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Mohamad Abou Ali, Fadi Dornaika and Ignacio Arganda-Carreras
Artificial intelligence (AI) has emerged as a cutting-edge tool, simultaneously accelerating, securing, and enhancing the diagnosis and treatment of patients. An exemplification of this capability is evident in the analysis of peripheral blood smears (PB...
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Mohamad Abou Ali, Fadi Dornaika and Ignacio Arganda-Carreras
Deep learning (DL) has made significant advances in computer vision with the advent of vision transformers (ViTs). Unlike convolutional neural networks (CNNs), ViTs use self-attention to extract both local and global features from image data, and then ap...
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Li Zhe Poh, Tee Connie, Thian Song Ong and Michael Kah Ong Goh
The growth in the number of automobiles in metropolitan areas has drawn attention to the need for more efficient carpark control in public spaces such as healthcare, retail stores, and office blocks. In this research, dynamic pricing is integrated with r...
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Wongwan Jung and Daejun Chang
This study proposed a deep reinforcement learning-based energy management strategy (DRL-EMS) that can be applied to a hybrid electric ship propulsion system (HSPS) integrating liquid hydrogen (LH2) fuel gas supply system (FGSS), proton-exchange membrane ...
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Yufeng Huang, Jun Tao, Gang Sun, Hao Zhang and Yan Hu
In this study, a prognostics and health management (PHM) framework is proposed for aero-engines, which combines a dynamic probability (DP) model and a long short-term memory neural network (LSTM). A DP model based on Gaussian mixture model-adaptive densi...
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Pedro Andrade, Catarina Silva, Bernardete Ribeiro and Bruno F. Santos
This paper presents a Reinforcement Learning (RL) approach to optimize the long-term scheduling of maintenance for an aircraft fleet. The problem considers fleet status, maintenance capacity, and other maintenance constraints to schedule hangar checks fo...
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