Prediction of Student's Performance with Deep Neural NetworksCSCJournals
The performance of education has a big part in people's life. The prediction of student's performance in advance is very important issue for education. School administrators and students' parents impact on students' performance. Hence, academic researchers have developed different types of models to improve student performance. The main goal to reveal of this study is to search the best model of neural network models for the prediction of the performance of the high school students. For this purpose, five different types of neural network models have been developed and compared to their results. The data set obtained from Taldykorgan Kazakh Turkish High School (in Kazakhstan) students was used. Test results show that proposed two types of neural network model are predicted students' real performance efficiently and provided better accuracy when the test of today’s and future’s samples have similar characteristics.
Proposing a new method of image classification based on the AdaBoost deep bel...TELKOMNIKA JOURNAL
Image classification has different applications. Up to now, various algorithms have been presented
for image classification. Each of these methods has its own weaknesses and strengths. Reducing error rate
is an issue which many researches have been carried out about it. This research intends to optimize
the problem with hybrid methods and deep learning. The hybrid methods were developed to improve
the results of the single-component methods. On the other hand, a deep belief network (DBN) is a generative
probabilistic modelwith multiple layers of latent variables and is used to solve the unlabeled problems. In
fact, this method is anunsupervised method, in which all layers are one-way directed layers except for
the last layer. So far, various methods have been proposed for image classification, and the goal of this
research project was to use a combination of the AdaBoost method and the deep belief network method to
classify images. The other objective was to obtain better results than the previous results. In this project, a
combination of the deep belief network and AdaBoost method was used to boost learning and the network
potential was enhanced by making the entire network recursive. This method was tested on the MINIST
dataset and the results were indicative of a decrease in the error rate with the proposed method as compared
to the AdaBoost and deep belief network methods.
Army Study: Ontology-based Adaptive Systems of Cyber DefenseRDECOM
The U.S. Army Research Laboratory is part of the U.S. Army Research, Development and Engineering Command, which has the mission to ensure decisive overmatch for unified land operations to empower the Army, the joint warfighter and our nation. RDECOM is a major subordinate command of the U.S. Army Materiel Command.
Analysis of Neocognitron of Neural Network Method in the String RecognitionIDES Editor
This paper aims that analysing neural network method
in pattern recognition. A neural network is a processing device,
whose design was inspired by the design and functioning of
human brain and their components. The proposed solutions
focus on applying Neocognitron Algorithm model for pattern
recognition. The primary function of which is to retrieve in a
pattern stored in memory, when an incomplete or noisy version
of that pattern is presented. An associative memory is a
storehouse of associated patterns that are encoded in some
form. In auto-association, an input pattern is associated with
itself and the states of input and output units coincide. When
the storehouse is incited with a given distorted or partial
pattern, the associated pattern pair stored in its perfect form
is recalled. Pattern recognition techniques are associated a
symbolic identity with the image of the pattern. This problem
of replication of patterns by machines (computers) involves
the machine printed patterns. There is no idle memory
containing data and programmed, but each neuron is
programmed and continuously active.
A systematic review on sequence-to-sequence learning with neural network and ...IJECEIAES
We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.
Symbolic-Connectionist Representational Model for Optimizing Decision Making ...IJECEIAES
Modeling higher order cognitive processes like human decision making come in three representational approaches namely symbolic, connectionist and symbolic-connectionist. Many connectionist neural network models are evolved over the decades for optimizing decision making behaviors and their agents are also in place. There had been attempts to implement symbolic structures within connectionist architectures with distributed representations. Our work was aimed at proposing an enhanced connectionist approach of optimizing the decisions within the framework of a symbolic cognitive model. The action selection module of this framework is forefront in evolving intelligent agents through a variety of soft computing models. As a continous effort, a Connectionist Cognitive Model (CCN) had been evolved by bringing a traditional symbolic cognitive process model proposed by LIDA as an inspiration to a feed forward neural network model for optimizing decion making behaviours in intelligent agents. Significanct progress was observed while comparing its performance with other varients.
Prediction of Student's Performance with Deep Neural NetworksCSCJournals
The performance of education has a big part in people's life. The prediction of student's performance in advance is very important issue for education. School administrators and students' parents impact on students' performance. Hence, academic researchers have developed different types of models to improve student performance. The main goal to reveal of this study is to search the best model of neural network models for the prediction of the performance of the high school students. For this purpose, five different types of neural network models have been developed and compared to their results. The data set obtained from Taldykorgan Kazakh Turkish High School (in Kazakhstan) students was used. Test results show that proposed two types of neural network model are predicted students' real performance efficiently and provided better accuracy when the test of today’s and future’s samples have similar characteristics.
Proposing a new method of image classification based on the AdaBoost deep bel...TELKOMNIKA JOURNAL
Image classification has different applications. Up to now, various algorithms have been presented
for image classification. Each of these methods has its own weaknesses and strengths. Reducing error rate
is an issue which many researches have been carried out about it. This research intends to optimize
the problem with hybrid methods and deep learning. The hybrid methods were developed to improve
the results of the single-component methods. On the other hand, a deep belief network (DBN) is a generative
probabilistic modelwith multiple layers of latent variables and is used to solve the unlabeled problems. In
fact, this method is anunsupervised method, in which all layers are one-way directed layers except for
the last layer. So far, various methods have been proposed for image classification, and the goal of this
research project was to use a combination of the AdaBoost method and the deep belief network method to
classify images. The other objective was to obtain better results than the previous results. In this project, a
combination of the deep belief network and AdaBoost method was used to boost learning and the network
potential was enhanced by making the entire network recursive. This method was tested on the MINIST
dataset and the results were indicative of a decrease in the error rate with the proposed method as compared
to the AdaBoost and deep belief network methods.
