Feed Forward Neural Network-Based Sensor Node Localization in Internet of Things

2018 ◽  
pp. 795-804 ◽  
Author(s):  
Ajay Kumar ◽  
V. K. Jain
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Sensor Node ◽  
Feed Forward Neural Network ◽  
Node Localization ◽  
Feed Forward

Patient Health Monitoring Using Feed Forward Neural Network With Cloud Based Internet of Things

2019 ◽  
pp. 65-77
Author(s):  
Piyush Kumar Shukla ◽  
◽  
◽  
Prashant Kumar Shukla
Keyword(s):  
Neural Network ◽  
Health Care ◽  
Support Vector Machine ◽  
Internet Of Things ◽  
New Technologies ◽  
Mobile Technologies ◽  
Healthcare Sector ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Feed Forward

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).

scholarly journals A fusion of data science and feed-forward neural network-based modelling of COVID-19 outbreak forecasting in IRAQ

2021 ◽  
Vol 118 ◽  
pp. 103766
Author(s):  
Ahmed J. Aljaaf ◽  
Thakir M. Mohsin ◽  
Dhiya Al-Jumeily ◽  
Mohamed Alloghani
Keyword(s):  
Neural Network ◽  
Data Science ◽  
Feed Forward Neural Network ◽  
Feed Forward

scholarly journals Determination of rheological properties of bio-asphalt binders through experimental and multilayer feed-forward neural network methods

2021 ◽  
Author(s):  
Abdulnaser M Al-Sabaeei ◽  
Madzlan B Napiah ◽  
Muslich H Sutanto ◽  
Suzielah Rahmad ◽  
Nur Izzi Md Yusoff ◽  
...  
Keyword(s):  
Neural Network ◽  
Rheological Properties ◽  
Asphalt Binders ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Network Methods

Feed forward neural network and interpolation function models to predict the soil and subsurface sediments distribution in Bam, Iran

2009 ◽  
Vol 57 (2) ◽  
pp. 271-293 ◽  
Author(s):  
Khalil Rezaei ◽  
Bernard Guest ◽  
Anke Friedrich ◽  
Farajollah Fayazi ◽  
Muhammad Nakhaei ◽  
...  
Keyword(s):  
Neural Network ◽  
Interpolation Function ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Subsurface Sediments ◽  
Function Models
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