Nature inspired population based evolutionary algorithms are very popular with
their competitive solutions for a wide variety of applications. Teaching Learning based
Optimization (TLBO) is a very recent population based evolutionary algorithm evolved
on the basis of Teaching Learning process of a class room. TLBO does not require any
algorithmic specific parameters. This paper proposes an automatic grouping of pixels into
different homogeneous regions using the TLBO. The experimental results have
demonstrated the effectiveness of TLBO in image segmentation.
A Combined Approach for Feature Subset Selection and Size Reduction for High ...IJERA Editor
selection of relevant feature from a given set of feature is one of the important issues in the field of
data mining as well as classification. In general the dataset may contain a number of features however it is not
necessary that the whole set features are important for particular analysis of decision making because the
features may share the common information‟s and can also be completely irrelevant to the undergoing
processing. This generally happen because of improper selection of features during the dataset formation or
because of improper information availability about the observed system. However in both cases the data will
contain the features that will just increase the processing burden which may ultimately cause the improper
outcome when used for analysis. Because of these reasons some kind of methods are required to detect and
remove these features hence in this paper we are presenting an efficient approach for not just removing the
unimportant features but also the size of complete dataset size. The proposed algorithm utilizes the information
theory to detect the information gain from each feature and minimum span tree to group the similar features
with that the fuzzy c-means clustering is used to remove the similar entries from the dataset. Finally the
algorithm is tested with SVM classifier using 35 publicly available real-world high-dimensional dataset and the
results shows that the presented algorithm not only reduces the feature set and data lengths but also improves the
performances of the classifier.
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...IJRES Journal
Data clustering is a common technique for statistical data analysis; it is defined as a class of
statistical techniques for classifying a set of observations into completely different groups. Cluster analysis
seeks to minimize group variance and maximize between group variance. In this study we formulate a
mathematical programming model that chooses the most important variables in cluster analysis. A nonlinear
binary model is suggested to select the most important variables in clustering a set of data. The idea of the
suggested model depends on clustering data by minimizing the distance between observations within groups.
Indicator variables are used to select the most important variables in the cluster analysis.
Comparision of methods for combination of multiple classifiers that predict b...IJERA Editor
Predictive analysis include techniques fromdata mining that analyze current and historical data and make
predictions about the future. Predictive analytics is used in actuarial science, financial services, retail, travel,
healthcare, insurance, pharmaceuticals, marketing, telecommunications and other fields.Predicting patterns can
be considered as a classification problem and combining the different classifiers gives better results. We will
study and compare three methods used to combine multiple classifiers. Bayesian networks perform
classification based on conditional probability. It is ineffective and easy to interpret as it assumes that the
predictors are independent. Tree augmented naïve Bayes (TAN) constructs a maximum weighted spanning tree
that maximizes the likelihood of the training data, to perform classification.This tree structure eliminates the
independent attribute assumption of naïve Bayesian networks. Behavior-knowledge space method works in two
phases and can provide very good performances if large and representative data sets are available.
A Threshold Fuzzy Entropy Based Feature Selection: Comparative StudyIJMER
Feature selection is one of the most common and critical tasks in database classification. It
reduces the computational cost by removing insignificant and unwanted features. Consequently, this
makes the diagnosis process accurate and comprehensible. This paper presents the measurement of
feature relevance based on fuzzy entropy, tested with Radial Basis Classifier (RBF) network,
Bagging(Bootstrap Aggregating), Boosting and stacking for various fields of datasets. Twenty
benchmarked datasets which are available in UCI Machine Learning Repository and KDD have been
used for this work. The accuracy obtained from these classification process shows that the proposed
method is capable of producing good and accurate results with fewer features than the original
datasets.
Optimal rule set generation using pso algorithmcsandit
Classification and Prediction is an important resea
rch area of data mining. Construction of
classifier model for any decision system is an impo
rtant job for many data mining applications.
The objective of developing such a classifier is to
classify unlabeled dataset into classes. Here
we have applied a discrete Particle Swarm Optimizat
ion (PSO) algorithm for selecting optimal
classification rule sets from huge number of rules
possibly exist in a dataset. In the proposed
DPSO algorithm, decision matrix approach was used f
or generation of initial possible
classification rules from a dataset. Then the propo
sed algorithm discovers important or
significant rules from all possible classification
rules without sacrificing predictive accuracy.
The proposed algorithm deals with discrete valued d
ata, and its initial population of candidate
solutions contains particles of different sizes. Th
e experiment has been done on the task of
optimal rule selection in the data sets collected f
rom UCI repository. Experimental results show
that the proposed algorithm can automatically evolv
e on average the small number of
conditions per rule and a few rules per rule set, a
nd achieved better classification performance
of predictive accuracy for few classes.
AUTOMATIC TRANSFER RATE ADJUSTMENT FOR TRANSFER REINFORCEMENT LEARNINGgerogepatton
This paper proposes a novel parameter for transfer reinforcement learning to avoid over-fitting when an
agent uses a transferred policy from a source task. Learning robot systems have recently been studied for
many applications, such as home robots, communication robots, and warehouse robots. However, if the
agent reuses the knowledge that has been sufficiently learned in the source task, deadlock may occur and
appropriate transfer learning may not be realized. In the previous work, a parameter called transfer rate
was proposed to adjust the ratio of transfer, and its contribution include avoiding dead lock in the target
task. However, adjusting the parameter depends on human intuition and experiences. Furthermore, the
method for deciding transfer rate has not discussed. Therefore, an automatic method for adjusting the
transfer rate is proposed in this paper using a sigmoid function. Further, computer simulations are used to
evaluate the effectiveness of the proposed method to improve the environmental adaptation performance in
a target task, which refers to the situation of reusing knowledge.
A Combined Approach for Feature Subset Selection and Size Reduction for High ...IJERA Editor
selection of relevant feature from a given set of feature is one of the important issues in the field of
data mining as well as classification. In general the dataset may contain a number of features however it is not
necessary that the whole set features are important for particular analysis of decision making because the
features may share the common information‟s and can also be completely irrelevant to the undergoing
processing. This generally happen because of improper selection of features during the dataset formation or
because of improper information availability about the observed system. However in both cases the data will
contain the features that will just increase the processing burden which may ultimately cause the improper
outcome when used for analysis. Because of these reasons some kind of methods are required to detect and
remove these features hence in this paper we are presenting an efficient approach for not just removing the
unimportant features but also the size of complete dataset size. The proposed algorithm utilizes the information
theory to detect the information gain from each feature and minimum span tree to group the similar features
with that the fuzzy c-means clustering is used to remove the similar entries from the dataset. Finally the
algorithm is tested with SVM classifier using 35 publicly available real-world high-dimensional dataset and the
results shows that the presented algorithm not only reduces the feature set and data lengths but also improves the
performances of the classifier.
