Most of students desire to know about their knowledge level to perfect their exams. In learning environment the fields of study overwhelm on page with collaboration or cooperation. Students can do their exercises either individually or collaboratively with their peers. The system provides the guidelines for students' learning system about interest fields as Java in this system. Especially the system feedbacks information about exam to know their grades without teachers. The participants who answered the exam can discuss with each others because of sharing e mail and list of them.
This paper presents a review & performs a comparative evaluation of few known machine learning
algorithms in terms of their suitability & code performance on any given data set of any size. In this paper,
we describe our Machine Learning ToolBox that we have built using python programming language. The
algorithms used in the toolbox consists of supervised classification algorithms such as Naïve Bayes,
Decision Trees, SVM, K-nearest Neighbors and Neural Network (Backpropagation). The algorithms are
tested on iris and diabetes dataset and are compared on the basis of their accuracy under different
conditions. However using our tool one can apply any of the implemented ML algorithms on any dataset of
any size. The main goal of building a toolbox is to provide users with a platform to test their datasets on
different Machine Learning algorithms and use the accuracy results to determine which algorithms fits the
data best. The toolbox allows the user to choose a dataset of his/her choice either in structured or
unstructured form and then can choose the features he/she wants to use for training the machine We have
given our concluding remarks on the performance of implemented algorithms based on experimental
analysis
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
COMPARISON INTELLIGENT ELECTRONIC ASSESSMENT WITH TRADITIONAL ASSESSMENT FOR ...cseij
In education, the use of electronic (E) examination systems is not a novel idea, as E-examination systems
have been used to conduct objective assessments for the last few years. This research deals with randomly
designed E-examinations and proposes an E-assessment system that can be used for subjective questions.
This system assesses answers to subjective questions by finding a matching ratio for the keywords in
instructor and student answers.
This paper presents a review & performs a comparative evaluation of few known machine learning
algorithms in terms of their suitability & code performance on any given data set of any size. In this paper,
we describe our Machine Learning ToolBox that we have built using python programming language. The
algorithms used in the toolbox consists of supervised classification algorithms such as Naïve Bayes,
Decision Trees, SVM, K-nearest Neighbors and Neural Network (Backpropagation). The algorithms are
tested on iris and diabetes dataset and are compared on the basis of their accuracy under different
conditions. However using our tool one can apply any of the implemented ML algorithms on any dataset of
any size. The main goal of building a toolbox is to provide users with a platform to test their datasets on
different Machine Learning algorithms and use the accuracy results to determine which algorithms fits the
data best. The toolbox allows the user to choose a dataset of his/her choice either in structured or
unstructured form and then can choose the features he/she wants to use for training the machine We have
given our concluding remarks on the performance of implemented algorithms based on experimental
analysis
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
COMPARISON INTELLIGENT ELECTRONIC ASSESSMENT WITH TRADITIONAL ASSESSMENT FOR ...cseij
In education, the use of electronic (E) examination systems is not a novel idea, as E-examination systems
have been used to conduct objective assessments for the last few years. This research deals with randomly
designed E-examinations and proposes an E-assessment system that can be used for subjective questions.
This system assesses answers to subjective questions by finding a matching ratio for the keywords in
instructor and student answers.
Predicting the Presence of Learning Motivation in Electronic Learning: A New ...TELKOMNIKA JOURNAL
Research affirms that electronic learning (e-learning) has a great deal of advantages to learning process, it’s provides learners with flexibility in terms of time, place, and pace. That easiness was not be a guarantee of a good learning outcomes though they got the same learning material and course, because the fact is the outcomes was not the same from one to another and even not satisfy enough. One of prerequisite to the successful of e-learning process is the presence of learning motivation and it was not easy to identify. We propose a novel model to predicting the presence of learning motivation in e-learning using those attributes that have been identified in previous research. This model has been built using WEKA toolkit by comparing fourteen algorithms for Tree Classifier and ten-fold cross validation testing methods to process 3.200 of data sets. The best accuracy reached at 91.1% and identified four parameters to predict the presence of learning motivation in e-learning, and aim to assist teachers in identify whether student needs motivation or does not. This study also confirmed that Tree Classifier still has the best accuracy to predict and classify academic performance, it was reached average 90.48% for fourteen algorithm for accuracy value.
