Titlebook: Intelligent Systems and Applications; Proceedings of the 2 Kohei Arai Conference proceedings 2024 The Editor(s) (if applicable) and The Aut

Intact

somnambulism

Affiliation

Wind Turbine Data-Driven Intelligent Fault Detection,. On the other hand, there is a limited amount of data to work with, which, along with the high processing needs, makes the training process resource-intensive. The purpose of this research is to offer a deep learning model that relies on a data-driven approach and is aimed to increase prediction ac

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gerontocracy

,Pre-trained Deep Learning Models for Chest X-Rays’ Classification: Views and Age-Groups, view of Chest X-rays. Moreover, an accurate radiological diagnosis should take into consideration the patient age-group, as well as the Chest X-ray view. In this paper, we aim to find the optimal classification deep learning model to classify Chest X-rays by age-group and by view. We trained seven

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Impact of Gender and Chest X-Ray View Imbalance in Pneumonia Classification Using Deep Learning,s’ performance. However, the optimal training requirements for each gender and the best use-cases of CXR views are yet to be discovered. Our objective is to determine the impact of the view (PA or AP) and the gender (Male or Female) on the performance of deep learning models in the classification of

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Index Tracking Via Learning to Predict Market Sensitivities,utual fund aiming to track the returns of a predefined market index (e.g., the S &P 500). A basic strategy to manage an index fund is replicating the index’s constituents and weights identically, which is, however, cost-ineffective and impractical. To address this issue, it is required to replicate

Endoscope

Deep Learning Models for Inventory Decisions: A Comparative Analysis,es forecasting accuracy; however the analysis of how the forecasting predictions translate to lower cost inventory decisions is still in incipient stage. The focus of this contribution is to compare how different deep learning architectures leverage the potential of data features to achieve better d

不能仁慈

Detecting Standard Library Functions in Obfuscated Code,tic analysis more challenging. In this work we use machine learning to detect standard library functions in compiled code which has been heavily obfuscated. First we create a C library function dataset augmented by obfuscation and diverse compiler options. We then train an ensemble of Paragraph Vect