scholarly journals Evaluation of the quality of functioning of artificial neural network with logic outputs in the diagnosis of diseases of hepatopancreatoduodenal zone

2017 ◽  
Vol 98 (6) ◽  
pp. 928-932 ◽  
Author(s):  
V A Lazarenko ◽  
A E Antonov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
False Negative ◽  
Activation Function ◽  
Negative Results ◽  
False Negative Results ◽  
Artificial Neural ◽  
Sensitivity Specificity ◽  
Diagnosis Of Diseases

Aim. To develop a set of information methods to improve the quality of neural network diagnosis of diseases of hepatopancreatoduodenal zone. Methods. The study involved 385 patients with peptic ulcer, cholecystitis and pancreatitis undergoing in-patient treatment in medical organizations of the city of Kursk. For data mining internally developed software «System of Intellectual Analysis and Diagnosis of Diseases» was used which is an environment for the creation, adjustment, training and practical clinical application of an artificial neural network, such as a multilayer perceptron with an activation function - hyperbolic tangent. Results. Hyperbolic tangent (activation function) of the output layer’s neuron takes the value OUT ∈ ℝ ∧ OUT ∈ (-1; 1) which requires an interpretation. For logic network gates, for example, presence/absence of a disease, it can be performed by comparison with an arbitrarily assigned threshold yB ∈ (0; 1). In this approach, the values are interpreted as false (if y ≤-yB), undefined if y ∈ (-yB; yB), or true (if y ≥yB). Network operation control includes calculation of sensitivity, specificity, false positive and false negative results, for which the comparison of arrays of pairs of calculated and empirical values is carried out. In case of artificial neural network use for diagnosing diseases of hepatopancreatoduodenal zone, the optimal mode was achieved assigning yB≈0.3 as a threshold of the output neuron activation function. Conclusion. Assessing the quality of the ability of artificial neural network with logic outputs to diagnose hepatopancreatoduodenal zone diseases, as well as its controlled setting, is most effective by evaluation of sensitivity, specificity, frequency of false positive and false negative results at the threshold value yB≈0.3; the demonstrated sensitivity (83-94.7%) and specificity (83-97.8%) levels are comparable to the traditionally used diagnostic methods.

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

scholarly journals P055 THE USE OF ARTIFICIAL NEURAL NETWORK TO PREDICT THE RISK OF INCISIONAL HERNIA AFTER MIDLINE LAPAROTOMY

2021 ◽  
Vol 108 (Supplement_8) ◽  
Author(s):  
Edgard Efren Lozada Hernandez ◽  
Tania Aglae Ramírez del Real ◽  
Dagoberto Armenta Medina ◽  
Jose Francisco Molina Rodriguez ◽  
Juan ramon Varela Reynoso
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Incisional Hernia ◽  
False Negative ◽  
Wound Dehiscence ◽  
Midline Laparotomy ◽  
Significant Difference ◽  
Positive Rate ◽  
Artificial Neural

Abstract Aim “Incisional Hernia (IH) has an incidence of 10-23%, which can increase to 38% in specific risk groups. The objective of this study was developed and validated an artificial neural network (ANN) model for the prediction of IH after midline laparotomy (ML) and this model can be used by surgeons to help judge a patient’s risk for IH.” Material and Methods “A retrospective, single arm, observational cohort trial was conducted from January 2016 to December 2020. Study participants were recruited from patients undergoing ML for elective or urgent surgical indication. Using logistic regression and ANN models, we evaluated surgical treated IH, wound dehiscence, morbidity, readmission, and mortality using the area under the receiver operating characteristic curves, true-positive rate, true-negative rate, false-positive rate, and false-negative rates.” Results “There was no significant difference in the power of the ANN and logistic regression for predicting IH, wound dehiscence, mortality, readmission, and all morbidities after ML. The resulting model consisted of 4 variables: surgical site infection, emergency surgery, previous laparotomy, and BMI(Kg/m2) > 26. The patient with the four positive factors has a 73% risk of developing incisional hernia. The area under the curve was 0.82 (95% IC 0.76-0.87). Conclusions “ANNs perform comparably to logistic regression models in the prediction of IH. ANNs may be a useful tool in risk factor analysis of IH and clinical applications.”

Power Forecasting from Solar Panels Using Artificial Neural Network in UTHM Parit Raja

2021 ◽  
Vol 02 (01) ◽  
Author(s):  
Natasha Munirah Mohd Fahmi ◽  
◽  
Nor Aira Zambri ◽  
Norhafiz Salim ◽  
Sim Sy Yi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Climatic Factors ◽  
Activation Function ◽  
Solar Panels ◽  
Ann Model ◽  
Power Characteristics ◽  
Step Procedure ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper presents a step-by-step procedure for the simulation of photovoltaic modules with numerical values, using MALTAB/Simulink software. The proposed model is developed based on the mathematical model of PV module, which based on PV solar cell employing one-diode equivalent circuit. The output current and power characteristics curves highly depend on some climatic factors such as radiation and temperature, are obtained by simulation of the selected module. The collected data are used in developing Artificial Neural Network (ANN) model. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) are the techniques used to forecast the outputs of the PV. Various types of activation function will be applied such as Linear, Logistic Sigmoid, Hyperbolic Tangent Sigmoid and Gaussian. The simulation results show that the Logistic Sigmoid is the best technique which produce minimal root mean square error for the system.

Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

2019 ◽  
pp. 158-177
Author(s):  
Tamer Emara
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Saving ◽  
Ieee 802.16 ◽  
Feed Forward Neural Network ◽  
Artificial Neural ◽  
Adaptive Power ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

scholarly journals Quality Prediction Modeling of Palm Oil Refining Plant in Malaysia Using Artificial Neural Network Models

2018 ◽  
Vol 7 (3.26) ◽  
pp. 19
Author(s):  
Nurul Sulaiha Sulaiman ◽  
Khairiyah Mohd-Yusof ◽  
Asngari Mohd-Saion
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Palm Oil ◽  
Oil Refinery ◽  
Oil Refining ◽  
Great Loss ◽  
Refining Plant ◽  
Artificial Neural ◽  
Refined Palm Oil

Malaysia is currently one of the biggest producers and exporters of palm oil and palm oil products. In the growth of palm oil industry in Malaysia, quality of the refined oil is a major concern where off-specification products will be rejected thus causing a great loss in profit. In this paper, predictive modeling of refined palm oil quality in one palm oil refining plant in Malaysia is proposed for online quality monitoring purposes. The color of the crude oil, Free Fatty acid (FFA) content, bleaching earth dosage, citric acid dosage, activated carbon dosage, deodorizer pressure and deodorizer temperature were studied in this paper. The industrial palm oil refinery data were used as input and output to the Artificial Neural Network (ANN) model. Various trials were examined for training all three ANN models using number of nodes in the hidden layer varying from 10 to 25. All three models were trained and tested reasonably well to predict FFA content, red and yellow color quality of the refined palm oil efficiently with small error. Therefore, the models can be further implemented in palm oil refinery plant as online prediction system.  

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