Army Study: Ontology-based Adaptive Systems of Cyber DefenseRDECOM
The U.S. Army Research Laboratory is part of the U.S. Army Research, Development and Engineering Command, which has the mission to ensure decisive overmatch for unified land operations to empower the Army, the joint warfighter and our nation. RDECOM is a major subordinate command of the U.S. Army Materiel Command.
Analysis of Neocognitron of Neural Network Method in the String RecognitionIDES Editor
This paper aims that analysing neural network method
in pattern recognition. A neural network is a processing device,
whose design was inspired by the design and functioning of
human brain and their components. The proposed solutions
focus on applying Neocognitron Algorithm model for pattern
recognition. The primary function of which is to retrieve in a
pattern stored in memory, when an incomplete or noisy version
of that pattern is presented. An associative memory is a
storehouse of associated patterns that are encoded in some
form. In auto-association, an input pattern is associated with
itself and the states of input and output units coincide. When
the storehouse is incited with a given distorted or partial
pattern, the associated pattern pair stored in its perfect form
is recalled. Pattern recognition techniques are associated a
symbolic identity with the image of the pattern. This problem
of replication of patterns by machines (computers) involves
the machine printed patterns. There is no idle memory
containing data and programmed, but each neuron is
programmed and continuously active.
A systematic review on sequence-to-sequence learning with neural network and ...IJECEIAES
We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.
Symbolic-Connectionist Representational Model for Optimizing Decision Making ...IJECEIAES
Modeling higher order cognitive processes like human decision making come in three representational approaches namely symbolic, connectionist and symbolic-connectionist. Many connectionist neural network models are evolved over the decades for optimizing decision making behaviors and their agents are also in place. There had been attempts to implement symbolic structures within connectionist architectures with distributed representations. Our work was aimed at proposing an enhanced connectionist approach of optimizing the decisions within the framework of a symbolic cognitive model. The action selection module of this framework is forefront in evolving intelligent agents through a variety of soft computing models. As a continous effort, a Connectionist Cognitive Model (CCN) had been evolved by bringing a traditional symbolic cognitive process model proposed by LIDA as an inspiration to a feed forward neural network model for optimizing decion making behaviours in intelligent agents. Significanct progress was observed while comparing its performance with other varients.
Computation of Neural Network using C# with Respect to BioinformaticsSarvesh Kumar
Neural network is the emerging field in the era of globalization which is fully based on the concept of soft-computing technique and bioinformatics. In the competitive market of new development process, Bioinformatics play the vital role to give the process of integration aspect as multidisciplinary subject like- biological Science, medicine science, computer science, engineering, chemical science, physical science as well as mathematical science who gives the experiences of artificial activities of human behaviour in the form of software. Now a days neural Network and its multidimensional approach give the idea for solving bioinformatics problems to handle imprecision, uncertainty in large and complex search spaces. This paper gives the emphasis on multidimensional approaches of neural network with soft computing paradigm using C# in bioinformatics with integrative research methodology. The overall process of multidimensional approaches of bioinformatics neurons can also be understood with the help of flow chart and diagram is the major concerned.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A SYSTEM OF SERIAL COMPUTATION FOR CLASSIFIED RULES PREDICTION IN NONREGULAR ...ijaia
Objects or structures that are regular take uniform dimensions. Based on the concepts of regular models,
our previous research work has developed a system of a regular ontology that models learning structures
in a multiagent system for uniform pre-assessments in a learning environment. This regular ontology has
led to the modelling of a classified rules learning algorithm that predicts the actual number of rules needed
for inductive learning processes and decision making in a multiagent system. But not all processes or
models are regular. Thus this paper presents a system of polynomial equation that can estimate and predict
the required number of rules of a non-regular ontology model given some defined parameters.
New Generation Routing Protocol over Mobile Ad Hoc Wireless Networks based on...ijasuc
There is a vast amount of researched literature available on Route Finding and Link Establishment in
MANET protocols based on various concepts such as “pro-active”, “reactive”, “power awareness”,
“cross-layering” etc. Most of these techniques are rather restrictive, taking into account a few of the
several aspects that go into effective route establishment. When we look at practical implementations of
MANETs, we have to take into account various factors in totality, not in isolation. The several factors that
decide and influence the routing have to be considered as a whole in the difficult task of finding the best
solution in route finding and optimization. The inputs to the system are manifold and apparently unrelated.
Most of the parameters are imprecise or non-crisp in nature. The uncertainty and imprecision lead to think
that intelligent routing techniques are essential and important in evolving robust and dependable solutions
to route finding. The obvious method by which this can be achieved is the deployment of soft computing
techniques such as Neural Nets, Fuzzy Logic and Genetic algorithms. Neural Networks help us to solve the
complex problem of transforming the inputs to outputs without apriori knowledge of what the relationship
is between inputs and outputs. Fuzzy Logic helps us to deal with imprecise and ill-conditioned data.
Genetic Algorithms help us to select the best possible solution from the solution space in an optimal sense.
Our paper presented here below seeks to explore new horizons in this direction. The results of our
experimentation have been very satisfactory and we have achieved the goal of optimal route finding to a
large extent. There is of course considerable room for further refinements.
The increased potential of the ontologies to reduce the human interference has wide range of applications. This paper identifies requirements for an ontology development platform to innovate artificially intelligent web. To facilitate this process, RDF and OWL have been developed as standard formats for the sharing and integration of data and knowledge. The knowledge in the form of rich conceptual schemas called ontologies. Based on the framework, an architectural paradigm is put forward in view of ontology engineering and development of ontology applications and a development portal designed to support ontology engineering, content authoring and application development with a view to maximal scalability in size and complexity of semantic knowledge and flexible reuse of ontology models and ontology application processes in a distributed and collaborative engineering environment.
