Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 4482

Search results for: Artificial Neural Network (ANNs)

4482 Artificial Neural Network Speed Controller for Excited DC Motor

Authors: Elabed Saud


This paper introduces the new ability of Artificial Neural Networks (ANNs) in estimating speed and controlling the separately excited DC motor. The neural control scheme consists of two parts. One is the neural estimator which is used to estimate the motor speed. The other is the neural controller which is used to generate a control signal for a converter. These two neutrals are training by Levenberg-Marquardt back-propagation algorithm. ANNs are the standard three layers feed-forward neural network with sigmoid activation functions in the input and hidden layers and purelin in the output layer. Simulation results are presented to demonstrate the effectiveness of this neural and advantage of the control system DC motor with ANNs in comparison with the conventional scheme without ANNs.

Keywords: Artificial Neural Network (ANNs), excited DC motor, convenional controller, speed Controller

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4481 Artificial Neural Networks and Geographic Information Systems for Coastal Erosion Prediction

Authors: Angeliki Peponi, Paulo Morgado, Jorge Trindade


Artificial Neural Networks (ANNs) and Geographic Information Systems (GIS) are applied as a robust tool for modeling and forecasting the erosion changes in Costa Caparica, Lisbon, Portugal, for 2021. ANNs present noteworthy advantages compared with other methods used for prediction and decision making in urban coastal areas. Multilayer perceptron type of ANNs was used. Sensitivity analysis was conducted on natural and social forces and dynamic relations in the dune-beach system of the study area. Variations in network’s parameters were performed in order to select the optimum topology of the network. The developed methodology appears fitted to reality; however further steps would make it better suited.

Keywords: artificial neural networks, backpropagation, coastal urban zones, erosion prediction

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4480 Predicting Durability of Self Compacting Concrete Using Artificial Neural Network

Authors: R. Boudjelthia


The aim of this study is to determine the influence of mix composition of concrete as the content of water and cement, water–binder ratio, and the replacement of fly ash on the durability of self compacting concrete (SCC) by using artificial neural networks (ANNs). To achieve this, an ANNs model is developed to predict the durability of self compacting concrete which is expressed in terms of chloride ions permeability in accordance with ASTM C1202-97 or AASHTO T277. Database gathered from the literature for the training and testing the model. A sensitivity analysis was also conducted using the trained and tested ANN model to investigate the effect of fly ash on the durability of SCC. The results indicate that the developed model is reliable and accurate. the durability of SCC expressed in terms of total charge passed over a 6-h period can be significantly improved by using at least 25% fly ash as replacement of cement. This study show that artificial neural network have strong potentialas a feasible tool for predicting accurately the durability of SCC containing fly ash.

Keywords: artificial neural networks, durability, chloride ions permeability, self compacting concrete

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4479 Efficient Neural and Fuzzy Models for the Identification of Dynamical Systems

Authors: Aouiche Abdelaziz, Soudani Mouhamed Salah, Aouiche El Moundhe


The present paper addresses the utilization of Artificial Neural Networks (ANNs) and Fuzzy Inference Systems (FISs) for the identification and control of dynamical systems with some degree of uncertainty. Because ANNs and FISs have an inherent ability to approximate functions and to adapt to changes in input and parameters, they can be used to control systems too complex for linear controllers. In this work, we show how ANNs and FISs can be put in order to form nets that can learn from external data. In sequence, it is presented structures of inputs that can be used along with ANNs and FISs to model non-linear systems. Four systems were used to test the identification and control of the structures proposed. The results show the ANNs and FISs (Back Propagation Algorithm) used were efficient in modeling and controlling the non-linear plants.

Keywords: non-linear systems, fuzzy set Models, neural network, control law

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4478 Application of Artificial Neural Network for Prediction of High Tensile Steel Strands in Post-Tensioned Slabs

Authors: Gaurav Sancheti


This study presents an impacting approach of Artificial Neural Networks (ANNs) in determining the quantity of High Tensile Steel (HTS) strands required in post-tensioned (PT) slabs. Various PT slab configurations were generated by varying the span and depth of the slab. For each of these slab configurations, quantity of required HTS strands were recorded. ANNs with backpropagation algorithm and varying architectures were developed and their performance was evaluated in terms of Mean Square Error (MSE). The recorded data for the quantity of HTS strands was used as a feeder database for training the developed ANNs. The networks were validated using various validation techniques. The results show that the proposed ANNs have a great potential with good prediction and generalization capability.

