pwc-archive/papers-with-abstracts huggingface dataset:
Driving pattern interpretation based on action phases clustering
Authors: Xue Yao, Simeon C. Calvert, Serge P. Hoogendoorn
Date: 2024-07-17 00:00:00
Tasks: Clustering, feature selection, Trajectory Prediction

Current approaches to identifying driving heterogeneity face challenges in comprehending fundamental patterns from the perspective of underlying driving behavior mechanisms. The concept of Action phases was proposed in our previous work, capturing the diversity of driving characteristics with physical meanings. This study presents a novel framework to further interpret driving patterns by classifying Action phases in an unsupervised manner. In this framework, a Resampling and Downsampling Method (RDM) is first applied to standardize the length of Action phases. Then the clustering calibration procedure including ''Feature Selection'', ''Clustering Analysis'', ''Difference/Similarity Evaluation'', and ''Action phases Re-extraction'' is iteratively applied until all differences among clusters and similarities within clusters reach the pre-determined criteria. Application of the framework using real-world datasets revealed six driving patterns in the I80 dataset, labeled as ''Catch up'', ''Keep away'', and ''Maintain distance'', with both ''Stable'' and ''Unstable'' states. Notably, Unstable patterns are more numerous than Stable ones. ''Maintain distance'' is the most common among Stable patterns. These observations align with the dynamic nature of driving. Two patterns ''Stable keep away'' and ''Unstable catch up'' are missing in the US101 dataset, which is in line with our expectations as this dataset was previously shown to have less heterogeneity. This demonstrates the potential of driving patterns in describing driving heterogeneity. The proposed framework promises advantages in addressing label scarcity in supervised learning and enhancing tasks such as driving behavior modeling and driving trajectory prediction.
Sample Enrichment via Temporary Operations on Subsequences for Sequential Recommendation
Authors: Shu Chen, Jinwei Luo, Weike Pan, Jiangxing Yu, Xin Huang, Zhong Ming
Date: 2024-07-25 00:00:00
Tasks: Sequential Recommendation

Sequential recommendation leverages interaction sequences to predict forthcoming user behaviors, crucial for crafting personalized recommendations. However, the true preferences of a user are inherently complex and high-dimensional, while the observed data is merely a simplified and low-dimensional projection of the rich preferences, which often leads to prevalent issues like data sparsity and inaccurate model training. To learn true preferences from the sparse data, most existing works endeavor to introduce some extra information or design some ingenious models. Although they have shown to be effective, extra information usually increases the cost of data collection, and complex models may result in difficulty in deployment. Innovatively, we avoid the use of extra information or alterations to the model; instead, we fill the transformation space between the observed data and the underlying preferences with randomness. Specifically, we propose a novel model-agnostic and highly generic framework for sequential recommendation called sample enrichment via temporary operations on subsequences (SETO), which temporarily and separately enriches the transformation space via sequence enhancement operations with rationality constraints in training. The transformation space not only exists in the process from input samples to preferences but also in preferences to target samples. We highlight our SETO's effectiveness and versatility over multiple representative and state-of-the-art sequential recommendation models (including six single-domain sequential models and two cross-domain sequential models) across multiple real-world datasets (including three single-domain datasets, three cross-domain datasets and a large-scale industry dataset).
Predicting student's dropout in university classes using two-layer ensemble machine learning approach: A novel stacked generalization
Authors: Jovial Niyogisubizo, Lyuchao Liao, Eric Nziyumva, Evariste Murwanashyaka, Pierre Claver Nshimyumukiza
Date: 2022-03-28 00:00:00
Proceeding: Computers and Education: Artificial Intelligence 2022 3
Tasks: Ensemble Learning, Student dropout

Student dropout is a serious problem globally. It affects not only the individual who drops out but also the former school, family, and society in general. With the current development of science and technology, big data is emphasized as the most significant technology in data analysis. From the recorded educational data, efficient prediction of students' dropout is currently a hot topic of research. Previous studies have focused only on the students' dropout based on specific levels such as individual, middle school, and university level. However, ensemble learning approaches have not received much research attention so far to predict students' dropout in university classes based on rare datasets. In this paper, we propose a novel stacking ensemble based on a hybrid of Random Forest (RF), Extreme Gradient Boosting (XGBoost), Gradient Boosting (GB), and Feed-forward Neural Networks (FNN) to predict student's dropout in university classes. On the dataset collected from 2016 to 2020 at Constantine the Philosopher University in Nitra, the proposed method has demonstrated greater performance when compared with the base models using testing accuracy and the area under the curve (AUC) evaluation metrics under the same conditions. Based on the findings of this study, students at the risk of dropping out the school can be identified based on influential factors and different agents of education can refer to this information for early intervention in the uncontrolled behavior that can lead to the risk of dropping out and take proactive precautionary measures before the issue arise.
A Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial Annotations
Authors: Hao Wu, Jovial Niyogisubizo, Keliang Zhao, Jintao Meng, Wenhui Xi, Hongchang Li, Yi Pan, Yanjie Wei
Date: 2023-11-07 00:00:00
Proceeding: International Journal of Molecular Sciences 2023 11
Tasks: Cell Detection, Cell Tracking, Weakly-supervised Learning, Weakly Supervised Object Detection

