Abstract Artificial Neural Networks Research Articles

MULTIPLES AND STOCK PRICE, NEW APPROACH FOR RELATIVE VALUATION THROUGH NEURAL NETWORK

Artificial Neural Networks (ANNs) has been used as a powerful modeling technique for forecasting. In this study, the relationship between multiples and stock prices has been investigated on the Pakistan Stock Exchange 100 Index by incorporating financial modeling through neural network. The aim is to develop multiple-based valuation model to check whether multiples are viable factor in predicting stock movements. Forecasting model has been developed by using neural network. Prediction accuracy of the developed forecasting model has been evaluated. Findings reveal that neural network outperforms in comparison to linear regression and forecasts stock prices with 98% accuracy.

A framework for the use of artificial neural networks for water treatment: development and application

Abstract Artificial neural networks (ANNs) are increasingly being used in water treatment applications because of their ability to model complex systems. The present study proposed a framework to develop and validate ANNs for drinking water treatment and distribution system water quality applications. The framework was used to develop ANNs to identify the optimal ozone dose required for effective UV disinfection and to meet regulatory requirements for disinfection by-products (DBPs) in the distribution system. Treatment at a full-scale treatment plant was successfully modelled, with treated water UV transmittance as the output variable. ANNs could be used to identify operating setpoints that minimize operating costs for effective disinfection during drinking water treatment. However, because of the limited data available to train and validate the distribution system ANNs (i.e. n = 48; 15 years of quarterly measurements), these could not be used to reliably identify operating setpoints that also ensure compliance with DBP regulations.

ANN-based dynamic modulus models of asphalt mixtures with similar input variables as Hirsch and Witczak models

ABSTRACT Artificial neural networks (ANNs) and Gb*-based regression models were used for the prediction of the dynamic modulus (|E*|) of South Carolina’s hot mix asphalt mixtures (HMAs) the majority of which contained recycled asphalt pavement (RAP). Models’ training and testing were done using a database that contained 1656 |E*| values from 93 HMA mixtures. Gb*-based models included the Hirsch, revised Hirsch, Bari-Witczak, revised Bari-Witczak, Al-Khateeb 1, Al-Khateeb 2, NCHRP 1-40D, and the simplified global models. The results showed that Gb*-based regression models had a significant bias in prediction; Coupling VMA and Gb* had the most influence on |E*|; four-layer ANNs generally had a better performance than three-layer ANNs on using Hirsch model’s related inputs; ANN 3-15-15-1 and ANN 8-15-15-1 (developed with similar input variables as the Hirsch and Witczak regression models respectively) showed very high performance of R 2 > 0.994 on testing. Therefore, ANNs could be considered to capture the influence of the binders’ rheological properties, mixture’s volumetric properties, and RAP on |E*| of HMA mixtures far better than regression-based models.

Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware

Artificial neural networks (ANNs) have emerged as hot topics in the research community. Despite the success of ANNs, it is challenging to train and deploy modern ANNs on commodity hardware due to the ever-increasing model size and the unprecedented growth in the data volumes. Particularly for microarray data, the very high dimensionality and the small number of samples make it difficult for machine learning techniques to handle. Furthermore, specialized hardware such as graphics processing unit (GPU) is expensive. Sparse neural networks are the leading approaches to address these challenges. However, off-the-shelf sparsity-inducing techniques either operate from a pretrained model or enforce the sparse structure via binary masks. The training efficiency of sparse neural networks cannot be obtained practically. In this paper, we introduce a technique allowing us to train truly sparse neural networks with fixed parameter count throughout training. Our experimental results demonstrate that our method can be applied directly to handle high-dimensional data, while achieving higher accuracy than the traditional two-phase approaches. Moreover, we have been able to create truly sparse multilayer perceptron models with over one million neurons and to train them on a typical laptop without GPU (https://github.com/dcmocanu/sparse-evolutionary-artificial-neural-networks/tree/master/SET-MLP-Sparse-Python-Data-Structures), this being way beyond what is possible with any state-of-the-art technique.

PEMODELAN PREDIKSI KUAT TEKAN BETON UMUR MUDA MENGGUNAKAN H2O'S DEEP LEARNING

Artificial Neural Network (ANN) is a Machine Learning (ML) algorithm which learn by itself and organize its thinking to solve problems. Although the learning process involves many hidden layers (Deep Learning) this algorithm still has weaknesses when faced with high noise data. Concrete mixture design data has a high enough noise caused by many unidentified / measurable aspects such as planning, design, manufacture of test specimens, maintenance, testing, diversity of physical and chemical properties, mixed formulas, mixed design errors, environmental conditions, and testing process. Information needs about the compressive strength of early age concrete (under 28 days) are often needed while the construction process is still ongoing. ANN has been tried to predict the compressive strength of concrete, but the results are less than optimal. This study aims to improve the ANN prediction model using an H2O’s Deep Learning based on a multi-layer feedforward artificial neural network that is trained with stochastic gradient descent using backpropagation. The H2O’s Deep Learning best model is achieved by 2 hidden layers- 50 hidden neurons and ReLU activation function with a RMSE value of 6,801. This Machine Learning model can be used as an alternative/ substitute for conventional mix designs, which are environmentally friendly, economical, and accurate. Future work with regard to the concrete industry, this model can be applied to create an intelligent Batching and Mixing Plants.