Adaptive Neural Fuzzy Inference System for Employability AssessmentEditor IJCATR
Employability is potential of a person for gaining and maintains employment. Employability is measure through the
education, personal development and understanding power. Employability is not the similar as ahead a graduate job, moderately it
implies something almost the capacity of the graduate to function in an employment and be capable to move between jobs, therefore
remaining employable through their life. This paper introduced a new adaptive neural fuzzy inference system for assessment of
employability with the help of some neuro fuzzy rules. The purpose and scope of this research is to examine the level of employability.
The concern research use both fuzzy inference systems and artificial neural network which is known as neuro fuzzy technique for
solve the problem of employability assessment. This paper use three employability skills as input and find a crisp value as output
which indicates the glassy of employee. It uses twenty seven neuro fuzzy rules, with the help of Sugeno type inference in Mat-lab and
finds single value output. The proposed system is named as Adaptive Neural Fuzzy Inference System for Employability Assessment
(ANFISEA).
6. kr paper journal nov 11, 2017 (edit a)IAESIJEECS
Knowledge Representation (KR) is a fascinating field across several areas of cognitive science and computer science. It is very hard to identify the requirement of a combination of many techniques and inference mechanism to achieve the accuracy for the problem domain. This research attempted to examine those techniques, and to apply them to implement a Cognitive Hybrid Sentence Modeling and Analyzer. The purpose of developing this system is to facilitate people who face the problem of using English language in daily life.
Text classification based on gated recurrent unit combines with support vecto...IJECEIAES
As the amount of unstructured text data that humanity produce largely and a lot of texts are grows on the Internet, so the one of the intelligent technique is require processing it and extracting different types of knowledge from it. Gated recurrent unit (GRU) and support vector machine (SVM) have been successfully used to Natural Language Processing (NLP) systems with comparative, remarkable results. GRU networks perform well in sequential learning tasks and overcome the issues of “vanishing and explosion of gradients in standard recurrent neural networks (RNNs) when captureing long-term dependencies. In this paper, we proposed a text classification model based on improved approaches to this norm by presenting a linear support vector machine (SVM) as the replacement of Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. Empirical results present that the proposed GRU-SVM model achieved comparatively better results than the baseline approaches BLSTM-C, DABN.
APPLYING NEURAL NETWORKS FOR SUPERVISED LEARNING OF MEDICAL DATAIJDKP
Constructing a classification model based on some given patterns is a form of learning from the environment perception. This modelling aims to discover new knowledge embedded in the input observations. Learning behaviour of the neural network model enhances the classification properties. This paper considers artificial neural networks for learning two different medical data sets in term of number of instances. The experiment results confirm that the back-propagation supervised learning algorithm has proved its efficiency for such non-linear classification issues.
Artificial Neural Networks: Applications In ManagementIOSR Journals
With the advancement of computer and communication technology, the tools used for management decisions have undergone a gigantic change. Finding the more effective solution and tools for managerial problems is one of the most important topics in the management studies today. Artificial Neural Networks (ANNs) are one of these tools that have become a critical component for business intelligence. The purpose of this article is to describe the basic behavior of neural networks as well as the works done in application of the same in management sciences and stimulate further research interests and efforts in the identified topics.
Computation of Neural Network using C# with Respect to BioinformaticsSarvesh Kumar
Neural network is the emerging field in the era of globalization which is fully based on the concept of soft-computing technique and bioinformatics. In the competitive market of new development process, Bioinformatics play the vital role to give the process of integration aspect as multidisciplinary subject like- biological Science, medicine science, computer science, engineering, chemical science, physical science as well as mathematical science who gives the experiences of artificial activities of human behaviour in the form of software. Now a days neural Network and its multidimensional approach give the idea for solving bioinformatics problems to handle imprecision, uncertainty in large and complex search spaces. This paper gives the emphasis on multidimensional approaches of neural network with soft computing paradigm using C# in bioinformatics with integrative research methodology. The overall process of multidimensional approaches of bioinformatics neurons can also be understood with the help of flow chart and diagram is the major concerned.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A SYSTEM OF SERIAL COMPUTATION FOR CLASSIFIED RULES PREDICTION IN NONREGULAR ...ijaia
Objects or structures that are regular take uniform dimensions. Based on the concepts of regular models,
our previous research work has developed a system of a regular ontology that models learning structures
in a multiagent system for uniform pre-assessments in a learning environment. This regular ontology has
led to the modelling of a classified rules learning algorithm that predicts the actual number of rules needed
for inductive learning processes and decision making in a multiagent system. But not all processes or
models are regular. Thus this paper presents a system of polynomial equation that can estimate and predict
the required number of rules of a non-regular ontology model given some defined parameters.
New Generation Routing Protocol over Mobile Ad Hoc Wireless Networks based on...ijasuc
There is a vast amount of researched literature available on Route Finding and Link Establishment in
MANET protocols based on various concepts such as “pro-active”, “reactive”, “power awareness”,
“cross-layering” etc. Most of these techniques are rather restrictive, taking into account a few of the
several aspects that go into effective route establishment. When we look at practical implementations of
MANETs, we have to take into account various factors in totality, not in isolation. The several factors that
decide and influence the routing have to be considered as a whole in the difficult task of finding the best
solution in route finding and optimization. The inputs to the system are manifold and apparently unrelated.
