Predicting Crash Injury Severity in Smart Cities: a Novel Computational Approach with Wide and Deep Learning Model

Niyogisubizo, Jovial; Liao, Lyuchao; Sun, Qi; Nziyumva, Eric; Wang, Yongqiang; Luo, Linsen; Lai, Shukun; Murwanashyaka, Evariste

doi:10.1007/s13177-023-00351-7

Cited by 4 publications

(1 citation statement)

References 82 publications

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“…Further, the text emphasizes the evaluation of overall errors and the calculation of subsequent iteration weights. In cases where prediction is challenging, greater significance is assigned, whereas less importance is attributed to easily predictable situations [35].…”

Section: Adaptive Boosting (Adaboost)mentioning

confidence: 99%

Towards Predicting the Quality of Red Wine Using Novel Machine Learning Methods for Classification, Data Visualization and Analysis

Niyogisubizo,

Ninteretse,

Nziyumva

et al. 2024

AIA

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here is a growing concern among consumers and the wine industry regarding the quality of wine. Traditionally, wine experts determined its quality through tasting, which was time-consuming. Therefore, there is a need to predict wine quality based on specific key features to streamline these tasks. Technological developments like machine learning (ML) approaches have replaced human assessments with computational methods. However, some of these methods have faced criticism due to their low accuracy and lack of interpretability for humans. In this paper, a stacking ensemble method is introduced and demonstrates superior predictive performance when compared to other classification techniques like Logistic Regression (LR), Decision Trees (DT), Gradient Boosting (GB), Adaptive Boosting (AdaBoost), and Random Forest (RF). This evaluation is based on classification metrics such as accuracy, precision, recall, and F1-Score, all under the same conditions. Additionally, outlier detection algorithms were employed to identify exceptional or subpar wines, though their results did not match the accuracy of classification approaches. Lastly, a feature analysis study was conducted to assess the significance of each feature in the model's performance.

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Section: Adaptive Boosting (Adaboost)mentioning

confidence: 99%

Towards Predicting the Quality of Red Wine Using Novel Machine Learning Methods for Classification, Data Visualization and Analysis

Niyogisubizo,

Ninteretse,

Nziyumva

et al. 2024

AIA

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Modeling motorcycle crash-injury severity utilizing explainable data-driven approaches

Se,

Sunkpho,

Wipulanusat

et al. 2024

Transportation Letters

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Road Crash Injury Severity Prediction Using a Graph Neural Network Framework

Sattar,

Ishak,

Affendey

et al. 2024

IEEE Access

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Crash severity prediction is a challenging research area, where the objective is to accurately assess the extent of severity of an injury resulting from road traffic accidents. The main aim of existing studies is to precisely assess the potential severity of crashes under diverse circumstances, such as weather conditions, vehicle attributes, road characteristics and layout, and traffic control factors. This effort aids authorities in establishing effective emergency response systems. The novelty and objective of our work involve contributing to this research area by employing a graph architecture to capture relationships among various crash records to uncover any hidden patterns that traditional ML models might overlook. The current study extends existing knowledge by leveraging Graph Neural Networks (GNN) and comparing their performance to popular ensemble-based models, which include Extreme Gradient Boosting (XGBoost) and Random Forest (RF) and Artificial Neural Networks (ANNs). Real data from the United Kingdom (UK) was employed to achieve our goal. All models underwent training using the training dataset, followed by performance evaluation using diverse metrics such as the accuracy, precision, recall, f1-score, Matthews Correlation Coefficient (MCC), confusion matrix, and computational cost on the test dataset. Overall, our proposed GNN-based model demonstrated better performance when compared to other models. Specifically, the GNN model outperformed all other models across all metrics. For instance, the accuracy stood at 85.55% for GNN as compared to 83.36%, 83.18%, and 83.27% for XGBoost, RF, and ANNs models, respectively. The GNN model assisted in identifying hidden patterns by considering non-linear relationships among crash records. Thus, the model had the potential to improve its ability to predict severe accidents, which could in turn significantly improve emergency response efforts and reduce the likelihood of severe accidents resulting in fatalities.

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Predicting Crash Injury Severity in Smart Cities: a Novel Computational Approach with Wide and Deep Learning Model

Cited by 4 publications

References 82 publications

Towards Predicting the Quality of Red Wine Using Novel Machine Learning Methods for Classification, Data Visualization and Analysis

Towards Predicting the Quality of Red Wine Using Novel Machine Learning Methods for Classification, Data Visualization and Analysis

Modeling motorcycle crash-injury severity utilizing explainable data-driven approaches

Road Crash Injury Severity Prediction Using a Graph Neural Network Framework

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