Keywords: artificial neural networks, back propagation, conceptual design, high tensile steel strands, post tensioned slabs, validation techniques

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4477 A Review on Artificial Neural Networks in Image Processing

Authors: B. Afsharipoor, E. Nazemi


Artificial neural networks (ANNs) are powerful tool for prediction which can be trained based on a set of examples and thus, it would be useful for nonlinear image processing. The present paper reviews several paper regarding applications of ANN in image processing to shed the light on advantage and disadvantage of ANNs in this field. Different steps in the image processing chain including pre-processing, enhancement, segmentation, object recognition, image understanding and optimization by using ANN are summarized. Furthermore, results on using multi artificial neural networks are presented.

Keywords: neural networks, image processing, segmentation, object recognition, image understanding, optimization, MANN

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4476 Optimization of Vertical Axis Wind Turbine Based on Artificial Neural Network

Authors: Mohammed Affanuddin H. Siddique, Jayesh S. Shukla, Chetan B. Meshram


The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization.

Keywords: VAWT, ANN, optimization, inverse design

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4475 Analysis of Q-Learning on Artificial Neural Networks for Robot Control Using Live Video Feed

Authors: Nihal Murali, Kunal Gupta, Surekha Bhanot


Training of artificial neural networks (ANNs) using reinforcement learning (RL) techniques is being widely discussed in the robot learning literature. The high model complexity of ANNs along with the model-free nature of RL algorithms provides a desirable combination for many robotics applications. There is a huge need for algorithms that generalize using raw sensory inputs, such as vision, without any hand-engineered features or domain heuristics. In this paper, the standard control problem of line following robot was used as a test-bed, and an ANN controller for the robot was trained on images from a live video feed using Q-learning. A virtual agent was first trained in simulation environment and then deployed onto a robot’s hardware. The robot successfully learns to traverse a wide range of curves and displays excellent generalization ability. Qualitative analysis of the evolution of policies, performance and weights of the network provide insights into the nature and convergence of the learning algorithm.

Keywords: artificial neural networks, q-learning, reinforcement learning, robot learning

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4474 Estimating Solar Irradiance on a Tilted Surface Using Artificial Neural Networks with Differential Outputs

Authors: Hsu-Yung Cheng, Kuo-Chang Hsu, Chi-Chang Chan, Mei-Hui Tseng, Chih-Chang Yu, Ya-Sheng Liu


Photovoltaics modules are usually not installed horizontally to avoid water or dust accumulation. However, the measured irradiance data on tilted surfaces are rarely available since installing pyranometers with various tilt angles induces high costs. Therefore, estimating solar irradiance on tilted surfaces is an important research topic. In this work, artificial neural networks (ANN) are utilized to construct the transfer model to estimate solar irradiance on tilted surfaces. Instead of predicting tilted irradiance directly, the proposed method estimates the differences between the horizontal irradiance and the irradiance on a tilted surface. The outputs of the ANNs in the proposed design are differential values. The experimental results have shown that the proposed ANNs with differential outputs can substantially improve the estimation accuracy compared to ANNs that estimate the titled irradiance directly.

Keywords: photovoltaics, artificial neural networks, tilted irradiance, solar energy

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4473 Prediction of Oil Recovery Factor Using Artificial Neural Network

Authors: O. P. Oladipo, O. A. Falode


The determination of Recovery Factor is of great importance to the reservoir engineer since it relates reserves to the initial oil in place. Reserves are the producible portion of reservoirs and give an indication of the profitability of a field Development. The core objective of this project is to develop an artificial neural network model using selected reservoir data to predict Recovery Factors (RF) of hydrocarbon reservoirs and compare the model with a couple of the existing correlations. The type of Artificial Neural Network model developed was the Single Layer Feed Forward Network. MATLAB was used as the network simulator and the network was trained using the supervised learning method, Afterwards, the network was tested with input data never seen by the network. The results of the predicted values of the recovery factors of the Artificial Neural Network Model, API Correlation for water drive reservoirs (Sands and Sandstones) and Guthrie and Greenberger Correlation Equation were obtained and compared. It was noted that the coefficient of correlation of the Artificial Neural Network Model was higher than the coefficient of correlations of the other two correlation equations, thus making it a more accurate prediction tool. The Artificial Neural Network, because of its accurate prediction ability is helpful in the correct prediction of hydrocarbon reservoir factors. Artificial Neural Network could be applied in the prediction of other Petroleum Engineering parameters because it is able to recognise complex patterns of data set and establish a relationship between them.