Learning Nonlinear Constitutive Laws Using Neural Network Models Based on Indirectly Measurable Data

Abstract 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.

Agent based Modeling for Activation Function in Enhancement Logic Programming in Hopfield Neural Network

Artificial Neural Network (ANN) uses many activation functions to update the state on neuron. The research and engineering have been used activation functions in the artificial neural network as the transfer functions. The most common reasons for using this transfer function were its unit interval boundaries, the functions and quick computability of its derivative, and several useful mathematical properties in the approximation of theory realm. Aim of this study is to figure out the best robust activation functions to accelerate HornSAT logic in the Hopfield Neural Network's context. In this paper we had developed Agent-based Modelling (ABM) assessed the performance of the Zeng Martinez Activation Function (ZMAF) and the Hyperbolic Tangent Activation Function (HTAF) beside the Wan Abdullah method to do Logic Programming (LP) in Hopfield Neural Network (HNN). These assessments are carried out on the basis of hamming distance (HD), the global minima ratio (zM), and CPU time. NETLOGO 5.3.1 software has been used for developing Agent-based Modeling (ABM) to test the proposed comparison of the efficaecy of these two activation functions HTAF and ZMAF.

Neural Networks with Asymptotics Control

Artificial Neural Networks (ANNs) have recently been proposed as accurate and fast approximators in various derivatives pricing applications. ANNs typically excel in fitting functions they approximate at the input parameters they are trained on, and often are quite good in interpolating between them. However, for standard ANNs, their extrapolation behavior – an important aspect for financial applications – cannot be controlled due to complex functional forms typically involved. We overcome this significant limitation and develop a new type of neural networks that incorporate large-value asymptotics, when known, allowing explicit control over extrapolation. This new type of asymptotics-controlled ANNs is based on two novel technical constructs, a multi-dimensional spline interpolator with prescribed asymptotic behavior, and a custom ANN layer that guarantees zero asymptotics in chosen directions. Asymptotics control brings a number of important benefits to ANN applications in finance such as well-controlled behavior under stress scenarios, graceful handling of regime switching, and improved interpretability.

Cataract Disease Diagnosis System Using Artificial Neural Network Learning Vector Quantization Method

Artificial Neural Network (ANN) is an information processing system that has certain performance characteristics that are artificial representatives based on human neural networks. ANN method has been widely applied to help human performance, one of which is health. In this research, ANN will be used to diagnose cataracts, especially Congenital Cataracts, Juvenile Cataracts, Senile Cataracts and Traumatic Cataracts based on the symptoms of the disease. The ANN method used is the Learning Vector Quantization (LVQ) method. The data used in this research were 146 data taken from the medical record data of RSUD Dr. M. Haulussy, Ambon. The data consists of 109 data as training data and 37 data as testing data. By using learning rate (α) = 0.1, decrease in learning rate (dec α) = 0.0001 and maximum epoch (max epoch) = 5, the accuracy rate obtained is 100%.

Experimental prediction and investigation of spring back in 'V' bending profile process modelling using artificial neural network

Artificial neural network (ANN) is accustomed to foreseeing responses of enormous range of parameters. A few scientists concentrated on the improvement of insightful the spring back and bend force in air bend of semi-legitimate and numerical models. ANN has utilised consistently in making estimated model on the grounds that ANN has high flexibility in fitting of educational accumulation. ANN has some exceptional characteristics, for example, power, adjustment to non-basic disappointment, parallel use. More that, it has ability to describe non-straight associations, joint efforts of procedure parameters. It can make it a promising gadget for showing many amassing issues. In this work is to distinguish the nature of ANN in 'V' bending profile among the specialists of sheet metal bend as the data included and the relations of parameters are very non-straight was contrasting and RSM.

