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Ilia Zaznov, Julian Martin Kunkel, Atta Badii and Alfonso Dufour
This paper introduces a novel deep learning approach for intraday stock price direction prediction, motivated by the need for more accurate models to enable profitable algorithmic trading. The key problems addressed are effectively modelling complex limi...
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Zahid Masood, Muhammad Usama, Shahroz Khan, Konstantinos Kostas and Panagiotis D. Kaklis
Generative models offer design diversity but tend to be computationally expensive, while non-generative models are computationally cost-effective but produce less diverse and often invalid designs. However, the limitations of non-generative models can be...
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Sharoon Saleem, Fawad Hussain and Naveed Khan Baloch
Network on Chip (NoC) has emerged as a potential substitute for the communication model in modern computer systems with extensive integration. Among the numerous design challenges, application mapping on the NoC system poses one of the most complex and d...
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Roman Major, Maciej Gawlikowski, Marcin Surmiak, Karolina Janiczak, Justyna Wiecek, Przemyslaw Kurtyka, Martin Schwentenwein, Ewa Jasek-Gajda, Magdalena Kopernik and Juergen M. Lackner
A major medical problem of state-of-the-art heart ventricular assist devices (LVADs) is device-induced thrombus formation due to inadequate blood-flow dynamics generated by the blood pump rotor. The latter is a highly complex device, with difficulties du...
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Norah Fahd Alhussainan, Belgacem Ben Youssef and Mohamed Maher Ben Ismail
Brain tumor diagnosis traditionally relies on the manual examination of magnetic resonance images (MRIs), a process that is prone to human error and is also time consuming. Recent advancements leverage machine learning models to categorize tumors, such a...
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