Artificial neural network applications in immunology

Artificial neural networks (ANN) is increasingly being used to solve engineering problems. Successful welding operations are at the top of engineering problems. The friction stir welding method, which is used to combine the materials under the melting temperature, is also a method that can be analyzed with ANN. In this study, Al-7075 workpieces (with and without aging treatment) were joined by the friction stir welding method. Tension tests were applied to the joined workpieces and the resulting values were modeled by ANN. In ANN model; The tensile strength of the assembled parts was tried to be estimated by input parameters (the type of the material, the number of revolutions and the feed rate). In the created ANN model; Different nerve numbers, different transfer functions, using different layer numbers, the models created by alternative network structures have been tested. In these experiments, prediction performance was measured using neuron and algorithm type variables. At the end of this study, it was seen that the ANN model for the parts joined with the FSW process made predictions with high consistency. The best estimates were obtained with the Levenberg Marquardt (LM) algorithm and the ANN model established with 3 neurons. This ANN model is seen as a suitable solution for this problem with 98% success.

Artificial neural network (ANN) model was used for predicting colour properties of 100% cotton denim fabrics, including colour yield (in terms of K/S value) and CIE L*, a*, b*, C*, and h° values, under the influence of cellulase treatment with various combinations of cellulase processing parameters. Variables examined in the ANN model included treatment temperature, treatment time, pH, mechanical agitation, and fabric yarn twist level. The ANN model was compared with a linear regression model where the ANN model produced superior results in the prediction of colour properties of cellulase-treated 100% cotton denim fabrics. The relative importance of the examined factors influencing colour properties was also investigated. The analysis revealed that cellulase treatment processing parameters played an important role in affecting the colour properties of the treated 100% denim cotton fabrics.

Artificial Neural Network (ANN) model along with Back Propagation Algorithm (BPA) has been applied in many fields, especially in hydrology and water resources management to simulate or forecast rainfall runoff process, discharge and water level - time series, and other hydrological variables. Several researches have recently been focusing to compare the applicability of ANN model with other theory-driven and data-driven approaches. The comparison of ANN with M5 model trees for rainfall-runoff forecasting, with ARMAX models for deriving flow series, with AR models and regression models for forecasting and estimating daily river flows have been carried out. The better results that were implemented by ANN model have been concluded. So, this research trend is continued for the comparison of ANN model with Tank, Harmonic, Thomas and Fiering models in simulation of the monthly runoffs at Dong Nai river basin, Viet Nam. The results proved ANN being the best choice among these models, if suitable and enough data sources were available.

Artificial Neural Networks (ANNs) have seen an explosion of interest over the last two decades and have been successfully applied in all fields of chemistry and particularly in analytical chemistry. Inspired from biological systems and originated from the perceptron, i.e. a program unit that learns concepts, ANNs are capable of gradual learning over time and modelling extremely complex functions. In addition to the traditional multivariate chemometric techniques, ANNs are often applied for prediction, clustering, classification, modelling of a property, process control, procedural optimisation and/or regression of the obtained data. This paper aims at presenting the most common network architectures such as Multi-layer Perceptrons (MLPs), Radial Basis Function (RBF) and Kohonen's self-organisations maps (SOM). Moreover, back-propagation (BP), the most widespread algorithm used today and its modifications, such as quick-propagation (QP) and Delta-bar-Delta, are also discussed. All architectures correlate input variables to output variables through non-linear, weighted, parameterised functions, called neurons. In addition, various training algorithms have been developed in order to minimise the prediction error made by the network. The applications of ANNs in water analysis and water quality assessment are also reviewed. Most of the ANNs works are focused on modelling and parameters prediction. In the case of water quality assessment, extended predictive models are constructed and optimised, while variables correlation and significance is usually estimated in the framework of the predictive or classifier models. On the contrary, ANNs models are not frequently used for clustering/classification purposes, although they seem to be an effective tool. ANNs proved to be a powerful, yet often complementary, tool for water quality assessment, prediction and classification.

Artificial neural networks (ANNs) are presented for the accurate and efficient small-signal modeling of active devices. Models are developed using measured data and are valid over ranges of parameters such as frequency, bias, and ambient temperature. Once generated, these ANN models are inserted into commercial microwave circuit simulators where they can be used for computer-aided design (CAD) and optimization of microwave/MM-wave circuits. Also, the developed ANN models can give physical insight into device behavior and scaling properties when used in conjunction with an equivalent circuit approach. An advantage of the ANN modeling approach is that it provides substantial data storage reduction over previously used modeling techniques without loss of accuracy. With increased model accuracy, the potential of first-pass design success may be realized, resulting in cost savings and decreased time-to-market for new products.

