FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4317425328> ?p ?o ?g. }

• W4317425328 endingPage "16" @default.
• W4317425328 startingPage "16" @default.
• W4317425328 abstract "Water is a valuable, necessary and unfortunately rare commodity in both developing and developed countries all over the world. It is undoubtedly the most important natural resource on the planet and constitutes an essential nutrient for human health. Geo-environmental pollution can be caused by many different types of waste, such as municipal solid, industrial, agricultural (e.g., pesticides and fertilisers), medical, etc., making the water unsuitable for use by any living being. Therefore, finding efficient methods to automate checking of water suitability is of great importance. In the context of this research work, we leveraged a supervised learning approach in order to design as accurate as possible predictive models from a labelled training dataset for the identification of water suitability, either for consumption or other uses. We assume a set of physiochemical and microbiological parameters as input features that help represent the water’s status and determine its suitability class (namely safe or nonsafe). From a methodological perspective, the problem is treated as a binary classification task, and the machine learning models’ performance (such as Naive Bayes–NB, Logistic Regression–LR, k Nearest Neighbours–kNN, tree-based classifiers and ensemble techniques) is evaluated with and without the application of class balancing (i.e., use or nonuse of Synthetic Minority Oversampling Technique–SMOTE), comparing them in terms of Accuracy, Recall, Precision and Area Under the Curve (AUC). In our demonstration, results show that the Stacking classification model after SMOTE with 10-fold cross-validation outperforms the others with an Accuracy and Recall of 98.1%, Precision of 100% and an AUC equal to 99.9%. In conclusion, in this article, a framework is presented that can support the researchers’ efforts toward water quality prediction using machine learning (ML)." @default.
• W4317425328 created "2023-01-19" @default.
• W4317425328 creator A5038233087 @default.
• W4317425328 creator A5053635944 @default.
• W4317425328 date "2023-01-18" @default.
• W4317425328 modified "2023-09-27" @default.
• W4317425328 title "Efficient Data-Driven Machine Learning Models for Water Quality Prediction" @default.
• W4317425328 cites W2152164967 @default.
• W4317425328 cites W2330219538 @default.
• W4317425328 cites W2785103246 @default.
• W4317425328 cites W2789059409 @default.
• W4317425328 cites W2789758093 @default.
• W4317425328 cites W2792565337 @default.
• W4317425328 cites W2793350103 @default.
• W4317425328 cites W2793798142 @default.
• W4317425328 cites W2807130444 @default.
• W4317425328 cites W2884859931 @default.
• W4317425328 cites W2887357732 @default.
• W4317425328 cites W2891385203 @default.
• W4317425328 cites W2891968149 @default.
• W4317425328 cites W2895303784 @default.
• W4317425328 cites W2898416261 @default.
• W4317425328 cites W2905418097 @default.
• W4317425328 cites W2908285635 @default.
• W4317425328 cites W2909354914 @default.
• W4317425328 cites W2913997948 @default.
• W4317425328 cites W2930216851 @default.
• W4317425328 cites W2930669685 @default.
• W4317425328 cites W2969889005 @default.
• W4317425328 cites W2970835038 @default.
• W4317425328 cites W2981586399 @default.
• W4317425328 cites W3006101764 @default.
• W4317425328 cites W3006326026 @default.
• W4317425328 cites W3008159215 @default.
• W4317425328 cites W3009964072 @default.
• W4317425328 cites W3011088070 @default.
• W4317425328 cites W3012447292 @default.
• W4317425328 cites W3015461837 @default.
• W4317425328 cites W3016321782 @default.
• W4317425328 cites W3026452615 @default.
• W4317425328 cites W3034010527 @default.
• W4317425328 cites W3044867970 @default.
• W4317425328 cites W3048857572 @default.
• W4317425328 cites W3099128240 @default.
• W4317425328 cites W3110182331 @default.
• W4317425328 cites W3118853240 @default.
• W4317425328 cites W3132521903 @default.
• W4317425328 cites W3181497692 @default.
• W4317425328 cites W3205275159 @default.
• W4317425328 cites W4210784138 @default.
• W4317425328 cites W4225507636 @default.
• W4317425328 cites W4280542118 @default.
• W4317425328 cites W4282920686 @default.
• W4317425328 cites W4285582316 @default.
• W4317425328 doi "https://doi.org/10.3390/computation11020016" @default.
• W4317425328 hasPublicationYear "2023" @default.
• W4317425328 type Work @default.
• W4317425328 citedByCount "7" @default.
• W4317425328 countsByYear W43174253282023 @default.
• W4317425328 crossrefType "journal-article" @default.
• W4317425328 hasAuthorship W4317425328A5038233087 @default.
• W4317425328 hasAuthorship W4317425328A5053635944 @default.
• W4317425328 hasBestOaLocation W43174253281 @default.
• W4317425328 hasConcept C116834253 @default.
• W4317425328 hasConcept C119857082 @default.
• W4317425328 hasConcept C12267149 @default.
• W4317425328 hasConcept C124101348 @default.
• W4317425328 hasConcept C151730666 @default.
• W4317425328 hasConcept C154945302 @default.
• W4317425328 hasConcept C2779343474 @default.
• W4317425328 hasConcept C41008148 @default.
• W4317425328 hasConcept C45942800 @default.
• W4317425328 hasConcept C52001869 @default.
• W4317425328 hasConcept C59822182 @default.
• W4317425328 hasConcept C66905080 @default.
• W4317425328 hasConcept C81669768 @default.
• W4317425328 hasConcept C84525736 @default.
• W4317425328 hasConcept C86803240 @default.
• W4317425328 hasConceptScore W4317425328C116834253 @default.
• W4317425328 hasConceptScore W4317425328C119857082 @default.
• W4317425328 hasConceptScore W4317425328C12267149 @default.
• W4317425328 hasConceptScore W4317425328C124101348 @default.
• W4317425328 hasConceptScore W4317425328C151730666 @default.
• W4317425328 hasConceptScore W4317425328C154945302 @default.
• W4317425328 hasConceptScore W4317425328C2779343474 @default.
• W4317425328 hasConceptScore W4317425328C41008148 @default.
• W4317425328 hasConceptScore W4317425328C45942800 @default.
• W4317425328 hasConceptScore W4317425328C52001869 @default.
• W4317425328 hasConceptScore W4317425328C59822182 @default.
• W4317425328 hasConceptScore W4317425328C66905080 @default.
• W4317425328 hasConceptScore W4317425328C81669768 @default.
• W4317425328 hasConceptScore W4317425328C84525736 @default.
• W4317425328 hasConceptScore W4317425328C86803240 @default.
• W4317425328 hasIssue "2" @default.
• W4317425328 hasLocation W43174253281 @default.
• W4317425328 hasLocation W43174253282 @default.
• W4317425328 hasOpenAccess W4317425328 @default.
• W4317425328 hasPrimaryLocation W43174253281 @default.

• next