The present study intends to propose identification methodologies for multistorey shear buildings using the powerful technique of Artificial Neural Network (ANN) models which can handle fuzzified data. Identification with crisp data is known, and also neural network method has already been used by various researchers for this case. Here, the input and output data may be in fuzzified form. This is because in general we may not get the corresponding input and output values exactly (in crisp form), but we have only the uncertain information of the data. This uncertain data is assumed in terms of fuzzy number, and the corresponding problem of system identification is investigated.
{"title":"Fuzzified Data Based Neural Network Modeling for Health Assessment of Multistorey Shear Buildings","authors":"Deepti Moyi Sahoo, S. Chakraverty","doi":"10.1155/2013/962734","DOIUrl":"https://doi.org/10.1155/2013/962734","url":null,"abstract":"The present study intends to propose identification methodologies for multistorey shear buildings using the powerful technique of Artificial Neural Network (ANN) models which can handle fuzzified data. Identification with crisp data is known, and also neural network method has already been used by various researchers for this case. Here, the input and output data may be in fuzzified form. This is because in general we may not get the corresponding input and output values exactly (in crisp form), but we have only the uncertain information of the data. This uncertain data is assumed in terms of fuzzy number, and the corresponding problem of system identification is investigated.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"33 1","pages":"962734:1-962734:12"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75978003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nystagmus recordings frequently include eye blinks, noise, or other corrupted segments that, with the exception of noise, cannot be dampened by filtering. Wemeasured the spontaneous nystagmus of 107 otoneurological patients to forma training set for machine learning-based classifiers to assess and separate valid nystagmus beats from artefacts. Video-oculography was used to record threedimensional nystagmus signals. Firstly, a procedure was implemented to accept or reject nystagmus beats according to the limits for nystagmus variables. Secondly, an expert perused all nystagmus beats manually. Thirdly, both the machine and the manual results were united to form the third variation of the training set for the machine learning-based classification. This improved accuracy results in classification; high accuracy values of up to 89% were obtained.
{"title":"The Classification of Valid and Invalid Beats of Three-Dimensional Nystagmus Eye Movement Signals Using Machine Learning Methods","authors":"M. Juhola, H. Aalto, H. Joutsijoki, T. Hirvonen","doi":"10.1155/2013/972412","DOIUrl":"https://doi.org/10.1155/2013/972412","url":null,"abstract":"Nystagmus recordings frequently include eye blinks, noise, or other corrupted segments that, with the exception of noise, cannot be dampened by filtering. Wemeasured the spontaneous nystagmus of 107 otoneurological patients to forma training set for machine learning-based classifiers to assess and separate valid nystagmus beats from artefacts. Video-oculography was used to record threedimensional nystagmus signals. Firstly, a procedure was implemented to accept or reject nystagmus beats according to the limits for nystagmus variables. Secondly, an expert perused all nystagmus beats manually. Thirdly, both the machine and the manual results were united to form the third variation of the training set for the machine learning-based classification. This improved accuracy results in classification; high accuracy values of up to 89% were obtained.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"15 1","pages":"972412:1-972412:11"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74382360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The present paper deals with treatment of slaughterhouse wastewater by conducting a laboratory scale sequencing batch reactor (SBR) with different input characterized samples, and the experimental results are explored for the formulation of feedforward backpropagation artificial neural network (ANN) to predict combined removal efficiency of chemical oxygen demand (COD) and ammonia nitrogen (NH4+-N). The reactor was operated under three different combinations of aerobic-anoxic sequence, namely, (4 + 4), (5 + 3), and (5 + 4) hour of total react period with influent COD and NH4+-N level of 2000 ± 100mg/L and 120 ± 10 mg/L, respectively. ANN modeling was carried out using neural network tools, with Levenberg-Marquardt training algorithm. Various trials were examined for training of three types of ANN models (Models "A," "B," and "C") using number of neurons in the hidden layer varying from 2 to 30. All together 29, data sets were used for each three types of model for which 15 data sets were used for training, 7 data sets for validation, and 7 data sets for testing. The experimental results were used for testing and validation of three types of ANN models. Three ANN models (Models "A," "B," and "C") were trained and tested reasonably well to predict COD and NH4+-N removal efficiently with 3.33% experimental error.
