcare sector is under pressure to embrace new technologies that are available on the market in order to enhance the overall quality of their services. Telecommunications systems are combined with computers, interconnection, mobility, data storage, and information analytics. Technology that is centred on the Internet of Things (IoT) is the order of the day. Because of the limited availability of human resources and infrastructure, it is becoming more necessary to monitor chronic patients on a continual basis as their conditions worsen. A cloud-based architecture, which can handle all of the aforementioned concerns, may offer effective solutions to the health-care sector. In order to create software that combines cloud computing and mobile technologies for health care monitoring systems, we have set a goal of developing software. A technique developed by proposed method is used to extract steady fractal values from electrocardiogram (ECG) data, which has never been tried before by any other researcher in the area of creating a computer-aided diagnostic system for arrhythmia. Based on the findings, it can be concluded that the support vector machine has achieved the highest possible classification accuracy for fractal features. While being compared to the other two classifiers, which are the feed forward and feedback neural network models, the support vector machine outperforms them both. In addition, it should be highlighted that the sensitivity of the feed forward neural network and the support vector machine provide results that are comparable (92.08 percent and 90.36 percent, respectively).","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"11 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":"125361660","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: 8

Federated Learning for Intelligent Resources Allocation in Internet of Things 面向物联网智能资源配置的联邦学习

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.070106

Mahmoud Ismail, S. Zaki

{"title":"Federated Learning for Intelligent Resources Allocation in Internet of Things","authors":"Mahmoud Ismail, S. Zaki","doi":"10.54216/jisiot.070106","DOIUrl":"https://doi.org/10.54216/jisiot.070106","url":null,"abstract":"By using federated learning (FL), multiple Internet-of-Things (IoT) devices can construct a shared learning model without sending raw data to a centralized server. While FL has come a long way, it still has a ways to go. Issues such as heterogeneous user equipment (UEs) and data that is not independently and uniformly distributed are still obstacles. Facilitating a numerous UEs to participate in the learning in each cycle poses a possible problem of the huge communication budget. A weighted adjoining factor is presented to the localized gradient descent, generalizing the present FedAvg to solve these concerns. At the start of each global round, the proposed FL method randomly selects a fraction of the UEs to perform stochastic gradient descent in parallel. Then, we utilize the suggested FL method in cellular IoT to reduce either total power usage or execution duration of FL, in which a straightforward but effective path-following method is constructed for its explanations. At last, obtained simulations on poorly balanced data are presented to show that the presented FL algorithm is superior to FedAvg in terms of performance with respect to fast convergence. Moreover, they show that the suggested algorithm needs significantly less time and energy to train than the FL algorithm does when users contribute heavily to the learning process. These findings provide strong support for the suggested FL algorithm as a potential paradigm change for training mobile IoT networks with limited bandwidth.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"18 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":"127405102","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

Forecasting COVID-19 Infection Using Encoder-Decoder LSTM and Attention LSTM Algorithms 利用编码器-解码器LSTM和注意力LSTM算法预测COVID-19感染

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.080202

K. ., A. A. Subhi, H. Alkattan, A. Kadi, Artem .., Irina .., Mostafa .., E. El-Kenawy

{"title":"Forecasting COVID-19 Infection Using Encoder-Decoder LSTM and Attention LSTM Algorithms","authors":"K. ., A. A. Subhi, H. Alkattan, A. Kadi, Artem .., Irina .., Mostafa .., E. El-Kenawy","doi":"10.54216/jisiot.080202","DOIUrl":"https://doi.org/10.54216/jisiot.080202","url":null,"abstract":"The COVID-19 epidemic has in fact placed the whole community in a dire predicament that has led to numerous tragedies, including an economic downturn, political unrest, and job losses. Forecasting and identifying COVID-19 infection cases is crucial for the government at all levels because the pandemic grows exponentially and results in fatalities. Hence, by giving information about the spread of the epidemic, the government can move quickly at multiple levels to establish new policies and modalities in order to minimize the trajectory of the COVID-19 pandemic's effects on both public health and the economic sectors. Forecasting models for COVID-19 infection cases in the Ural region in Russia were developed using two deep Long Short-Term Memory (LSTM) learning-based approaches namely Encoder–Decoder LSTM and Attention LSTM algorithms. The models were evaluated based on five standard performance evaluation metrics which include Mean Square Error (MSE), Mean Absolute Error (MAE), Root MSE (RMSE), Relative RMSE (RRMSE), and coefficient of determination (R2). However, the Encoder–Decoder LSTM deep learning-based forecasting model achieved the best performance results (MSE=32794.09, MAE=168.56, RMSE=181.09, RRMSE=13.46, and R2=0.87) compared to the model developed with Attention LSTM models.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"34 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":"116770434","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

Characterizing wavelet coefficients with decomposition for medical images 基于分解的医学图像小波系数表征

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.020103

J. A. Eleiwy

引用次数: 5

An Approach Based on Decision-Making Algorithms for Qos-Aware Iot Services Composition 一种基于qos感知的物联网服务组合决策算法

Journal of Intelligent Systems and Internet of Things Pub Date : 1900-01-01 DOI: 10.54216/jisiot.080101

A. Salamai

引用次数: 3