Artificial Neural Data M Network Tools for Co ...

Artificial Neural Network Tools for Coomputerised Data Modeling M and Processinng Jiri Krenek( ), Kamil Kuca,,Ondrej Krejcar, Petra Maresova, Vladimir Sobesslav, Pavel Blazek Center for Basic and Applied Research Faculty of Informatics and Management, University of Hradec Kralove, Rokittanskeho 62 Hradec Kralove, Czech Republic [email protected], kamil.kucaa, ondrej.krejcar, petra.maresova, vladimir.sobeslav, [email protected] Abstract—Artificial Neural Networks (ANN) represent progressive method for information processsing. Due to their capability to solve complex problems by maanipulation of high volume data the designation, training and usagge of ANN requires computer environment. Due to the wide posssibilities of ANN in current data processing, many of SW solutionss can be used while the problem is in finding the best suitable for desired problem. This paper reviews available special ANN sofftware separated in various categories specified by licensing poolicy, the software environment, etc..The article focuses namelyy on open source license variations. It compares different useer interfaces from simple diagram-like visual designs to special programming language scripting. Contribution of this paper is to give to reader a complex summary on which ANN SW is thee best suitable for a specific problem. Keywords—artificial neural network; simulations; software; licensing;open source

data

Transfer function can be liinear but due to fact that ANNs are used for solving complex nonlinear problems the transfer function is most often nonllinear, for example sigmoidal function. Except the input and outpuut layer ANN can have one or more hidden layers. Signal cann be transferred from one layer to the next layer and such network is call feed forward network. There exist also recurrrent networks where signal can be either transferred to the preevious layer or can make a loop in the same layer or even saame neuron. The results of the ANN are then exposed in the output layer.

processing;

I. INTRODUCTION Artificial neural networks (ANN) play a significant role in a prediction of data processing,manipulation, extraction and information. ANNs are mathematical proccessing structures inspired by biological structure - a human brrain. Human brain is a complicated structure consisting of billions neuron cells connected in complex network[11]. Every cell consists of cell body called soma, input transferring axons and output signal transferring dendrites. Axons of one neurron are connected with dendrites of other neurons, typically thoousands, and these connections are called synapses. Similarly thhe artificial neuron consists of cell body called processing eleements and inputs and outputs[12]. Significance of every input is manipulated by multiplication of input values by assignned weights. All adjusted inputs including the bias are in i the processing element utilized bysummation andtransfer fuunctionFig. (1). The mathematical description of artificiial neuron can be written as equation (1)

⎛ n ⎞ y = f ⎜ ∑ wi ⋅ xi + b ⎟ ⎝ i =0 ⎠

(1)

where: y is the output value of the neurron; xi is the input value; wi is the weight value, b is the bias annd f is the transfer function.

Fig. 1. Artificial neuron as a mathemaatical processing unit.

Manipulation of input weigghts is called training[1, 7]. Data processed within ANN has to be divided in two groups. One u for training of the ANN group called training set is used model. The other group calleed validation set contains data different from data present in the training set. The validation data are used for evaluation of conformity rate of trained ANN model.Once the network is suuccessfully trained, there is no way to describe the relationshiips and formulas used by ANN. Therefore the ANNs are callled ”black boxes”[6]. Neurons behave like simple tresholdingg unit and the ANN models are able to represent simple Boollean operations AND and OR. Unfortunately the ANN cannott represent the operation XOR – exclusive or, where the outputt gets the true value just in case where one or the other input is i true,but not both at the same time[13]. There exist variety of topologies of ANNs [5, 6, MLP), Kohonen self-organising 9]like multilayer perceptron (M maps (SOM), Bayesian probaabilistic networks (PNN), radial basis function networks (RBF)),where each is suitable to solve