The automatic detection of cells in microscopy image sequences is a significant task in biomedical research. However, routine microscopy images with cells, which are taken during the process whereby constant division and differentiation occur, are notoriously difficult to detect due to changes in their appearance and number. Recently, convolutional neural network (CNN)-based methods have made significant progress in cell detection and tracking. However, these approaches require many manually annotated data for fully supervised training, which is time-consuming and often requires professional researchers. To alleviate such tiresome and labor-intensive costs, we propose a novel weakly supervised learning cell detection and tracking framework that trains the deep neural network using incomplete initial labels. Our approach uses incomplete cell markers obtained from fluorescent images for initial training on the Induced Pluripotent Stem (iPS) cell dataset, which is rarely studied for cell detection and tracking. During training, the incomplete initial labels were updated iteratively by combining detection and tracking results to obtain a model with better robustness. Our method was evaluated using two fields of the iPS cell dataset, along with the cell detection accuracy (DET) evaluation metric from the Cell Tracking Challenge (CTC) initiative, and it achieved 0.862 and 0.924 DET, respectively. The transferability of the developed model was tested using the public dataset FluoN2DH-GOWT1, which was taken from CTC; this contains two datasets with reference annotations. We randomly removed parts of the annotations in each labeled data to simulate the initial annotations on the public dataset. After training the model on the two datasets, with labels that comprise 10% cell markers, the DET improved from 0.130 to 0.903 and 0.116 to 0.877. When trained with labels that comprise 60% cell markers, the performance was better than the model trained using the supervised learning method. This outcome indicates that the model’s performance improved as the quality of the labels used for training increased.
A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking
Authors: Keliang Zhao, Jovial Niyogisubizo, Linxia Xiao, Yi Pan, Dongqing Wei, Didi Rosiyadi, Yanjie Wei
Date: 2024-01-18 00:00:00
Proceeding: IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2024 1
Tasks: Cell Detection, Cell Segmentation, Cell Tracking, Graph Neural Network, Instance Segmentation, Outlier Detection, Segmentation, Semantic Segmentation

The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation and tracking of cells is time-consuming and often require professional experiences. Besides, segmenting cells in the images with a low signal-to-noise ratio remains difficult. While deep learning (DL) has become a common method for cell segmentation, few DL- based methods address concurrent cell segmentation and tracking. In this paper, we propose a novel DL approach featuring graph-based tracking for cell segmentation and tracking in microscopy images. We combine Deeplabv3+ for semantic segmentation and ResNet50 for enhanced feature extraction, enabling comprehensive cell detection and instance segmentation. Post-processing, involving non-maxima suppression and outlier detection, refines predictions and produces final segmentation. The tracking method is based on the relative position of graph nodes to track segmented cells, encompassing cell division and apoptosis. We conduct our experiments on the induced pluripotent stem (iPS) cell datasets, and the results show that the segmentation and tracking performance of our method yields superior performance compared to the benchmark models. More specifically, our approach achieved DET values of 0.955 and 0.913, TRA values of 0.951 and 0.906, and SEG values of 0.690 and 0.665 on two iPS dataset videos, respectively. Additionally, the performance assessment encompasses four real microscopy datasets from the Cell Tracking Challenge (CTC). Our method greatly reduces the cost of manual labeling and labor-intensive costs.
Predicting crash injury severity in smart cities: a novel computational approach with wide and deep learning model
Authors: Jovial Niyogisubizo, Lyuchao Liao, Qi Sun, Eric Nziyumva, Yongqiang Wang, Linsen Luo, Shukun Lai, Evariste Murwanashyaka
Date: 2023-03-04 00:00:00
Proceeding: International Journal of Intelligent Transportation Systems Research 2023 3
Tasks: Classification, Crash injury severity, Interpretable Machine Learning, severity prediction, Traffic Prediction