{"title":"Artificial Neural Network Modeling for Biological Removal of Organic Carbon and Nitrogen from Slaughterhouse Wastewater in a Sequencing Batch Reactor","authors":"Pradyut Kundu, A. Debsarkar, S. Mukherjee","doi":"10.1155/2013/268064","DOIUrl":"https://doi.org/10.1155/2013/268064","url":null,"abstract":"The present paper deals with treatment of slaughterhouse wastewater by conducting a laboratory scale sequencing batch reactor (SBR) with different input characterized samples, and the experimental results are explored for the formulation of feedforward backpropagation artificial neural network (ANN) to predict combined removal efficiency of chemical oxygen demand (COD) and ammonia nitrogen (NH4+-N). The reactor was operated under three different combinations of aerobic-anoxic sequence, namely, (4 + 4), (5 + 3), and (5 + 4) hour of total react period with influent COD and NH4+-N level of 2000 ± 100mg/L and 120 ± 10 mg/L, respectively. ANN modeling was carried out using neural network tools, with Levenberg-Marquardt training algorithm. Various trials were examined for training of three types of ANN models (Models \"A,\" \"B,\" and \"C\") using number of neurons in the hidden layer varying from 2 to 30. All together 29, data sets were used for each three types of model for which 15 data sets were used for training, 7 data sets for validation, and 7 data sets for testing. The experimental results were used for testing and validation of three types of ANN models. Three ANN models (Models \"A,\" \"B,\" and \"C\") were trained and tested reasonably well to predict COD and NH4+-N removal efficiently with 3.33% experimental error.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"109 3 1","pages":"268064:1-268064:15"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89733369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper investigates the solution of Ordinary Differential Equations (ODEs) with initial conditions using Regression Based Algorithm (RBA) and compares the results with arbitrary- and regression-based initial weights for different numbers of nodes in hidden layer. Here, we have used feed forward neural network and error back propagation method for minimizing the error function and for the modification of the parameters (weights and biases). Initial weights are taken as combination of randomas well as by the proposed regression based model. We present the method for solving a variety of problems and the results are compared. Here, the number of nodes in hidden layer has been fixed according to the degree of polynomial in the regression fitting. For this, the input and output data are fitted first with various degree polynomials using regression analysis and the coefficients involved are taken as initial weights to start with the neural training. Fixing of the hidden nodes depends upon the degree of the polynomial. For the example problems, the analytical results have been compared with neural results with arbitrary and regression based weights with four, five, and six nodes in hidden layer and are found to be in good agreement.
{"title":"Comparison of Artificial Neural Network Architecture in Solving Ordinary Differential Equations","authors":"Susmita Mall, S. Chakraverty","doi":"10.1155/2013/181895","DOIUrl":"https://doi.org/10.1155/2013/181895","url":null,"abstract":"This paper investigates the solution of Ordinary Differential Equations (ODEs) with initial conditions using Regression Based Algorithm (RBA) and compares the results with arbitrary- and regression-based initial weights for different numbers of nodes in hidden layer. Here, we have used feed forward neural network and error back propagation method for minimizing the error function and for the modification of the parameters (weights and biases). Initial weights are taken as combination of randomas well as by the proposed regression based model. We present the method for solving a variety of problems and the results are compared. Here, the number of nodes in hidden layer has been fixed according to the degree of polynomial in the regression fitting. For this, the input and output data are fitted first with various degree polynomials using regression analysis and the coefficients involved are taken as initial weights to start with the neural training. Fixing of the hidden nodes depends upon the degree of the polynomial. For the example problems, the analytical results have been compared with neural results with arbitrary and regression based weights with four, five, and six nodes in hidden layer and are found to be in good agreement.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"17 1","pages":"181895:1-181895:12"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81037343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial neural networks due to their general-purpose nature are used to solve problems in diverse fields. Artificial neural networks (ANNs) are very useful for fractal antenna analysis as the development of mathematical models of such antennas is very difficult due to complex shapes and geometries. As such empirical approach doing experiments is costly and time consuming, in this paper, application of artificial neural networks analysis is presented taking the Sierpinski gasket fractal antenna as an example. The performance of three different types of networks is evaluated and the best network for this type of applications has been proposed. The comparison of ANN results with experimental results validates that this technique is an alternative to experimental analysis. This low cost method of antenna analysis will be very useful to understand various aspects of fractal antennas.