Most of the parameters are imprecise or non-crisp in nature. The uncertainty and imprecision lead to think
that intelligent routing techniques are essential and important in evolving robust and dependable solutions
to route finding. The obvious method by which this can be achieved is the deployment of soft computing
techniques such as Neural Nets, Fuzzy Logic and Genetic algorithms. Neural Networks help us to solve the
complex problem of transforming the inputs to outputs without apriori knowledge of what the relationship
is between inputs and outputs. Fuzzy Logic helps us to deal with imprecise and ill-conditioned data.
Genetic Algorithms help us to select the best possible solution from the solution space in an optimal sense.
Our paper presented here below seeks to explore new horizons in this direction. The results of our
experimentation have been very satisfactory and we have achieved the goal of optimal route finding to a
large extent. There is of course considerable room for further refinements.
The increased potential of the ontologies to reduce the human interference has wide range of applications. This paper identifies requirements for an ontology development platform to innovate artificially intelligent web. To facilitate this process, RDF and OWL have been developed as standard formats for the sharing and integration of data and knowledge. The knowledge in the form of rich conceptual schemas called ontologies. Based on the framework, an architectural paradigm is put forward in view of ontology engineering and development of ontology applications and a development portal designed to support ontology engineering, content authoring and application development with a view to maximal scalability in size and complexity of semantic knowledge and flexible reuse of ontology models and ontology application processes in a distributed and collaborative engineering environment.
Adaptive Neural Fuzzy Inference System for Employability AssessmentEditor IJCATR
Employability is potential of a person for gaining and maintains employment. Employability is measure through the
education, personal development and understanding power. Employability is not the similar as ahead a graduate job, moderately it
implies something almost the capacity of the graduate to function in an employment and be capable to move between jobs, therefore
remaining employable through their life. This paper introduced a new adaptive neural fuzzy inference system for assessment of
employability with the help of some neuro fuzzy rules. The purpose and scope of this research is to examine the level of employability.
The concern research use both fuzzy inference systems and artificial neural network which is known as neuro fuzzy technique for
solve the problem of employability assessment. This paper use three employability skills as input and find a crisp value as output
which indicates the glassy of employee. It uses twenty seven neuro fuzzy rules, with the help of Sugeno type inference in Mat-lab and
finds single value output. The proposed system is named as Adaptive Neural Fuzzy Inference System for Employability Assessment
(ANFISEA).
6. kr paper journal nov 11, 2017 (edit a)IAESIJEECS
Knowledge Representation (KR) is a fascinating field across several areas of cognitive science and computer science. It is very hard to identify the requirement of a combination of many techniques and inference mechanism to achieve the accuracy for the problem domain. This research attempted to examine those techniques, and to apply them to implement a Cognitive Hybrid Sentence Modeling and Analyzer. The purpose of developing this system is to facilitate people who face the problem of using English language in daily life.
Text classification based on gated recurrent unit combines with support vecto...IJECEIAES
As the amount of unstructured text data that humanity produce largely and a lot of texts are grows on the Internet, so the one of the intelligent technique is require processing it and extracting different types of knowledge from it. Gated recurrent unit (GRU) and support vector machine (SVM) have been successfully used to Natural Language Processing (NLP) systems with comparative, remarkable results. GRU networks perform well in sequential learning tasks and overcome the issues of “vanishing and explosion of gradients in standard recurrent neural networks (RNNs) when captureing long-term dependencies. In this paper, we proposed a text classification model based on improved approaches to this norm by presenting a linear support vector machine (SVM) as the replacement of Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. Empirical results present that the proposed GRU-SVM model achieved comparatively better results than the baseline approaches BLSTM-C, DABN.
APPLYING NEURAL NETWORKS FOR SUPERVISED LEARNING OF MEDICAL DATAIJDKP
Constructing a classification model based on some given patterns is a form of learning from the environment perception. This modelling aims to discover new knowledge embedded in the input observations. Learning behaviour of the neural network model enhances the classification properties. This paper considers artificial neural networks for learning two different medical data sets in term of number of instances. The experiment results confirm that the back-propagation supervised learning algorithm has proved its efficiency for such non-linear classification issues.
Artificial Neural Networks: Applications In ManagementIOSR Journals
With the advancement of computer and communication technology, the tools used for management decisions have undergone a gigantic change. Finding the more effective solution and tools for managerial problems is one of the most important topics in the management studies today. Artificial Neural Networks (ANNs) are one of these tools that have become a critical component for business intelligence. The purpose of this article is to describe the basic behavior of neural networks as well as the works done in application of the same in management sciences and stimulate further research interests and efforts in the identified topics.
An Eye On Google, Executive Summary PresentationKetzirah Lesser
Executive summary presentation I developed for the RTCRM whitepaper of the same name written by Sara Weiner, Croom Lawrence, and Carlen Lea Lesser that provides guidance for digital marketing after the 14 simultaneous warning letters the FDA issued in April 2009 to pharmaceutical companies in regards to their Google Ads. The impact of these warning letters goes far beyond PPC ads, and ideas on how to deal with this new regulation are outlined in the presentation and more deeply in the whitepaper (http://www.rtcrm.com/whitepapers).
With the surge in modern research focus towards Pervasive Computing, lot of techniques and challenges
needs to be addressed so as to effectively create smart spaces and achieve miniaturization. In the process of
scaling down to compact devices, the real things to ponder upon are the Information Retrieval challenges.
In this work, we discuss the aspects of multimedia which makes information access challenging. An
Example Pattern Recognition scenario is presented and the mathematical techniques that can be used to
model uncertainty are also presented for developing a system that can sense, compute and communicate in
a way that can make human life easy with smart objects assisting from around his surroundings.
