AnatomyDiseasesChemicals and DrugsAnalytical, Diagnostic and Therapeutic Techniques and EquipmentPhenomena and ProcessesDisciplines and OccupationsHumanitiesInformation ScienceHealth Care
Neural Networks (Computer)AlgorithmsArtificial IntelligencePattern Recognition, AutomatedFuzzy LogicDiagnosis, Computer-AssistedComputer SimulationROC CurveReproducibility of ResultsNerve NetSensitivity and SpecificitySoftwareQuantitative Structure-Activity RelationshipComputational BiologyDiscriminant AnalysisModels, BiologicalSupport Vector MachinesNeuromuscular Junction DiseasesSignal Processing, Computer-AssistedSequence Analysis, ProteinElectronic NoseRemote Sensing TechnologyImage Interpretation, Computer-AssistedBack PainLow Back PainModels, NeurologicalRadiographic Image Interpretation, Computer-AssistedDatabases, FactualImage Processing, Computer-AssistedDecision TreesGene Regulatory NetworksModels, StatisticalWavelet AnalysisPredictive Value of TestsPrincipal Component AnalysisModels, TheoreticalPheniramineAccelerationBackNonlinear DynamicsExpert SystemsCluster Analysis
ANNsMultilayerSupervised neural network basedLevenberg-MarquardtConvolutionalPredictionGradient DescentJETNETSigmoidBPNNRadialFeed forwardRecurrentFeedforwardGapsRunoffMathematicsPythonNonlinearModelEfficiencyExperimentalTemperatureConcentrationsStack ExchangeStatisticalIntelligenceModelsFunctionsSystemSelf-orgTrainWorkTechniquesExposureResultsAssessmentApplicationMachineDesignPaperInputInputsPatternProcessDeepStructureTimePhysicalApproachSpecificallyProblemStudy
ANNs6
  • Article: Modelling runoff in a river basin, India: an integration for developing un-gauged catchment Journal: International Journal of Hydrology Science and Technology (IJHST) 2020 Vol.10 No.3 pp.248 - 266 Abstract: Stage-runoff model based on nonlinear multilayer regression (NLMR) and artificial neural networks (ANNs) are developed in the present study. (inderscience.com)
  • Stage-runoff model based on nonlinear multilayer regression (NLMR) and artificial neural networks (ANNs) are developed in the present study. (inderscience.com)
  • The training process of deep Artificial Neural Networks (ANNs) is based on the backpropagation algorithm. (mldawn.com)
  • Artificial neural networks (ANNs) are discussed in Chapter 8. (ascelibrary.org)
  • Beginning with a discussion of where they originated, the chapter presents basics of ANNs, unconstrained optimization techniques, perceptron, types of activation functions, types of ANNs, learning modes and learning, back propagation (BP) algorithm, feedback systems, and problems and limitations. (ascelibrary.org)
  • Four Artificial Neural Networks (ANNs) models were developed to be applied to internal micro-channels machined in PMMA using a Nd:YVO4 laser. (dcu.ie)
Multilayer6
  • The Artificial Neural Network-Multilayer Perceptron (ANN-MLP) was employed to forecast the upcoming 15 years rainfall across India. (nature.com)
  • The basic ingredients are the multilayer perceptron back-propagation algorithm and the topological self-organizing map. (lu.se)
  • Multilayer Perceptron Network was designed for forecasting. (edu.ng)
  • Objective of this paper is to resolve this problem through Multilayer feed-forward back-propagation algorithm using two hidden layer. (ijsce.org)
  • Multilayer Perceptron neural networks (MLP) are a popular type of ANN due totheir simplicity and efficiency. (doionline.org)
  • In particular feed-forward multilayer perceptron ( MLP ) networks are widely used due to their simplicity and excellent performance. (lu.se)
Supervised neural network based1
  • We will discuss the architecture of the supervised neural network based on the algorithm named Robust Error Back Propagation, trained and optimized so to maximize the number of True Positive ROIs, i.e., the actual tumor regions. (springer.com)
Levenberg-Marquardt3
  • The back propagation training technique and Levenberg-Marquardt algorithm was employed to develop ANN model. (techno-press.org)
  • This paper presents the application of back-propagation neural networks and the testing of the Levenberg-Marquardt, Quasi-Newton and Bayesian Regularization algorithms. (aston.ac.uk)
  • For this purpose the Levenberg-Marquardt back-propagation algorithm was used. (uitm.edu.my)
Convolutional1
  • To do so, we use convolutional neural networks - a way of structuring a deep learning system inspired by the architecture of the human brain. (fb.com)
Prediction6
  • Prediction of EMG signals of trunk muscles in manual lifting using a neural network model. (cdc.gov)
  • An EMG (electromyography) signal prediction model is built using artificial neural network. (cdc.gov)
  • A novel structure of feedforward neural network is proposed in This work to obtain better accuracy of prediction. (cdc.gov)
  • We developed a new algorithm for subcellular location prediction that uses residue exposure signatures. (biomedcentral.com)