ANN-Based Stop Criteria for a Genetic Algorithm Applied to Air Impingement Design

Artificial Neural Networks (ANNs) have proven to be a powerful tool in many fields of knowledge. At the same time, evolutionary algorithms show a very efficient technique in optimization tasks. Historically, ANNs are used in the training process of supervising networks by decreasing the error between the output and the target. However, we propose another approach in order to improve these two techniques together. The ANN is trained with the points obtained during an optimization process by a genetic algorithm and a flower pollination algorithm. The performance of this ANN is used as a stop criterion for the optimization process. This new configuration aims to reduce the number of iterations executed by the genetic optimizer when learning the cost function by an ANN. As a first step, this approach is tested with eight benchmark functions. As a second step, the authors apply it to an air jet impingement design process, optimizing the surface temperature and the fan efficiency. Finally, a comparison between the results of a regular optimization and the results obtained in the present study is presented.

Development and application of a GIS-based artificial neural network system for water quality prediction: a case study at the Lake Champlain area

Artificial Neural Network (ANN) models have been extensively applied in the prediction of water resource variables, and Geographical Information System (GIS) includes powerful functions to visualize spatial data. In order to provide an efficient tool for environmental assessment and management that combines the advantages of these two modules, a GIS-based ANN water quality prediction system was developed in the present study. The ANN module and ArcGIS Engine module, along with a dynamic database, were imbedded in the system, which integrates water quality prediction via the ANN model and spatial presentation of the model results. The structure of the ANN model could be modified through the graphical user interface to optimize the model performance. The developed system was applied to a real case study for the prediction of the total phosphorus concentration in the Lake Champlain area. The prediction results were verified with the monitoring data, and the performance of the developed model was further evaluated through graphical techniques and quantitative statistical methods. Overall, the developed system provided satisfactory prediction results, and spatial distribution maps of the predicted results were obtained, which coincided with the monitored values. The developed GIS-based ANN water quality prediction system could serve as an efficient tool for engineers and decision makers.

Artificial Neural Network Prediction of Silicon and Nickel recovery in Al-Si-Ni alloy Manufactured by Stir Casting

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.

Long-term prediction of biosignals from greenhouse-grown tomato

Artificial neural networks (ANN) have been successfully used for modelling climate and plant signals in greenhouses. This work shows the combination of two ANN models, built and trained separately, and then coupled together. Both models were trained using a data set consisting of 3 complete cultivation periods (2011 until 2013) of 2 venlo-type greenhouses located at the Humboldt-Universitat zu Berlin. The greenhouses were used for soilless cultivation of tomato. The first model considered (climate prediction: CP) estimated the expected values of temperature and relative humidity in the near future (one-step prediction, OSP). The output was then recursively fed to the model in order to make further predictions. Each time step represented 5 min, and the maximum number of prediction steps was set to 6. On the other hand, the output of the second model (plant signals: PM) was not a prediction in time, but an estimation of plant signals (leaf temperature, transpiration rate, photosynthesis rate) as expected to be measured by a phytomonitoring system (BERMONIS). The combined models took the 6 predicted steps of climate (long-term prediction: LTP) produced by CM and used them to feed PM, thus generating a Biosignals LTP. As expected, the fit of the LTP decreased with the number of forward steps. For example, the R2 for leaf temperature range from 0.946 for the first simulation until 0.704 for the 6th simulated step. For transpiration and photosynthesis rate, these ranges were (0.874, 0.809) and (0.772, 0.692), respectively. These results show that the estimated plant responses could support a predictive control system, in order to avoid plant damage due to extreme climate conditions.

ARTIFICIAL NEURAL NETWORK IN THE MODELLING OF THE EFFECT OF CHROMIUM DOPANTS ON THE MECHANICAL PROPERTIES OF AL-4%CU ALLOY

Artificial Neural Network (ANN) was used to model the effect of Chromium dopants on the mechanical properties duralumin (Al-4 %Cu). The results showed that the hardness, yield strength, and ultimate tensile strength increased, while the energy absorbed and percentage elongation decreased, with increasing %wt of Chromium dopants. Simulation results of ANN show strong agreement with experimental values, having satisfactory R-values of Mean Square Error. ANN can suitably be used to predict the mechanical properties of Al-4%Cu doped with Chromium.

Dynamic Modeling of Streptomyces hygroscopicus Fermentation Broth Microfiltration by Artificial Neural Networks

Artificial neural networks (ANNs) have been used to dynamically model cross-flow microfiltration of Streptomyces hygroscopicus fermentation broths. The aim is to predict permeate flux as a function of temperature, feed flow, transmembrane pressure and processing time. Dynamic modeling of microfiltration performance of complex systems (such as broths) is very important for design of new processes and better understanding of the present. The results of ANN model analysis suggest that the coefficients of the determination have high values. The application of the Bayesian regularization gave better results to the performance of the neural network compared to the Levenberg-Marquet algorithm. The optimal number of neurons in the hidden layer is eight. Analysis of the absolute relative error showed excellent permeate flux estimates for 100 % of the data points, with an error less than 5 % for the data obtained during microfiltration in the presence of a turbulence promoter. Whilst in the case of microfiltration without turbulence promoter 90 % of predictions have an error less than 10 %. The results of applying the concept of neural networks in the dynamic modeling of microfiltration of Streptomyces hygroscopicus fermentative broths with and without a turbulence promoter clearly show the validity of proposed method for simulation and prediction of microfiltration experimental results.