{"title":"Artificial Neural Network Analysis of Sierpinski Gasket Fractal Antenna: A Low Cost Alternative to Experimentation","authors":"B. S. Dhaliwal, S. S. Pattnaik","doi":"10.1155/2013/560969","DOIUrl":"https://doi.org/10.1155/2013/560969","url":null,"abstract":"Artificial neural networks due to their general-purpose nature are used to solve problems in diverse fields. Artificial neural networks (ANNs) are very useful for fractal antenna analysis as the development of mathematical models of such antennas is very difficult due to complex shapes and geometries. As such empirical approach doing experiments is costly and time consuming, in this paper, application of artificial neural networks analysis is presented taking the Sierpinski gasket fractal antenna as an example. The performance of three different types of networks is evaluated and the best network for this type of applications has been proposed. The comparison of ANN results with experimental results validates that this technique is an alternative to experimental analysis. This low cost method of antenna analysis will be very useful to understand various aspects of fractal antennas.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"4 6 1","pages":"560969:1-560969:7"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76794768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A constrained neural network optimization algorithm is presented for factorizing simultaneously the numerator and denominator polynomials of the transfer functions of 2-D IIR filters. The method minimizes a cost function based on the frequency response of the filters, along with simultaneous satisfaction of appropriate constraints, so that factorization is facilitated and the stability of the resulting filter is respected.
{"title":"An Efficient Constrained Learning Algorithm for Stable 2D IIR Filter Factorization","authors":"N. Ampazis, S. Perantonis","doi":"10.1155/2013/292567","DOIUrl":"https://doi.org/10.1155/2013/292567","url":null,"abstract":"A constrained neural network optimization algorithm is presented for factorizing simultaneously the numerator and denominator polynomials of the transfer functions of 2-D IIR filters. The method minimizes a cost function based on the frequency response of the filters, along with simultaneous satisfaction of appropriate constraints, so that factorization is facilitated and the stability of the resulting filter is respected.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"5 1","pages":"292567:1-292567:7"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77021417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, the static pull-in instability of beam-type micro-electromechanical system (MEMS) is theoretically investigated. Considering the mid-plane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. Two supervised neural networks, namely, back propagation (BP) and radial basis function (RBF), have been used formodeling the static pull-in instability of microcantilever beam. These networks have four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. Numerical data employed for training the networks and capabilities of the models in predicting the pull-in instability behavior has been verified. Based on verification errors, it is shown that the radial basis function of neural network is superior in this particular case and has the average errors of 4.55% in predicting pull-in voltage of cantilever microbeam. Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results ofmodeling with numerical considerations show a good agreement, which also proves the feasibility and effectiveness of the adopted approach.
{"title":"Estimation of Static Pull-In Instability Voltage of Geometrically Nonlinear Euler-Bernoulli Microbeam Based on Modified Couple Stress Theory by Artificial Neural Network Model","authors":"M. Heidari, Y. Beni, H. Homaei","doi":"10.1155/2013/741896","DOIUrl":"https://doi.org/10.1155/2013/741896","url":null,"abstract":"In this study, the static pull-in instability of beam-type micro-electromechanical system (MEMS) is theoretically investigated. Considering the mid-plane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. Two supervised neural networks, namely, back propagation (BP) and radial basis function (RBF), have been used formodeling the static pull-in instability of microcantilever beam. These networks have four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. Numerical data employed for training the networks and capabilities of the models in predicting the pull-in instability behavior has been verified. Based on verification errors, it is shown that the radial basis function of neural network is superior in this particular case and has the average errors of 4.55% in predicting pull-in voltage of cantilever microbeam. Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results ofmodeling with numerical considerations show a good agreement, which also proves the feasibility and effectiveness of the adopted approach.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"703 1","pages":"741896:1-741896:10"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75765976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper develops a process whereby a high-dimensional clustering problem is solved using a neural network and a lowdimensional cluster diagram of the results is produced using the Mapper method from topological data analysis. The lowdimensional cluster diagram makes the neural network's solution to the high-dimensional clustering problem easy to visualize, interpret, and understand. As a case study, a clustering problem froma diabetes study is solved using a neural network. The clusters in this neural network are visualized using the Mapper method during several stages of the iterative process used to construct the neural network. The neural network and Mapper clustering diagram results for the diabetes study are validated by comparison to principal component analysis.