Artificial neural network (ANN) analysis was used to predict the permeability of selected compounds through Caco-2 cell monolayers. Previously reported models, which were shown to be useful in the prediction of permeability values, use many structural parameters. More complex equations have also been proposed using both linear and non-linear relationships, including ANN analysis and various structural parameters. But proposed models still need to be developed using different neuron patterns for more precise predictions and a better understanding of which factors affect the permeation. To develop a simple and useful model or method for easy prediction is also a general need. Permeability coefficients (log kp) were obtained from various literature sources. Some structural parameters were calculated using computer programs. Multiple linear regression analysis (MLRA) was used to predict Caco-2 cell permeability for the set of 50 compounds (r2 = 0.403). A successful ANN model was developed, and the ANN produced log kp values that correlated well with the experimental ones (r2 = 0.952). The permeability of a compound, famotidine, which has not previously been studied, through the Caco-2 cell monolayer was investigated, and its permeability coefficient determined. It was then possible to compare the experimental data with that predicted using the trained ANN with previously determined Caco-2 cell permeability values and structural parameters of compounds. The model was also tested using literature values. The developed and described ANN model in this publication does not require any experimental parameters; it could potentially provide useful and precise prediction of permeability for new drugs or other penetrants.

Artificial neural network (ANN) models are used to learn the nonlinear constitutive laws based on indirectly measurable data. The real input and output of the ANN model are derived from indirect data using a mechanical system, which is composed of several subsystems including the ANN model. As the ANN model is coupled with other subsystems, the input of the ANN model needs to be determined during the training. This approach integrates measurable data, mechanics, and ANN models so that the ANN models can be trained without direct data which is usually not available from experiments. Two examples are provided as an illustration of the proposed approach. The first example uses two-dimensional (2D) finite element (FE) analysis to train an ANN model to learn the nonlinear in-plane shear constitutive law. The second example applies a continuum damage model to train an ANN model to learn the damage accumulation law. The results show that the trained ANN models achieve great accuracy based on the proposed approach.

Artificial neural networks (ANNs) were developed very quickly and applied very widely in recent years due to their strong ability to solve the nonlinear problems. The artificial neural network-based method was also widely applied to the geotechnical engineering. The complexity of the geotechnical engineering problems because of the strong nonlinear relationship between knows and unknowns of the problems can be mapped very well by artificial neural networks. Researches on the application of artificial neural network in geotechnical engineering are reviewed and appraised in this paper. All the networks mentioned are trained with the back-propagation algorithm which is widely used by a great number of researchers. Research reveals that the method is feasible and it will be interested for more geotechnical engineers.

38 - Artificial Neural Network Applications in Power Electronics and Electrical Drives

Artificial neural networks (ANNs) have been widely used in modelling and control of many practical industrial nonlinear processes. However, most of them have focused on a general approach for input selection. In this paper, a hierarchical hybrid approach of partial least squared (PLS) analysis and ANNs has been applied for predictive control of a real drying process in ceramic tile manufacturing. This approach is employed in order to promote the reliability of neural network model via reduction of the input set dimension. First, PLS analysis is done to arrive at the significant factors that influence the spray drying quality the most. Also, the significant factors are used to construct the predictive neural network model so called the Focus-NN model. Next, the reliability of the Focus-NN model is compared with the ANN model. In order to develop a predictive-control strategy using a more reliable model, i.e. the Focus-NN model, several scenarios as an accurate, fast running and inexpensive method are deployed to identify the optimal process settings considering the desired output.

Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%–16%) more accuracy than without using clustering, while the percentage split achieves (2%–5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

Artificial neural networks (ANNs) have been widely used for prediction and classification problems. In particular, many methods for building ANNs have appeared in the last two decades. One of the continuing important limitations of using ANNs, however, is their poor ability in analyzing small data sets because of overfitting. Several methods have been proposed in the literature to overcome this problem. Based on our study, radial basis functions (RBF) with stacked generalization can effectively decrease the error of the prediction when there is an underlying relationship between the variables. We have applied this method to determine the factors controlling and related to fracture spacing in the Lisburne Formation. Comparing our results with those from other ANN methods, we find that the proposed method gives a substantially smaller error than alternative methods. The errors in predicted fracture spacing for the Lisburne using the conventional ANN methods are about 50 to 200% larger than those obtained using the proposed method. By having a method which predicts fracture spacing more accurately, we were able to more reliably identify the effects of such factors as bed thickness, lithology, structural position, and degree of folding on the spacing. In petroleum engineering and geosciences, there are many cases where the number of data is limited because of expense or logistical limitations, e.g., limited core, poor borehole conditions, or restricted logging suites. Thus, the methods used in this study should be attractive in many petroleum engineering contexts where complex, non-linear relationships need to be modeled using small datasets.