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...IJRES Journal
Data clustering is a common technique for statistical data analysis; it is defined as a class of
statistical techniques for classifying a set of observations into completely different groups. Cluster analysis
seeks to minimize group variance and maximize between group variance. In this study we formulate a
mathematical programming model that chooses the most important variables in cluster analysis. A nonlinear
binary model is suggested to select the most important variables in clustering a set of data. The idea of the
suggested model depends on clustering data by minimizing the distance between observations within groups.
Indicator variables are used to select the most important variables in the cluster analysis.
Comparision of methods for combination of multiple classifiers that predict b...IJERA Editor
Predictive analysis include techniques fromdata mining that analyze current and historical data and make
predictions about the future. Predictive analytics is used in actuarial science, financial services, retail, travel,
healthcare, insurance, pharmaceuticals, marketing, telecommunications and other fields.Predicting patterns can
be considered as a classification problem and combining the different classifiers gives better results. We will
study and compare three methods used to combine multiple classifiers. Bayesian networks perform
classification based on conditional probability. It is ineffective and easy to interpret as it assumes that the
predictors are independent. Tree augmented naïve Bayes (TAN) constructs a maximum weighted spanning tree
that maximizes the likelihood of the training data, to perform classification.This tree structure eliminates the
independent attribute assumption of naïve Bayesian networks. Behavior-knowledge space method works in two
phases and can provide very good performances if large and representative data sets are available.
A Threshold Fuzzy Entropy Based Feature Selection: Comparative StudyIJMER
Feature selection is one of the most common and critical tasks in database classification. It
reduces the computational cost by removing insignificant and unwanted features. Consequently, this
makes the diagnosis process accurate and comprehensible. This paper presents the measurement of
feature relevance based on fuzzy entropy, tested with Radial Basis Classifier (RBF) network,
Bagging(Bootstrap Aggregating), Boosting and stacking for various fields of datasets. Twenty
benchmarked datasets which are available in UCI Machine Learning Repository and KDD have been
used for this work. The accuracy obtained from these classification process shows that the proposed
method is capable of producing good and accurate results with fewer features than the original
datasets.
Optimal rule set generation using pso algorithmcsandit
Classification and Prediction is an important resea
rch area of data mining. Construction of
classifier model for any decision system is an impo
rtant job for many data mining applications.
The objective of developing such a classifier is to
classify unlabeled dataset into classes. Here
we have applied a discrete Particle Swarm Optimizat
ion (PSO) algorithm for selecting optimal
classification rule sets from huge number of rules
possibly exist in a dataset. In the proposed
DPSO algorithm, decision matrix approach was used f
or generation of initial possible
classification rules from a dataset. Then the propo
sed algorithm discovers important or
significant rules from all possible classification
rules without sacrificing predictive accuracy.
The proposed algorithm deals with discrete valued d
ata, and its initial population of candidate
solutions contains particles of different sizes. Th
e experiment has been done on the task of
optimal rule selection in the data sets collected f
rom UCI repository. Experimental results show
that the proposed algorithm can automatically evolv
e on average the small number of
conditions per rule and a few rules per rule set, a
nd achieved better classification performance
of predictive accuracy for few classes.
AUTOMATIC TRANSFER RATE ADJUSTMENT FOR TRANSFER REINFORCEMENT LEARNINGgerogepatton
This paper proposes a novel parameter for transfer reinforcement learning to avoid over-fitting when an
agent uses a transferred policy from a source task. Learning robot systems have recently been studied for
many applications, such as home robots, communication robots, and warehouse robots. However, if the
agent reuses the knowledge that has been sufficiently learned in the source task, deadlock may occur and
appropriate transfer learning may not be realized. In the previous work, a parameter called transfer rate
was proposed to adjust the ratio of transfer, and its contribution include avoiding dead lock in the target
task. However, adjusting the parameter depends on human intuition and experiences. Furthermore, the
method for deciding transfer rate has not discussed. Therefore, an automatic method for adjusting the
transfer rate is proposed in this paper using a sigmoid function. Further, computer simulations are used to
evaluate the effectiveness of the proposed method to improve the environmental adaptation performance in
a target task, which refers to the situation of reusing knowledge.
Centralized Class Specific Dictionary Learning for wearable sensors based phy...Sherin Mathews
With recent progress in pervasive healthcare,
physical activity recognition with wearable body sensors has
become an important and challenging area in both research and
industrial communities. Here, we address a novel technique for
a sensor platform that performs physical activity recognition by
leveraging a class specific regularizer term into the dictionary
pair learning objective function. The proposed algorithm jointly
learns a synthesis dictionary and an analysis dictionary in
order to simultaneously perform signal representation and
classification once the time-domain features have been extracted.
Specifically, the class specific regularizer term ensures that the
sparse codes belonging to the same class will be concentrated
thereby proving beneficial for the classification stage. In order
to develop a more practical approach, we employ a combination
of an alternating direction method of multipliers and a l1 − ls
minimization method to approximately minimize the objective
function. We validate the effectiveness of our proposed model
by employing it on two activity recognition problem and an
intensity estimation problem, both of which include a large
number of physical activities. Experimental results demonstrate
that classifiers built in this dictionary learning based framework
outperforms state of art algorithms by using simple features,
thereby achieving competitive results when compared with
classical systems built upon features with prior knowledge
A Novel Approach to Mathematical Concepts in Data Miningijdmtaiir
-This paper describes three different fundamental
mathematical programming approaches that are relevant to
data mining. They are: Feature Selection, Clustering and
Robust Representation. This paper comprises of two clustering
algorithms such as K-mean algorithm and K-median
algorithms. Clustering is illustrated by the unsupervised
learning of patterns and clusters that may exist in a given
databases and useful tool for Knowledge Discovery in
Database (KDD). The results of k-median algorithm are used
to collecting the blood cancer patient from a medical database.
K-mean clustering is a data mining/machine learning algorithm
used to cluster observations into groups of related observations
without any prior knowledge of those relationships. The kmean algorithm is one of the simplest clustering techniques
and it is commonly used in medical imaging, biometrics and
related fields.
Analysis On Classification Techniques In Mammographic Mass Data SetIJERA Editor
Data mining, the extraction of hidden information from large databases, is to predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Data-Mining classification techniques deals with determining to which group each data instances are associated with. It can deal with a wide variety of data so that large amount of data can be involved in processing. This paper deals with analysis on various data mining classification techniques such as Decision Tree Induction, Naïve Bayes , k-Nearest Neighbour (KNN) classifiers in mammographic mass dataset.
