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

ALB

https://doi.org/10.1007/978-3-662-49459-2d vehicle. This setup facilitated the collection of large datasets, which were subsequently processed using the YOLO AI algorithm to effectively detect and classify road pavement conditions. The experiment‘s results underscore the effectiveness of combining mobile mapping technology, programming, an

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Deep Learning-Based Object Detection of Relevant Morphological Traits for Enhancing Automatic Classinces in recent years, especially with deep learning techniques, the complexity of emerging models still needs to accurately capture the fine morphological features that are key to manual taxonomic classification. This paper examines how a semantic detector like YOLO performs when dealing with fine-g

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Improvement in?the?Management of?Potable Water Distribution Using Data Science for?the?Detection andapproach between computational capabilities and expert judgment results in useful models that contribute to the optimal management of the water service and the utilization of modern technological tools.

glomeruli

Wrist Motion Pattern Recognition from?EMG Signal Processing Using Machine Learning and?Neural Networifier achieved an accuracy of 75%. In contrast, the neural network, specifically a multilayer neural network, achieved an accuracy of 90%. Including PCA for feature selection significantly contributed to the overall performance improvement in both classifiers. This study’s findings show the potentia

ascetic

Precursor

Enhancing the?Diagnostic Accuracy of?Diabetes and?Prediabetes with?Neural Network-Based Area Under t OGTT. Artificial neural networks (ANNs) have shown significant potential in enhancing the diagnosis of diabetes and prediabetes. This study explores the application of ANNs for diagnosing diabetes and prediabetes, utilizing AUCG and AUCI as diagnostic metrics. A data set of 188 individuals diagnose