characteristic problems. Training process exists also in multiple different mechanisms and can be basically divided into two groups: supervised and unsupervised training. The supervised training is the one where the dataset is split into two groups – training and validation. During the training iterations there is evaluated the conformity rate on validation dataset. The unsupervised training is used for optimisation of the system like to minimise the energy or maximise the profit. The potential of the ANN lies in the connection complexity and the number of neurons used. In case the training data set is too small and the number of neurons in ANN too high there can be very low conformity rate of ANN results. On the other side, too complex input data with too high number of input neurons can lead to over-fitting, the common phenomenon of incorrectly designed network. Therefore the data has to be optimised prior to processing in ANN. On the top of it, too high number of input neurons overload the process of calculations within ANN structure. While the number of inputs raises linearly the number of calculations raises exponentially. For this purpose there were designed several tools like correlation analysis, multilinear regression or genetic algorithms for data pre-processing. ANNs are called systems of artificial intelligence (AI) but the definition is limited with the fact, that the trained ANN model can solve specific problem only. It can manage the task which it was trained for. It cannot be used to solve another problems. The AI is fulfilled just in the way that the ANN can be trained on known training dataset. It later results in the biggest advantage that trained ANN can handle set of incomplete data, can cover and operate with noisy data and can predict unknown data based on defined attributes and the relationships between the known data[2]. ANNs are widely used for numerous predictions in diverse fields. They are frequently used in economics, weather forecast, chemistry, medicine, pharmacy, industry and it’s modern products. From articles of daily use the well knownapplications of ANNs are in digital cameras, adaptive systems in cars, software for optical character recognition – OCR, etc. From the above described findings and the fact that ANNs are used for processing of high volume data it is clear that the only option is to use environment of computer to work with ANN. In the first step the data has to be pre-processed, in the second step the data has to be processed within ANN and in the last step there comes into play extensive statistical evaluation. There exist numerous different SW that can be segmented by: designation to operating system, license policy, SW environment, etc. Due to fact that the Windows operating system (OS) is the most extended between the computer users, this paper focuses on the SW compatible with this OS [3]. It can be basically said that from the Windows compatibility point of view, systems are supported from version of Windows XP with installed service pack 3. Minimum hardware configuration is generally defined as Pentium 4 processor or AMD-compatible, cca 100 MB free harddrive space and the minimum video resolution is 1024x768. From the above mentioned definitions it is clear that almost any computer not older then 8 years is capable to run the ANN SW. It is based on design of SW focused to work with raw data and for practical purposes and

not to high end graphical outputs.The program is limited by the hardware of the computer which has direct influence on SW performance. The target of user’s focus on improvementofprocessingtimeshould be more on number and frequency of processors, RAM capacity and the bus frequency rather than on other HW parameters. SW licenses used for ANN software are commercial, trial, shareware, open source. Commercial SW permits the holder to use SW just in case of license purchase and can be used for any (business or private) purpose. To give the users chance to test the SW prior to purchase, the SW vendors offervarious limited versions of their programs, called trial or shareware. Trial is commonly fully working version of SW with no limitation on SW function for defined time period. Most occasionally it is 30 days. Shareware version is such modification of commercial SW, that is not limited by time period for usage but it uses limitation on functions like disabled work progress and results saving, no results export option or by limitation of some producer’sspecific functions. Open source licenses are specific in their policy. In general they can be freely used, distributed and shared, or changed by anyone. The list of all different licenses covered by Open Source Initiative definition are available at www.opensource.org. Compared to widely known freeware license which in most of cases cannot be used for commercial purposes, open source licensed SW can be used for commercial purpose. SW licensing will be used for further ANN SW tools categorisation. Overview of available software with www address is in Tab. I. II. STAND ALONE APPLICATIONS Stand alone applications are such programs that are installed directly on operating system and their function is not dependent on any other applications or environment. Such application has it’s own license and with the exception of operating system’s license it does not require any other additional license. A. Commercial stand alone applications SPSS Modeler is mainly designed as the data mining software by IBM. It was originally developed by Integral Solutions limited and was called Clementine. The name Modeler is used since the acquisition by IBM in 2009. IBM SPSS Modeler is available in numerous of different commercial licenses differing by functionality. Neural network option is part of basic license. The advantage of the Modeler is in it’s focus on data mining which massively improves data pre- and post-processing. The environment of Modeler is called stream and it consists of different nodes. Working with nodes is very easy, they can be connected in numerous waysand each of them has specific function and settings. Manipulation with nodes is by drag and drop functionality. The only option to test the Modeler is to contact IBM representative directly for trial version. Neurosolutions is the commercial SW by NeurodimensionInc, designed specially for ANN applications. It can be run either as a stand alone application or can be integrated to another SW like MS Excel, Matlab, etc. It’s ANN editor is icon based and easy to use. The Neurosolutions contain very useful tool called Neural Builder that helps to

