The Use Of Artificial Intelligence Techniques For The-Books Pdf

The use of artificial intelligence techniques for the
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Proceedings of the International Conference on Industrial Engineering and Operations Management. Bandung Indonesia March 6 8 2018, 1 Background, Artificial neural networks are inspired by the biological neural system and its ability to learn through. example 1 Mathematical models based on artificial intelligence now serve in support of certain. diagnoses 2 4 Neural networks have the capacity to learn how to make a diagnosis through the. information presented to them 5 8 The history of neural networks dates backs to the mid 20th century. The neural networks may seem complicated at first but they can be easily merged with a medical. environment 9 Today due to the development of knowledge in the medical field as well as complexity. of the decisions related to diagnosis and treatment specialists pay attention to smart tools and decision. support systems in medical issues and the use of different kinds of smart systems in medicine has been. increasing 10 11 Using these tools and systems can decrease the potential errors that may arise due to. the medical specialists tiredness or their inexperience in the diagnosis and treatment of diseases In. addition by using these systems we can analyze the medical database in much less time and in more. detail 10 12 So for this purpose we must use the models that have minimum errors and maximum. confidence Oz den et al 2014 in their study titled Periodontal disease diagnosis using classification. algorithms found that the decision tree and supporting vector machine with high precision were. suitable for periodontal disease classification 13 In 2012 a study conducted by Kositbowornhcahi et. al titled The neural network function for diagnosing vertical fracture of tooth root found that the. neural network designed for their research had high insensitivity accuracy and verity in vertical tooth. root diagnosis 14 In 2008 in their study titled The multilayer perceptron neural network for diagnosis. of proximal plaque DeVito et al reported that according to specialists there was an improvement of. 39 5 in diagnosis 15 Martina et al 2006 showed that neural network can be used as an important. tool for improving medical behaviors and maximizing the profit of treatment costs 16 In a study titled. Estimation of dental ceramics chemical resistance using neural network Zivco Babic et al 2008. reported that artificial neural network has high potential as an additional method in investigating the. properties of dental materials 17 In 2013 Amiri et al s study titled Determining the effect of. qualitative and quantitative prediction of survival of patients with gastric cancer using hierarchical. neural network models concluded that compared to Cox model neural network can accurately. anticipate the probability of survival of patients with gastric cancer 18 Shankarapillai et al 2012. showed that natural network trained by Levenberg Marquardet algorithm can be used effectively in. diagnoses of periodontal disease risk 19 Moghimi et al 2012 conducted a study titled Designing. and using a combination of genetic algorithm and artificial neural network for anticipating the size of. hidden canines and premolar size which showed that the proposed method was an efficient tool for. IEOM Society International, Proceedings of the International Conference on Industrial Engineering and Operations Management. Bandung Indonesia March 6 8 2018, anticipating the size of hidden canines and premolar with high accuracy in comparison to regression. analysis 20 According to the previous studies it can be said that the unique capability of artificial. neural networks to differentiate categorize and diagnose diseases can be efficient and useful 21. Periodontitis is a common inflammatory disease 22 in humans and its main cause is long term. bacterial infection 23 Research on the pathobiology of periodontal disease increases our knowledge. of this disease 24, Artificial neural network, Each artificial neural network is made of input hidden and output layers There are some processing. elements neurons and nodes in each layer A neural network is a set of processors in which each. processor is associated with the processor in the next layer The relations between the network layers. are possible according to weight coefficients and bias of each processor and the threshold and transfer. functions Finally the network output can be regarded as the simulated value resulting from the training. network While training the network it is necessary to minimize the network simulation error by. choosing a suitable learning algorithm In the back propagation error method the main goal is to reduce. the network error rate 25 In this study we used multilayer feed forward neural network with. Levenberg Marquardet algorithms and three major factors of disease diagnosis probing pocket depth. clinical attachment loss and plaque index to diagnose periodontal disease and the results of the. algorithm were studied, 2 Objectives This study aims to introduce a model for periodontal disease diagnosis using.