Keywords: recovery factor, reservoir, reserves, artificial neural network, hydrocarbon, MATLAB, API, Guthrie, Greenberger

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4472 Estimation of Chronic Kidney Disease Using Artificial Neural Network

Authors: Ilker Ali Ozkan


In this study, an artificial neural network model has been developed to estimate chronic kidney failure which is a common disease. The patients’ age, their blood and biochemical values, and 24 input data which consists of various chronic diseases are used for the estimation process. The input data have been subjected to preprocessing because they contain both missing values and nominal values. 147 patient data which was obtained from the preprocessing have been divided into as 70% training and 30% testing data. As a result of the study, artificial neural network model with 25 neurons in the hidden layer has been found as the model with the lowest error value. Chronic kidney failure disease has been able to be estimated accurately at the rate of 99.3% using this artificial neural network model. The developed artificial neural network has been found successful for the estimation of chronic kidney failure disease using clinical data.

Keywords: estimation, artificial neural network, chronic kidney failure disease, disease diagnosis

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4471 Assessing Artificial Neural Network Models on Forecasting the Return of Stock Market Index

Authors: Hamid Rostami Jaz, Kamran Ameri Siahooei


Up to now different methods have been used to forecast the index returns and the index rate. Artificial intelligence and artificial neural networks have been one of the methods of index returns forecasting. This study attempts to carry out a comparative study on the performance of different Radial Base Neural Network and Feed-Forward Perceptron Neural Network to forecast investment returns on the index. To achieve this goal, the return on investment in Tehran Stock Exchange index is evaluated and the performance of Radial Base Neural Network and Feed-Forward Perceptron Neural Network are compared. Neural networks performance test is applied based on the least square error in two approaches of in-sample and out-of-sample. The research results show the superiority of the radial base neural network in the in-sample approach and the superiority of perceptron neural network in the out-of-sample approach.

Keywords: exchange index, forecasting, perceptron neural network, Tehran stock exchange

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4470 Application of Artificial Neural Network to Prediction of Feature Academic Performance of Students

Authors: J. K. Alhassan, C. S. Actsu


This study is on the prediction of feature performance of undergraduate students with Artificial Neural Networks (ANN). With the growing decline in the quality academic performance of undergraduate students, it has become essential to predict the students’ feature academic performance early in their courses of first and second years and to take the necessary precautions using such prediction-based information. The feed forward multilayer neural network model was used to train and develop a network and the test carried out with some of the input variables. A result of 80% accuracy was obtained from the test which was carried out, with an average error of 0.009781.

Keywords: academic performance, artificial neural network, prediction, students

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4469 A Two-Step Framework for Unsupervised Speaker Segmentation Using BIC and Artificial Neural Network

Authors: Ahmad Alwosheel, Ahmed Alqaraawi


This work proposes a new speaker segmentation approach for two speakers. It is an online approach that does not require a prior information about speaker models. It has two phases, a conventional approach such as unsupervised BIC-based is utilized in the first phase to detect speaker changes and train a Neural Network, while in the second phase, the output trained parameters from the Neural Network are used to predict next incoming audio stream. Using this approach, a comparable accuracy to similar BIC-based approaches is achieved with a significant improvement in terms of computation time.

Keywords: artificial neural network, diarization, speaker indexing, speaker segmentation

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4468 Influential Parameters in Estimating Soil Properties from Cone Penetrating Test: An Artificial Neural Network Study

Authors: Ahmed G. Mahgoub, Dahlia H. Hafez, Mostafa A. Abu Kiefa


The Cone Penetration Test (CPT) is a common in-situ test which generally investigates a much greater volume of soil more quickly than possible from sampling and laboratory tests. Therefore, it has the potential to realize both cost savings and assessment of soil properties rapidly and continuously. The principle objective of this paper is to demonstrate the feasibility and efficiency of using artificial neural networks (ANNs) to predict the soil angle of internal friction (Φ) and the soil modulus of elasticity (E) from CPT results considering the uncertainties and non-linearities of the soil. In addition, ANNs are used to study the influence of different parameters and recommend which parameters should be included as input parameters to improve the prediction. Neural networks discover relationships in the input data sets through the iterative presentation of the data and intrinsic mapping characteristics of neural topologies. General Regression Neural Network (GRNN) is one of the powerful neural network architectures which is utilized in this study. A large amount of field and experimental data including CPT results, plate load tests, direct shear box, grain size distribution and calculated data of overburden pressure was obtained from a large project in the United Arab Emirates. This data was used for the training and the validation of the neural network. A comparison was made between the obtained results from the ANN's approach, and some common traditional correlations that predict Φ and E from CPT results with respect to the actual results of the collected data. The results show that the ANN is a very powerful tool. Very good agreement was obtained between estimated results from ANN and actual measured results with comparison to other correlations available in the literature. The study recommends some easily available parameters that should be included in the estimation of the soil properties to improve the prediction models. It is shown that the use of friction ration in the estimation of Φ and the use of fines content in the estimation of E considerable improve the prediction models.