A Comparative Study of Various Data Mining Techniques: Statistics, Decision T...Editor IJCATR
In this paper we focus on some techniques for solving data mining tasks such as: Statistics, Decision Trees and Neural
Networks. The new approach has succeed in defining some new criteria for the evaluation process, and it has obtained valuable results
based on what the technique is, the environment of using each techniques, the advantages and disadvantages of each technique, the
consequences of choosing any of these techniques to extract hidden predictive information from large databases, and the methods of
implementation of each technique. Finally, the paper has presented some valuable recommendations in this field.
Classifier Model using Artificial Neural NetworkAI Publications
When it comes to AI and ML, precision in categorization is of the utmost importance. In this research, the use of supervised instance selection (SIS) to improve the performance of artificial neural networks (ANNs) in classification is investigated. The goal of SIS is to enhance the accuracy of future classification tasks by identifying and selecting a subset of examples from the original dataset. The purpose of this research is to provide light on how useful SIS is as a preprocessing tool for artificial neural network-based classification. The work aims to improve the input dataset to ANNs by using SIS, which may help with problems caused by noisy or redundant data. The ultimate goal is to improve ANNs' ability to identify data points properly across a wide range of application areas.
PREDICTION OF MALIGNANCY IN SUSPECTED THYROID TUMOUR PATIENTS BY THREE DIFFER...cscpconf
In the present study, the abilities of three classification methods of data mining namely artificial
neural networks with feed-forward back propagation algorithm, J48 decision tree method and
logistic regression analysis are compared in a medical real dataset. The prediction of
malignancy in suspected thyroid tumour patients is the objective of the study. The accuracy of
the correct predictions (the minimum error rate), the amount of time consuming in the
modelling process and the interpretability and simplicity of the results for clinical experts are
the factors considered to choose the best method
An Iterative Improved k-means ClusteringIDES Editor
Clustering is a data mining (machine learning),
unsupervised learning technique used to place data elements
into related groups without advance knowledge of the group
definitions. One of the most popular and widely studied
clustering methods that minimize the clustering error for
points in Euclidean space is called K-means clustering.
However, the k-means method converges to one of many local
minima, and it is known that the final results depend on the
initial starting points (means). In this research paper, we have
introduced and tested an improved algorithm to start the kmeans
with good starting points (means). The good initial
starting points allow k-means to converge to a better local
minimum; also the numbers of iteration over the full dataset
are being decreased. Experimental results show that initial
starting points lead to good solution reducing the number of
iterations to form a cluster.
Ontology Based PMSE with Manifold PreferenceIJCERT
International journal from http://www.ijcert.org
IJCERT Standard on-line Journal
ISSN(Online):2349-7084,(An ISO 9001:2008 Certified Journal)
iso nicir csir
IJCERT (ISSN 2349–7084 (Online)) is approved by National Science Library (NSL), National Institute of Science Communication And Information Resources (NISCAIR), Council of Scientific and Industrial Research, New Delhi, India.
Top cited articles 2020 - Advanced Computational Intelligence: An Internation...aciijournal
Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
REPRESENTATION OF UNCERTAIN DATA USING POSSIBILISTIC NETWORK MODELScscpconf
Uncertainty is a pervasive in real world environment due to vagueness, is associated with the
difficulty of making sharp distinctions and ambiguity, is associated with situations in which the
choices among several precise alternatives cannot be perfectly resolved. Analysis of large
collections of uncertain data is a primary task in the real world applications, because data is
incomplete, inaccurate and inefficient. Representation of uncertain data in various forms such
as Data Stream models, Linkage models, Graphical models and so on, which is the most simple,
natural way to process and produce the optimized results through Query processing. In this
paper, we propose the Uncertain Data model can be represented as Possibilistic data model
and vice versa for the process of uncertain data using various data models such as possibilistic
linkage model, Data streams, Possibilistic Graphs. This paper presents representation and
process of Possiblistic Linkage model through Possible Worlds with the use of product-based
operator.
Pattern recognition using context dependent memory model (cdmm) in multimodal...ijfcstjournal
Pattern recognition is one of the prime concepts in current technologies in both private and public sectors.
The analysis and recognition of two or more patterns is a complex task due to several factors. The
consideration of two or more patterns requires huge space for keeping the storage media as well as
computational aspect. Vector logic gives very good strategy for recognition of patterns. This paper
proposes pattern recognition in multimodal authentication system with the use of vector logic and makes
the computation model hard and less error rate. Using PCA two to three biometric patterns will be fusion
and then various key sizes will be extracted using LU factorization approach. The selected keys will be
combined using vector logic, which introduces a memory model often called Context Dependent Memory
Model (CDMM) as computational model in multimodal authentication system that gives very accurate and
very effective outcome for authentication as well as verification. In the verification step, Mean Square
Error (MSE) and Normalized Correlation (NC) as metrics to minimize the error rate for the proposed
model and the performance analysis will be presented.