Maximum Correntropy Based Dictionary Learning Framework for Physical Activity...sherinmm
Due to its symbolic role in ubiquitous health monitoring,
physical activity recognition with wearable body sensors has been in the
limelight in both research and industrial communities. Physical activity
recognition is difficult due to the inherent complexity involved with different
walking styles and human body movements. Thus we present a
correntropy induced dictionary pair learning framework to achieve this
recognition. Our algorithm for this framework jointly learns a synthesis
dictionary and an analysis dictionary in order to simultaneously perform
signal representation and classification once the time-domain features
have been extracted. In particular, the dictionary pair learning algorithm
is developed based on the maximum correntropy criterion, which
is much more insensitive to outliers. In order to develop a more tractable
and practical approach, we employ a combination of alternating direction
method of multipliers and an iteratively reweighted method to approximately
minimize the objective function. We validate the effectiveness of
our proposed model by employing it on an activity recognition problem
and an intensity estimation problem, both of which include a large number
of physical activities from the recently released PAMAP2 dataset.
Experimental results indicate that classifiers built using this correntropy
induced dictionary learning based framework achieve high accuracy by
using simple features, and that this approach gives results competitive
with classical systems built upon features with prior knowledge.
A Preference Model on Adaptive Affinity PropagationIJECEIAES
In recent years, two new data clustering algorithms have been proposed. One of them is Affinity Propagation (AP). AP is a new data clustering technique that use iterative message passing and consider all data points as potential exemplars. Two important inputs of AP are a similarity matrix (SM) of the data and the parameter ”preference” p. Although the original AP algorithm has shown much success in data clustering, it still suffer from one limitation: it is not easy to determine the value of the parameter ”preference” p which can result an optimal clustering solution. To resolve this limitation, we propose a new model of the parameter ”preference” p, i.e. it is modeled based on the similarity distribution. Having the SM and p, Modified Adaptive AP (MAAP) procedure is running. MAAP procedure means that we omit the adaptive p-scanning algorithm as in original Adaptive-AP (AAP) procedure. Experimental results on random non-partition and partition data sets show that (i) the proposed algorithm, MAAP-DDP, is slower than original AP for random non-partition dataset, (ii) for random 4-partition dataset and real datasets the proposed algorithm has succeeded to identify clusters according to the number of dataset’s true labels with the execution times that are comparable with those original AP. Beside that the MAAP-DDP algorithm demonstrates more feasible and effective than original AAP procedure.
Fuzzy clustering has been widely studied and applied in a variety of key areas of science and
engineering. In this paper the Improved Teaching Learning Based Optimization (ITLBO)
algorithm is used for data clustering, in which the objects in the same cluster are similar. This
algorithm has been tested on several datasets and compared with some other popular algorithm
in clustering. Results have been shown that the proposed method improves the output of
clustering and can be efficiently used for fuzzy clustering.
Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithmaciijournal
In this paper we attempt to solve an automatic clustering problem by optimizing multiple objectives such as automatic k-determination and a set of cluster validity indices concurrently. The proposed automatic clustering technique uses the most recent optimization algorithm Jaya as an underlying optimization stratagem. This evolutionary technique always aims to attain global best solution rather than a local best solution in larger datasets. The explorations and exploitations imposed on the proposed work results to detect the number of automatic clusters, appropriate partitioning present in data sets and mere optimal values towards CVIs frontiers. Twelve datasets of different intricacy are used to endorse the performance of aimed algorithm. The experiments lay bare that the conjectural advantages of multi objective clustering optimized with evolutionary approaches decipher into realistic and scalable performance paybacks.
Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithmaciijournal
In this paper we attempt to solve an automatic clustering problem by optimizing multiple objectives such as
automatic k-determination and a set of cluster validity indices concurrently. The proposed automatic
clustering technique uses the most recent optimization algorithm Jaya as an underlying optimization
stratagem. This evolutionary technique always aims
to attain global best solution rather than a local best solution in larger datasets. The explorations and exploitations imposed on the proposed work results to
detect the number of automatic clusters, appropriate partitioning present in data sets and mere optimal
values towards CVIs frontiers. Twelve datasets of different intricacy are used to endorse the performance
of aimed algorithm. The experiments lay bare that the conjectural advantages of multi objective clustering optimized with evolutionary approaches decipher into realistic and scalable performance paybacks.
Centralized Class Specific Dictionary Learning for wearable sensors based phy...Sherin Mathews
With recent progress in pervasive healthcare,
physical activity recognition with wearable body sensors has
become an important and challenging area in both research and
industrial communities. Here, we address a novel technique for
a sensor platform that performs physical activity recognition by
leveraging a class specific regularizer term into the dictionary
pair learning objective function. The proposed algorithm jointly
learns a synthesis dictionary and an analysis dictionary in
order to simultaneously perform signal representation and
classification once the time-domain features have been extracted.
Specifically, the class specific regularizer term ensures that the
sparse codes belonging to the same class will be concentrated
thereby proving beneficial for the classification stage. In order
to develop a more practical approach, we employ a combination
of an alternating direction method of multipliers and a l1 − ls
minimization method to approximately minimize the objective
function. We validate the effectiveness of our proposed model
by employing it on two activity recognition problem and an
intensity estimation problem, both of which include a large
number of physical activities. Experimental results demonstrate
that classifiers built in this dictionary learning based framework
outperforms state of art algorithms by using simple features,
thereby achieving competitive results when compared with
classical systems built upon features with prior knowledge
A Novel Approach to Mathematical Concepts in Data Miningijdmtaiir
-This paper describes three different fundamental
mathematical programming approaches that are relevant to
data mining. They are: Feature Selection, Clustering and
Robust Representation. This paper comprises of two clustering
algorithms such as K-mean algorithm and K-median
algorithms. Clustering is illustrated by the unsupervised
learning of patterns and clusters that may exist in a given
databases and useful tool for Knowledge Discovery in
Database (KDD). The results of k-median algorithm are used
to collecting the blood cancer patient from a medical database.
K-mean clustering is a data mining/machine learning algorithm
used to cluster observations into groups of related observations
without any prior knowledge of those relationships. The kmean algorithm is one of the simplest clustering techniques
and it is commonly used in medical imaging, biometrics and
related fields.
Analysis On Classification Techniques In Mammographic Mass Data SetIJERA Editor
Data mining, the extraction of hidden information from large databases, is to predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Data-Mining classification techniques deals with determining to which group each data instances are associated with. It can deal with a wide variety of data so that large amount of data can be involved in processing. This paper deals with analysis on various data mining classification techniques such as Decision Tree Induction, Naïve Bayes , k-Nearest Neighbour (KNN) classifiers in mammographic mass dataset.
Maximum Correntropy Based Dictionary Learning Framework for Physical Activity...sherinmm
Due to its symbolic role in ubiquitous health monitoring,
physical activity recognition with wearable body sensors has been in the
limelight in both research and industrial communities. Physical activity
recognition is difficult due to the inherent complexity involved with different
walking styles and human body movements. Thus we present a
correntropy induced dictionary pair learning framework to achieve this
recognition. Our algorithm for this framework jointly learns a synthesis
dictionary and an analysis dictionary in order to simultaneously perform
signal representation and classification once the time-domain features
have been extracted. In particular, the dictionary pair learning algorithm
is developed based on the maximum correntropy criterion, which
is much more insensitive to outliers. In order to develop a more tractable
and practical approach, we employ a combination of alternating direction
method of multipliers and an iteratively reweighted method to approximately
minimize the objective function. We validate the effectiveness of
our proposed model by employing it on an activity recognition problem
and an intensity estimation problem, both of which include a large number
of physical activities from the recently released PAMAP2 dataset.