design the neural network. The user has to choose the initial neural model and follow the tutorial instructions and commands to define test and validation data and other neural network options, for example training parameters. For testing TABLE I. Tool name IBM SPSS Modeler

purpose is the Neurosolutions available as a monthly trial with limited saving option, no results can be saved or exported while the designed ANN model storage is still available.

LIST OF ARTIFICIAL NEURAL NETWORK TOOLS

Environment SAa

License Commercial

http://www-01.ibm.com/software/analytics/spss/products/modeler/

SAa

Commercial

http://www.neurosolutions.com/neurosolutions/

Neurointelligence

a

SA

Commercial

http://www.alyuda.com/neural-networks-software.htm

Neural Designer

SAa

Commercial

http://www.intelnics.com/neuraldesigner/introduction

NeuralSight

SAa

Commercial

http://www.neuralware.com/

a

Commercial

http://www.gmdhshell.com/neural-network-software

NeuroShell 2

a

SA

Commercial

http://www.wardsystems.com/neuroshell2.asp

EasyNN plus

SAa

Commercial

http://www.easynn.com/

JustNN

SAa

Freeware

http://www.justnn.com/

Neurosolutions

GMDH Shell Neural Network Software

Neuroph Studio

SA

Link

a

Appache 2.0

http://neuroph.sourceforge.net/

a

http://sourceforge.net/projects/neurallibs/

SA

Neuro Libs (NeuroSimulator)

SA

GNU LGPL

Weka

SAa

GNU GPL

http://www.cs.waikato.ac.nz/ml/weka/

Fast artificial neural network library (FANN)

SAa

GNU GPL

http://leenissen.dk/fann/wp/

GPL

http://mbp.sourceforge.net/

Multiple Back Propagation (MBP) Nengo

a

SA

a

SA

Appache

Statistica

Commercial

http://www.statsoft.com/Products/STATISTICA/Automated-Neural-Networks

Matlab

Commercial

http://www.mathworks.de/products/neural-network/

Mathematica

Commercial

http://www.wolfram.com/products/applications/neuralnetworks/

NeuroXL

MS Excel

Commercial

http://neuroxl.com/

NeuralWorks Predict

MS Excel

Commercial

http://www.neuralware.com/

Neural Network Program (NNP)

Visual Prolog

Commercial

http://wiki.visual-prolog.com/index.php?title=Neural_Network_Program

Open Neural Networks Library (OpenNN)

Visual Prolog

Commercial

http://sourceforge.net/projects/opennn/

Statistica Automated Neural Networks Neural Network Toolbox Neural Networks

http://www.nengo.ca/

PyBrain

Python

GPL

http://pybrain.org/pages/home

Brian spiking neural network simulator

Python

GPL

http://briansimulator.org/

Feed-forward neural network solution

Python

GPL

http://ffnet.sourceforge.net/

Java

Freeware

Bayesian Network tools

Java

GNU GPL

http://bnj.sourceforge.net/

Simbrain

Java

GNU

http://www.simbrain.net/

UnBBayes

Java

GNU GPL

Java Neural Network Simulator

http://www.ra.cs.uni-tuebingen.de/software/JavaNNS/

http://unbbayes.sourceforge.net/ a.

