Secure Internet of Things-Based Cloud Framework to Control Zika Virus Outbreak

Sareen, Sanjay; Sood, Sandeep K.; Gupta, Sunil Kumar

doi:10.1017/s0266462317000113

Cited by 35 publications

(29 citation statements)

References 18 publications

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“…Zika virus (ZikaV) is currently one of the most important emerging viruses in the world that have caused outbreaks and epidemics and has also been associated with severe clinical manifestations and congenital malformations. Sanjay Sareen et al [8] introduced a cloud-based system for detecting and controlling the ZikaV using a mobile device. The authors in the article [9] presented and identified the different ways to implement the edge computing paradigm, by using M2M communications in dense networked systems via social connectivity from two different perspectives: the offered reliability for delay-tolerant (delaysensitive) services and the energy conservation over reliability provision.…”

Section: Literature Reviewmentioning

confidence: 99%

Modified Machine Learning Techique for Curve Fitting on Regression Models for COVID-19 projections

Andreas

Mavromoustakis

Mastorakis

et al. 2020

2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

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COrona VIrus Disease 2019 (COVID-19) is a disease caused by Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) and was first diagnosed in China in December, 2019. Dr. Tedros Adhanom Ghebreyesus, World Health Organization (WHO) director-general on March 11th declared the COVID-19 pandemic. The cumulative cases of infected individuals and deaths due to COVID-19 develop a graph that could be interpreted by an exponential function. Mathematical models are therefore fundamental to understanding the evolution of the pandemic. Applying machine learning prediction methods in conjunction with cloud computing to such models will be beneficial in designing effective control strategies for the current or future spread of infectious diseases. Initially, we compare the trendlines of the following three models: linear, exponential and polynomial using R-squared, to determine which model best interprets the prevailing data sets of cumulative infectious cases and cumulative deaths due to COVID-19 disease. We propose the development of an improved mathematical forecasting framework based on machine learning and the cloud computing system with data from a real-time cloud data repository. Our goal is to predict the progress of the curve as accurately as possible in order to understand the spread of the virus from an early stage so that strategies and policies can be implemented.

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Section: Literature Reviewmentioning

confidence: 99%

Modified Machine Learning Techique for Curve Fitting on Regression Models for COVID-19 projections

Andreas

Mavromoustakis

Mastorakis

et al. 2020

2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

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“…So if the symptoms persist more than 7 days the person has to check for early prediction method that is using our proposed method. If they found to be infected that person has to go for a RNA test in which it is completely analyzed [19] [20]. Otherwise no need to go for any other test.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Using the LR-Linear Regression the specificity 69%, Sensitivity is 68, accuracy is 65 [21]. For NN the specificity 85%, Sensitivity is 84, accuracy is 83 [22] and for NBN the specificity 73%, Sensitivity is 80, accuracy is 87 [20]. Finally the MLP our proposed classifier gives the result of specificity 97%, Sensitivity is 98, and accuracy is 97.9.…”

Section: Figure 5: Comparsion Of Lr Nn Nbn Mlpmentioning

confidence: 99%

Prediction of Zika Virus by Multilayer Perceptron Neural Network (MLPNN) using Cloud

2019

IJRTE

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Zika virus a mosquito borne flavivirus disease, which is spreading hastily across all over the world. Nearly 95 countries are infected with Zika, Aedes aegypti Mosquitoes is the source of spreading the virus. Microcephaly, myelitis, Guillain – Barre Syndrome and neuropathy are the causes of ZVD. Miscarriages and preterm birth also possible also occur during the time of infection. To overcome an early prediction system is used for detecting the virus using symptoms. The zika dataset is stored in cloud and in our proposed work a Multilayer Perceptron Neural Network classifier used for predicting the Zika virus. The classifier produces accuracy level of 97% the highest accuracy level. Based on the symptoms ZVD is predicted at earlier stage, if they found as infected RNA test will be taken for the concerned person.

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“…It introduces the facility of local processing leading to reduction in data size, lower latency, high throughput and power efficiency of the cloudbased systems. Fog computing technology has been successfully implemented in smart cities [31] and healthcare [23,22,42,49,50,51]. Fog devices are embedded computers such as Intel Edison or Raspberry Pi that acts a gateway between cloud and mobile 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 devices.…”

Section: Fog Computingmentioning

confidence: 99%

GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis

Barik

Dubey

Mankodiya

et al. 2018

J Ambient Intell Human Comput

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Spatial Data Infrastructure (SDI) is an important framework for sharing geospatial big data using the web. Integration of SDI with cloud computing lead to emergence of Cloud-SDI as a tool for transmission, processing and analysis of geospatial data. Fog computing is a paradigm where embedded devices are employed to increase the throughput and reduce latency at the edge of network. In this study, we developed and evaluated a Fog-based SDI framework named GeoFog4Health for mining analytics from geo-health big data. We built a prototype using both Intel Edison and Raspberry Pi for performing a comparative study. We performed a case study on Malaria vector borne disease positive maps of Maharastra state in India. The proposed framework had provision of lossless data compression. Also, overlay analysis of geospatial data could be performed. In addition, we discussed energy saving, cost analysis and scalability of proposed framework for efficient data processing. We compared the performance of proposed framework with state of the art Cloud-SDI in terms of analysis time. Results and discussions showed the efficacy of proposed system for enhanced analysis of geo-health big data generated from a variety of sensing frameworks.

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Secure Internet of Things-Based Cloud Framework to Control Zika Virus Outbreak

Abstract: The cloud-based proposed system contributed to the accurate NBN-based classification of infected users and accurate identification of risk-prone areas using Google Maps.

Cited by 35 publications

References 18 publications

Modified Machine Learning Techique for Curve Fitting on Regression Models for COVID-19 projections

Modified Machine Learning Techique for Curve Fitting on Regression Models for COVID-19 projections

Prediction of Zika Virus by Multilayer Perceptron Neural Network (MLPNN) using Cloud

GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis

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