Experimental results indicate that classifiers built using this correntropy
induced dictionary learning based framework achieve high accuracy by
using simple features, and that this approach gives results competitive
with classical systems built upon features with prior knowledge.
A Preference Model on Adaptive Affinity PropagationIJECEIAES
In recent years, two new data clustering algorithms have been proposed. One of them is Affinity Propagation (AP). AP is a new data clustering technique that use iterative message passing and consider all data points as potential exemplars. Two important inputs of AP are a similarity matrix (SM) of the data and the parameter ”preference” p. Although the original AP algorithm has shown much success in data clustering, it still suffer from one limitation: it is not easy to determine the value of the parameter ”preference” p which can result an optimal clustering solution. To resolve this limitation, we propose a new model of the parameter ”preference” p, i.e. it is modeled based on the similarity distribution. Having the SM and p, Modified Adaptive AP (MAAP) procedure is running. MAAP procedure means that we omit the adaptive p-scanning algorithm as in original Adaptive-AP (AAP) procedure. Experimental results on random non-partition and partition data sets show that (i) the proposed algorithm, MAAP-DDP, is slower than original AP for random non-partition dataset, (ii) for random 4-partition dataset and real datasets the proposed algorithm has succeeded to identify clusters according to the number of dataset’s true labels with the execution times that are comparable with those original AP. Beside that the MAAP-DDP algorithm demonstrates more feasible and effective than original AAP procedure.
Fuzzy clustering has been widely studied and applied in a variety of key areas of science and
engineering. In this paper the Improved Teaching Learning Based Optimization (ITLBO)
algorithm is used for data clustering, in which the objects in the same cluster are similar. This
algorithm has been tested on several datasets and compared with some other popular algorithm
in clustering. Results have been shown that the proposed method improves the output of
clustering and can be efficiently used for fuzzy clustering.
Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithmaciijournal
In this paper we attempt to solve an automatic clustering problem by optimizing multiple objectives such as automatic k-determination and a set of cluster validity indices concurrently. The proposed automatic clustering technique uses the most recent optimization algorithm Jaya as an underlying optimization stratagem. This evolutionary technique always aims to attain global best solution rather than a local best solution in larger datasets. The explorations and exploitations imposed on the proposed work results to detect the number of automatic clusters, appropriate partitioning present in data sets and mere optimal values towards CVIs frontiers. Twelve datasets of different intricacy are used to endorse the performance of aimed algorithm. The experiments lay bare that the conjectural advantages of multi objective clustering optimized with evolutionary approaches decipher into realistic and scalable performance paybacks.
Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithmaciijournal
In this paper we attempt to solve an automatic clustering problem by optimizing multiple objectives such as
automatic k-determination and a set of cluster validity indices concurrently. The proposed automatic
clustering technique uses the most recent optimization algorithm Jaya as an underlying optimization
stratagem. This evolutionary technique always aims
to attain global best solution rather than a local best solution in larger datasets. The explorations and exploitations imposed on the proposed work results to
detect the number of automatic clusters, appropriate partitioning present in data sets and mere optimal
values towards CVIs frontiers. Twelve datasets of different intricacy are used to endorse the performance
of aimed algorithm. The experiments lay bare that the conjectural advantages of multi objective clustering optimized with evolutionary approaches decipher into realistic and scalable performance paybacks.
A hybrid constructive algorithm incorporating teaching-learning based optimiz...IJECEIAES
In neural networks, simultaneous determination of the optimum structure and weights is a challenge. This paper proposes a combination of teachinglearning based optimization (TLBO) algorithm and a constructive algorithm (CA) to cope with the challenge. In literature, TLBO is used to choose proper weights, while CA is adopted to construct different structures in order to select the proper one. In this study, the basic TLBO algorithm along with an improved version of this algorithm for network weights selection are utilized. Meanwhile, as a constructive algorithm, a novel modification to multiple operations, using statistical tests (MOST), is applied and tested to choose the proper structure. The proposed combinatorial algorithms are applied to ten classification problems and two-time-series prediction problems, as the benchmark. The results are evaluated based on training and testing error, network complexity and mean-square error. The experimental results illustrate that the proposed hybrid method of the modified MOST constructive algorithm and the improved TLBO (MCO-ITLBO) algorithm outperform the others; moreover, they have been proven by Wilcoxon statistical tests as well. The proposed method demonstrates less average error with less complexity in the network structure.
GRAY SCALE IMAGE SEGMENTATION USING OTSU THRESHOLDING OPTIMAL APPROACHJournal For Research
Image segmentation is often used to distinguish the foreground from the background. Image segmentation is one of the difficult research problems in the machine vision industry and pattern recognition. Thresholding is a simple but effective method to separate objects from the background. A commonly used method, the Otsu method, improves the image segmentation effect obviously. It can be implemented by two different approaches: Iteration approach and Custom approach. In this paper both approaches has been implemented on MATLAB and give the comparison of them and show that both has given almost the same threshold value for segmenting image but the custom approach requires less computations. So if this method will be implemented on hardware in an optimized way then custom approach is the best option.
Max stable set problem to found the initial centroids in clustering problemnooriasukmaningtyas
In this paper, we propose a new approach to solve the document-clustering using the K-Means algorithm. The latter is sensitive to the random selection of the k cluster centroids in the initialization phase. To evaluate the quality of K-Means clustering we propose to model the text document clustering problem as the max stable set problem (MSSP) and use continuous Hopfield network to solve the MSSP problem to have initial centroids. The idea is inspired by the fact that MSSP and clustering share the same principle, MSSP consists to find the largest set of nodes completely disconnected in a graph, and in clustering, all objects are divided into disjoint clusters. Simulation results demonstrate that the proposed K-Means improved by MSSP (KM_MSSP) is efficient of large data sets, is much optimized in terms of time, and provides better quality of clustering than other methods.
DESIGN AND IMPLEMENTATION OF BINARY NEURAL NETWORK LEARNING WITH FUZZY CLUSTE...cscpconf
In this paper, Design and Implementation of Binary Neural Network Learning with Fuzzy
Clustering (DIBNNFC), is proposed to classify semisupervised data, it is based on the
concept of binary neural network and geometrical expansion. Parameters are updated
according to the geometrical location of the training samples in the input space, and each
sample in the training set is learned only once. It’s a semisupervised based approach, the
training samples are semi-labelled i.e. for some samples, labels are known and for some
samples data labels are not known. The method starts with classification, which is done by
using the concept of ETL algorithm. In classification process various classes are formed.