  • Feed-forward neural networks are the most widely adopted methodology for the prediction and forecasting of water quality and quantity variables. (aston.ac.uk)
  • The neural networks were statistically assessed for their goodness of prediction of outflow water quality with respect to ammonia-nitrogen, nitrate-nitrogen, and ortho-phosphate-phosphorus by numerical computation of the mean absolute error, root square mean error, mean absolute relative error and the coefficient of correlation for the prediction versus measured dataset. (aston.ac.uk)
Gradient Descent3
  • BPNN are developed using extended gradient descent-based delta-learning algorithm and radial basis function network (RBFN) are developed using Gaussian potential functions. (inderscience.com)
  • According to Wikipedia gradient descent (also often called steepest descent ) is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. (mql5.com)
  • The gradient descent is a very important algorithm in machine learning as it helps us find the parameters for the best model for our dataset. (mql5.com)
JETNET1
  • A F77 package of adaptive artificial neural network algorithms, JETNET 2.0, is presented. (lu.se)
Sigmoid2
  • How do I implement softmax forward propagation and backpropagation to replace sigmoid in a neural network? (stackexchange.com)
  • A standard three layer feed-forward neural network with tan-sigmoid (tansig) activation functions in the hidden layer and purelin at the output layer is used for this work. (uitm.edu.my)
BPNN4
  • The results confirmed that back propagation neural network (BPNN) model is an important alternative to regression models. (inderscience.com)
  • An ergonomic reliability model based on an improved backpropagation neural network (BPNN) and human cognition reliability (HCR) is proposed for predicting and evaluating operation flows according to medical equipment VDTs. (techscience.com)
  • Firstly, a small data sample can be used to train BPNN to generate a network that can ensure suitable accuracy. (techscience.com)
  • To prevent the model from falling into local optimal solutions, the bat algorithm is introduced to improve the BPNN. (techscience.com)
Radial1
  • Radial basis function (RBF) neural networks are discussed in Chapter 9. (ascelibrary.org)
Feed forward5
  • I have just gotten myself into Artificial Neural Networks and I have covered the Multi-Layer Perceptron, feed-forward and Back-propagation algorithm. (stackexchange.com)
  • These models were varied in terms of the selection and the quantity of training data set and constructed using a multi-layered, feed-forward structure with a the back-propagation algorithm. (dcu.ie)
  • Feed forward Network was used to improve the efficiency of the back propagation. (edu.ng)
  • The back-propagation and feed-forward neural network models performed optimally as pollutant removal predictors with regard to these two sustainable technologies. (aston.ac.uk)
  • Using an input layer consisting of pH, temperature, electric conductivity, turbidity, total dissolved solids, dissolved oxygen, and redox potential, the back-propagation feed forward neural network models performed optimally as nutrient predictors with regard to sustainable (urban) drainage systems such as permeable pavements. (aston.ac.uk)
Recurrent1
  • Networks with cycles are commonly called recurrent. (wikipedia.org)
Feedforward1
  • Networks such as the previous one are commonly called feedforward, because their graph is a directed acyclic graph. (wikipedia.org)
Gaps2
  • This paper reviews and discusses the applications of GA with ANN and the future scope of applying GA for the training of ANN based machinery fault diagnosis in order to fill the gaps of traditional Back Propagation algorithm (BP). (doionline.org)
  • FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies. (cdc.gov)
Runoff1
  • Artificial intelligence techniques such as neural networks are modelling tools that can be applied to analyse urban runoff water quality issues. (aston.ac.uk)
Mathematics1
  • A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language. (onlinebooksreview.com)
Python2
Nonlinear1
  • Artificial Neural Networks ( ANN ) constitute powerful nonlinear extensions of the conventional methods. (lu.se)
Model7
  • In this work, an artificial neural network foreign exchange rate forecasting model (AFERFM) was designed for foreign exchange rate forecasting to correct some of these problems. (edu.ng)
  • This study focuses on using an artificial neural network (ANN) based model for predicting the performance of high strength concrete (HSC) beams strengthened with surface mounted FRP laminates. (ac.ir)
  • A back-propagation artificial neural network (BP-ANN) model was used to analyse these mixture optode spectra. (edu.hk)
  • Further, Artificial neural network based model has been developed to predict the compressive strength of various SCC mixes. (techno-press.org)