June 2020: Top Read Articles in Advanced Computational Intelligenceaciijournal
Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
T OWARDS A S YSTEM D YNAMICS M ODELING M E- THOD B ASED ON DEMATELijcsit
If System Dynamics (SD) models are constructed based
solely on decision makers' mental models and u
n-
derstanding of the context subject to study, then the resulting systems must necessarily bear some d
e
gree of
deficiency due to the subjective, limited, and internally inconsistent mental models which led to t
he conce
p-
tion of these systems. As such, a systematic method for constructing SD models could be esse
n
tially helpful
in overcoming the biases dictated by the human mind's limited understanding and conceptualization of
complex systems. This paper proposes a
novel combined method to su
p
port SD model construction. The
classical Dec
i
sion Making Trial and Evaluation Laboratory (DEMATEL) technique is used to define causal
relationships among variables of a system, and to construct the corresponding Impact Relatio
n Maps
(IRMs). The novelty of this paper stems from the use of the resulting total influence m
a
trix to derive the
system dynamic's Causal Loop Diagram (CLD) and then define variable weights in the stock
-
flow chart
equations. This new method allows to overc
ome the subjectivity bias of SD
mode
ling while projecting D
E-
MATEL in a more d
y
namic simulation environment, which could significantly improve the strategic choices
made by an
a
lysts and policy makers
Comparison of relational and attribute-IEEE-1999-published ...
1. Kovalerchuk, B., Vityaev E., Comparison of relational methods and attribute-based methods for data mining in
intelligent systems, 1999 IEEE Int. Symposium on Intelligent Control/Intelligent Systems, Cambridge, Mass,
1999. pp. 162-166.
Comparison of relational methods and attribute-based methods
for data mining in intelligent systems
Boris Kovalerchuk
Dept. of Computer Science, Central Washington University,
Ellensburg, WA, 98926-7520, USA
borisk@cwu.edu
Evgenii Vityaev
Institute of Mathematics, Russian Academy of Science,
Novosibirsk, 630090, Russia
vityaev@math.nsc.ru
Abstract that automatically improve with experience. In recent years
Most of the data mining methods in real-world intelligent many successful machine learning applications have been
systems are attribute-based machine learning methods such developed including autonomous vehicles that learn to
as neural networks, nearest neighbors and decision trees. drive on public [1].
They are relatively simple, efficient, and can handle noisy
data. However, these methods have two strong limitations: Currently statistical and Artificial Neural Network
(1) a limited form of expressing the background methods dominate in design of intelligent systems and data
knowledge and (2) the lack of relations other than “object- mining. There are three shortages of Neural Networks [1]
attribute” makes the concept description language for forecasting related to:
inappropriate for some applications. 1) Explainability,
2) Use of logical relations and
Relational hybrid data mining methods based on first-order 3) Tolerance for sparse data.
logic were developed to meet these challenges. In the
paper they are compared with Neural Networks and other Alternative relational (symbolic) machine learning
benchmark methods. The comparison shows several methods had shown their effectiveness in robotics
advantages of relational methods. (navigation, 3-dimensional scene analysis) and drug design
(selection of the most promising components for drug
design). . In practice, learning systems based on first-order
1. Problem definition and objectives representations have been successfully applied to many
problems in engineering, chemistry, physics, medicine.
Performance of intelligent systems such as intelligent finance and other fields [1,2]. Traditionally symbolic
control systems can be significantly improved if control methods are used in the areas with a lot of non-numeric
signals are generated using prediction of future behavior of (symbolic) knowledge. In robot navigation this is relative
the controlled system. In this study we assume that at each location of obstacles (on the right, on the left and so on).
moment t performance of the system is measured by gain We discuss the key algorithms and theory that form the
function G(t,yt,yt-1,u), were yt and yt-1 are system’s states core of symbolic machine learning methods for
in t and previous moment t-1, respectively and u is control applications with dominating numerical data. The
signal at the same moment t. Implementing this approach mathematical formalisms of first order logic rules
requires discovering regularities in system’s behavior and described in [1,3,4] are used. Note that a class of general
computing y ~ , predicted values of system’s state using
t propositional and first-order logic rules, covered by
discovered regularities. relational methods is wider than a class of decision trees
[1, pp. 274-275].
Data mining methods are design to for discovering hidden
regularities in databases. Data mining has two major
sources to infer regularities: database and machine learning Specifically Relational hybrid Data Mining (RHDM)
technologies. The field of machine learning is concerned combines inductive logic programming (ILP) with
with the question of how to construct computer programs probabilistic inference. The combination benefits from
2. noise robust probabilistic inference and highly expressive rely only on them then there are more chances that these
and understandable first-order logic rules employed in ILP. rules will not deliver a right forecast on other data.
Relational Data Mining technology is a data modeling
What is the motivation to use suggested MMDR method in
algorithm that does not assume the functional form of
particular? MMDR uses hypothesis/rule generation and
the relationship being modeled a priori. It can
selection process, based on fundamental representative
automatically consider a large number of inputs (e.g., time
measurement theory [3] The original challenge for MMDR
series characterization parameters) and learn how to
was the simulation of discovering scientific laws from
combine these to produce estimates for future values of a
empirical data in chemistry and physics. There is a well-
specific output variable.
known difference between “black box” models and
fundamental models (laws) in modern physics. The last
Relational data mining (RDM) has roots in logic
ones have much longer life, wider scope and a solid
programming which provides the solid theoretical basis for
background. Mitchell in [1] noted the importance that
RDM. On the other hand at present existing Inductive
relational assertions “can be conveniently expressed using
Logic Programming systems representing RDM are
first-order representations, while they are very difficult to
relatively inefficient and have rather limited facilities for
describe using propositional representations”.
handling numerical data [5]. We developed a hybrid ILP
and probabilistic technique (MMDR method) that handles
Many well-known rule learners such as AQ, CN2 are
numerical data efficiently [2,6].
propositional [1, p.279, 283]. Note that decision tree
methods represent a particular type of propositional
One of the main advantages of RDM over attribute-based
representation [1, p.275]. Therefore decision tree methods
learning is ILP’s generality of representation for
such as ID3 and its successor C4.5 fit better to tasks
background knowledge. This enables the user to provide,
without relational assertions. Mitchell argues and gives
in a more natural way, domain-specific background
examples that propositional representations offer no
knowledge to be used in learning. The use of background
general way to describe the essential relations among the
knowledge enables the user both to develop a suitable
values of the attributes [1, pp. 283-284]. Below we follow
problem representation and to introduce problem-specific
his simple illustrative example. In contrast with
constraints into the learning process. By contrast, attribute-
propositional rules, a program using first-order
based learners can typically accept background knowledge
representations could learn the following general rule:
in rather limited form only [5].