Smart cities came out as highly knowledgeable bio-networks, offering intelligent services and innovative solutions to urban problems. With rapid development, urbanization, and population pressure, traffic congestion and collisions are increasing substantially in road highway zones. Recently, traffic collisions have become one of the hugest national health problems in many cities of the world. Hence, crash injury severity prediction is vital for informing responsible authorities and the public to find alternative ways of dealing with its adverse effects, accordingly improving traffic safety and reducing traffic congestion. In predicting crash injury severity, researchers have explored and applied several techniques to aid in traffic injury management. However, the performance of many techniques suffers from some inherent limitations, including overgeneralization, lack of interpretability for humans, and low-performance accuracy. To address these issues, this paper proposes a novel computational framework based on improved wide and deep learning methods to predict accurately crash injury severity in the context of smart cities. On the crash dataset of New Zealand cities from 2000 to 2020, the proposed model has demonstrated better performance in comparison with the benchmark algorithms. Moreover, SHAP (SHapley Additive exPlanation) is employed to interpret the results and analyze the importance of each determinant of crash severity. The proposed method can help to prevent traffic crashes in smart cities and take proactive measures before the occurrence, also trauma centers can refer to this information to dispatch proper emergency service equipment swiftly and assist injuries to get direct medical care regardless of the crash location.
Predicting traffic crash severity using hybrid of balanced bagging classification and light gradient boosting machine
Authors: Jovial Niyogisubizo, Lyuchao Liao, Fumin Zou, Guangjie Han, Eric Nziyumva, Ben Li, Yuyuan Lin
Date: 2023-01-01 00:00:00
Proceeding: Intelligent Data Analysis 2023 1
Tasks: Feature Importance, Hybrid Machine Learning, severity prediction, Traffic Prediction

Accident severity prediction is a hot topic of research aimed at ensuring road safety as well as taking precautionary measures for anticipated future road crashes. In the past decades, both classical statistical methods and machine learning algorithms have been used to predict traffic crash severity. However, most of these models suffer from several drawbacks including low accuracy, and lack of interpretability for people. To address these issues, this paper proposed a hybrid of Balanced Bagging Classification (BBC) and Light Gradient Boosting Machine (LGBM) to improve the accuracy of crash severity prediction and eliminate the issues of bias and variance. To the best of the author’s knowledge, this is one of the pioneer studies which explores the application of BBC-LGBM to predict traffic crash severity. On the accident dataset of Great Britain (UK) from 2013 to 2019, the proposed model has demonstrated better performance when compared with other models such as Gaussian Naïve Bayes (GNB), Support vector machines (SVM), and Random Forest (RF). More specifically, the proposed model managed to achieve better performance among all metrics for the testing dataset (accuracy = 77.7%, precision = 75%, recall = 73%, F1-Score = 68%). Moreover, permutation importance is used to interpret the results and analyze the importance of each factor influencing crash severity. The accuracy-enhanced model is significant to several stakeholders including drivers for early alarm and government departments, insurance companies, and even hospitals for the services concerned about human lives and property damage in road crashes.
Automatic ECG classification using discrete wavelet transform and one-dimensional convolutional neural network
Authors: armin shoughi, Mohammad Bagher Dowlatshahi, Arefeh Amiri, Marjan Kuchaki Rafsanjani & Ranbir Singh Batth
Date: 2023-12-23 00:00:00
Proceeding: Computing 2023 12
Tasks: Diagnostic, ECG Classification

This paper presents an approach based on deep learning for accurate Electrocardiogram signal classification. The electrocardiogram is a significant signal in the realm of medical affairs, which gives vital information about the cardiovascular status of patients to heart specialists. Manually meticulous analysis of signals needs high and specific skills, and it is a time-consuming job too. The existence of noise, the inflexibility of signals, and the irregularity of heartbeats keep heart specialists in trouble. Cardiovascular diseases (CVDs) are the most important factor of fatality globally, which annually caused the deaths of 17.9 million people. Totally 31% of all death in the world are related to CVDs, which the age of 1/3 of patients that died because of CVDs is below 70 Because of the high percentage of mortality in cardiovascular patients, accurate diagnosis of this disease is an important matter. We present an approach to the analysis of electrocardiogram signals based on the convolutional neural network, discrete wavelet transformation with db2 mother wavelet, and synthetic minority over-sampling technique (SMOTE) on the MIT-BIH dataset according to the association for the advancement of medical instrumentation (AAMI) standards to increase the accuracy in electrocardiogram signal classifications. The evaluation results show this approach with 50 epoch training that the time of each epoch is 39 s, achieved 99.71% accuracy for category F, 98.69% accuracy for category N, 99.45% accuracy for category S, 99.33% accuracy for category V and 99.82% accuracy for category Q. It is worth mentioning that it can potentially be used as a clinical auxiliary diagnostic tool. The source code is available at https://github.com/arminshoughi/ecg-classification-cnn.
Electrical load forecasting using hybrid of extreme gradient boosting and light gradient boosting machine
Authors: Eric Nziyumva, Rong Hu, Chih-Yu Hsu, Jovial Niyogisubizo
Date: 2022-03-03 00:00:00
Proceeding: The International Conference on Image, Vision and Intelligent Systems (ICIVIS 2021) 2022 3
Tasks: Electrical Engineering, Ensemble Learning, Hybrid Machine Learning, Load Forecasting

