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Saleh Albahli
Since the introduction of just-in-time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods, which can predict the defect inducing changes in a software product. In order to predict these changes, it ...
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Hilmil Pradana
Predicting traffic risk incidents in first-person helps to ensure a safety reaction can occur before the incident happens for a wide range of driving scenarios and conditions. One challenge to building advanced driver assistance systems is to create an e...
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Han Hu, Changming Wang, Zhu Liang, Ruiyuan Gao and Bailong Li
Landslides frequently occur because of natural or human factors. Landslides cause huge losses to the economy as well as human beings every year around the globe. Landslide susceptibility prediction (LSP) plays a key role in the prevention of landslides a...
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Yuxun Lu and Ryutaro Ichise
Knowledge graph completion (KGC) models are a feasible approach for manipulating facts in knowledge graphs. However, the lack of entity types in current KGC models results in inaccurate link prediction results. Most existing type-aware KGC models require...
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Zhimeng Li, Wen Zhong, Weiwen Liao, Yiqun Cai, Jian Zhao and Guofeng Wang
Real-time tool condition monitoring (TCM) is becoming more and more important to meet the increased requirement of reducing downtime and ensuring the machining quality of manufacturing systems. However, it is difficult to satisfy both robustness and effe...
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Zhihao Guo, Shengyuan Chen, Xiao Huang, Zhiqiang Qian, Chunsing Yu, Yan Xu and Fang Ding
Most machine-learning algorithms assume that instances are independent of each other. This does not hold for networked data. Node representation learning (NRL) aims to learn low-dimensional vectors to represent nodes in a network, such that all actionabl...
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Lingling Zhao and Anping Zhao
To facilitate product developers capturing the varying requirements from users to support their feature evolution process, requirements evolution prediction from massive review texts is in fact of great importance. The proposed framework combines a super...
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Tulio Silveira-Santos, Anestis Papanikolaou, Thais Rangel and Jose Manuel Vassallo
App-based ride-hailing mobility services are becoming increasingly popular in cities worldwide. However, key drivers explaining the balance between supply and demand to set final prices remain to a considerable extent unknown. This research intends to un...
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Gaurang Sonkavde, Deepak Sudhakar Dharrao, Anupkumar M. Bongale, Sarika T. Deokate, Deepak Doreswamy and Subraya Krishna Bhat
The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning ...
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Roman Putter, Andre Neubohn, Andre Leschke and Roland Lachmayer
Traffic accident avoidance and mitigation are the main targets of accident research and vehicle safety development worldwide. Despite improving advanced driver assistance systems (ADAS) and active safety systems, it will not be possible to avoid all vehi...
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Olurotimi Oguntola and Steven Simske
This study proposes a framework for a systems engineering-based approach to context-aware personalization, which is applied to e-commerce through the understanding and modeling of user behavior from their interactions with sales channels and media. The f...
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Michael McCord, Daniel Lo, Peadar Davis, John McCord, Luc Hermans and Paul Bidanset
Prediction accuracy for mass appraisal purposes has evolved substantially over the last few decades, facilitated by the evolution in big data, data availability and open source software. Accompanying these advances, newer forms of geo-spatial approaches ...
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Wagner Costa, Déborah Idier, Jérémy Rohmer, Melisa Menendez and Paula Camus
Increasing our capacity to predict extreme storm surges is one of the key issues in terms of coastal flood risk prevention and adaptation. Dynamically forecasting storm surges is computationally expensive. Here, we focus on an alternative data-driven app...
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Robert Truong, Olga Gkountouna, Dieter Pfoser and Andreas Züfle
The problem of traffic prediction is paramount in a plethora of applications, ranging from individual trip planning to urban planning. Existing work mainly focuses on traffic prediction on road networks. Yet, public transportation contributes a significa...
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Tala Talaei Khoei and Naima Kaabouch
Intrusion Detection Systems are expected to detect and prevent malicious activities in a network, such as a smart grid. However, they are the main systems targeted by cyber-attacks. A number of approaches have been proposed to classify and detect these a...
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Eva Romano-Moreno, Antonio Tomás, Gabriel Diaz-Hernandez, Javier L. Lara, Rafael Molina and Javier García-Valdecasas
The good performance of the port activities in terminals is mainly conditioned by the dynamic response of the moored ship system at a berth. An adequate definition of the highly multivariate processes involved in the response of a moored ship at a berth ...
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Jiahao Guo, Xiaohuo Yu and Lu Wang
Industrial quality control is an important task. Most of the existing vision-based unsupervised industrial anomaly detection and segmentation methods require that the training set only consists of normal samples, which is difficult to ensure in practice....
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Van Quy Khuc and Duc Trung Tran
This paper introduces an advanced method that integrates contingent valuation and machine learning (CVML) to estimate residents? demand for reducing or mitigating environmental pollution and climate change. To be precise, CVML is an innovative hybrid mac...
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Yubo Zheng, Yingying Luo, Hengyi Shao, Lin Zhang and Lei Li
Contrastive learning, as an unsupervised technique, has emerged as a prominent method in time series representation learning tasks, serving as a viable solution to the scarcity of annotated data. However, the application of data augmentation methods duri...
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Amna Al-Sayed, Mashael M. Khayyat and Nuha Zamzami
Different data types are frequently included in clinical data. Applying machine learning algorithms to mixed data can be difficult and impact the output accuracy and quality. This paper proposes a hybrid model of unsupervised and supervised learning tech...
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