artificial neural network In this study Levenberg Marquardet algorithm were used. 3 Materials and Methods, In this study a neural network was designed that diagnosed periodontal disease according to the input. variables The system was evaluated by using a data set related to patients with periodontal disease in. the periodontics department of Zahedan Dentistry University in the period between 2014 and 2015 The. features and functions available in Matlab software version 2015 were used for network implementation. According to the specialist the input variables introduced were age sex probing pocket depth clinical. attachment loss and plaque index The overall structure of the artificial neural network was inspired by. the human biological neural network and is a simplified model of the central neural system As an. information processing system the brain is composed of structural main elements named neurons A set. of related neurons comprise tissues called nerves which transfer information and messages from one. point to the other in the body Artificial neural networks include a set of connected neurons each of. which is called a layer 26 Figure 1 shows a single input neuron structure in which p and a are the. neuron input and output respectively, IEOM Society International. Proceedings of the International Conference on Industrial Engineering and Operations Management. Bandung Indonesia March 6 8 2018, Figure 1 Single input neuron model. The effect of p on a is determined by w value Another input is the constant value of 1 which is. multiplied by b italic sentence and then summed with WP The sum is the n net input for conversion or. activation motive function of f So the neuron output is defined as Eq 1. a f WP b 1, Where parameters w and b are adjustable and the motive function of f is determined by the. designer The parameters w and b are set according to the selection of f and the type of learning. algorithm In fact learning means that w and b change so that the relation of neuron input and. output are set with a special goal Finally the neurons are attached by the activation motive. functions to create layers 19 Despite their diversity the artificial neural networks have similar. structures 26 A neural network is usually composed of three layers input hidden and output 27. The input layer only receives the information and acts as an independent variable and therefore the. number of neurons in the input layer is determined on the basis of the problem s nature The output layer. acts as a dependent variable and the number of its neurons depends on the number of independent. variables but unlike the input and output layers the hidden layer shows no meaning and is just an. intermediate result in the process of calculating the output value 28 Feed forward neural networks are. the most applied type of artificial neural networks 26 because the feed forward neural networks with. one hidden layer logistic activation function in the hidden layer linear activation function in the output. layer and enough neurons in the hidden layer can approximate any function with arbitrary accuracy. 27 For this reason this kind of neural network with the above structure is called comprehensive. approximation It means that by having enough numbers of hidden units and suitable numbers of neurons. in this layer the network can almost approximate every linear and nonlinear function with an arbitrary. accuracy level 28 Accordingly a feed forward neural network has been used in this study Data should. be divided into two different sets of train and test samples for designing and training an artificial neural. network because it is necessary to use train and test data for network design 29 Train sample is a set. of network inputs and outputs that is used for training a special work to the network After network. training and learning procedure stop the test sample is used for investigating the network efficiency. 28 Most researchers select the train and test samples with either one of the rules of 90 against 10. 80 against 20 or 70 against 30 30 Naturally the selection of any rule depends on the problem. type But different researches have indicated that increasing the number of train samples improves the. network operation in the field of anticipation 29 In this study about 80 of data was used as train. IEOM Society International, Proceedings of the International Conference on Industrial Engineering and Operations Management.
Bandung Indonesia March 6 8 2018, sample and 20 of data as test sample For training were used during the design phase in the data. related to 160 patients train The 30 remaining data were used to simulate neural network models for. each of the algorithms test applied The input variables factors for periodontal disease diagnosis were. investigated in all the 190 patients as is evident in Table 1 The data were imported to the Matlab. software as input values Periodontal disease diagnosis on each patient s record was made in 1 4 interval. by one specialist so that target parameter 1 was regarded for the values of attachment loss index that. were between 1 and 2 target parameter 2 for the values of attachment loss index in 2 and 3 intervals. target parameter 3 for the values between 3 and 4 and target parameter 4 for the values between 4 and. 5 Therefore 40 data were considered with target parameter 1 40 data with target parameter 2 40 data. with target parameter 3 and 40 data with target parameter 4 for the train phase The 30 remaining data. including seven data with target parameter 1 seven data with target parameter 2 eight data with target. parameter 3 and eight data with target parameter 4 were considered The mean square error and. regression parameters with maximal 1000 epoch were considered for the two algorithms Descending. slope with momentum weight and bias learning function and mean square error function were used for. Levenberg Marquardet algorithms The Sigmoid transfer function was selected for both layers The. Levenberg Marquardet algorithm was trained with 1000 epochs and minimum tangent of 1e 010 and. infinite time First 160 samples were trained for designing the natural network by Levenberg. Marquardet algorithm The outputs of both the trained networks were saved and the results were. compared for determining the most efficient algorithm for periodontal disease diagnosis. Table 1 Factors of periodontal disease diagnosis and their values range as input parameters. Factors The range of values, Sex Female male, Probing Pocket Depth 1 4. Clinical Attachment 0 4, Plaque Index 0 100, The Matlab programming environment version 2015 was used to implement the algorithms An artificial. neural network modeling process was performed by a set of training data First 160 samples were used. to train the neural network for both the Levenberg Marquardet algorithms and then 30 remaining. IEOM Society International, Proceedings of the International Conference on Industrial Engineering and Operations Management. Bandung Indonesia March 6 8 2018, samples were used to test the neural network By fitting different artificial neural networks a model was.
designed with two hidden layers 20 neurons in the first hidden layer 4 neurons in the second hidden. layer and 5 neurons are output 5 20 4 4 Table 2 shows the designed neural network output after the. implementation of the multilayer perceptron neural network in the Matlab software by the Levenberg. Marquardet algorithms The train phase for the Levenberg Marquardet algorithm was performed in. 6 5870 seconds with six validations in 16 iterations The rates of regression for train validation and test. phase were computed as 0 9649 0 8687 and 0 7354 respectively The compound regression for the. three phases of train validation and test was computed as 0 9054 The optimal Gradient in this study. was 0 0012 In the present study the best performance of error validation in the Levenberg Marquardet. algorithms in periodontal disease diagnosis indicated that the Levenberg Marquardet algorithm training. in 22 performances gained 0 0098 for mean square error there by showing that the Levenberg. Marquardet algorithm has a best performance in error management The numbers of iterations for the. Levenberg Marquardet algorithm were 16 so the algorithm arrived with low iterations According to. the results The Levenberg Marquardet algorithms were performed in 6 5870 seconds respectively. indicating that the Levenberg Marquardet algorithm performed soon Among the three factors of error. Artificial neural networks are inspired by the biological neural system and its ability to learn through example 1 Mathematical models based on artificial intelligence now serve in support of certain diagnoses 2 4 Neural networks have the capacity to learn how to make a diagnosis through the information presented to them 5 8 The history of neural networks dates backs to the mid

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