Keywords: angle of internal friction, cone penetrating test, general regression neural network, soil modulus of elasticity

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4467 Predicting the Success of Bank Telemarketing Using Artificial Neural Network

Authors: Mokrane Selma


The shift towards decision making (DM) based on artificial intelligence (AI) techniques will change the way in which consumer markets and our societies function. Through AI, predictive analytics is being used by businesses to identify these patterns and major trends with the objective to improve the DM and influence future business outcomes. This paper proposes an Artificial Neural Network (ANN) approach to predict the success of telemarketing calls for selling bank long-term deposits. To validate the proposed model, we uses the bank marketing data of 41188 phone calls. The ANN attains 98.93% of accuracy which outperforms other conventional classifiers and confirms that it is credible and valuable approach for telemarketing campaign managers.

Keywords: bank telemarketing, prediction, decision making, artificial intelligence, artificial neural network

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4466 Intermittent Demand Forecast in Telecommunication Service Provider by Using Artificial Neural Network

Authors: Widyani Fatwa Dewi, Subroto Athor


In a telecommunication service provider, quantity and interval of customer demand often difficult to predict due to high dependency on customer expansion strategy and technological development. Demand arrives when a customer needs to add capacity to an existing site or build a network in a new site. Because demand is uncertain for each period, and sometimes there is a null demand for several equipments, it is categorized as intermittent. This research aims to improve demand forecast quality in Indonesia's telecommunication service providers by using Artificial Neural Network. In Artificial Neural Network, the pattern or relationship within data will be analyzed using the training process, followed by the learning process as validation stage. Historical demand data for 36 periods is used to support this research. It is found that demand forecast by using Artificial Neural Network outperforms the existing method if it is reviewed on two criteria: the forecast accuracy, using Mean Absolute Deviation (MAD), Mean of the sum of the Squares of the Forecasting Error (MSE), Mean Error (ME) and service level which is shown through inventory cost. This research is expected to increase the reference for a telecommunication demand forecast, which is currently still limited.

Keywords: artificial neural network, demand forecast, forecast accuracy, intermittent, service level, telecommunication

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4465 Artificial Neural Networks for Cognitive Radio Network: A Survey

Authors: Vishnu Pratap Singh Kirar


The main aim of the communication system is to achieve maximum performance. In cognitive radio, any user or transceiver have the ability to sense best suitable channel, while the channel is not in use. It means an unlicensed user can share the spectrum of licensed user without any interference. Though the spectrum sensing consumes a large amount of energy and it can reduce by applying various artificial intelligent methods for determining proper spectrum holes. It also increases the efficiency of Cognitive Radio Network (CRN). In this survey paper, we discuss the use of different learning models and implementation of Artificial Neural Network (ANN) to increase the learning and decision-making capacity of CRN without affecting bandwidth, cost and signal rate.

Keywords: artificial neural network, cognitive radio, cognitive radio networks, back propagation, spectrum sensing

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4464 Process Modeling of Electric Discharge Machining of Inconel 825 Using Artificial Neural Network

Authors: Himanshu Payal, Sachin Maheshwari, Pushpendra S. Bharti


Electrical discharge machining (EDM), a non-conventional machining process, finds wide applications for shaping difficult-to-cut alloys. Process modeling of EDM is required to exploit the process to the fullest. Process modeling of EDM is a challenging task owing to involvement of so many electrical and non-electrical parameters. This work is an attempt to model the EDM process using artificial neural network (ANN). Experiments were carried out on die-sinking EDM taking Inconel 825 as work material. ANN modeling has been performed using experimental data. The prediction ability of trained network has been verified experimentally. Results indicate that ANN can predict the values of performance measures of EDM satisfactorily.