These classes classify samples in to two classes after that considers each class as a region and calculates the average of the entire region separately. This average is centres of the region which is used for the purpose of clustering by using FCM algorithm. Once clustering process over labelling of semi supervised data is done, then whole samples would be classify by (DIBNNFC). The method proposes here is exhaustively tested with different benchmark datasets and it is found that, on increasing value of training parameters number of hidden neurons and training time both are getting decrease. The result reported, using real character recognition data set and result will compare with existing semi-supervised classifier, the proposed approach learned with semi-supervised leads to higher classification accuracy.
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...cscpconf
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
MULTIPROCESSOR SCHEDULING AND PERFORMANCE EVALUATION USING ELITIST NON DOMINA...ijcsa
Task scheduling plays an important part in the improvement of parallel and distributed systems. The problem of task scheduling has been shown to be NP hard. The time consuming is more to solve the problem in deterministic techniques. There are algorithms developed to schedule tasks for distributed environment, which focus on single objective. The problem becomes more complex, while considering biobjective.This paper presents bi-objective independent task scheduling algorithm using elitist Nondominated
sorting genetic algorithm (NSGA-II) to minimize the makespan and flowtime. This algorithm generates pareto global optimal solutions for this bi-objective task scheduling problem. NSGA-II is implemented by using the set of benchmark instances. The experimental result shows NSGA-II generates efficient optimal schedules.
Comparison Between Clustering Algorithms for Microarray Data AnalysisIOSR Journals
Currently, there are two techniques used for large-scale gene-expression profiling; microarray and
RNA-Sequence (RNA-Seq).This paper is intended to study and compare different clustering algorithms that used
in microarray data analysis. Microarray is a DNA molecules array which allows multiple hybridization
experiments to be carried out simultaneously and trace expression levels of thousands of genes. It is a highthroughput
technology for gene expression analysis and becomes an effective tool for biomedical research.
Microarray analysis aims to interpret the data produced from experiments on DNA, RNA, and protein
microarrays, which enable researchers to investigate the expression state of a large number of genes. Data
clustering represents the first and main process in microarray data analysis. The k-means, fuzzy c-mean, selforganizing
map, and hierarchical clustering algorithms are under investigation in this paper. These algorithms
are compared based on their clustering model.
Similar to An Automatic Medical Image Segmentation using Teaching Learning Based Optimization (20)
Now-a-days, Internet has become an important part of human’s life, a person
can shop, invest, and perform all the banking task online. Almost, all the organizations have
their own website, where customer can perform all the task like shopping, they only have to
provide their credit card details. Online banking and e-commerce organizations have been
experiencing the increase in credit card transaction and other modes of on-line transaction.
Due to this credit card fraud becomes a very popular issue for credit card industry, it causes
many financial losses for customer and also for the organization. Many techniques like
Decision Tree, Neural Networks, Genetic Algorithm based on modern techniques like
Artificial Intelligence, Machine Learning, and Fuzzy Logic have been already developed for
credit card fraud detection. In this paper, an evolutionary Simulated Annealing algorithm is
used to train the Neural Networks for Credit Card fraud detection in real-time scenario.
This paper shows how this technique can be used for credit card fraud detection and
present all the detailed experimental results found when using this technique on real world
financial data (data are taken from UCI repository) to show the effectiveness of this
technique. The algorithm used in this paper are likely beneficial for the organizations and
for individual users in terms of cost and time efficiency. Still there are many cases which are
misclassified i.e. A genuine customer is classified as fraud customer or vise-versa.
Wireless sensor networks (WSN) have been widely used in various applications.
In these networks nodes collect data from the attached sensors and send their data to a base
station. However, nodes in WSN have limited power supply in form of battery so the nodes
are expected to minimize energy consumption in order to maximize the lifetime of WSN. A
number of techniques have been proposed in the literature to reduce the energy
consumption significantly. In this paper, we propose a new clustering based technique
which is a modification of the popular LEACH algorithm. In this technique, first cluster
heads are elected using the improved LEACH algorithm as usual, and then a cluster of
nodes is formed based on the distance between node and cluster head. Finally, data from
node is transferred to cluster head. Cluster heads forward data, after applying aggregation,
to the cluster head that is closer to it than sink in forward direction or directly to the sink.
This reduction in distance travelled improves the performance over LEACH algorithm
significantly.
The next generation wireless networks comprises of mobile users moving
between heterogeneous networks, using terminals with multiple access interfaces and
services. The most important issue in such environment is ABC (Always Best Connected) i.e.
allowing the best connectivity to applications anywhere at any time. For always best
connectivity requirement various vertical handover strategies for decision making have
been proposed. This paper provides an overview of the most interesting and recent
strategies.
This paper presents the design and performance comparison of a two stage
operational amplifier topology using CMOS and BiCMOS technology. This conventional op
amp circuit was designed by using RF model of BSIM3V3 in 0.6 μm CMOS technology and
0.35 μm BiCMOS technology. Both the op amp circuits were designed and simulated,
analyzed and performance parameters are compared. The performance parameters such as
gain, phase margin, CMRR, PSRR, power consumption etc achieved are compared. Finally,
we conclude the suitability of CMOS technology over BiCMOS technology for low power
RF design.
In Cognitive Radio Networks (CRN), Cooperative Spectrum Sensing (CSS) is
used to improve performance of spectrum sensing techniques used for detection of licensed
(Primary) user’s signal. In CSS, the spectrum sensing information from multiple unlicensed
(Secondary) users are combined to take final decision about presence of primary signal. The
mixing techniques used to generate final decision about presence of PU’s signal are also
called as Fusion techniques / rules. The fusion techniques are further classified as data
fusion and decision fusion techniques. In data fusion technique all the secondary users
(SUs) share their raw information of spectrum detection like detected energy or other
statistical information, while in decision fusion technique all the SUs take their local
decisions and share the decision by sending ‘0’ or ‘1’ corresponding to absence and presence
of PU’s signal respectively. The rules used in decision fusion techniques are OR rule, AND
rule and K-out-of-N rule. The CSS is further classified as distributed CSS and centralized
CSS. In distributed CSS all the SUs share the spectrum detection information with each
other and by mixing the shared information; all the SUs take final decision individually. In
centralized CSS all the SUs send their detected information to a secondary base station /
central unit which combines the shared information and takes final decision. The secondary
base station shares the final decision with all the SUs in the CRN. This paper covers
overview of information fusion methods used for CSS and analysis of decision fusion rules
with simulation results.
ZigBee has been developed to support lower data rates and low power consuming
applications. This paper targets to analyze various parameters of ZigBee physical (PHY).
Performance of ZigBee PHY is evaluated on the basis of energy consumption in
transmitting and receiving mode and throughput. Effect of variation in network size is
studied on these performance attributes. Some modulation schemes are also compared and
the best modulation scheme is suggested with tradeoffs between different performance
metrics.
This paper gives a brief idea of the moving objects tracking and its application.
