Titlebook: Applied Computer Sciences in Engineering; 11th Workshop on Eng Juan Carlos Figueroa-García,German Hernández,Elvis Conference proceedings 20

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SEMI

Neural Networks Informed by?Physics Applied to?Solving an?Optimal Investment-Consumption ProblemThis work considers the optimal portfolio problem of Merton in the context of an uncertain financial market composed of two types of assets. The Hamilton-Jacobi-Bellman equation associated with this type of problem is posed, and it is shown how physics-informed neural networks (PINNs) can be implemented to find its solution.

Occipital-Lobe

https://doi.org/10.1007/978-981-15-7226-5te the performance of both open-source (Flan-T5) and proprietary LLMs (ChatGPT, GPT-4) on the well-known SpamAssassin dataset. Two classification approaches are explored: (1) truncated raw content from email subject and body, and (2) classification based on summaries generated by ChatGPT. Our empiri

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HIV Diagnosis, Management and Prognosis,rt System for Public Transport Robberies based on Deep Learning with Transfer Learning (SAATP-ATP), which focuses on analyzing audio signals. The system utilizes advanced deep learning techniques and transfer learning to enhance accuracy in detecting suspicious activities based on specific sounds as

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Pathology of HIV-Associated Liver Disease,f the development of methods in other areas of knowledge such as mathematics, physics or statistics, which have set a precedent either by offering greater precision or by being more efficient methods to solve problems that originated in the framework of these other branches of knowledge. It is not i

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SpringerBriefs in Public Healthders (AE) and variational autoencoders (VAE) within deep neural networks, incorporating cardinality constraints to limit the number of assets in the portfolio. Our methodology entails training the models on historical stock returns from 2019 to 2022 and validating their performance in 2023. The resu

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芳香一點(diǎn)

Learning to Age Successfully with HIVreduce these losses, improving operational management is of paramount importance. This paper presents a methodology that integrates the technical expertise of operational engineers with data science to improve the management, monitoring, and maintenance of water distribution networks, focusing on ef

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