Validation Of Artificial Neural Network Research Articles

Experimental Validation of Artificial Neural Network Based Road Condition Classifier and its Complementation

Experimental Validation of Artificial Neural Network Based Road Condition Classifier and its Complementation

Development and Validation of Artificial Neural Networks for Survival Prediction Model for Patients with Spontaneous Hepatocellular Carcinoma Rupture After Transcatheter Arterial Embolization.

BackgroundSpontaneous rupture bleeding is a fatal hepatocellular carcinoma (HCC) complication and a significant determinant of survival outcomes. This study aimed to develop and validate a novel artificial neural network (ANN)-based survival prediction model for patients with spontaneous HCC rupture after transcatheter arterial embolization (TAE).MethodsPatients with spontaneous HCC rupture bleeding who underwent TAE at our hospital between January 2010 and December 2018 were included in our study. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to screen clinical variables related to prognosis. We incorporated the above clinical variables identified by LASSO Cox regression into the ANNs model. Multilayer perceptron ANNs were used to develop the 1-year overall survival (OS) prediction model for patients with spontaneous HCC ruptured bleeding in the training set. The area under the receiver operating characteristic curve and decision curve analysis were used to compare the predictive capability of the ANNs model with that of existing conventional prediction models.ResultsThe median survival time for the whole set was 11.8 months, and the 1-year OS rate was 47.5%. LASSO Cox regression revealed that sex, extrahepatic metastasis, macroscopic vascular invasion, tumor number, hepatitis B surface antigen, hepatitis B e antigen, tumor size, alpha-fetoprotein, fibrinogen, direct bilirubin, red blood cell, and γ-glutamyltransferase were risk factors for OS. An ANNs model with 12 input nodes, seven hidden nodes, and two corresponding prognostic outcomes was constructed. In the training set and the validation set, AUCs for the ability of the ANNs model to predict the 1-year OS of patients with spontaneous HCC rupture bleeding were 0.923 (95% CI, 0.890–0.956) and 0.930 (95% CI, 0.875–0.985), respectively, which were higher than that of the existing conventional models (all P < 0.0001).ConclusionThe ANNs model that we established has better survival prediction performance.

인공신경망 모형을 이용한 하천 수위예측에 관한 연구

In this study, artificial neural network model was constructed in order to predict the water level. The target stream is the main stream between Yongdam Dam and Daecheong Dam in the Geum river Basin, and as input data at the upstream, the observation water level at the Sutong and the Hotan observatory and the water level at the Songcheon observatory, which is a tributary, are considered as input data. As an output value, an artificial neural network model was constructed to predict the water level of the Okcheon station at the downstream with 3 hours and 6 hours lead time. The artificial neural network model was constructed around a single hidden layer, and the sensitivity to the prediction accuracy of three learning variables was analyzed: Epoch number, Batch size, and Learning rate. For training, testing, and validation of artificial neural networks, learning was conducted using observed water level data for 13 years from 2000 to 2012, and water level data for two years from 2013 to 2014. The optimal model was selected through the used test. In addition, water level prediction from 2015 to 2016 was performed using the selected optimal model.

Design and validation of an artificial neural network based on analog circuits

This paper focuses on the design and validation of an analog artificial neural network. Basic building blocks of the analog ANN have been constructed in UMC 90 nm device technology. Performance metrics of the building blocks have been demonstrated through circuit simulations. The weights of the ANN have been estimated through an automated back-propagation algorithm, which is running circuit simulations during weight optimization. Two case studies, the operation an XOR logic gate and a full adder circuit have been captured using the proposed analog ANN. Monte Carlo analysis of the XOR gate reveals that the analog ANN operates with an accuracy of 99.85%.

Development and Validation of Artificial Neural Network-Based Tools for Forecasting of Power System Inertia With Wind Farms Penetration

Increased penetration of power electronics interfaced renewable energy sources-based generation, for instance, wind farms, that displaces much of the conventional synchronous generation, has a profound effect on the inertia of modern/future power system networks. This article presents the development and validation of artificial neural network (ANN)-based tools, utilizing the power system variables measured by phasor measurement units through wide-area measurements systems, for estimation/forecasting of power system inertia with high penetration of wind farms. The development stage involves the correlation analysis to identify the best power system variables that can be nominated as inputs, and the training of the proposed inertia forecasting tools with the best-nominated inputs that are highly correlated with the power system inertia. Whereas, in the validation stage, the functionality of the trained ANN-based inertia forecasting tools have been validated using the hardware-in-the-loop testing facility developed at the University of Manchester. The development and validation procedures of the proposed inertia forecasting tools have been demonstrated on the IEEE 9-bus modified test system. The validation results revealed the effectiveness of the proposed inertia forecasting tools in estimating the inertia of modern/future power system networks with high penetration of wind farms.