ARTIFICIAL NEURAL NETWORK IN THE MODELLING OF THE EFFECT OF CHROMIUM DOPANTS ON THE MECHANICAL PROPERTIES OF AL-4%CU ALLOY

Artificial Neural Network (ANN) was used to model the effect of Chromium dopants on the mechanical properties duralumin (Al-4 %Cu). The results showed that the hardness, yield strength, and ultimate tensile strength increased, while the energy absorbed and percentage elongation decreased, with increasing %wt of Chromium dopants. Simulation results of ANN show strong agreement with experimental values, having satisfactory R-values of Mean Square Error. ANN can suitably be used to predict the mechanical properties of Al-4%Cu doped with Chromium.

Reliable Neural Networks for Proportioning of Concrete Mixes Containing Cement Replacement Materials

Abstract Artificial neural network (ANN) is one of the most suitable analytical methods used for modeling complex relationships between concrete properties and concrete mix ingredients. In the present study, common ANN is first trained to predict concrete mix proportions using experimental data sets. Experimental data sets include the content of water, cementitious materials (cement, silica fume, rice husk ash, and natural pozzolanas), coarse and fine aggregates, superplasticizer and the compressive strength, the slump, and the maximum size of the aggregates. Also, statistical analyses are used to correlate the results and the acceptable error. Then, the neural network is retrained using experimental data sets together with analytical data sets that are produced using the Iranian national concrete mix design method, empirical relationships, or both. Finally, the performance of the retrained neural networks is compared with the first one. The results show that the neural networks, which are trained using both analytical and experimental data sets, are more reliable and can be reliably used for modeling of complex relationships.

Application of neural networks in membrane separation

Abstract Artificial neural networks (ANNs) as a powerful technique for solving complicated problems in membrane separation processes have been employed in a wide range of chemical engineering applications. ANNs can be used in the modeling of different processes more easily than other modeling methods. Besides that, the computing time in the design of a membrane separation plant is shorter compared to many mass transfer models. The membrane separation field requires an alternative model that can work alone or in parallel with theoretical or numerical types, which can be quicker and, many a time, much more reliable. They are helpful in cases when scientists do not thoroughly know the physical and chemical rules that govern systems. In ANN modeling, there is no requirement for a deep knowledge of the processes and mathematical equations that govern them. Neural networks are commonly used for the estimation of membrane performance characteristics such as the permeate flux and rejection over the entire range of the process variables, such as pressure, solute concentration, temperature, superficial flow velocity, etc. This review investigates the important aspects of ANNs such as methods of development and training, and modeling strategies in correlation with different types of applications [microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), electrodialysis (ED), etc.]. It also deals with particular types of ANNs that have been confirmed to be effective in practical applications and points out the advantages and disadvantages of using them. The combination of ANN with accurate model predictions and a mechanistic model with less accurate predictions that render physical and chemical laws can provide a thorough understanding of a process.

Modelling Diabetes Mellitus among Adult Kenyan Population Using Artificial Neural Network

Artificial Neural Network (ANN) is a parallel connection of a set of nodes called neurons which mimic biological neural system. Statistically, ANN represents a class of non-parametric models which is capable of approximating a non-linear function by a composition of low dimensional ridge functions. This study aimed at modeling diabetes mellitus among adult Kenyan population using 2015 stepwise survey data from Kenya National Bureau of Statistics. Data analysis was carried out using R statistical software version 3.5.0. Among the input variables Age, Sex, Alcoholic status, Sugar consumption, Physical Inactivity, Obesity status, Systolic and Diastolic blood pressure had a significant relationship with diabetic status at 5% level of significance. A multi layered feed-forward neural network with a back propagation algorithm and a logistic activation function was used. Considering a parsimonious model, the model selected had the eight input variables with two neurons in the hidden layer since it gave a minimum MSE of 0.0580 reported. 75% of data was used for training while 25% was used for testing. The sensitivity of the trained network was reported as 75% while specificity was 94.29%. The overall accuracy of the model was 84.64% . This implied that the model could correctly classify an individual as either diabetic or not with an accuracy rate of 84.64%. A 10-fold cross validation was carried out and an average MSE of 0.0686 reported. Kolmogorov-Smirnov test of normality was carried out and at 5% level of significance, for most parameter estimates, we failed to reject the null hypothesis and concluded that the network parameter estimates were asymptotically normal and consistent. With a good choice of risk factors for diabetes, neural network structures could be successfully used to accurately model diabetes melitus among Kenyan adult population.