With the emergence of virtualization and cloud computing technologies, several services are housed on virtualization platform. Virtualization is the technology that many cloud service providers rely on for efficient management and coordination of the resource pool. As essential services are also housed on cloud platform, it is necessary to ensure continuous availability by implementing all necessary measures. Windows Active Directory is one such service that Microsoft developed for Windows domain networks. It is included in Windows Server operating systems as a set of processes and services for authentication and authorization of users and computers in a Windows domain type network. The service is required to run continuously without downtime. As a result, there are chances of accumulation of errors or garbage leading to software aging which in turn may lead to system failure and associated consequences. This results in software aging. In this work, software aging patterns of Windows active directory service is studied. Software aging of active directory needs to be predicted properly so that rejuvenation can be triggered to ensure continuous service delivery. In order to predict the accurate time, a model that uses time series forecasting technique is built.
Modeling Text Independent Speaker Identification with Vector QuantizationTELKOMNIKA JOURNAL
Speaker identification is one of the most important technologies nowadays. Many fields such as
bioinformatics and security are using speaker identification. Also, almost all electronic devices are using
this technology too. Based on number of text, speaker identification divided into text dependent and text
independent. On many fields, text independent is mostly used because number of text is unlimited. So, text
independent is generally more challenging than text dependent. In this research, speaker identification text
independent with Indonesian speaker data was modelled with Vector Quantization (VQ). In this research
VQ with K-Means initialization was used. K-Means clustering also was used to initialize mean and
Hierarchical Agglomerative Clustering was used to identify K value for VQ. The best VQ accuracy was
59.67% when k was 5. According to the result, Indonesian language could be modelled by VQ. This
research can be developed using optimization method for VQ parameters such as Genetic Algorithm or
Particle Swarm Optimization.
The process of determining cuts tuition for students are usually given with the same nominal. And in this paper is the determination of the pieces tuition for students who are less able to be different, depending on how much income parents and the number of children covered. For income parents who get discounted tuition fee of IDR Rp.1,500,000 and for the number of children in these families also determine the number of pieces obtained. Tsukamoto Fuzzy system is the model used in this paper. Each input variable is divided into three membership functions. In this paper, Nine Tsukamoto Fuzzy model rules have been applied. The system also provides a consequent change of parameters if the current parameter values to be changed. The smaller the parent's income, the greater the pieces obtained. The more children insured the greater the college acquired pieces.
Research scholars evaluation based on guides view using id3eSAT Journals
Abstract Research Scholars finds many problems in their Research and Development activities for the completion of their research work in universities. This paper gives a proficient way for analyzing the performance of Research Scholar based on guides and experts feedback. A dataset is formed using this information. The outcome class attribute will be in view of guides about the scholars. We apply decision tree algorithm ID3 on this dataset to construct the decision tree. Then the scholars can enter the testing data that has comprised with attribute values to get the view of guides for that testing dataset. Guidelines to the scholar can be provided by considering this constructed tree to improve their outcomes.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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.
Profile Analysis of Users in Data Analytics DomainDrjabez
Data Analytics and Data Science is in the fast forward
mode recently. We see a lot of companies hiring people for data
analysis and data science, especially in India. Also, many
recruiting firms use stackoverflow to fish their potential
candidates. The industry has also started to recruit people based
on the shapes of expertise. Expertise of a personal is
metaphorically outlined by shapes of letters like I, T, M and
hyphen betting on her experiencein a section (depth) and
therefore the variety of areas of interest (width).This proposal
builds upon the work of mining shapes of user expertise in a
typical online social Question and Answer (Q&A) community
where expert users often answer questions posed by other
users.We have dealt with the temporal analysis of the expertise
among the Q&A community users in terms how the user/ expert
have evolved over time.
Keywords— Shapes of expertise, Graph communities, Expertise
evolution, Q&A community
Audio Signal Identification and Search Approach for Minimizing the Search Tim...aciijournal
Audio or music fingerprints can be utilize to implement an economical music identification system on a
million-song library, however the system needs great deal of memory to carry the fingerprints and indexes.