Another commercial stand alone application is AlyudaNeurointelligence. Comparing with Neurosolutions and Modeler the work in Neurointelligence is through the classic dialogs known from standard Windows applications and the environment of this SW is similar to table processor. For neural network modelling it uses one dataset with possibility to set attributes for identification of training and validation data. Data attributes can be set by manual entry or by automated randomization algorithms. Operating the Neurointelligence is very intuitive and from the preprocessing until the reach of predicted results very easy. During the testing phase there is very well worked out option of visualization of conformance rate of each dataset. Neurointelligence is available as monthly trial without any functional limitation.

Stand alone application

Neural Designer by Intelnics TM is a commercial application available as 30 days trial with GUI similar to Neurontelligence. The software package contains useful tools Assistant with detailed tutorials and examples and Viewer to display and store neural network design as a picture. NeuralWare is a developer which offers set of ANN related applications from which the NeuralWorks represent the fundamental prediction engine for the rest of NeuralWare products. There are available special mutations, for example NeuralWorks Predict for creation and use of ANNs by scripting or as a MS Excel add-on, NeuralSight as a visual GUI for easy creation and configuration of ANNs or a NeuralPower with integrations of electricity industry specific details. GMDH Shell Neural Network Software by Geos Research Group, LLC.isavailable in different versions according to

purpose of use. It is feasible to automatically create neural network models without preliminary input data normalisationand beside the ANN it is a powerful data mining, fitting and visualisation tool. Ward Systems Group, Inc.(NeuroShell2) and Neural Planner Software (EasyNN plus)are developers who offer different ANN software packages from which the most important are noted in brackets. On the top of the commercial tools the Neural Planner Software offers a free tool JustNNwhich has no limitation on ANN design but from EasyNN plus it has for example no macros or scripting options. B. Open-Source licensed stand alone applications Fast artificial neural network library (FANN) is a neural network library implementing multilayer ANNs.FANN is not completely a stand alone application, there has to be installed additional graphical user interfaces (GUI) as well from which the FANN Tool is highly recommended by developer. Design of ANN can have up to three hidden layers and there are present various activation functions for hidden and output layers. FANN is especially characteristic with it’s programming language binding with approx. 20 languages like C++, PHP, Python, Java, Matlab, R, Visual Prolog, etc [3, 4, 8, 10, 20]. It can recommend network and training architecture based on available data and their internal relationships. FANN is also very well documented, additionally with basic neural network theory and can be run on multiple platforms for example Windows,Linux or iOS. Weka is Java based GNU GPL software with functionality focused on machine learning and data mining. ANN designs present in Weka Explorer are Bayesian neural networks (BNN), multilayer perceptrons (MLP) and radial basis functions (RBF). The SW has preprocessing functions incl. an option of filtering by many parameters, and the bigdata processing tools are separated in groups for classification, clustering and association.On the top of the standard functionalities it has built-in tools for different data visualisation and for editing the input data in it’s specific format. Neuroph Studio is Java based ANN development environment licensed under Appache 2.0 license compatible with GPL license of OpenSource foundation. It supports most of the typical ANN designs and on the top of it the network designs can be completely modified or even created in a very flexible matter. For creation of own design the software uses simple drag and drop method by choosing types of nodes, layers, connections, input and transfer functions and the learning rules. Visually the SW is very simple and intuitive. All the work is saved in project with separate files for neural networks, test data and training data. Neural Libs (Neuro simulator) is GNU LGPL package combining various programming language but it is limited by the functionality. The design of ANN is limited up to two hidden layers, five activation functions and an option for bias of each layer. Unfortunately there is no support for opening files from external programs or import external datasets. All data entry has to be set manually within the SW environment. Multiple Back Propagation (MBP) is application licensed under GPL license and is specifically designed for training neural networks with back propagation algorithm. The MBP