Validation of artificial neural networks to model the acoustic behaviour of induction motors

In the last decade, the sound quality of electric induction motors is a hot topic in the research field. Specially, due to its high number of applications, the population is exposed to physical and psychological discomfort caused by the noise emission. Therefore, it is necessary to minimise its psychological impact on the population. In this way, the main goal of this work is to evaluate the use of multitask artificial neural networks as a modelling technique for simultaneously predicting psychoacoustic parameters of induction motors. Several inputs are used, such as, the electrical magnitudes of the motor power signal and the number of poles, instead of separating the noise of the electric motor from the environmental noise. Two different kind of artificial neural networks are proposed to evaluate the acoustic quality of induction motors, by using the equivalent sound pressure, the loudness, the roughness and the sharpness as outputs. Concretely, two different topologies have been considered: simple models and more complex models. The former are more interpretable, while the later lead to higher accuracy at the cost of hiding the cause-effect relationship. Focusing on the simple interpretable models, product unit neural networks achieved the best results: 38.77 for MSE and 13.11 for SEP. The main benefit of this product unit model is its simplicity, since only 10 inputs variables are used, outlining the effective transfer mechanism of multitask artificial neural networks to extract common features of multiple tasks. Finally, a deep analysis of the acoustic quality of induction motors in done using the best product unit neural networks.

Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation.

In 2014, we reported a model for donor-recipient (D-R) matching in liver transplantation (LT) based on artificial neural networks (ANNs) from a Spanish multicenter study (Model for Allocation of Donor and Recipient in España [MADR-E]). The aim is to test the ANN-based methodology in a different European health care system in order to validate it. An ANN model was designed using a cohort of patients from King's College Hospital (KCH; n = 822). The ANN was trained and tested using KCH pairs for both 3- and 12-month survival models. End points were probability of graft survival (correct classification rate [CCR]) and nonsurvival (minimum sensitivity [MS]). The final model is a rule-based system for facilitating the decision about the most appropriate D-R matching. Models designed for KCH had excellent prediction capabilities for both 3 months (CCR-area under the curve [AUC] = 0.94; MS-AUC = 0.94) and 12 months (CCR-AUC = 0.78; MS-AUC = 0.82), almost 15% higher than the best obtained by other known scores such as Model for End-Stage Liver Disease and balance of risk. Moreover, these results improve the previously reported ones in the multicentric MADR-E database. In conclusion, the use of ANN for D-R matching in LT in other health care systems achieved excellent prediction capabilities supporting the validation of these tools. It should be considered as the most advanced, objective, and useful tool to date for the management of waiting lists. Liver Transplantation 24 192-203 2018 AASLD.

Risk prediction of knee osteoarthritis using artificial neural network

Purpose: Knee osteoarthritis (OA) is the most common joint disease of adults worldwide. Since there has been no effective therapy, treatments of advanced radiographic knee OA are limited. Studies have shown that the early diagnosis and treatment of OA could help preventing aggravation of symptoms. Late diagnosis results in the socio-economic burden of illness associated with OA. Therefore, clinicians face a significant challenge of identifying patients who are at high risk of radiographic knee OA in a timely and appropriate way. This study aimed at the first development and validation of artificial neural network (ANN) model for radiographic knee OA risk prediction. Few reports have investigated the ability of ANN for knee OA risk prediction in a clinical manner. Logistic regression (LR), which is the gold standard method for analyzing binary medical data, is also used to compare its performance with that of ANN. Methods: ANN, one of the widely used approaches in machine learning, is an area of artificial intelligence technology and a mathematical system which mimic biological neural networks. The networks can be trained to recognize underlying patterns of diseases. ANN has been successfully used in medical decision support system. The 5th Korea National Health and Nutrition Examination Surveys (KNHANES V-1) data were used to develop ANN and LR models for radiographic knee OA. A logistic regression analysis was used to determine predictors for the models. The dataset were separated randomly into two independent groups, training and internal validation groups. The training group, comprised of two thirds (1777 participants) of the entire dataset, was used to construct the ANN and LR models. The internal validation group, comprised of one third (888 participants) of the entire dataset, was used to assess the ability to predict radiographic knee OA. In the KNHANES V-1, bilateral antero-posterior, lateral, and weight-bearing antero-posterior plain radiographs of knees were taken. Radiographic changes relating to OA were assessed using the Kellgren/Lawrence (KL) grade. The radiographic images were graded by trained two radiologists with concordant grades accepted. We defined radiographic knee OA as having KL grade ≥2 in one or both knees. Area under the curve (AUC) of the receiver operating characteristic (ROC), accuracy, sensitivity, and specificity were calculated to compare the performance of the two prediction models. Results: The ANN and LR were built using the independent predictors including sex, age, body mass index, educational status, hypertension, moderate physical activity, and knee pain. In the internal validation, ANN and LR predicted radiographic knee OA risk with AUC of 0.81 and 0.76, accuracy of 73.6% and 68.0%, sensitivity of 73.2% and 73.8%, and specificity of 73.9% and 65.1%, respectively. Figure 1 shows ROC curves of ANN and LR for radiographic knee OA in the internal validation group. ANN had significantly better AUC than LR. Conclusions: To our knowledge, we were the first to develop an ANN model for radiographic knee OA risk prediction using large population-based data. The ANN model might be a cost-effective screening tool identifying patients with untreated knee OA. These patients can then be received further evaluation such as knee radiograph and physical examination. The selected predictors including sex, age, body mass index, educational status, hypertension, moderate physical activity, and knee pain can be self-assessed or easily identified by the public health center. Therefore, the ANN can be easily used and might contribute to the advancement of clinical decision tools. Further studies should be targeted at constructing an extended prediction model for progressive knee OA through the collection of prospective data.

