Analysis on the prediction of central line-associated bloodstream infections (CLABSI) using deep neural network classification

Yuvaraj, N.; Raja, R. Arshath; Kousik, N.V.; Johri, Prashant; Diván, Mario José

doi:10.1016/b978-0-12-820604-1.00016-9

Cited by 15 publications

(8 citation statements)

References 19 publications

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“…ANN has an architecture consisting of layers of neurons with nodes connected. Interactions and relationships between each node in each layer have different weight and bias values which can function as regression or classification [13]. ANN performs learning through forward propagation to compute input values from data into initial node neurons to produce the expected output node.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Enhancing the accuracy of low-cost thermocouple devices through deep-wavelet neural network calibration

Julian,

Wahyuni,

Dewantara

et al. 2024

IJECE

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Data collection using thermocouple sensors in low-cost data acquisition is prone to noise interference, which could reduce the data quality. Noise sources such as cold junction compensators, electromagnetic interference, and Johnson noise can significantly affect the reliability and accuracy of conventional measurements. This study aims to improve the quality of thermocouple sensor readings on low-cost data acquisition using calibration method based on deep learning and the denoising process using a wavelet transform. This taken approach successfully increase the accuracy value of 97.67% with a mean absolute error (MAE) of 0.2. The precision also increases of 262.7% as indicated by the result of signal-to-noise ratio (SNR) with a value of 105.29 dB. Comparative analysis was carried out against National Instruments® device and it was found that deep-wavelet method had a lower and higher of MAE and SNRdB values of 16.67% and 0.8% respectively. This study shows that the denoising-calibration method with deep-wavelet can improve the accuracy and reliability of data from low-cost thermocouple devices.

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Section: Artificial Neural Networkmentioning

confidence: 99%

“…The global thresholding value will be used for the denoising process for each wavelet coefficient. The SNR equation measures the wavelet transform's denoising performance against the equation's actual value (13). The uncertainty value is calculated using the mean deviation in (12) to find out the reliability data.…”

Section: Wavelet Transformmentioning

confidence: 99%

Enhancing the accuracy of low-cost thermocouple devices through deep-wavelet neural network calibration

Julian,

Wahyuni,

Dewantara

et al. 2024

IJECE

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“…The DNN classifier combines the feature extraction with classification into learning and allows for decision making. Figure 9 shows the DNN structure [41]. There are several neuron layers in the DNN classifier.…”

Section: Deep Neural Network (Dnn)mentioning

confidence: 99%

Machine Learning Based on Resampling Approaches and Deep Reinforcement Learning for Credit Card Fraud Detection Systems

et al. 2021

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The problem of imbalanced datasets is a significant concern when creating reliable credit card fraud (CCF) detection systems. In this work, we study and evaluate recent advances in machine learning (ML) algorithms and deep reinforcement learning (DRL) used for CCF detection systems, including fraud and non-fraud labels. Based on two resampling approaches, SMOTE and ADASYN are used to resample the imbalanced CCF dataset. ML algorithms are, then, applied to this balanced dataset to establish CCF detection systems. Next, DRL is employed to create detection systems based on the imbalanced CCF dataset. The diverse classification metrics are indicated to thoroughly evaluate the performance of these ML and DRL models. Through empirical experiments, we identify the reliable degree of ML models based on two resampling approaches and DRL models for CCF detection. When SMOTE and ADASYN are used to resampling original CCF datasets before training/test split, the ML models show very high outcomes of above 99% accuracy. However, when these techniques are employed to resample for only the training CCF datasets, these ML models show lower results, particularly in terms of logistic regression with 1.81% precision and 3.55% F1 score for using ADASYN. Our work reveals the DRL model is ineffective and achieves low performance, with only 34.8% accuracy.

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“…, 2016). The mathematical formulation of the activation function (ReLU) is expressed in Eqn (1) (Yuvaraj et al. , 2020).…”

Section: Deep and Machine Learning Techniquesmentioning

confidence: 99%

“…This is called the rectified linear unit (ReLU) (Goodfellow et al, 2016). The mathematical formulation of the activation function (ReLU) is expressed in Eqn (1) (Yuvaraj et al, 2020). The ReLUs are rapidly augmented since the derivative is either 0 or a positive constant value through the domain, which transforms the gradient direction far more beneficial for the prediction than it would be with activation functions with nonvanishing and higher-order derivatives ðxÞ is zero when xis less than zero and RðxÞ is equal to x when x is above or equal to zero as shown in Eqn (3).…”

Section: Deep and Machine Learning Approachesmentioning

confidence: 99%

Deep and machine learning approaches for forecasting the residual value of heavy construction equipment: a management decision support model

Alshboul

Shehadeh

Al-Kasasbeh

et al. 2021

ECAM

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PurposeHeavy equipment residual value forecasting is dynamic as it relies on the age, type, brand and model of the equipment, ranking condition, place of sale, operating hours and other macroeconomic gauges. The main objective of this study is to predict the residual value of the main types of heavy construction equipment. The residual value of heavy construction equipment is predicted via deep learning (DL) and machine learning (ML) approaches.Design/methodology/approachBased on deep and machine learning regression network integrated with data mining, random forest (RF), decision tree (DT), deep neural network (DNN) and linear regression (LR)-based modeling decision support models are developed. This research aims to forecast the residual value for different types of heavy construction equipment. A comprehensive investigation of publicly accessible auction data related to various types and categories of construction equipment was utilized to generate the model's training and testing datasets. In total, four performance metrics (i.e. the mean absolute error (MAE), mean squared error (MSE), the mean absolute percentage error (MAPE) and coefficient of determination (R2)) were used to measure and compare the developed algorithms' accuracy.FindingsThe developed algorithm's efficiency has been demonstrated by comparing the deep and machine learning predictions with real residual value. The accuracy of the results obtained by different proposed modeling techniques was comparable based on the performance evaluation metrics. DT shows the highest accuracy of 0.9111 versus RF with an accuracy of 0.8123, followed by DNN with an accuracy of 0.7755 and the linear regression with an accuracy of 0.5967.Originality/valueThe proposed novel model is designed as a supportive tool for construction project managers for equipment selling, purchasing, overhauling, repairing, disposing and replacing decisions.

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Analysis on the prediction of central line-associated bloodstream infections (CLABSI) using deep neural network classification

Cited by 15 publications

References 19 publications

Enhancing the accuracy of low-cost thermocouple devices through deep-wavelet neural network calibration

Enhancing the accuracy of low-cost thermocouple devices through deep-wavelet neural network calibration

Machine Learning Based on Resampling Approaches and Deep Reinforcement Learning for Credit Card Fraud Detection Systems

Deep and machine learning approaches for forecasting the residual value of heavy construction equipment: a management decision support model

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