In sport it is challenging to track and detect motion of players in video frames. Task
represents optical flow analysis to do motion detection and particle filter to track players
and taking consideration of regions with movement of players in sports video. Optical flow
vector calculation gives motion of players in video frame. This paper presents improved
Luacs Kanade algorithm explained for optical flow computation for large displacement and
more accuracy in motion estimation.
A rapid progress is seen in the field of robotics both in educational and industrial
automation sectors. The Robotics education in particular is gaining technological advances
and providing more learning opportunities. In automotive sector, there is a necessity and
demand to automate daily human activities by robot. With such an advancement and
demand for robotics, the realization of a popular computer game will help students to learn
and acquire skills in the field of robotics. The computer game such as Pacman offers
challenges on both software and hardware fronts. In software, it provides challenges in
developing algorithms for a robot to escape from the pool of attacking robots and to develop
algorithms for multiple ghost robots to attack the Pacman. On the hardware front, it
provides a challenge to integrate various systems to realize the game. This project aims to
demonstrate the pacman game in real world as well as in simulation. For simulation
purpose Player/Stage is used to develop single-client and multi-client architectures. The
multi- client architecture in player/stage uses one global simulation proxy to which all the
robot models are connected. This reduces the overhead to manage multiple robots proxy.
The single-client architecture enables only two robot models to connect to the simulation
proxy. Multi-client approach offers flexibility to add sensors to each port which will be used
distinctly by the client attached to the respective robot. The robots are named as Pacman
and Ghosts, which try to escape and attack respectively. Use of Network Camera has been
done to detect the global positions of the robots and data is shared through inter-process
communication.
In Content-Based Image Retrieval (CBIR) systems, the visual contents of the
images in the database are took out and represented by multi-dimensional characteristic
vectors. A well known CBIR system that retrieves images by unsupervised method known
as cluster based image retrieval system. For enhancing the performance and retrieval rate
of CBIR system, we fuse the visual contents of an image. Recently, we developed two
cluster-based CBIR systems by fusing the scores of two visual contents of an image. In this
paper, we analyzed the performance of the two recommended CBIR systems at different
levels of precision using images of varying sizes and resolutions. We also compared the
performance of the recommended systems with that of the other two existing CBIR systems
namely UFM and CLUE. Experimentally, we find that the recommended systems
outperform the other two existing systems and one recommended system also comparatively
performed better in every resolution of image.
Information Systems and Networks are subjected to electronic attacks. When
network attacks hit, organizations are thrown into crisis mode. From the IT department to
call centers, to the board room and beyond, all are fraught with danger until the situation is
under control. Traditional methods which are used to overcome these threats (e.g. firewall,
antivirus software, password protection etc.) do not provide complete security to the system.
This encourages the researchers to develop an Intrusion Detection System which is capable
of detecting and responding to such events. This review paper presents a comprehensive
study of Genetic Algorithm (GA) based Intrusion Detection System (IDS). It provides a
brief overview of rule-based IDS, elaborates the implementation issues of Genetic Algorithm
and also presents a comparative analysis of existing studies.
Step by step operations by which we make a group of objects in which attributes
of all the objects are nearly similar, known as clustering. So, a cluster is a collection of
objects that acquire nearly same attribute values. The property of an object in a cluster is
similar to other objects in same cluster but different with objects of other clusters.
Clustering is used in wide range of applications like pattern recognition, image processing,
data analysis, machine learning etc. Nowadays, more attention has been put on categorical
data rather than numerical data. Where, the range of numerical attributes organizes in a
class like small, medium, high, and so on. There is wide range of algorithm that used to
make clusters of given categorical data. Our approach is to enhance the working on well-
known clustering algorithm k-modes to improve accuracy of algorithm. We proposed a new
approach named “High Accuracy Clustering Algorithm for Categorical datasets”.
Brain tumor is a malformed growth of cells within brain which may be
cancerous or non-cancerous. The term ‘malformed’ indicates the existence of tumor. The
tumor may be benign or malignant and it needs medical support for further classification.
Brain tumor must be detected, diagnosed and evaluated in earliest stage. The medical
problems become grave if tumor is detected at the later stage. Out of various technologies
available for diagnosis of brain tumor, MRI is the preferred technology which enables the
diagnosis and evaluation of brain tumor. The current work presents various clustering
techniques that are employed to detect brain tumor. The classification involves classification
of images into normal and malformed (if detected the tumor). The algorithm deals with
steps such as preprocessing, segmentation, feature extraction and classification of MR brain
images. Finally, the confirmatory step is specifying the tumor area by technique called
region of interest.
A Proxy signature scheme enables a proxy signer to sign a message on behalf of
the original signer. In this paper, we propose ECDLP based solution for chen et. al [1]
scheme. We describe efficient and secure Proxy multi signature scheme that satisfy all the
proxy requirements and require only elliptic curve multiplication and elliptic curve addition
which needs less computation overhead compared to modular exponentiations also our
scheme is withstand against original signer forgery and public key substitution attack.
Water marking has been proposed as a method to enhance data security. Text
water marking requires extreme care when embedding additional data within the images
because the additional information must not affect the image quality. Digital water marking
is a method through which we can authenticate images, videos and even texts. Add text
water mark and image water mark to your photos or animated image, protect your
copyright avoid unauthorized use. Water marking functions are not only authentication, but
also protection for such documents against malicious intentions to change such documents
or even claim the rights of such documents. Water marking scheme that hides water
marking in method, not affect the image quality. In this paper method of hiding a data using
LSB replacement technique is proposed.
Today among various medium of data transmission or storage our sensitive data
are not secured with a third-party, that we used to take help of. Cryptography plays an
important role in securing our data from malicious attack. This paper present a partial
image encryption based on bit-planes permutation using Peter De Jong chaotic map for
secure image transmission and storage. The proposed partial image encryption is a raw data
encryption method where bits of some bit-planes are shuffled among other bit-planes based
on chaotic maps proposed by Peter De Jong. By using the chaotic behavior of the Peter De
Jong map the position of all the bit-planes are permuted. The result of the several
experimental, correlation analysis and sensitivity test shows that the proposed image
encryption scheme provides an efficient and secure way for real-time image encryption and
decryption.
This paper presents a survey of Dependency Analysis of Service Oriented
Architecture (SOA) based systems. SOA presents newer aspects of dependency analysis due
to its different architectural style and programming paradigm. This paper surveys the
previous work taken on dependency analysis of service oriented systems. This study shows
the strengths and weaknesses of current approaches and tools available for dependency
analysis task in context of SOA. The main motivation of this work is to summarize the
recent approaches in this field of research, identify major issue and challenges in
dependency analysis of SOA based systems and motivate further research on this topic.