{"title":"Visualizing Clusters in Artificial Neural Networks Using Morse Theory","authors":"Paul T. Pearson","doi":"10.1155/2013/486363","DOIUrl":"https://doi.org/10.1155/2013/486363","url":null,"abstract":"This paper develops a process whereby a high-dimensional clustering problem is solved using a neural network and a lowdimensional cluster diagram of the results is produced using the Mapper method from topological data analysis. The lowdimensional cluster diagram makes the neural network's solution to the high-dimensional clustering problem easy to visualize, interpret, and understand. As a case study, a clustering problem froma diabetes study is solved using a neural network. The clusters in this neural network are visualized using the Mapper method during several stages of the iterative process used to construct the neural network. The neural network and Mapper clustering diagram results for the diabetes study are validated by comparison to principal component analysis.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"69 1","pages":"486363:1-486363:8"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78689015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Meyer-Bäse, S. Lespinats, J. Górriz, O. Bastien
1 Department of Scientific Computing, Florida State University, Tallahassee, FL 32306-4120, USA 2 Laboratoire des Systemes Solaires (L2S), Institut National de l’Energie Solaire (CEA/INES), BP 332, 73377 Le Bourget du Lac, France 3 Department of Signal Theory, Telematics and Communications, Facultad de Ciencias, Universidad de Granada Fuentenueva, s/n, 18071 Granada, Spain 4 Laboratoire de Physiologie Cellulaire Végétale, UMR 5168 CEA-CNRS-INRA-Université Joseph Fourier, CEA Grenoble, 38054 Grenoble Cedex 09, France
1 Department of Scientific Computing,佛罗里达州立大学、哈西、FL 32306-4120太阳能系统实验室()公布,美国2太阳能研究所(eca / INES)、BP 332 73377航展湖、法国电视三台Department of Theory,遥测信号、通信学院de Ciencias Universidad de Granada Fuentenueva、n、s / 18071 Granada,西班牙4植物细胞生理学实验室(UMR 5168 CEA-CNRS-INRA-UniversitéCEA (Grenoble)、约瑟夫·傅里叶法国格勒诺布尔Cedex 09
{"title":"Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine","authors":"A. Meyer-Bäse, S. Lespinats, J. Górriz, O. Bastien","doi":"10.1155/2012/219860","DOIUrl":"https://doi.org/10.1155/2012/219860","url":null,"abstract":"1 Department of Scientific Computing, Florida State University, Tallahassee, FL 32306-4120, USA 2 Laboratoire des Systemes Solaires (L2S), Institut National de l’Energie Solaire (CEA/INES), BP 332, 73377 Le Bourget du Lac, France 3 Department of Signal Theory, Telematics and Communications, Facultad de Ciencias, Universidad de Granada Fuentenueva, s/n, 18071 Granada, Spain 4 Laboratoire de Physiologie Cellulaire Végétale, UMR 5168 CEA-CNRS-INRA-Université Joseph Fourier, CEA Grenoble, 38054 Grenoble Cedex 09, France","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"83 1","pages":"219860:1-219860:2"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90148299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most attempts at training computers for the difficult and time-consuming task of sleep stage classification involve a feature extraction step. Due to the complexity of multimodal sleep data, the size of the feature space can grow to the extent that it is also necessary to include a feature selection step. In this paper, we propose the use of an unsupervised feature learning architecture called deep belief nets (DBNs) and show how to apply it to sleep data in order to eliminate the use of handmade features. Using a postprocessing step of hidden Markov model (HMM) to accurately capture sleep stage switching, we compare our results to a feature-based approach. A study of anomaly detection with the application to home environment data collection is also presented. The results using raw data with a deep architecture, such as the DBN, were comparable to a feature-based approach when validated on clinical datasets.
{"title":"Sleep Stage Classification Using Unsupervised Feature Learning","authors":"Martin Längkvist, L. Karlsson, A. Loutfi","doi":"10.1155/2012/107046","DOIUrl":"https://doi.org/10.1155/2012/107046","url":null,"abstract":"Most attempts at training computers for the difficult and time-consuming task of sleep stage classification involve a feature extraction step. Due to the complexity of multimodal sleep data, the size of the feature space can grow to the extent that it is also necessary to include a feature selection step. In this paper, we propose the use of an unsupervised feature learning architecture called deep belief nets (DBNs) and show how to apply it to sleep data in order to eliminate the use of handmade features. Using a postprocessing step of hidden Markov model (HMM) to accurately capture sleep stage switching, we compare our results to a feature-based approach. A study of anomaly detection with the application to home environment data collection is also presented. The results using raw data with a deep architecture, such as the DBN, were comparable to a feature-based approach when validated on clinical datasets.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"129 1","pages":"107046:1-107046:9"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79576218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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