2020 3rd International Conference on Computer and Informatics Engineering (IC2IE) 2020
DOI: 10.1109/ic2ie50715.2020.9274581
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Accidental Prone Area Detection in Bangladesh using Machine Learning Model

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Cited by 17 publications
(3 citation statements)
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“…The independent variables in a classification or regression problem are represented by the internal nodes. The solution to the issue, or the dependent variable, is represented by the leaf nodes [15].…”
Section: 16random Forest and Decision Treementioning
confidence: 99%
“…The independent variables in a classification or regression problem are represented by the internal nodes. The solution to the issue, or the dependent variable, is represented by the leaf nodes [15].…”
Section: 16random Forest and Decision Treementioning
confidence: 99%
“…Various ML algorithms are used for various purposes like graph theory [10], graph generation [11] accident-prone area detection [12], medical field [13], etc. Critical user assessments are enabled in many fields which makes sentiment analysis a goal for relevant research.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Therefore, the corresponding accident analysis of these areas has gradually become a research topic in recent years [2][3][4][5]. Especially in recent years, machine learning algorithms have been widely used to divide and analyze accident-prone areas [6][7][8].…”
Section: Introductionmentioning
confidence: 99%