Therefore, for a large-scale audio library, memory
imposes a restriction on the speed of music
identifications. So, we propose an efficient music
identification system which used a kind of space-saving
audio fingerprints. For saving space, original finger representations are sub-sample and only one quarters
of the original data is reserved. In this approach,
memory demand is far reduced and therefore the search
speed is criticalincreasing whereas the lustiness and dependability are well preserved. Mapping
audio information to time and frequency domain for
the classification, retrieval or identification tasks
presents four principal challenges. The dimension o
f the input should be considerably reduced;
the ensuing options should be strong to possible distortions of the input; the feature should be informative
for the task at hand simple. We propose distortion
free system which fulfils all four of these requirements.
Extensive study has been done to compare our system
with the already existing ones, and the results sh
ow
that our system requires less memory, provides fast
results and achieves comparable accuracy for a large-
scale database.
Web spam classification using supervised artificial neural network algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised learning algorithms of artificial neural network by creating classifiers for the complex problem of latest web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
LOCALIZATION OF OVERLAID TEXT BASED ON NOISE INCONSISTENCIESaciijournal
In this paper, we present a novel technique for localization of caption text in video frames based on noise inconsistencies. Text is artificially added to the video after it has been captured and as such does not form part of the original video graphics. Typically, the amount of noise level is uniform across the entire captured video frame, thus, artificially embedding or overlaying text on the video introduces yet another segment of noise level. Therefore detection of various noise levels in the video frame may signify availability of overlaid text. Hence we exploited this property by detecting regions with various noise levels to localize overlaid text in video frames. Experimental results obtained shows a great improvement in line with overlaid text localization, where we have performed metric measure based on Recall, Precision and f-measure.
Erca energy efficient routing and reclusteringaciijournal
The pervasive application of wireless sensor networks (WNSs) is challenged by the scarce energy constraints of sensor nodes. En-route filtering schemes, especially commutative cipher based en-route filtering (CCEF) can saves energy with better filtering capacity. However, this approach suffer from fixed paths and inefficient underlying routing designed for ad-hoc networks. Moreover, with decrease in remaining sensor nodes, the probability of network partition increases. In this paper, we propose energy-efficient routing and re-clustering algorithm (ERCA) to address these limitations. In proposed scheme with reduction in the number of sensor nodes to certain thresh-hold the cluster size and transmission range dynamically maintain cluster node-density. Performance results show that our approach demonstrate filtering-power, better energy-efficiency, and an average gain over 285% in network lifetime.
TRUST METRICS IN RECOMMENDER SYSTEMS: A SURVEYaciijournal
Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing of
information that lead following the information flow in real world be impossible. Recommender systems, as
the most successful application of information filtering, help users to find items of their interest from huge
datasets. Collaborative filtering, as the most successful technique for recommendation, utilises social
behaviours of users to detect their interests. Traditional challenges of Collaborative filtering, such as cold
start, sparcity problem, accuracy and malicious attacks, derived researchers to use new metadata to
improve accuracy of recommenders and solve the traditional problems. Trust based recommender systems
focus on trustworthy value on relation among users to make more reliable and accurate recommends. In
this paper our focus is on trust based approach and discuss about the process of making recommendation
in these method. Furthermore, we review different proposed trust metrics, as the most important step in this
process.
Predicting the Presence of Learning Motivation in Electronic Learning: A New ...TELKOMNIKA JOURNAL
Research affirms that electronic learning (e-learning) has a great deal of advantages to learning process, it’s provides learners with flexibility in terms of time, place, and pace. That easiness was not be a guarantee of a good learning outcomes though they got the same learning material and course, because the fact is the outcomes was not the same from one to another and even not satisfy enough. One of prerequisite to the successful of e-learning process is the presence of learning motivation and it was not easy to identify. We propose a novel model to predicting the presence of learning motivation in e-learning using those attributes that have been identified in previous research. This model has been built using WEKA toolkit by comparing fourteen algorithms for Tree Classifier and ten-fold cross validation testing methods to process 3.200 of data sets. The best accuracy reached at 91.1% and identified four parameters to predict the presence of learning motivation in e-learning, and aim to assist teachers in identify whether student needs motivation or does not. This study also confirmed that Tree Classifier still has the best accuracy to predict and classify academic performance, it was reached average 90.48% for fourteen algorithm for accuracy value.
