Performance evaluation of ANN and SVM multiclass models for intelligent water quality classification using Dempster-Shafer Theory

Ladjal, Mohamed; Bouamar, M.; Djerioui, Mohamed; Brik, Youcef

doi:10.1109/eitech.2016.7519588

Cited by 14 publications

(9 citation statements)

References 19 publications

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“…The result revealed that SVM shows the best performance without any error in calibration and validation process. Ladjal et al, (2016) have also conducted the study of water quality classification using Dempster-Shafer theory. In this study, Ladjal et al, used four parameters: temperature, pH, conductivity, and turbidity, and used Neural Network and SVM.…”

Section: Related Workmentioning

confidence: 99%

“…Spark MLlib has supported several algorithms that could be used for classification, clustering, or regression. As proposed by (Modaresi and Araghinejad, 2014), (Ladjal et al, 2016), (Jaloree et al, 2014), (Saghebian et al, 2014), we use two classification algorithms, where the results would be compared to choose the best one. Classification algorithms which would be used were Support Vector Machine and Decision Tree.…”

Section: Learning Processmentioning

confidence: 99%

“…If ( , ) is dataset with = 1, … , , where is the vector containing features, and ∈ {−1,1} is the class label related to . SVM solves the following primal problem (Modaresi and Araghinejad, 2014), (Ladjal et al, 2016), (Suykens and Vandewalle, 1999).…”

Section: Learning Processmentioning

confidence: 99%

“…It describes nonlinearity mapping from the input space to the higher dimensional space features. The dual problem of SVM Lagrange turns into (Modaresi and Araghinejad, 2014), (Ladjal et al, 2016), (Jaloree et al, 2014), (Ito and Kunisch, 2008).…”

Section: Learning Processmentioning

confidence: 99%

“…After obtaining an optimal parameter , the decision function of the classification becomes (Modaresi and Araghinejad, 2014), (Ladjal et al, 2016).…”

Section: Learning Processmentioning

confidence: 99%

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Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in real-time system

Arridha

Sukaridhoto

Pramadihanto

et al. 2017

IJSSC

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Abstract:Monitoring water conditions in real-time is a critical mission to preserve the water ecosystem in maritime and archipelagic countries, such as Indonesia that is relying on the wealth of water resources. To integrate the water monitoring system into the big data technology for realtime analysis, we have engaged in the ongoing project named SEMAR (Smart Environment Monitoring and Analytic in Real-time system), which provides the IoT-Big Data platform for water monitoring. However, SEMAR does not have an analytical system yet. This paper proposes the analytical system for water quality classification using Pollution Index method, which is an extension of SEMAR. Besides, the communication protocol is updated from REST to MQTT. Furthermore, the real-time user interface is implemented for visualisation. The evaluations confirmed that the data analytic function adopting the linear SVM and Decision Tree algorithms achieves more than 90% for the estimation accuracy with 0.019075 for the MSE.

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Section: Related Workmentioning

confidence: 99%

Section: Learning Processmentioning

confidence: 99%

Section: Learning Processmentioning

confidence: 99%

Section: Learning Processmentioning

confidence: 99%

“…After obtaining an optimal parameter , the decision function of the classification becomes (Modaresi and Araghinejad, 2014), (Ladjal et al, 2016).…”

Section: Learning Processmentioning

confidence: 99%

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