needs to read train and test data sets and allows option to configure training parameters. It provides graphical comparison of original and the desired data for training and testing data sets. Nengo licensed under Apache license is a large-scale neural network simulator. It supports in addition from classical ANN topologies and training algorithms the very complex problems of machine learning and a for example there exist a functional brain model. The main difference from the other ANN software is, that the synaptic weights are adjusted in different manner. Beside the functionaloity from classical models where the weights are set manually or randomly here in Nengo the weights could be additionally specified by a function and the SW will set the weights to approximate the function. Nengospecifical function is an interactive plot which can be viewed during training phase. III. APPLICATION ADD-ONS AND MODULES Application add-ons and modules are tools that run in the environment of another SW. It can be a special SW that was originally designed for another purpose (Matlab, Statistica, ...) or it can be a programming language engine in which the addons run (Java, Python, Visual Prolog). The disadvantage of commercial add-ons and modules is that there has to be purchased two licenses – one for the sw environment and the other for the ANN tool. A. Commercial add-ons and modules Statistica Automated Neural Networks (SANN) is a part of professional statistical SW Statistica from StatSoft Inc. It’s advantage is a well sofisticated and detailed evaluation of computed data during pre- and post-processing phases. The ease of use is supported by availability of ANN design wizard for computer-supported choice of pre-built network topologies. The ANNs designed in SANN can be almost unlimited in size which borders with the reasonability and effectivity of the neural network application with need of over-fitting elimination. The integration with Statistica SW gives the opportunity to use high variety of graphical outputs and support for different input and output data formats. Neural Network Toolbox (NNT) for Matlab by The Mathworks Inc. and Neural Networks for Mathematica (NNM) by Wolfram are another versatile tools running inside of well known environment. Matlab and Mathematica are both originally used for various numerical computation tasks to solve mathematical and engineering problems, to visualize the data and to program basic applications and software components [4, 8, 10]. Each of the NNT and the NNM supports work of the advanced and unexperienced users by visual and intuitive network design wizards. Due to flexibility of the Matlab and Mathematica are the data pre- and postprocessing very resourceful. NeuroXL by Olsoft LLC., is an add-on to standard Microsoft Excel and it is a package made of two modules Predictor and Clusterizer. The advantage of this software is usability in mass-distributed and used spreadsheet program. It is available as 10 days trial. Both packages follow the logic of work in Excel, ie. the choice of inputs and outputs is done by choosing the cells knownfrom standard spreadsheet usage.

The limitation of this software is already obvious from it’s module names, ie. clustering and predicting. B. Visual prolog add-ons, modules and applications Visual Prolog (VP) by Prolog Development Center A/S (PDC) is a commercial programming environment. It is free to use for private purpose if registered at PDC. The business use is allowed with commercial license only. The disadvantage of the VP lies in the need to purchase the environment although the specific ANN applications are licensed under opensource conditions. Similarly to further described Python programming language all applications for ANNs made in VP are programmed and checked by scripting. The Neural Network Program (NNP) and Open Neural Networks Library (OpenNN) developed in Visual Prolog can build and work with a neural network. Unfortunately NNP does not have the option to train the designed networks. IV. OPEN-SOURCE ADD-ONS, MODULES AND APPLICATIONS A. Python programming language Python is a dynamic open source object oriented programming languagedeveloped in early 90’s. It is a part of standard sw package in many Linux operating system distributions. The main work in Python is based on scripting and therefore it is not user friendly and requires good knowledge of the Python programming language and this phenomenon is common in all here introduced Python addons. PyBrain is modular machine learning library, and is an abbreviation of Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library. It containst all basic algorithms and topologies for processing of ANNs and is capable to run training algorithms with supervised and unsupervised learning. The Brian spiking neural network simulatoris next Python programming language ANN tool. Brian uses to increase computing speed all vectorised computations rather than numeric values. Feed-forward neural network solution (FFNET) for Python is tool limited for use of feed-forward ANN architecture only and is capable to export data in Fortran (programming language) format and to make them useable in another Fortran compatible applications. B. Java SDK add-ons, modules and applications Java is a programming language and environment developed by Oracle Corporation. The Java Platform SDK allows to programm different applications. Applications made in Java are mainly designed as user-friendly environment oriented on dialogswith command options like standard Windows applications. Java Neural Network Simulator (JNNS) is application licensed under GNU-GPL with well made and comfortable GUI. Simbrain is a visual neural network simulator in Java programming language with simple GUI licensed under GNUGPL. It has functions with which it is possible to watch dynamic learning process of the designed ANN. Bayesian Network tools in Java(BNJ) is licensed under GNU-GPL and together with another java