IF Father(x,y) & Female(y), THEN Daugher(x,y),
2. Relational methods
where x and y are variables that can be bound to any
person. For the target concept Daughter1,2 propositional
A machine learning type of method, called Machine rule learner such as CN2 or C4.5, the result would be a
Methods for Discovering Regularities (MMDR) is applied collection of very specific rules such as
for forecasting time series. Then this forecast is used to
compute control signals to get a better value of gain IF (Father1=Bob)&Name2=Bob)&Female1=True) THEN
function G. Daughter1,2=True.
MMDR method expresses patterns in first order logic and Although it is correct, this rule is so specific that it will
assigns probabilities to rules generated by composing rarely, if ever, be useful in classifying future pairs of
patterns. As any technique based on first order logic, people [4, pp.283-284]. It is obvious that similar problem
MMDR allows one to get human-readable forecasting exists for autoregression methods like ARIMA and Neural
rules [1, ch. 10], i.e. understandable in ordinary language Networks methods. First-order logic rules have an
in addition to the forecast. A field expert can evaluate the advantage in discovering relational assertions because they
performance of the forecast as well as a forecasting rule. capture relations directly, e.g., Father(x,y) in the example
above.
Also, as any technique based on probabilistic estimates,
this technique delivers rules tested on their statistical In addition, first order rules allow one to express naturally
significance. Statistically significant rules have advantage the other more general hypotheses not only the relation
in comparison with rules tested only for their performance between pairs of attributes [3]. These more general rules
on training and test data [1, ch. 5]. Training and testing can be as for classification problems as for an interval
data can be too limited and/or not representative. If rules
3. forecast of continuous variable. Moreover these rules are The next critical issue in applying data-driven forecasting
able to catch Markov chain type of models used for time systems is generalization. The "Discovery" system
series forecast. We share Mitchell’s opinion about the developed as software implementation MMDR [6]
importance of algorithms designed to learn sets of first- generalizes data through “lawlike” logical probabilistic
order rules that contain variables because first-order rules rules. Discovered rules have similar statistical estimate
are much more expressive than propositional rules” [1, and significance on training and test sets of studied time
p.274]. series. Theoretical advantages of MMDR generalization
are presented in [6, 1].
What is the difference between MMDR and other Machine
Learning methods dealing with first-order logic [1,4,5]? 3. Method for discovering regularities
From our viewpoint the main accent in other first-order
methods is on two computational complexity issues: how Figure 1 describes the steps of MMDR. On the first step
wide is the class of hypotheses tested by the particular we select and/or generate a class first–order logic rules
machine learning algorithms and how to construct a suitable for a particular task.
learning algorithm to find deterministic rules. The
emphasis of MMDR is on probabilistic first-order rules The next step is learning the particular first-order logic
and measurement issues, i.e., how we can move from a rules using available training data. Then we test first-order
real measurement to first-order logic representation. This is logic rules on training and test data using Fisher statistical
a non-trivial task [3]. For example, how to represent criterion. After that we select
temperature measurement in terms of first-order logic
MMDR models
without losing the essence of the attribute (temperature in (selecting/generating logical rules
this case) and without inputting unnecessary conventional with variables x,y,..,z:
properties? For instance, Fahrenheit and Celsius zeros of IF A(x,y,…,z)THEN B(x,y,…,z)
temperature are our conventions in contrast with Kelvin
scale where the zero is a real physical zero. There are no
Learning logical rules on training Testing and selecting
temperatures less than this zero. Therefore incorporating data using conditional logical rules (Occam’s
properties of the Fahrenheit zero into first-order rules may probabilities of inference razor, Fisher criterion)
force us to discover/learn properties of this convention P(B(x,y,…,z)/A(x,y,…z))
along with more significant scale invariant forecasting
rules. Learning algorithms in the space with those kind of Creating interval and threshold
accidental properties may be very time consuming and forecasts using rules
IF A(x,y,…,z) THEN B(x,y,…,z)
may produce inappropriate rules.
and p-quintiles
It is well known that the general problem of rule Figure 1. Flow diagram for MMDR: steps and technique applied
generating and testing is NP-complete. Therefore the
discussion above is closely related to the following statistically significant rules and apply Occam’s razor
questions. What determines the number of rules and when principle: prefer the simplest hypothesis (rules) that fits the
to stop generating rules? What is the justification for data [1, p. 65]. Simultaneously we use the rules’
specifying particular expressions instead of any other performance on training and test data for their selection.
expressions? Using the approach from [3] we select rules We may iterate back and forth among these three steps
which are simplest and consistent with measurement scales several times to find the best rules. The last step is creating
for a particular task. The algorithm stops generating new interval and threshold forecasts using selected first-order
rules when they become too complex (i.e., statistically logic rules:
insignificant for the data) in spite of possible high accuracy
on training data. The obvious other stop criterion is time IF A(x,y,…,z) THEN B(x,y,…,z).
limitation. Detailed discussion about a mechanism of
initial rule selection from measurement theory [3] As we mentioned above conceptually law-like rules came
viewpoint is out of the scope of this paper. A special study from philosophy of science. These rules attempt to
may result in a catalogue of initial rules/hypotheses to be mathematically capture the essential features of scientific
tested (learned) for particular applications. In this way any laws:
field analyst can choose rules to be tested without (1) High level of generalization;
generating them. (2) Simplicity (Occam’s razor); and,
(3) Refutability.