ANN in Hardware with Floating Point and Activation Function Using Hybrid Methods

Artificial neural networks are bio-inspired models used mainly in the problems solution with nonlinear behavior. Reconfigurable devices (FPGA) are widely employed in the implementation of artificial neural networks. The contributions of this work is in the implementation of a Multilayer ANN with four neurons, one hidden layer and hyperbolic tangent for activation function. A nonlinear function approximation performed for system validation of artificial neural network. This paper makes analysis of nine scenarios with several methods to implementation of the activation function in ANN. Two hybrid methods are used in the approximations related to PWL method, Combinational and RALUT. Results are compared between distinct scenarios and the activation function with recent literature. The results were examined by performance, FPGA area and errors absolute and relative. Activation function has achieved the best average error in 10-4 and the ANN in 10-3. ANN system is suitable for target application and with portability to others platforms.

Acoustic Performance of Exhaust Muffler Based Genetic Algorithms and Artificial Neural Network

The noise level wa s one of the important indicators as a measure of the quality and performance of the diesel engine.Exhaust noise in diesel engines machine accounted for an important proportion of installed performance exhaust muffler and it w as an effective way to control exhaust noise. This article using orthogonal test program for the muffler structure parameters as input to the sound pressure level and diesel fuel each output artificial neural network (BP network) learning sample. Matlab artificial neural network toolbox to complete the training of the network, and better noise performance and fuel consumption rate performance muffler internal structure parameters combination was obtained through genetic algorithm gifted collaborative validation of artificial neural networks and genetic algorithms to optimize application exhaust muffler design is entirely feasible.

Noninvertibility in neural networks

We present and discuss an inherent shortcoming of neural networks used as discrete-time models in system identification, time series processing, and prediction. Trajectories of nonlinear ordinary differential equations (ODEs) can, under reasonable assumptions, be integrated uniquely backward in time. Discrete-time neural network mappings derived from time series, on the other hand, can give rise to multiple trajectories when followed backward in time: they are in principle noninvertible. This fundamental difference can lead to model predictions that are not only slightly quantitatively different, but qualitatively inconsistent with continuous time series. We discuss how noninvertibility arises, present key analytical concepts and some of its phenomenology. Using two illustrative examples (one experimental and one computational), we demonstrate when noninvertibility becomes an important factor in the validity of artificial neural network (ANN) predictions, and show some of the overall complexity of the predicted pathological dynamical behavior. These concepts can be used to probe the validity of ANN time series models, as well as provide guidelines for the acquisition of additional training data.

Predicting fish distribution in a mesotrophic lake by hydroacoustic survey and artificial neural networks

The present work describes the development and validation of Artificial Neural Networks (ANN) by comparison with classical and more advanced parametric and nonparametric statistical modeling methods such as Multiple Regression (MR), Generalized Additive Models (GAM), and Alternating Conditional Expectations (ACE) to esti‐mate spatial distribution of fish in a mesotrophic reservoir. The modeling approaches were developed and tested using 60 hydroacoustic transects covering the whole lake. Each transect was divided into 100‐m‐long sections, constituting a total of 732 sampling units. For each of them, the relationships between topology, chemical characteristics, and fish abundance were studied. The models had six independent topological (i.e., depth, distance from the bank, slope, and stratum) and chemical (i.e., temperature and dissolved oxygen) variables and one dependent output variable (fish density, FD). The data matrix was divided into two parts. The first contained units where FD was nonnil (i.e., 399 sampling units), and the second contained only cases without fish (i.e., 333 sampling units). Model training and testing procedures were run on the first submatrix after log(FD + 1) transformation. As linear MR results were not satisfactory (r2 = 0.42 in the training set, and r2 = 0.51 in the testing set) compared with ANN (r2 = 0.81 in the training set, and r2 = 0.77 in the testing set), we tried nonlinear transformations of the variables such as logarithmic, lowess (for the GAM), and an optimal nonlinear transformation using the SAS Transreg procedure (for the ACE model), but the determination coefficients remained clearly lower than those obtained using ANN (r2 = 0.60 in the training set for ACE, and r2 = 0.66 in the training set for GAM). The results of a second test on the nil submatrix stressed that, compared with other statistical techniques, ANN and, to a certain extent, GAM models were able to clearly define the potential FDs in samples where no fish were actually found. The model showed, on the basis of the topological and chemical variables taken into account, that the predicted potential FDs in the surface stratum are higher than in the underlying stratum. Finally, on the basis of the sensitivity analyses performed on the ANN and GAM results, we established relationships between FDs and the six environmental variables. Our results exhibit a clear summer habitat preferendum, the fish (predominantly roach) being located mainly in the surface stratum, in the warm shallow littoral areas. These observations led us to discuss the ecological significance of such a fish distribution, which may be due to a trade‐off between feeding, predation avoidance, and endogenous fish requirements.