Ensemble learning methods have been used to improve performance accuracy through bias-variance trade-off techniques. However, there is still room to improve. This paper proposes an ensemble model to forecast the electrical load behavior based on a hybrid of Extreme Gradient Boosting (XGBoost) and Light gradient boosting machine (LGBM). Extreme gradient boosting (XGBoost), a Light gradient boosting machine (LGBM) and a hybrid of XGBoost and LGBM models are trained, evaluated, and compared. The experiments show that the proposed model outperforms other methods by reducing more than 1% in mean absolute percentage error (MAPE), root mean squared percentage error (RMSPE), and mean absolute error (MAE). The dataset from the Pennsylvania-New Jersey-Maryland interconnection power grid was used to validate the evolutionary capability of the proposed method and the finding of optimal accuracy of the model.
A novel stacking framework based on hybrid of gradient boosting-adaptive boosting-multilayer perceptron for crash injury severity prediction and analysis
Authors: Jovial Niyogisubizo, Lyuchao Liao, Yuyuan Lin, Linsen Luo, Eric Nziyumva, Evariste Murwanashyaka
Date: 2021-12-17 00:00:00
Proceeding: 2021 IEEE 4th International Conference on Electronics and Communication Engineering (ICECE) 2021 12
Tasks: Crash injury severity, Ensemble Learning, Feature Importance, Management, severity prediction, Traffic Prediction

Crash injury severity prediction is a promising area of interest in traffic safety and management. Recently, machine learning approaches are becoming popular due to their ability to enhance the prediction performance through the bias-variance trade-off-technique. However, some of these methods are criticized to perform like a ‘black box’ approach while predicting and analyzing crash injury severity and produce low accuracy. In this study, we propose a novel stacking framework based on a hybrid of Gradient Boosting (GB), Adaptive Boosting (AdaBoost), and Multilayer Perceptron (MLP) to predict accurately crash injury severity. On the traffic collision dataset provided by the Seattle City Department of Transportation from 2004 to 2021, the proposed model has demonstrated superior performance when compared with the base models. Furthermore, SHAP (SHapley Additive exPlanation) is used to interpret the contribution of every feature on model performance and provide recommendations to responsible authorities.
A Comparative Study on Machine Learning-based Approaches for Improving Traffic Accident Severity Prediction
Authors: Jovial Niyogisubizo, Evariste Murwanashyaka, Eric Nziyumva
Date: 2021-11-02 00:00:00
Proceeding: International Journal of Engineering Research & Technology (IJERT) 2021 11
Tasks: Classification, Crash injury severity, Feature Importance, severity prediction, Traffic Prediction

Traffic accidents are the leading cause of many deaths, property damages, injuries, and fatalities as well as financial losses every year. Accurate traffic accident severity prediction would be very crucial to evaluate the major determinants associated with road accidents, offer precautions before occurrence based on the predicted outcomes and thus minimize all negative impacts caused by accidents. In the past decades, traditional techniques and machine learning have been used to predict traffic accidents. However, machine learning models are criticized because they perform like black box and lack interpretations for humans. The main purpose of this research is to employ machine learning-based approaches to predict crash injury severity and analyze the most influential factors contributing to road crashes as well as giving recommendations to concerned stakeholders. In this study, four classification approaches were employed: Random Forest (RF), Multinomial NaÃ¯ve Bayes (MNB), K-Means Clustering (KC), and K-Nearest Neighbors (KNN) to predict accident severity and analyze feature importance. On the road accident dataset from 2015 to 2020 provided by the State of Victoria in Australia, the RF outperformed the remaining methods in terms of accuracy, precision, recall, and F1 Score. Month, time of day, female drivers, male drivers, total persons, speed zone, day of the week, passengers, etc., were found as the major determinants of accident severity. The accuracy enhanced model can help in giving recommendations such as safe route planning, preparing emergency vehicle allocation, reducing property damage, placing additional signage where necessary, and roadway design to concerned stakeholders to eradicate the number of fatalities and injuries resulting from traffic accidents.
Towards Predicting the Quality of Red Wine Using Novel Machine Learning Methods for Classification, Data Visualization and Analysis
Authors: Jovial Niyogisubizo, Jean de Dieu Ninteretse, Eric Nziyumva, Marc Nshimiyimana, Evariste Murwanashyaka, Erneste Habiyakare
Date: 2024-01-18 00:00:00
Proceeding: Artificial Intelligence and Applications 2024 1
Tasks: Data Visualization, Ensemble Learning, Outlier Detection, Red Wine Quality Prediction

There 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.