Keywords: artificial neural network, EDM, metal removal rate, modeling, surface roughness

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4463 Transport Related Air Pollution Modeling Using Artificial Neural Network

Authors: K. D. Sharma, M. Parida, S. S. Jain, Anju Saini, V. K. Katiyar


Air quality models form one of the most important components of an urban air quality management plan. Various statistical modeling techniques (regression, multiple regression and time series analysis) have been used to predict air pollution concentrations in the urban environment. These models calculate pollution concentrations due to observed traffic, meteorological and pollution data after an appropriate relationship has been obtained empirically between these parameters. Artificial neural network (ANN) is increasingly used as an alternative tool for modeling the pollutants from vehicular traffic particularly in urban areas. In the present paper, an attempt has been made to model traffic air pollution, specifically CO concentration using neural networks. In case of CO concentration, two scenarios were considered. First, with only classified traffic volume input and the second with both classified traffic volume and meteorological variables. The results showed that CO concentration can be predicted with good accuracy using artificial neural network (ANN).

Keywords: air quality management, artificial neural network, meteorological variables, statistical modeling

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4462 Massively-Parallel Bit-Serial Neural Networks for Fast Epilepsy Diagnosis: A Feasibility Study

Authors: Si Mon Kueh, Tom J. Kazmierski


There are about 1% of the world population suffering from the hidden disability known as epilepsy and major developing countries are not fully equipped to counter this problem. In order to reduce the inconvenience and danger of epilepsy, different methods have been researched by using a artificial neural network (ANN) classification to distinguish epileptic waveforms from normal brain waveforms. This paper outlines the aim of achieving massive ANN parallelization through a dedicated hardware using bit-serial processing. The design of this bit-serial Neural Processing Element (NPE) is presented which implements the functionality of a complete neuron using variable accuracy. The proposed design has been tested taking into consideration non-idealities of a hardware ANN. The NPE consists of a bit-serial multiplier which uses only 16 logic elements on an Altera Cyclone IV FPGA and a bit-serial ALU as well as a look-up table. Arrays of NPEs can be driven by a single controller which executes the neural processing algorithm. In conclusion, the proposed compact NPE design allows the construction of complex hardware ANNs that can be implemented in a portable equipment that suits the needs of a single epileptic patient in his or her daily activities to predict the occurrences of impending tonic conic seizures.

Keywords: Artificial Neural Networks (ANN), bit-serial neural processor, FPGA, Neural Processing Element (NPE)

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4461 Artificial Neural Network-Based Short-Term Load Forecasting for Mymensingh Area of Bangladesh

Authors: S. M. Anowarul Haque, Md. Asiful Islam


Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh.

Keywords: load forecasting, artificial neural network, particle swarm optimization

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4460 Selecting the Best RBF Neural Network Using PSO Algorithm for ECG Signal Prediction

Authors: Najmeh Mohsenifar, Narjes Mohsenifar, Abbas Kargar


In this paper, has been presented a stable method for predicting the ECG signals through the RBF neural networks, by the PSO algorithm. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electro cardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited neural networks that are capable of complicated nonlinear mapping. Although the architecture and spread of RBF networks are usually selected through trial and error, the PSO algorithm has been used for choosing the best neural network. In this way, 2 second of a recorded ECG signal is employed to predict duration of 20 second in advance. Our simulations show that PSO algorithm can find the RBF neural network with minimum MSE and the accuracy of the predicted ECG signal is 97 %.

Keywords: electrocardiogram, RBF artificial neural network, PSO algorithm, predict, accuracy

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4459 Demand Forecasting Using Artificial Neural Networks Optimized by Particle Swarm Optimization

Authors: Daham Owaid Matrood, Naqaa Hussein Raheem


Evolutionary algorithms and Artificial neural networks (ANN) are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of Particle Swarm Optimization (PSO) to train a multi-layer feed forward neural network for demand forecasting. We use in this paper weekly demand data for packed cement and towels, which have been outfitted by the Northern General Company for Cement and General Company of prepared clothes respectively. The results showed superiority of trained neural networks using particle swarm optimization on neural networks trained using error back propagation because their ability to escape from local optima.