Prospective validation of artificial neural network trained to identify acute myocardial infarction: W.G. Baxt, J. Skora. Department of Emergency Medicine, Hospital University of Pennsylvania, Philadelphia, PA. Lancet 1996;347:12–5

Prospective validation of artificial neural network trained to identify acute myocardial infarction: W.G. Baxt, J. Skora. Department of Emergency Medicine, Hospital University of Pennsylvania, Philadelphia, PA. Lancet 1996;347:12–5

An artificial neural network was more accurate for identifying acute myocardial infarction

DiagnosisMay 1, 1996An artificial neural network was more accurate for identifying acute myocardial infarctionJeremy C. Wyatt, MDJeremy C. Wyatt, MDImperial Cancer Research Fund Laboratories, London, England, UK (J.C.W.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/ACPJC-1996-124-3-073 SectionsAboutFull Text ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinkedInRedditEmail Source CitationBaxt WG, Skora J. Prospective validation of artificial neural network trained to identify acute myocardial infarction. Lancet. 1996 Jan 6;347:12-5.References1 Baxt WG. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann Intern Med. 1991;115:843-8. Google Scholar2 Kennedy RL, Harrison RF, Marshall SJ, Hardisty CA. Analysis of clinical and electrocardiographic data from patients with acute chest pain using a neurocomputer. Q J Med. 1991;80:788-9. Google Scholar3 Wyatt JC. Nervous about artificial neural networks? Lancet. 1995;346:1175-7. Google Scholar4 Wyatt JC, Altman DG. Prognostic models: clinically useful, or quickly forgotten? BMJ. 1995;311:1539-41. Google Scholar Author, Article, and Disclosure InformationAffiliations: Imperial Cancer Research Fund Laboratories, London, England, UK (J.C.W.) PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails May 1, 1996Volume 124, Issue 3Page: 73KeywordsAnginaChartsComputersElectrocardiographyEnzymesIschemiaMyocardial infarctionNeural networksSpecificityUnstable angina ePublished: 9 March 2020 Issue Published: May 1, 1996 Copyright & PermissionsCopyright © 1996 by American College of Physicians. All Rights Reserved.Loading ...

Prospective validation of artificial neural network trained to identify acute myocardial infarction

SummaryBackground Artificial neural networks apply non-linear statistics to pattern recognition problems. One such problem is acute myocardial infarction (AMI), a diagnosis which, in a patient presenting as an emergency, can be difficult to confirm. We report here a prospective comparison of the diagnostic accuracy of a network and that of physicians, on the same patients with suspected AMI.Methods Emergency department physicians who evaluated 1070 patients 18 years or older presenting to the emergency department of a teaching hospital in California, USA with anterior chest pain indicated whether they thought these patients had sustained a myocardial infarction. The network analysed the patient data collected by the physicians during their evaluations and also generated a diagnosis.Findings The physicians had a diagnostic sensitivity and specificity for myocardial infarction of 73·3% (95% confidence interval 63·3-83·3%) and 81·1% (78·7-83·5%), respectively, while the network had a diagnostic sensitivity and specificity of 96·0% (91·2-100%) and 96·0% (94·8-97·2%), respectively. Only 7% of patients had had an AMI, a low frequency but typical for anterior chest pain.Interpretation The application of non-linear neural computational analysis via an artificial neural network to the clinical diagnosis of myocardial infarction appears to have significant potential.

Colored information from a black box?: Validation and evaluation of neural networks

Artificial neural networks are used in many fields and have proven their worth. Until recently the applications in analytical chemistry are scarce, but now many groups are working in this field. One of the reasons for the hesitation in accepting artificial neural networks is the weak scientific understanding of the system. In fact it works as a black box. However, the applications in analytical chemistry so far are so encouraging that more research will be conducted. One very important aspect will be the critical validation of artificial neural networks as long as no sound theoretical background is available. Some criteria will be discussed and some practical examples will be given.