In this paper, proposed a novel implementation of a Soft-Core system using
micro-blaze processor with virtex-5 FPGA. Till now Hard-Core processors are used in
FPGA processor cores. Hard cores are a fixed gate-level IP functions within the FPGA
fabrics. Now the proposed processor is Soft-Core Processor, this is a microprocessor fully
described in software, usually in an HDL. This can be implemented by using EDK tool. In
this paper, developed a system which is having a micro-blaze processor is the combination
of both hardware & Software. By using this system, user can control and communicate all
the peripherals which are in the supported board by using Xilinx platform to develop an
embedded system. Implementing of Soft-Core process system with different peripherals like
UART interface, SPA flash interface, SRAM interface has to be designed using Xilinx
Embedded Development Kit (EDK) tools.
The article presents a simple algorithm to construct minimum spanning tree and
to find shortest path between pair of vertices in a graph. Our illustration includes the proof
of termination. The complexity analysis and simulation results have also been included.
Wimax technology has reshaped the framework of broadband wireless internet
service. It provides the internet service to unconnected or detached areas such as east South
Africa, rural areas of America and Asia region. Full duplex helpers employed with one of
the relay stations selection and indexing method that is Randomized Distributed Space Time
are used to expand the coverage area of primary Wimax station. The basic problem was
identified at cell edge due to weather conditions (rain, fog), insertion of destruction because
of multiple paths in the same communication channel and due to interference created by
other users in that communication. It is impractical task for the receiver station to decode
the transmitted signal successfully at the cell edges, which increases the high packet loss and
retransmissions. But Wimax is a outstanding technology which is used for improving the
quality of internet service and also it offers various services like Voice over Internet
Protocol, Video conferencing and Multimedia broadcast etc where a little delay in packet
transmission can cause a big loss in the communication. Even setup and initialization of
another Wimax station nearer to each other is not a good alternate, where any mobile
station can easily handover to another base station if it gets a strong signal from other one.
But in rural areas, for few numbers of customers, installation of base station nearer to each
other is costlier task. In this review article, we present a scheme using R-DSTC technique to
choose and select helpers (relay nodes) randomly to expand the coverage area and help to
mobile station as a helper to provide secure communication with base station. In this work,
we use full duplex helpers for better utilization of bandwidth.
Radio Frequency identification (RFID) technology has become emerging
technique for tracking and items identification. Depend upon the function; various RFID
technologies could be used. Drawback of passive RFID technology, associated to the range
of reading tags and assurance in difficult environmental condition, puts boundaries on
performance in the real life situation [1]. To improve the range of reading tags and
assurance, we consider implementing active backscattering tag technology. For making
mobiles of multiple radio standards in 4G network; the Software Defined Radio (SDR)
technology is used. Restrictions in Existing RFID technologies and SDR technology, can be
eliminated by the development and implementation of the Software Defined Radio (SDR)
active backscattering tag compatible with the EPC global UHF Class 1 Generation 2 (Gen2)
RFID standard. Such technology can be used for many of applications and services.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
2. PS-measure as the objective functions [13]. Teaching Learning Based Optimization is a very recent
population
based evolutionary algorithm [14]. Rao and Patel have introduced the TeachingLearning-Based Optimization (TLBO) algorithm which does not require any algorithm specific parameters.
TLBO is developed based on the natural phenomena of teaching and learning process of a class room.
TLBO contains two phases as teacher phase and learning phase [15]. As in any population based algorithms
the TLBO is also contains population. Solution vectors are the learners and dimensions of each vector is
termed as subjects. Best learner in the population is a teacher. This paper proposes an automatic
clustering algorithm using TLBO that determines homogeneous groups automatically from grey
image datasets. Experimental results on various images have shown the accuracy and efficiency of
TLBO in image segmentation. Methodology is included in Section II, Experimental results are provided
in Section III and Conclusions are presented in Section IV.
I. METHODOLOGY
The paper is mainly focused on the applicability of TLBO
in
finding
optimal
clusters
automatically. The following subsections contain the procedure of TLBO and the proposed Automatic
Clustering using TLBO (AUTOTLBO). The chromosome contains inpk, threshold values for active
centroids and inpk centroids as in ACDE.
TLBO
TLBO is a recent evolutionary algorithm which providing competitive solutions for various applications
and does not require any program specific parameters compared to other existing evolutionary algorithms.
The process of TLBO is as follows
Initialization
The population X, is randomly initialized by a given data set of n rows and d columns using the
following equation.
X i , j (0)
X min
j
rand (1) * X max
j
X i (t )
X min
j
X i ,1 (t ), X i , 2 (t ),..., X i ,d (t )
(1)
Xi,j Creation of a population of learners or individuals. The ith learner of the population X at current
generation t with d subjects is as follows,
(2)
Teacher phase
The mean value of each subject, j, of the population in generation t is given as
M (t ) [M 1 (t ), M 2 (t ),..., M d (t )
(3)
The teacher is the best learner with minimum objective function value in the current population. The Teacher
phase tries to increase the mean result of the learners and always tries to shift the learners towards the
teacher. A new set of improved learners can be generated by adding a difference of teacher and mean vector
to each learner in the current population as follows.
X i (t 1)
X i (t ) r * ( X best (t ) TF M (t ))
(4)
TF is the teaching factor with value between 1 and 2, and riis the random number in the range [0, 1]. The
value of TF can be found using the following equation (5)
TF
round (1 rand (1))
(5)
Learner phase
The knowledge of the learners can be increased by the interaction of one another in the class. For a learner, i,
another learner is selected, j, randomly from the class.
Xi (t 1
)
Xi (t) r *(Xi (t) Xj (t)), ((Xi (t)) f (Xj (t))
iff
Xi (t) r *(Xj (t) Xi (t)), ((Xj (t)) f (Xi (t))
iff
(6)
The two phases are repeated till a stopping criterion has met. Best learner is the best solution in the run.
Stopping criteria
The stopping criteria in the present work is “Stop by convergence or stagnation”. The convergence of the
9
3. algorithm is based on the fitness value of the fittest individual. The difference of fitness value of fittest
individuals in any two successive generations is less than 0.0001, is the stopping
B. Automatic Clustering Using Tlbo (Autotlbo).
The new AUTOTLBO is to find optimal clusters automatically. Any cluster validity measure
can be selected as fitness function. Here, CS Index is selected as fitness function [8]. The algorithm for
the AUTOTLBO is as follows. Let X is a given data set with n, elements.
Step 1) Initialize each learner to contain Maxk, maximum number of randomly selected cluster centers
and Maxk (randomly chosen) activation thresholds in [0, 1]. Learner is represented in the following figure
figure1.
Active Centroids
0.4
0.7
0.9
…….
Activation thresholds for centroids
1
2
……
20
50
110
Centroids
Maxk
Figure1. Learner Representation
Step 2) Find the active cluster centers with value greater than 0.5,in each learner.
Step 3) For t = 1 to tmax do
a) For each data vector Xp, calculate its difference from all active cluster centers.
b) Assign Xp to closest cluster
c)Evaluate each learner quality and find Teacher, the best learner using CS Index.
d) Update the learners according to the TLBO algorithm described in the section 2.1.
