An approach for classification of health risks based on air quality levels

Gore, Ranjana; Deshpande, Deepa S.

doi:10.1109/icisim.2017.8122148

Cited by 48 publications

(21 citation statements)

References 5 publications

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“…The accuracy of dataset by using Naïve Bayes was 86.66% and the accuracy with J48 decision tree algorithm was 91.99%. And author also justify that J48 algorithm gives more accurate results than Naïve Bayes algorithm [1] . In 2018 Dr. Sandhya P proposed a method in which author aim is to predict the PM2.5 by using random forest, Naïve Bayes, and decision tree algorithm [2] .…”

Section: Literature Reviewmentioning

confidence: 84%

Air Quality Index Class prediction using Data Mining Techniques

Usmani¹

2019

IJRASET

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India, Being a developing country focuses on industrial and personal development. An increasing number of industries and vehicles produces various harmful chemicals as their byproducts. Nowadays the fresh air of the environment is getting polluted due to the generation of various harmful particles, biological molecules, and other poisonous elements.Air pollution can be determined by the organisations with using Index based parameter of Air Quality.Index holds several parameters for poisionous and harmful elements contains in apolluted air. In this research work, the main focus is on scrutiny of air pollution data of delhi by using data mining techniques and also build an efficient model for class wise prediction of air pollution data.

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Section: Literature Reviewmentioning

confidence: 84%

Air Quality Index Class prediction using Data Mining Techniques

Usmani¹

2019

IJRASET

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“…The Naïve Bayes classifier was used as the reference [1,16,28]. One of the classic algorithms in machine learning is the Naïve Bayes Classifier, which is based on the Bayes theorem for determining the posterior probability of an event occurring.…”

Section: Classification Methodsmentioning

confidence: 99%

Urban Environmental Quality Assessment by Shape and Spectral Indices of Mulberry Leaves

Dineva¹,

Zlatev²

2019

ARTTE

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In this paper, an analysis of the potential use of the surface and geometric characteristics of mulberry leaves as parameters for environmental quality assessment is made. Methods have been used to reduce the amount of data of latent variables, linear and kernel variants of principal components. It has been found that a kernel variant of the principal components, combined with nonlinear separating functions of discriminant analysis and a method of support vector machines, are an appropriate methods for distinguishing the degree of air pollution from the mulberry leaf data. The results obtained could be used as preliminary baseline data for future evaluations and studies related to remote monitoring of urban air quality.

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“…Atmospheric processes and factors have been studied by meteorologists and climatologists, respectively, with the focus on weather forecasting and characterization of the climate in macro-and micro-regions [2,3]. The factors of the weather in a region, also called meteorological variables, such as humidity, temperature, precipitation, and wind direction that have been analyzed over decades allow characterization of the climate according to the seasons of the year.…”

Section: Introductionmentioning

confidence: 99%

A Standard-Based Internet of Things Platform and Data Flow Modeling for Smart Environmental Monitoring

Filho¹,

Fernando

Rabelo

et al. 2021

Sensors

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The environment consists of the interaction between the physical, biotic, and anthropic means. As this interaction is dynamic, environmental characteristics tend to change naturally over time, requiring continuous monitoring. In this scenario, the internet of things (IoT), together with traditional sensor networks, allows for the monitoring of various environmental aspects such as air, water, atmospheric, and soil conditions, and sending data to different users and remote applications. This paper proposes a Standard-based Internet of Things Platform and Data Flow Modeling for Smart Environmental Monitoring. The platform consists of an IoT network based on the IEEE 1451 standard which has the network capable application processor (NCAP) node (coordinator) and multiple wireless transducers interface module (WTIM) nodes. A WTIM node consists of one or more transducers, a data transfer interface and a processing unit. Thus, with the developed network, it is possible to collect environmental data at different points within a city landscape, to perform analysis of the communication distance between the WTIM nodes, and monitor the number of bytes transferred according to each network node. In addition, a dynamic model of data flow is proposed where the performance of the NCAP and WTIM nodes are described through state variables, relating directly to the information exchange dynamics between the communicating nodes in the mesh network. The modeling results showed stability in the network. Such stability means that the network has capacity of preserve its flow of information, for a long period of time, without loss frames or packets due to congestion.

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An approach for classification of health risks based on air quality levels

Cited by 48 publications

References 5 publications

Air Quality Index Class prediction using Data Mining Techniques

Air Quality Index Class prediction using Data Mining Techniques

Urban Environmental Quality Assessment by Shape and Spectral Indices of Mulberry Leaves

A Standard-Based Internet of Things Platform and Data Flow Modeling for Smart Environmental Monitoring

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