With the emergence of virtualization and cloud computing technologies, several services are housed on virtualization platform. Virtualization is the technology that many cloud service providers rely on for efficient management and coordination of the resource pool. As essential services are also housed on cloud platform, it is necessary to ensure continuous availability by implementing all necessary measures. Windows Active Directory is one such service that Microsoft developed for Windows domain networks. It is included in Windows Server operating systems as a set of processes and services for authentication and authorization of users and computers in a Windows domain type network. The service is required to run continuously without downtime. As a result, there are chances of accumulation of errors or garbage leading to software aging which in turn may lead to system failure and associated consequences. This results in software aging. In this work, software aging patterns of Windows active directory service is studied. Software aging of active directory needs to be predicted properly so that rejuvenation can be triggered to ensure continuous service delivery. In order to predict the accurate time, a model that uses time series forecasting technique is built.
Modeling Text Independent Speaker Identification with Vector QuantizationTELKOMNIKA JOURNAL
Speaker identification is one of the most important technologies nowadays. Many fields such as
bioinformatics and security are using speaker identification. Also, almost all electronic devices are using
this technology too. Based on number of text, speaker identification divided into text dependent and text
independent. On many fields, text independent is mostly used because number of text is unlimited. So, text
independent is generally more challenging than text dependent. In this research, speaker identification text
independent with Indonesian speaker data was modelled with Vector Quantization (VQ). In this research
VQ with K-Means initialization was used. K-Means clustering also was used to initialize mean and
Hierarchical Agglomerative Clustering was used to identify K value for VQ. The best VQ accuracy was
59.67% when k was 5. According to the result, Indonesian language could be modelled by VQ. This
research can be developed using optimization method for VQ parameters such as Genetic Algorithm or
Particle Swarm Optimization.
The process of determining cuts tuition for students are usually given with the same nominal. And in this paper is the determination of the pieces tuition for students who are less able to be different, depending on how much income parents and the number of children covered. For income parents who get discounted tuition fee of IDR Rp.1,500,000 and for the number of children in these families also determine the number of pieces obtained. Tsukamoto Fuzzy system is the model used in this paper. Each input variable is divided into three membership functions. In this paper, Nine Tsukamoto Fuzzy model rules have been applied. The system also provides a consequent change of parameters if the current parameter values to be changed. The smaller the parent's income, the greater the pieces obtained. The more children insured the greater the college acquired pieces.
Research scholars evaluation based on guides view using id3eSAT Journals
Abstract Research Scholars finds many problems in their Research and Development activities for the completion of their research work in universities. This paper gives a proficient way for analyzing the performance of Research Scholar based on guides and experts feedback. A dataset is formed using this information. The outcome class attribute will be in view of guides about the scholars. We apply decision tree algorithm ID3 on this dataset to construct the decision tree. Then the scholars can enter the testing data that has comprised with attribute values to get the view of guides for that testing dataset. Guidelines to the scholar can be provided by considering this constructed tree to improve their outcomes.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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.
Profile Analysis of Users in Data Analytics DomainDrjabez
Data Analytics and Data Science is in the fast forward
mode recently. We see a lot of companies hiring people for data
analysis and data science, especially in India. Also, many
recruiting firms use stackoverflow to fish their potential
candidates. The industry has also started to recruit people based
on the shapes of expertise. Expertise of a personal is
metaphorically outlined by shapes of letters like I, T, M and
hyphen betting on her experiencein a section (depth) and
therefore the variety of areas of interest (width).This proposal
builds upon the work of mining shapes of user expertise in a
typical online social Question and Answer (Q&A) community
where expert users often answer questions posed by other
users.We have dealt with the temporal analysis of the expertise
among the Q&A community users in terms how the user/ expert
have evolved over time.
Keywords— Shapes of expertise, Graph communities, Expertise
evolution, Q&A community
Audio Signal Identification and Search Approach for Minimizing the Search Tim...aciijournal
Audio or music fingerprints can be utilize to implement an economical music identification system on a
million-song library, however the system needs great deal of memory to carry the fingerprints and indexes.
