Hybrid Approach for Driver Behavior Analysis with Machine Learning, Feature Optimization, and Explainable AI
Department of CSE, DUET Gazipur, Bangladesh；Department of IST, George Mason University Fairfax, USA；AI \& Big Data Department, Woosong University Daejeon, South Korea
Mehedi Hasan Shuvo, Md. Raihan Tapader, Nur Mohammad Tamjid, Sajjadul Islam, Ahnaf Atef Choudhury, Jia Uddin
Progressive driver behavior analytics is crucial for improving road safety and mitigating the issues caused by aggressive or inattentive driving. Previous studies have employed machine learning and deep learning techniques, which often result in low feature optimization, thereby compromising both high performance and interpretability. To fill these voids, this paper proposes a hybrid approach to driver behavior analysis that uses a 12,857-row and 18-column data set taken from Kaggle. After applying preprocessing techniques such as label encoding, random oversampling, and standard scaling, 13 machine learning algorithms were tested. The Random Forest Classifier achieved an accuracy of 95%. After deploying the LIME technique in XAI, the top 10 features with the most significant positive and negative influence on accuracy were identified, and the same algorithms were retrained. The accuracy of the Random Forest Classifier decreased slightly to 94.2%, confirming that the efficiency of the model can be improved without sacrificing performance. This hybrid model can provide a return on investment in terms of the predictive power and explainability of the driver behavior process.
ExBigBang: A Dynamic Approach for Explainable Persona Classification through Contextualized Hybrid Transformer Analysis
School of Computing, Macquarie University（麦考瑞大学计算机学院）；Department of Computer Engineering and Information Technology, Shiraz University of Technology（谢里兹技术大学计算机工程与信息科技系）；John Walsh Centre for Rehabilitation Research, Kolling Institute, University of Sydney（悉尼大学约翰·沃什康复研究中心）；College of Engineering and Information Technology, University of Dubai（迪拜大学工程与信息科技学院）
Electroencephalogram (EEG) has become increasingly popular in driver fatigue monitoring systems. Several decomposition methods have been attempted to analyze the EEG signals that are complex, nonlinear and non-stationary and improve the EEG decoding performance in different applications. However, it remains challenging to extract more distinguishable features from different decomposed components for driver fatigue recognition. In this work, we propose a novel decomposition-based hybrid ensemble convolutional neural network (CNN) framework to enhance the capability of decoding EEG signals. Four…
Explainable AI for Curie Temperature Prediction in Magnetic Materials
Department of Data Sciences, Penn State Abington（宾夕法尼亚州立大学阿伯丁分校数据科学系）；Department of Physics, Penn State Abington（宾夕法尼亚州立大学阿伯丁分校物理系）；Department of Physics,University of Delaware（特拉华大学物理系）
The University of Michigan（密歇根大学）；The University of Wisconsin（威斯康星大学）；Ford Motor Company（福特汽车公司）
Research indicates that monotonous automated driving increases the incidence of fatigued driving. Although many prediction models based on advanced machine learning techniques were proposed to monitor driver fatigue, especially in manual driving, little is known about how these black-box machine learning models work. In this paper, we proposed a combination of eXtreme Gradient Boosting (XGBoost) and SHAP (SHapley Additive exPlanations) to predict driver fatigue with explanations due to their efficiency and accuracy. First, in order to obtain the ground truth of driver fatigue, PERCLOS (percent…
State of the art music recommender systems mainly rely on either matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction by learning from temporal sequences of user actions. Despite advances in deep learning for song recommendation, none has taken advantage of the sequential nature of songs by learning sequence models that are based on content. Aside from the importance of prediction accuracy, other significant aspects are important, such as explainab…
University of Technology Sydney (UTS)（悉尼科技大学）；Deakin University（迪肯大学）；University of Essex（埃塞克斯大学）
Model interpretability has become important to engenders appropriate user trust by providing the insight into the model prediction. However, most of the existing machine learning methods provide no interpretability for depression prediction, hence their predictions are obscure to human. In this work, we propose interpretive Multi-Modal Depression Detection with Hierarchical Attention Network MDHAN, for detection depressed users on social media and explain the model prediction. We have considered user posts along with Twitter-based multi-modal features, specifically, we encode user posts using …
Global optimization of the energy consumption of dual power source vehicles such as hybrid electric vehicles, plug-in hybrid electric vehicles, and plug in fuel cell electric vehicles requires knowledge of the complete route characteristics at the beginning of the trip. One of the main characteristics is the vehicle speed profile across the route. The profile will translate directly into energy requirements for a given vehicle. However, the vehicle speed that a given driver chooses will vary from driver to driver and from time to time, and may be slower, equal to, or faster than the average tr…