opensourceUnBBayes developed to solve problems with probabilistic networks. It supports unexperienced users by neural network designing and learning wizard. V. COMPARISON OF SELECTED SW There are thousands papers published with miscellaneous investigations using ANNs for clasification, clustering and prediction. Owing to the fact that our research group is focused on Biomedicine, ANNs are used for Biomedical data processing which is a very specific topic [14-19]. In pharmaceutical industry the tools of AI support the development of new potential drugs to save resources by predicting new potential compounds, simulating drug release profiles. Medicinal usage focuses on treatment and diagnosis, the ANNs have been used in radiology, CT colonography, diagnosis of Alzheimer’s disease or analysis of electroencephalogram signals, ... During last years there have been a huge progress in using ANNs for early diagnosis of cancer and classification of tumors on malign and benign. From the above mentioned SW packages some are used in papers more often than the others. Matlab was succesfullyused in works [23, 24] for detection and diagnosis of cancer tumors. Both used ANNs for analysis of digitalised images. For breast cancer diagnosis [23] there were used infrared thermal images, in case of brain tumors detection [24] the pictures came from magnetical resonance imaging. Neurointelligence showed reliable results in breast cancer diagnosis [25] achieving performance with 95% classification accuracy. Another published application of Neurointelligence wasin prediction of neonatal diseases [26] where it reached the best results comparing with other techniques. Processing of electrocardiography (ECG) [27] and electromyography (EMG) [28] signals was task for Neurosolutions. These signals contain certain amount of noise that has to be reduced or even removed and after that signal is classified. ECG classification of arrythmia diseases [27] has reached 82% accuracy. One of the open source representatives WEKA was used for comparison of different techniques [21]for classification of breast cancer. There were compared RBF, PNN, decision trees techniques and other techniques from which the best results 90% was obtained with PNN. Some of the SW packages were studied in direct performance comparison on the same datasets. Neurosolutionsand Neuroshellwere used for modeling of process of nanoparticle preparation [22] where the authors lean to use theNeurosolutions due to it’s wide flexibility of ANN topology designs and a pre-processing features. Neurosolutionsand Modeler were compared on the same dataset in three modifications differing by the distribution of dependent variables from 1:1 to 3:1 [31].With distribution rate increase the better prediction accuracy was reached with Modeler.Neurosolutions demonstrated more stable results of predicting individual values independent on dependent variable distribution rate while Modeler was not successful in stable prediction accuracy of both variables. The authors propose Modeler as more suitable for predictions of problems like classification of breast cancer while the Neurosolutions fits more to prediction of individual values. The fact of steady results of Neurosolutions on individual values was also

verified in SW comparison on prediction of hydraulic data [32] with highlight on intuitive and easy use of this tool. VI. CONCLUSION There are available numerous software tools to design and manage the ANNs. Beside the commercial programs there are available various open source projects with capabilities close to professional applications. The decision aboutsuitable software package could be facilitated by availability of someSW by upgrade with add-on tools or by knowledge of programming language and developing own script based ANN models. Some open source projects show strong potential promoted by worldwide community support and further SW development and evolution.Our future goal is to perform direct comparison of some SW packages on biomedicinal datanot only between with commercial programs but together with open source projects. ACKNOWLEDGMENT This work and the contribution were supported by project “SP/2014 - Smart Solutions for Ubiquitous Computing Environments” Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic. This work was also supported by the project No. CZ.1.07/2.2.00/28.0327 Innovation and support of doctoral study program (INDOP), financed from EU and Czech Republic funds. REFERENCES [1]

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