  • Artificial neural networks are frequently used to model various highly variable and non-linear physical phenomena in the water and environmental engineering fields. (aston.ac.uk)
  • A neural network is a powerful computational data model that is able to capture and represent complex input/output relationships. (onlinebooksreview.com)
  • MRI-Based Back Propagation Neural Network Model as a Powerful Tool for Predicting the Response to Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma. (cdc.gov)
Efficiency1
  • Three neural network models were assessed for their efficiency in accurately simulating effluent water quality parameters from various experimental pavement systems. (aston.ac.uk)
Experimental3
  • Then, to differentiate these samples, two supervised chemometric procedures were applied to process the experimental data: linear discriminant analysis (LDA) and artificial neural network (ANN) using the back-propagation algorithm. (unboundmedicine.com)
  • In the present study, comparison has been made between the experimental results and those predicted through neural network modeling. (ac.ir)
  • Experimental results show the ability and high performance of proposed algorithm. (edu.iq)
Temperature1
  • In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. (uni-weimar.de)
Concentrations1
  • The results showed that for varied effluent concentrations of nutrients, applications of artificial neural networks computed a convincing outcome. (aston.ac.uk)
Stack Exchange2
  • Stack Exchange network consists of 183 Q&A communities including Stack Overflow , the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. (stackexchange.com)
  • Artificial Intelligence Stack Exchange is a question and answer site for people interested in conceptual questions about life and challenges in a world where 'cognitive' functions can be mimicked in purely digital environment. (stackexchange.com)
Statistical1
  • A set of rules-of-thumb on when, why and how to use the various options is presented in this manual and the relation between the underlying algorithms and standard statistical methods is pointed out. (lu.se)
Intelligence9
Models4
  • Neural network models can be viewed as defining a function that takes an input (observation) and produces an output (decision) f : X → Y {\displaystyle \textstyle f:X\rightarrow Y} or a distribution over X {\displaystyle \textstyle X} or both X {\displaystyle \textstyle X} and Y {\displaystyle \textstyle Y} . Sometimes models are intimately associated with a particular learning rule. (wikipedia.org)
  • We explore parameterized families of molecular dynamics (MD) and smoothed particle hydrodynamics (SPH) models for simulating coarse-grained Lagrangian turbulence and for validating our learning algorithms. (aps.org)
  • The back-propagation neural network models incorporating these algorithms performed classification and regression tasks without knowledge of the underlying physical processes occurring throughout the pavement system. (aston.ac.uk)
  • The neural network models were functions of the readily available water quality parameters. (aston.ac.uk)
Functions2
  • Mathematically, a neuron's network function f ( x ) {\displaystyle \textstyle f(x)} is defined as a composition of other functions g i ( x ) {\displaystyle \textstyle g_{i}(x)} , that can further be decomposed into other functions. (wikipedia.org)
  • This can be conveniently represented as a network structure, with arrows depicting the dependencies between functions. (wikipedia.org)
System3
  • The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. (onlinebooksreview.com)
  • we have used artificial neural network system to decide about whether each pixel is edge or not. (edu.iq)
  • First standard deviation values are computed for mask (3*3), Then after training a neural network system to recognize structural patterns (these pattern represents edges), it decides on each pixel if its edge or not. (edu.iq)
Self-org2
  • In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. (uni-weimar.de)
  • 1 ] implemented ``vanilla'' versions of such networks using the back-propagation updating rule, and included a self-organizing map algorithm as well. (lu.se)
Train3
  • In the training phase, back propagation algorithm was used to train the foreign exchange rates and learn how to approximate input. (edu.ng)
  • The process is called machine learning, and with the right algorithms we can train a computer to recognize objects in images. (fb.com)
  • This paper has proposed to train the network in summer for reducing load shedding and in winter, holidays to minimize the power loss as well as the cost of generation. (ss-pub.org)
Work2
  • This research work explores the impact of virtual data positions (VDPs) on financial time series forecasting using three different data exploration techniques with artificial neural network (ANN) trained by back propagation. (inderscience.com)
