A new offline method for extracting I-V characteristic curve for photovoltaic modules using artificial neural networks

Background The independent role of mild autonomous cortisol secretion (ACS) in influencing the cardiovascular event (CVE) occurrence is a topic of interest. We investigated the role of mild ACS in the CVE occurrence in patients with adrenal incidentaloma (AI) by standard statistics and artificial neural networks (ANNs). Methods We analyzed a retrospective record of 518 AI patients. Data regarding cortisol levels after 1 mg dexamethasone suppression (1 mg DST) and the presence of obesity (OB), hypertension (AH), type-2 diabetes (T2DM), dyslipidemia (DL), familial CVE history, smoking habit and CVE were collected. Results The receiver-operating characteristic curve analysis suggested that 1 mg DST, at a cut-off of 1.8 µg/dL, had the best accuracy for detecting patients with increased CVE risk. In patients with 1 mg-DST ≥1.8 µg/dL (DST+, n = 223), age and prevalence of AH, T2DM, DL and CVE (66 years, 74.5, 25.9, 41.4 and 26.8% respectively) were higher than that of patients with 1 mg-DST ≤1.8 µg/dL (61.9 years, 60.7, 18.5, 32.9 and 10%, respectively, P < 0.05 for all). The CVE were associated with DST+ (OR: 2.46, 95% CI: 1.5–4.1, P = 0.01), regardless of T2DM, AH, DL, smoking habit, gender, observation period and age. The presence of at least two among AH, T2DM, DL and OB plus DST+ had 61.1% sensitivity in detecting patients with CVE. By using the variables selected by ANNs (familial CVE history, age, T2DM, AH, DL and DST+) 78.7% sensitivity was reached. Conclusions Cortisol after 1 mg-DST is independently associated with the CVE occurrence. The ANNs might help for assessing the CVE risk in AI patients.

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Modelling photovoltaic modules by a numerical method and artificial neural networks

African Journal of Science Technology Innovation and Development ◽

10.1080/20421338.2015.1118869 ◽

2016 ◽

Vol 8 (4) ◽

pp. 331-339 ◽

Cited By ~ 1

Author(s):

Zahra Meziani ◽

Zohir Dibi

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Numerical Method ◽

Photovoltaic Modules ◽

Artificial Neural

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Detecting Dengue Fever in Children Using A Combined Scheme Compared with Individual Algorithms: An APP Development and Usability Study

10.21203/rs.3.rs-551659/v1 ◽

2021 ◽

Author(s):

Julie Chi Chow ◽

CHIEN TSAI WEI ◽

Willy Chou

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Prediction Accuracy ◽

Early Stage ◽

Statistical Significance ◽

Characteristic Curve ◽

Nearest Neighbors ◽

Public Health Issue ◽

K Nearest Neighbors ◽

Artificial Neural

Abstract BackgroundDengue fever (DF) is an important public health issue in Asia. However, the disease is extremely hard to detect using traditional dichotomous (i.e., absent vs. present) evaluations of symptoms. Convolution neural network (CNN) and artificial neural networks(ANN) can improve prediction accuracy on account of its usage of a large number of parameters for modeling. A hypothesis using a combined scheme of algorithms, including convolutional neural networks(CNN), artificial neural networks(ANN), K-nearest Neighbors Algorithm(KNN), and logis-tical regression(LR), was made to improve the prediction DF accuracy for children. MethodsWe extracted 19 feature variables of DF-related symptoms from 177 pediatric patients (69 diagnosed with DF). A 11-variables were eligible by observing the statistical significance in predicting DF risk. The prediction accuracy was based on two training (80%) and testing (20%) sets on model accuracy of the area under the receiver operating characteristic curve (AUC) greater than 0.80 and 0.70, respectively, for discriminating DF+ and DF− in the two sets. Two scenarios of the combined scheme and individual algorithms were compared using the training set to predict the testing set. ResultsWe observed that (i) k-nearest neighbors algorithm has poorer AUC(<0.50), (ii)LR has relatively higher AUC(=0.70), and (ii) the three alternatives have almost equal AUC(=0.68), but smaller than the individual algorithms of NaiveBayes, Logistic regression in raw data and NaiveBayes in normalized data. ConclusionAn LR-based APP was designed to detect DF in children. The 11-item model is suggested to develop the APP for helping patients, family members, and clinicians discriminate DF from other febrile illnesses at an early stage.

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Microseismic Signal Classification Based on Artificial Neural Networks

Shock and Vibration ◽

10.1155/2021/6697948 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Chong-wei Xin ◽

Fu-xing Jiang ◽

Guo-dong Jin

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Operating Characteristic ◽

Characteristic Curve ◽

Activation Function ◽

Data Set ◽

Waveform Data ◽

Artificial Neural ◽

Microseismic Signal

The classification of multichannel microseismic waveform is essential for real-time monitoring and hazard prediction. The accuracy and efficiency could not be guaranteed by manual identification. Thus, based on 37310 waveform data of Junde Coal Mine, eight features of statistics, spectrum, and waveform were extracted to generate a complete data set. An automatic classification algorithm based on artificial neural networks (ANNs) has been proposed. The model presented an excellent performance in identifying three preclassified signals in the test set. Operated with two hidden layers and the Logistic activation function, the multiclass area under the receiver operating characteristic curve (AUC) reached 98.6%.

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