Predicting traffic incident duration is important for effective and real-time traffic incident management (TIM), which helps to minimize traffic congestion, environmental pollution, and secondary incident related to this incident. Traffic incident duration prediction methods often use more input variables to obtain better prediction results. However, the problems that available variables are limited at the beginning of an incident and how to select significant variables are ignored to some extent. In this paper, a novel prediction method named NCA-BOA-RF is proposed using the Neighborhood Components Analysis (NCA) and the Bayesian Optimization Algorithm (BOA)-optimized Random Forest (RF) model. Firstly, the NCA is applied to select feature variables for traffic incident duration. Then, RF model is trained based on the training set constructed using feature variables, and the BOA is employed to optimize the RF parameters. Finally, confusion matrix is introduced to measure the optimized RF model performance and compare with other methods. In addition, the performance is also tested in the absence of some feature variables. The results demonstrate that the proposed method not only has high accuracy, but also exhibits excellent reliability and robustness.

https://downloads.hindawi.com/journals/jat/2019/4202735.pdf

A Hybrid Method for Traffic Incident Duration Prediction Using BOA-Optimized Random Forest Combined with Neighborhood Components Analysis

Editorial on special issue: Machine learning on MCI.

Directing research on Alzheimer’s towards only early prediction and accuracy cannot be considered a feasible approach towards tackling a ubiquitous degenerative disease today. Applying deep learning (DL), Explainable artificial intelligence(XAI) and advancing towards the human-computer interface(HCI) model can be a leap forward in medical research. This research aims to propose a robust explainable HCI model using shapley additive explanation (SHAP), local interpretable model-agnostic explanations (LIME) and DL algorithms. The use of DL algorithms: logistic regression(80.87%), support vector machine (85.8%), k-nearest neighbour(87.24%), multilayer perceptron(91.94%), decision tree(100%) and explainability can help exploring untapped avenues for research in medical sciences that can mould the future of HCI models. The outcomes of the proposed model depict higher prediction accuracy bringing efficient computer interface in decision making, and suggests a high level of relevance in the field of medical and clinical research.

/pdf/explanation-driven-hci-model-to-examine-the-mini-mental-19m7rkiw.pdf

Weishu Deng

Papers

Predication of different stages of Alzheimer's disease using neighborhood component analysis and ensemble decision tree.