  • DIGITAL FILTER USING ARTIFICIAL NEURAL NETWORK In proposed work, low pass FIR digital filter has been designed and analyzed using artificial neural network (ANN) with Bartlett-Hanning and Blackman-Harris window function. (bartleby.com)
Techniques2
  • Design of Experiments (DOE) and the Artificial Neural Networks (ANN) are two methodologies that can be used as estimation techniques. (dcu.ie)
  • However, in the last decade, there has been strong resurgence of neural networks as processing techniques where they have been applied to many real-world problems. (samuelcheng.us)
Exposure2
  • The ELEMF exposure stimulated the electrical network activity and intensified the structure of bursts. (frontiersin.org)
  • Further, the exposure to electromagnetic fields within the first 28 days in vitro of the differentiation of the network activity induced also reorganization within the burst structure. (frontiersin.org)
Results1
  • Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. (uni-weimar.de)
Assessment1
  • Artificial neural networks are a promising tool for environmental process assessment and modelling. (aston.ac.uk)
Application1
  • Evaluation of Combined Cancer Markers With Lactate Dehydrogenase and Application of Machine Learning Algorithms for Differentiating Benign Disease From Malignant Ovarian Cancer. (cdc.gov)
Machine2
  • We propose an unsupervised machine learning algorithm for anomaly detection that exploits self-learnt features of monodimensional time series. (ss-pub.org)
  • Intended as a humorous and educational look at what machine learning is capable of, RoboRecipes is the product of three different Neural Networks. (onlinebooksreview.com)
Design1
  • 1. Design rule-base and fuzzy expert systems, artificial neural networks with back propagation learning algorithm and competitive learning, genetic algorithms and hybrid intelligent systems for solving practical problems. (edu.au)
Paper3
  • In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. (uni-weimar.de)
  • This paper presents the load forecasting for the Power Grid Company Bangladesh Ltd. (PGCB) by using Advanced Back Propagation Algorithm. (ss-pub.org)
  • This paper presents a proposed neural network based edge detection algorithm. (edu.iq)
Input4
  • An input neuron has no predecessor but serves as input interface for the whole network. (wikipedia.org)
  • By adding regional connections between the input and the output, the new architecture of the neural network can have both global features and regional features extracted from the input. (cdc.gov)
  • The datasets from oanda website were used as input in the back propagation for the evaluation and forecasting of foreign exchange rates. (edu.ng)
  • Are my computations of the forward and backward pass of a neural network with one input, hidden and output neurons correct? (stackexchange.com)
Inputs1
  • Let N {\displaystyle N} be a network with e {\displaystyle e} connections, m {\displaystyle m} inputs and n {\displaystyle n} outputs. (wikipedia.org)
Pattern1
  • An artificial neural network (ANN) combines biological principles with advanced statistics to solve problems in domains such as pattern recognition and game-play. (wikipedia.org)
Process1
  • The process of finding the gradient of an equation is the most important step of all of them and I wish my mathematical teachers told me back in the day that the purpose of differentiating the function is so that we get the gradient of a function. (mql5.com)
Deep1
  • For deep learning artificial neural networks in 2023, we have listed some good books review that helps you to learn from beginner to master level. (onlinebooksreview.com)
Structure1
  • A basic structure of neural network designed for this problem is discussed. (cdc.gov)
Time1
  • Keep the algorithm learning rate to smaller values but not very small like 0.0001 by doing so you are increasing the program execution time as it might take longer for the algorithm to reach the minimum values. (mql5.com)
Physical1
  • Specifically, we utilize symplectic integrators consistent with back propagation over the parameter space while embedding physical constraints within artificial neural networks. (aps.org)
Approach1
  • Bevilacqua, V.: Three-dimensional virtual colonoscopy for automatic polyps detection by artificial neural network approach: new tests on an enlarged cohort of polyps. (springer.com)
Specifically1
  • Assignment 2, Q1 specifically is about training the CIFAR data set using a two layer neural network. (stackexchange.com)
Problem1
  • See Back Pain: Find the Cause, Watch for the Comeback , a Critical Images slideshow, to help diagnose and manage this common problem. (medscape.com)
Study1
  • In this study, differentiating murine cortical networks on multiwell microelectrode arrays were repeatedly exposed to an extremely low-electromagnetic field (ELEMF) with alternating 10 and 16 Hz frequencies piggy backed onto a 150 MHz carrier frequency. (frontiersin.org)