Step 4) Report the final solution obtained by the globally best learner (one yielding the highest value of the
fitness function) at time t = tmax.
C. Cluster validity measures [8].
Assessing the clustering results and interpreting the clusters found are as important as generating the clusters.
Cluster Validity is the procedure of evaluating, quantitatively, the results of a clustering algorithm. Cluster
validity indices correspond to the statistical– mathematical functions used to evaluate the results of a
clustering algorithm on a quantitative basis. Using Internal Criteria, we are going to verify whether the
clustering structure produced by a clustering algorithm fit the data, but using only information inherent to the
data set.
CS Index
Chou et al. have proposed the CS measure for evaluating the validity of a clustering scheme. The centroid of
a cluster is computed by averaging the elements that belong to the same cluster using
mi
k
CS
i 1
1
Ni
1
Ni
Xj
X j Ci
max{d ( X i , X q )}
X i Ci
xq Ci
k
min {d (mi , mq )}
i 1
j k, j i
CS measure is a function of the ratio of the sum of within-cluster distance to between-cluster distance. The
cluster configuration that minimizes CS is taken as the optimal number of clusters, k.
Dunn index
The Dunn index defines the ratio between the minimal intra-cluster distance to maximal inter-cluster
distance.
The index is given by:
D = dmin / dmax ,
10
4. Where, dmin denote the smallest distance between two objects from different clusters, and dmax the largest
distance of two objects from the same cluster. The Dunn index is limited to the interval [0, 1] and should be
maximized.
The Davies-Bouldin Index
The Davies-Bouldin index aims at identifying sets of clusters that are compact and well separated. The
Davies-Bouldin validation index, DB, is defined as:
DB( X )
1
k
k
max
i 1
i j
Ci
Cj
D Ci , C j
Where, D(Ci, Cj) defines the distance between clusters C i and Cj (inter cluster distance);
p) represents the
intra cluster distance of cluster Cp, and k is the number of clusters of data set X. Small values of DB
correspond to clusters that are compact, and whose centers are far away from each other. Therefore, the
cluster configuration that minimizes DB is taken as the optimal number of clusters, k.
III. E XPERIMENTAL RESULTS
The AUTOTLBO performance is studied using two other Evolutionary algorithms Genetic Algorithm (GA),
Differential Evolution, ACDE and with classical k-means algorithm. In the present work, population size is
taken as 20. In the following tables first image is the original image, (a) is the output from k-means, (b) is
the output generated by GA, (c) is from DE, output from ACDE provided as (d) and (e) is the segmentation
result from the proposed AUTOTLBO algorithm. In each image, K-represents the number of clusters
of the output image. In the images (d) and (e) the input number of clusters is specified as inpk. The
segmentation results are validated using Dunn, DB, and CS clustering validity measures and the values are
tabulated in Table 7.
TABLE I. SEGMENTATION RESULTS OF PEPPER IMAGE
TABLE II. SEGMENTATION RESULTS OF BIRD IMAGE
ORIGINAL
(A) K-8
(B) K-8
Original
(a)
(b)
(C) K-8
(D) INPK-20, K12
(E) INPK-20,
K-8
(c)
(d)
(e)
TABLE III. SEGMENTATION RESULTS OF LEENA
Original
(a)
(b)
(c)
(d)
TABLE IV. SEGMENTATION RESULTS OF BEAR
(e)
11
5. TABLE IV. SEGMENTATION R ESULTS OF 3 BIRDS
(c)
(a)
(b)
(d)
Original
TABLE VI. SEGMENTATION R ESULTS OF DOG
(e)
TABLE VII. VALIDITY MEASURES OBSERVED IN VARIOUS ALGORITHMS
ACDE
DE
TLBO
AUTOTLBO
GA
kmeans
Pepper
cs
0.9268
0.2939
0.9805
0.9339
0.8487
0.7581
Leena
db
dunn
cs
0.5608
0.0323
0.0050
0.5570
0.0303
0.4450
0.5662
0.0333
1.2126
0.5887
0.0149
0.8277
0.5443
0.0127
1.1580
0.5304
0.0370
0.7223
Dog
db
dunn
cs
0.5872
0.0357
0.1947
0.5553
0.0263
0.6776
0.5123
0.0222
2.2213
0.5207
0.0175
0.7242
0.5530
0.0152
1.8319
0.5116
0.0333
0.7690
Bear
db
dunn
cs
0.5116
0.0286
0.1730
0.5362
0.0294
0.3369
0.6355
0.0222
1.2316
0.4653
0.0175
0.9266
0.6068
0.0156
1.1083
0.5626
0.0345
0.7705
3birds
db
dunn
cs
0.5148
0.0370
0.0108
0.5455
0.0333
0.3436
0.5568
0.0238
1.1633
0.5278
0.0175
0.8211
0.5334
0.0233
0.9646
0.5136
0.0303
0.6800
db
0.7314
0.5127
0.5588
0.5204
0.5892
0.5333
dunn
cs
0.0303
0.3102
0.0357
0.4057
0.0192
1.2624
0.0213
0.8378
0.0145
1.2954
0.0303
0.7549
db
dunn
0.5539
0.0417
0.5391
0.0313
0.5084
0.0250
0.5168
0.0154
0.5759
0.0182
0.5305
0.0250
Bird
Table1-6 shows the six original images and segmented portions of the images from various
algorithms. The tables clearly show the efficiency of AUTOTLBO in segmenting the given images.
Compared to DE the TLBO is very fast and simple. We have extended the concept of segmentation using
TLBO to Medical imaging also. The results are tabulated in the following Table8. The values from
AUTOTLBO are as equal as compared to the other methods.
IV. CONCLUSIONS
TLBO is the very recent population based evolutionary algorithm that provided competitive solutions in
mechanical engineering optimization. TLBO is very simple, fast, and doesn’t required algorithm
specific parameters. This paper proposes automatic clustering using TLBO for image segmentation.
The performance of the proposed algorithm is studied by conducting tests on various images and the results
are also compared with the existing evolutionary, classical, and automatic clustering techniques. The
experimental results have shown in the accuracy and efficiency of AUTOTLBO in image segmentation.
Successful image segmentation is also observed by AUTOTLBO in medical image segmentation.
ACKNOWLEDGMENT
This work was supported by grant from DST, New Delhi
12
6. T ABLE VIII. SEGMENTATION RESULTS OF MEDICAL IMAGES
Original Image
AUTOTLBO image
Comments
For the given 20, 8 segments are identified. Shape of breast cancer tissue has
been correctly segmented.
Breast mammogram
Coronol section of human head
Horizontal head section
The cortex and the cerebellum are well segmented. In addition, the
brainstem and the ventricle lying at the center of the brain are correctly
separated
Original gray-level image showing a coronal section of a human head. The entire
brain is segmented as a region, as are the extracranial tissue and the neck muscle.
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