University of Southampton（南安普顿大学）；The Alexandra Institute（亚历山德拉研究所）
Monitoring machine learning models once they are deployed is challenging. It is even more challenging to decide when to retrain models in real-case scenarios when labeled data is beyond reach, and monitoring performance metrics becomes unfeasible. In this work, we use non-parametric bootstrapped uncertainty estimates and SHAP values to provide explainable uncertainty estimation as a technique that aims to monitor the deterioration of machine learning models in deployment environments, as well as determine the source of model deterioration when target labels are not available. Classical methods…
Hybrid Attribution Priors for Explainable and Robust Model Training
School of Computer Science, Peking University（北京大学计算机科学学院）；Key Lab of High Confidence Software Technologies, Peking University（北京大学高可信软件技术重点实验室）；Kling Team（Kling团队）；Department of Systems Engineering and Engineering Management, CUHK（CUHK系统工程与工程管理系）
UNAD+: An Explainable Hybrid Framework for Unknown Network Attack Detection
Department of Computer Science, University of Exeter（埃克塞特大学计算机科学系）；German Research Center for Artificial Intelligence GmbH (DFKI)（德国人工智能研究中心（DFKI））；Marine Perception（海洋感知）
Purpose: To explore the potential of Explainable Machine Learning in the prediction and detection of drivers of cleared homicides at the national- and state-levels in the United States. Methods: First, nine algorithmic approaches are compared to assess the best performance in predicting cleared homicides country-wise, using data from the Murder Accountability Project. The most accurate algorithm among all (XGBoost) is then used for predicting clearance outcomes state-wise. Second, SHAP, a framework for Explainable Artificial Intelligence, is employed to capture the most important features in e…
Accurately forecasting patient arrivals at Urgent Care Clinics (UCCs) and Emergency Departments (EDs) is important for effective resourcing and patient care. However, correctly estimating patient flows is not straightforward since it depends on many drivers. The predictability of patient arrivals has recently been further complicated by the COVID-19 pandemic conditions and the resulting lockdowns. This study investigates how a suite of novel quasi-real-time variables like Google search terms, pedestrian traffic, the prevailing incidence levels of influenza, as well as the COVID-19 Alert Level …
The recognition or understanding of the scenes observed with a SAR system requires a broader range of cues, beyond the spatial context. These encompass but are not limited to: imaging geometry, imaging mode, properties of the Fourier spectrum of the images or the behavior of the polarimetric signatures. In this paper, we propose a change of paradigm for explainability in data science for the case of Synthetic Aperture Radar (SAR) data to ground the explainable AI for SAR. It aims to use explainable data transformations based on well-established models to generate inputs for AI methods, to prov…
National Observatory of Athens（雅典国家天文台）；National Technical University of Athens（雅典国立技术大学）
Helicoverpa Armigera, or cotton bollworm, is a serious insect pest of cotton crops that threatens the yield and the quality of lint. The timely knowledge of the presence of the insects in the field is crucial for effective farm interventions. Meteo-climatic and vegetation conditions have been identified as key drivers of crop pest abundance. In this work, we applied an interpretable classifier, i.e., Explainable Boosting Machine, which uses earth observation vegetation indices, numerical weather predictions and insect trap catches to predict the onset of bollworm harmfulness in cotton fields i…
Driver drowsiness is one of main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been considered as one of the best physiological signals to detect drivers drowsy states, since it directly measures neurophysiological activities in the brain. However, designing a calibration-free system for driver drowsiness detection with EEG is still a challenging task, as EEG suffers from serious mental and physical drifts across different subjects. In this paper, we propose a compact and interpretable Convolutional Neural Network (CNN) to disco…
A Novel Approach to Explainable AI with Quantized Active Ingredients in Decision Making
Department of Computational Mathematics University of Moratuwa Sri Lanka（计算数学系 卢特瓦大学 斯里兰卡）；Department of Mathematics University of Colombo Sri Lanka（数学系 科布姆大学 斯里兰卡）
Remaining Useful Life Prediction for Batteries Utilizing an Explainable AI Approach with a Predictive Application for Decision-Making
Nepal Engineering College, Pokhara University（博卡拉大学尼泊尔工程学院）；University of Delaware（特拉华大学）；Delft University of Technology（代尔夫特理工大学）；The Hague University of Applied Science（海牙应用科学大学）；Institute of Engineering, Pulchowk Campus, Tribhuvan University（特里布文大学工程学院普尔乔克校区）
