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Esmaeil Zahedi, Mohamad Saraee, Fatemeh Sadat Masoumi and Mohsen Yazdinejad
Unsupervised anomalous sound detection, especially self-supervised methods, plays a crucial role in differentiating unknown abnormal sounds of machines from normal sounds. Self-supervised learning can be divided into two main categories: Generative and C...
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Turki Turki, Sanjiban Sekhar Roy and Y.-H. Taguchi
It is difficult to identify histone modification from datasets that contain high-throughput sequencing data. Although multiple methods have been developed to identify histone modification, most of these methods are not specific to histone modification bu...
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Abrar Alamr and Abdelmonim Artoli
Anomaly detection is one of the basic issues in data processing that addresses different problems in healthcare sensory data. Technology has made it easier to collect large and highly variant time series data; however, complex predictive analysis models ...
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Tingting Chen, Vignesh Sampath, Marvin Carl May, Shuo Shan, Oliver Jonas Jorg, Juan José Aguilar Martín, Florian Stamer, Gualtiero Fantoni, Guido Tosello and Matteo Calaon
While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of producti...
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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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