Distributed Analytics For Big Data: A Survey

Berloco, Francesco; Bevilacqua, Vitoantonio; Colucci, Simona

doi:10.1016/j.neucom.2024.127258

Cited by 2 publications

(1 citation statement)

References 66 publications

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“…In recent years, research in machine learning and deep learning has made it feasible to predict sample indicators by integrating past large datasets with existing sample features [28,29]. Existing studies indicate that models based on machine learning and deep learning can predict the compressive strength of concrete with relatively high accuracy [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Pervious Concrete Compressive Strength Based on a Convolutional Neural Network

Yu,

Zhu,

Xiang

2024

Buildings

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To overcome limitations inherent in existing mechanical performance prediction models for pervious concrete, including material constraints, limited applicability, and inadequate accuracy, this study employs a deep learning approach to construct a Convolutional Neural Network (CNN) model with three convolutional modules. The primary objective of the model is to precisely predict the 28-day compressive strength of pervious concrete. Eight input variables, encompassing coarse and fine aggregate content, water content, admixture content, cement content, fly ash content, and silica fume content, were selected for the model. The dataset utilized for both model training and testing consists of 111 sample sets. To ensure the model’s coverage within the practical range of pervious concrete strength and to enhance its robustness in real-world applications, an additional 12 sets of experimental data were incorporated for training and testing. The research findings indicate that, in comparison to the conventional machine learning method of Backpropagation (BP) neural networks, the developed CNN prediction model in this paper demonstrates a higher coefficient of determination, reaching 0.938, on the test dataset. The mean absolute percentage error is 9.13%, signifying that the proposed prediction model exhibits notable accuracy and universality in predicting the 28-day compressive strength of pervious concrete, regardless of the materials used in its preparation.

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Section: Introductionmentioning

confidence: 99%

The Prediction of Pervious Concrete Compressive Strength Based on a Convolutional Neural Network

Yu,

Zhu,

Xiang

2024

Buildings

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Enhancing Survival Analysis Model Selection through XAI(t) in Healthcare

Berloco,

Marvulli,

Suglia

et al. 2024

Applied Sciences

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Artificial intelligence algorithms have become extensively utilized in survival analysis for high-dimensional, multi-source data. However, due to their complexity, these methods often yield poorly interpretable outcomes, posing challenges in the analysis of several conditions. One of these conditions is obstructive sleep apnea, a sleep disorder characterized by the simultaneous occurrence of comorbidities. Survival analysis provides a potential solution for assessing and categorizing the severity of obstructive sleep apnea, aiding personalized treatment strategies. Given the critical role of time in such scenarios and considering limitations in model interpretability, time-dependent explainable artificial intelligence algorithms have been developed in recent years for direct application to basic Machine Learning models, such as Cox regression and survival random forest. Our work aims to enhance model selection in OSA survival analysis using time-dependent XAI for Machine Learning and Deep Learning models. We developed an end-to-end pipeline, training several survival models and selecting the best performers. Our top models—Cox regression, Cox time, and logistic hazard—achieved good performance, with C-index scores of 0.81, 0.78, and 0.77, and Brier scores of 0.10, 0.12, and 0.11 on the test set. We applied SurvSHAP methods to Cox regression and logistic hazard to investigate their behavior. Although the models showed similar performance, our analysis established that the results of the log hazard model were more reliable and useful in clinical practice compared to those of Cox regression in OSA scenarios.

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Distributed Analytics For Big Data: A Survey

Cited by 2 publications

References 66 publications

The Prediction of Pervious Concrete Compressive Strength Based on a Convolutional Neural Network

The Prediction of Pervious Concrete Compressive Strength Based on a Convolutional Neural Network

Enhancing Survival Analysis Model Selection through XAI(t) in Healthcare

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