4. The first feature -- generalization -- means that any other “Law-like” rules defined in this way hold all three
regularity covering the same events would be less general, mentioned above properties of scientific laws: generality,
i.e., applicable only to the part of events covered by the simplicity, and refutability..
law-like regularity. The second feature – simplicity-- The “Discovery” software searches all chains
reflects the fact that a law-like rule is shorter than other
rules. A law-like rule, R1 is more refutable than another C1 , C2, …, Cm-1, Cm
rule R2 if there are more testing examples which refute R1
than R2, but the testing examples fail to refute R1. of nested “law-like” subrules, where C1 is a subrule of rule
C2 , C1 = sub(C2), C2 is a subrule of rule C3, C2 = sub(C3)
Formally, we present an IF-THEN rule C as and finally Cm-1 is a subrule of rule Cm, Cm-1 = sub(Cm). In
addition, they satisfy an important property:
A1& …&Ak ⇒ A0,
Prob(C1) < Prob(C2), … , Prob(Cm-1) < Prob(Cm).
where the IF-part, A1&...&Ak, consists of true/false logical
statements A1,…,Ak ,and the THEN-part consists of a This property is the base for the following
single logical statement A0. Each statements Ai is a given theorem [6]:
refutable statements or its negations, which are also
refutable. Rule C allows us to generate sub-rules with a All rules, which have a maximum value of
truncated IF part, e.g. conditional probability, can be found at the end of
such chains.
A1&A2 ⇒ A0 , A1&A2&A3 ⇒ A0
This theorem basically means that the MMDR algorithm
and so on. For rule C its conditional probability does not miss the best rules. The algorithm stops
generating new rules when they become too complex (i.e.,
Prob(C) = Prob(A0/A1&...&Ak) statistically insignificant for the data) even if the rules are
highly accurate on training data. The Fisher statistical
is defined. Similarly conditional probabilities criterion is used in this algorithm for testing statistical
significance. The obvious other stop criterion is time
Prob(A0/Ai1&...&Aih) limitation.
are defined for sub-rules Ci of the form 4. Performance
Ai1& …&Aih ⇒ A0. We consider a control task with three control actions u=1,
u=0 and u=-1, set as follows:
We use conditional probability 1, if y ~ > y
t t -1
~
Prob(C) = Prob(A0/A1&...&Ak) u = 0, if y t = y t -1
~
for estimating forecasting power of the rule to predict A0. - 1, if y t < y t -1
where yt-1 is an actual value of the time series for t-1
The goal is to find ‘law-like” rules. The rule is “law-like” moment and y ~ is the forecasted value of y for t moment.
t
if and only if all of its sub-rules have less conditional Forecasted values are generated by all four studied
probability than the rule, and statistical significance of methods including Neural Network method and relational
that is established. Each sub-rule Ci generalizes rule C, MMDR method.
i.e., potentially Ci is true for larger set of instances.
Another definition of “law-like” rules can be stated in The gain function G is computed for every t after all actual
terms of generalization. values of yt became known. Gain function G(t,u,yt ,yt-1)
depends on u and actual values of output yt ,yt-1:
The rule is “law-like” if and only if it can not be
generalized without producing a statistically significant
reduction in its conditional probability.
5. y ~ − y t -1 , if u = 1
t
5. Conclusion
G(t, u, y t , y t -1 ) = 0, if u = 0
~ In average human-readable and understadable regularities
- (y t − y t -1 ), if u = −1
generated by the relational method MMDR outperformed
other methods including widely used neural networks
The total gain is defined as (table 1).
Gtotal=Σt=1,n G(t,u, yt ,yt-1)
6. References
The adaptive linear method in essence works according to
~
the following fomula: y t =ay t-1+ byt-2 +c. 1. Mitchell T. “Machine Learning”, McGraw-Hill, NY,
1997
“Passive control” method ignores forecast yt and in all 2. Kovalerchuk, B., Vityaev E. “Discovering Law-like
cases generates the same control signal “do nothing”: Regularities in Financial Time Series”, Journal of
Computational Intelligence in Finance, Vol.6, No.3,
If (yt ~>yt-1 or yt ~≤yt-1) then u=0 (do nothing) 1998, pp.12-26,
Table 1 shows performance of MMDR in conparison with 3. Krantz D.H., Luce R.D., Suppes P., and Tversky A.
three other methods: Adaptive Linear, Passive Contol, and “Foundations of measurement”. vol.1-3, Acad. Press,
Neural Networks. NY, London, 1971, 1989, 1990
4. Russel S., Norvig P. “Artificial Intelligence. A modern
Table 1. Simulated gain approach”, Prentice Hall, 1995
5. Bratko, I., Muggleton. S. “Applications of
Method Gain (%)
inductive logic programming”, Communications
Data Data Average
set 1 set 2 (two data of ACM, vol.38, N. 11,1995, pp.65-70.
(477 (424 sets, 901 6. Vityaev E., Moskvitin A. “Introduction to Discovery
instances) instances) instances)
theory: Discovery system”, Computational Systems,
Adaptive Linear 21.9 18.28 20.09 v.148, Novosibirsk, pp.117-163, 1993 (in Russian).
MMDR 26.69 43.83 35.26
Passive control 30.39 20.56 25.47
Neural Network 18.94 16.07 17.5