Keywords: artificial neural network, demand forecasting, particle swarm optimization, weight optimization

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4458 Modelling Fluoride Pollution of Groundwater Using Artificial Neural Network in the Western Parts of Jharkhand

Authors: Neeta Kumari, Gopal Pathak


Artificial neural network has been proved to be an efficient tool for non-parametric modeling of data in various applications where output is non-linearly associated with input. It is a preferred tool for many predictive data mining applications because of its power , flexibility, and ease of use. A standard feed forward networks (FFN) is used to predict the groundwater fluoride content. The ANN model is trained using back propagated algorithm, Tansig and Logsig activation function having varying number of neurons. The models are evaluated on the basis of statistical performance criteria like Root Mean Squarred Error (RMSE) and Regression coefficient (R2), bias (mean error), Coefficient of variation (CV), Nash-Sutcliffe efficiency (NSE), and the index of agreement (IOA). The results of the study indicate that Artificial neural network (ANN) can be used for groundwater fluoride prediction in the limited data situation in the hard rock region like western parts of Jharkhand with sufficiently good accuracy.

Keywords: Artificial neural network (ANN), FFN (Feed-forward network), backpropagation algorithm, Levenberg-Marquardt algorithm, groundwater fluoride contamination

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4457 Modeling the Philippine Stock Exchange Index Closing Value Using Artificial Neural Network

Authors: Frankie Burgos, Emely Munar, Conrado Basa


This paper aimed at developing an artificial neural network (ANN) model specifically for the Philippine Stock Exchange index closing value. The inputs to the ANN are US Dollar and Philippine Peso(USD-PHP) exchange rate, GDP growth of the country, quarterly inflation rate, 10-year bond yield, credit rating of the country, previous open, high, low, close values and volume of trade of the Philippine Stock Exchange Index (PSEi), gold price of the previous day, National Association of Securities Dealers Automated Quotations (NASDAQ), Standard and Poor’s 500 (S & P 500) and the iShares MSCI Philippines ETF (EPHE) previous closing value. The target is composed of the closing value of the PSEi during the 627 trading days from November 3, 2011, to May 30, 2014. MATLAB’s Neural Network toolbox was employed to create, train and simulate the network using multi-layer feed forward neural network with back-propagation algorithm. The results satisfactorily show that the neural network developed has the ability to model the PSEi, which is affected by both internal and external economic factors. It was found out that the inputs used are the main factors that influence the movement of the PSEi closing value.

Keywords: artificial neural networks, artificial intelligence, philippine stocks exchange index, stocks trading

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4456 Using Artificial Neural Networks for Optical Imaging of Fluorescent Biomarkers

Authors: K. A. Laptinskiy, S. A. Burikov, A. M. Vervald, S. A. Dolenko, T. A. Dolenko


The article presents the results of the application of artificial neural networks to separate the fluorescent contribution of nanodiamonds used as biomarkers, adsorbents and carriers of drugs in biomedicine, from a fluorescent background of own biological fluorophores. The principal possibility of solving this problem is shown. Use of neural network architecture let to detect fluorescence of nanodiamonds against the background autofluorescence of egg white with high accuracy - better than 3 ug/ml.

Keywords: artificial neural networks, fluorescence, data aggregation, biomarkers

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4455 Feedforward Neural Network with Backpropagation for Epilepsy Seizure Detection

Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner


Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG.

Keywords: Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection , Seizure.

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4454 Evaluating Performance of an Anomaly Detection Module with Artificial Neural Network Implementation

Authors: Edward Guillén, Jhordany Rodriguez, Rafael Páez


Anomaly detection techniques have been focused on two main components: data extraction and selection and the second one is the analysis performed over the obtained data. The goal of this paper is to analyze the influence that each of these components has over the system performance by evaluating detection over network scenarios with different setups. The independent variables are as follows: the number of system inputs, the way the inputs are codified and the complexity of the analysis techniques. For the analysis, some approaches of artificial neural networks are implemented with different number of layers. The obtained results show the influence that each of these variables has in the system performance.

Keywords: network intrusion detection, machine learning, artificial neural network, anomaly detection module

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4453 Employing Bayesian Artificial Neural Network for Evaluation of Cold Rolling Force

Authors: P. Kooche Baghy, S. Eskandari, E.javanmard


Neural network has been used as a predictive means of cold rolling force in this dissertation. Thus, imposed average force on rollers as a mere input and five pertaining parameters to its as a outputs are regarded. According to our study, feed-forward multilayer perceptron network has been selected. Besides, Bayesian algorithm based on the feed-forward back propagation method has been selected due to noisy data. Further, 470 out of 585 all tests were used for network learning and others (115 tests) were considered as assessment criteria. Eventually, by 30 times running the MATLAB software, mean error was obtained 3.84 percent as a criteria of network learning. As a consequence, this the mentioned error on par with other approaches such as numerical and empirical methods is acceptable admittedly.

Keywords: artificial neural network, Bayesian, cold rolling, force evaluation

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