This article discusses the applications of Artificial Neural Network (ANN) and Fuzzy Logic Control (FLC) strategies in a control context of a grid connected Wind Energy Conversion System (WECS), using a Doubly-Fed Induction Generator (DFIG). To rigorously explore the performance of the controllers, a systemic approach based on an ideal model and a nominal model of the wind turbine is used for the comparative analysis between PI, FLC and ANN. It turns out that the PI is less efficient than ANN and FLC. It turns out that the PI is less efficient than ANN and FLC. Whereas, for a high level of control, the FLC has better performance than the ANN. For a low level, the performance of the ANN is very slightly superior to the FLC. In addition, one notes a good rejection of disturbance and a good robustness with the FLC compared to the ANN. The latter has a Total Harmonic Distortion (THD) and cos ϕ ≈ 1 slightly better than the FLC. In short, the ANN and FLC present great advantages for the WECS and the results obtained are satisfactory. The platform used for modeling and simulation studies is MATLAB/Simulink.KeywordsWind Energy Conversion SystemFuzzy Logic Control (FLC)Artificial Neural Network (ANN)DFIGPower grid

Artificial neural network (ANN) is a non-linear statistical technique that being used to describe material behavior. This paper proposes using artificial neural networks (ANNS) in predicting silicon and nickel recovery. During the experimental work, the used optimum parameters are reaction time is 25 min., temperature is 950 ᵒC, Ni2O3 /Al wt. (weight) ratio is 0.082, and Na2SiF6 / Al wt. ratio is 1. Some tests such as chemical analysis, microstructure examination (EDX mapping), XRD diffraction were carried out on the produced alloys. The obtained experimental results are used to train the artificial neural network (ANN). While reaction time, temperature, Ni2O3 /Al wt. ratio, and Na2SiF6 / Al wt. ratio are used as ANN's inputs. Silicon and nickel recovery are used as ANN's outputs. The used ANN consists of three layers; Input layer that includes 4 neurons, the hidden layer includes 9 neurons, while the output layer contains 2 neurons. The Levenberg-Marquardt (LM) is used as the training function. Optimal mean square errors (MSE) for the ANN during predicting and estimating silicon and nickel recovery equal 0.0358, 0.0034, respectively, when reaction time is the variable and other parameters are kept constant, MSE equal 1.4007e-04, 1.3478e-04 when temperature is variable and other parameters are kept constant, MSE equal 1.3839e-04, 9.9891e-05 when Ni2O3/Al wt. ratio was the variable and other parameters are kept constant and finally MSE equal 0.0287, 0.0263 when Na2SiF6 / Al wt. ratio is variable and other parameters are kept constant.

Artificial neural network (ANN) is a simplified model of the biological nervous system consisting of nerve cells or neurons. The application of ANN to food process engineering is relatively novel. ANN had been employed in diverse applications like food safety and quality analyses, food image analysis, and modeling of various thermal and non-thermal food-processing operations. ANN has the ability to map nonlinear relationships without any prior knowledge and predicts responses even with incomplete information. Every neural network possesses data in the form of connection weights interconnecting lines between the input to hidden layer neurons and weights of hidden to output layer neurons, which has a significant role in predicting the output data. The applications of ANN in different unit operations in food processing were described that includes theoretical developments using intelligent characteristics for adaptability, automatic learning, classification, and prediction. The parallel architecture of ANN resulted in a fast response and low computational time making it suitable for application in real-time systems of different food process operations. The predicted responses obtained by the ANN model exhibited high accuracy due to lower relative deviation and root mean squared error and higher correlation coefficient. This paper presented the various applications of ANN for modeling nonlinear food engineering problems. The application of ANN in the modeling of the processes such as extraction, extrusion, drying, filtration, canning, fermentation, baking, dairy processing, and quality evaluation was reviewed. HIGHLIGHTS 1. This paper discusses application of ANN in different emerging trends in food process. 2. Application of ANN to develop non-linear multivariate modeling is illustrated. 3. ANNs have been shown to be useful tool for prediction of outcomes with high accuracy. 4. ANN resulted in fast response making it suitable for application in real time systems.