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., Aggarwal, C. C., & Salehi, M. (2025). CARLA: Self-supervised contrastive representation learning for time series anomaly detection. Pattern Recognition, 157, 110874. [2] Zamanzadeh Darban, Z., Webb, G. I
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This project examines how films produced in Asian markets perform in terms of commercial success and critical recognition using real-world industry data. Students will compile a dataset of films
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to examine vast amounts of textual data, identifying keywords, phrases, and sentiment that may indicate extremist views or intentions. Analysing audio involves techniques such as speech recognition, keyword
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-supervised contrastive representation learning for time series anomaly detection. Pattern Recognition, 157, 110874. [3] Foumani, N. M., Tan, C. W., Webb, G. I., & Salehi, M. (2024). Improving position encoding
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at prediction and pattern recognition tasks but still fails at very simple planning and decision-making problems. This project will develop predictive and prescriptive analytics algorithms that combine
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vision and pattern recognition methods, will be utilized to automate the process of fingertip detection. These methods will be trained to learn patterns from fingertip features and detect them using object
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Over the past decades, we have witnessed the emergence and rapid development of deep learning. DL has been successfully deployed in many real-life applications, including face recognition, automatic
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, Guodong Long, Chengqi Zhang: Finding the best not the most: regularized loss minimization subgraph selection for graph classification. Pattern Recognition 48(11): 3783-3796 (2015) Shirui Pan, Jia Wu