Therefore, for a large-scale audio library, memory
imposes a restriction on the speed of music
identifications. So, we propose an efficient music
identification system which used a kind of space-saving
audio fingerprints. For saving space, original finger representations are sub-sample and only one quarters
of the original data is reserved. In this approach,
memory demand is far reduced and therefore the search
speed is criticalincreasing whereas the lustiness and dependability are well preserved. Mapping
audio information to time and frequency domain for
the classification, retrieval or identification tasks
presents four principal challenges. The dimension o
f the input should be considerably reduced;
the ensuing options should be strong to possible distortions of the input; the feature should be informative
for the task at hand simple. We propose distortion
free system which fulfils all four of these requirements.
Extensive study has been done to compare our system
with the already existing ones, and the results sh
ow
that our system requires less memory, provides fast
results and achieves comparable accuracy for a large-
scale database.
Web spam classification using supervised artificial neural network algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised learning algorithms of artificial neural network by creating classifiers for the complex problem of latest web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
LOCALIZATION OF OVERLAID TEXT BASED ON NOISE INCONSISTENCIESaciijournal
In this paper, we present a novel technique for localization of caption text in video frames based on noise inconsistencies. Text is artificially added to the video after it has been captured and as such does not form part of the original video graphics. Typically, the amount of noise level is uniform across the entire captured video frame, thus, artificially embedding or overlaying text on the video introduces yet another segment of noise level. Therefore detection of various noise levels in the video frame may signify availability of overlaid text. Hence we exploited this property by detecting regions with various noise levels to localize overlaid text in video frames. Experimental results obtained shows a great improvement in line with overlaid text localization, where we have performed metric measure based on Recall, Precision and f-measure.
Erca energy efficient routing and reclusteringaciijournal
The pervasive application of wireless sensor networks (WNSs) is challenged by the scarce energy constraints of sensor nodes. En-route filtering schemes, especially commutative cipher based en-route filtering (CCEF) can saves energy with better filtering capacity. However, this approach suffer from fixed paths and inefficient underlying routing designed for ad-hoc networks. Moreover, with decrease in remaining sensor nodes, the probability of network partition increases. In this paper, we propose energy-efficient routing and re-clustering algorithm (ERCA) to address these limitations. In proposed scheme with reduction in the number of sensor nodes to certain thresh-hold the cluster size and transmission range dynamically maintain cluster node-density. Performance results show that our approach demonstrate filtering-power, better energy-efficiency, and an average gain over 285% in network lifetime.
TRUST METRICS IN RECOMMENDER SYSTEMS: A SURVEYaciijournal
Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing of
information that lead following the information flow in real world be impossible. Recommender systems, as
the most successful application of information filtering, help users to find items of their interest from huge
datasets. Collaborative filtering, as the most successful technique for recommendation, utilises social
behaviours of users to detect their interests. Traditional challenges of Collaborative filtering, such as cold
start, sparcity problem, accuracy and malicious attacks, derived researchers to use new metadata to
improve accuracy of recommenders and solve the traditional problems. Trust based recommender systems
focus on trustworthy value on relation among users to make more reliable and accurate recommends. In
this paper our focus is on trust based approach and discuss about the process of making recommendation
in these method. Furthermore, we review different proposed trust metrics, as the most important step in this
process.
Recommender systems have grown to be a critical research subject after the emergence of the first paper on collaborative filtering in the Nineties. Despite the fact that educational studies on recommender systems, has extended extensively over the last 10 years, there are deficiencies in the complete literature evaluation and classification of that research. Because of this, we reviewed articles on recommender structures, and then classified those based on sentiment analysis. The articles are categorized into three techniques of recommender system, i.e.; collaborative filtering (CF), content based and context based. We have tried to find out the research papers related to sentimental analysis based recommender system. To classify research done by authors in this field, we have shown different approaches of recommender system based on sentimental analysis with the help of tables. Our studies give statistics, approximately trends in recommender structures research, and gives practitioners and researchers with perception and destiny route on the recommender system using sentimental analysis. We hope that this paper enables all and sundry who is interested in recommender systems research with insight for destiny.
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.
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SURVEY SURFER: A WEB BASED DATA GATHERING AND ANALYSIS APPLICATIONaciijournal
The most important need for a web based survey technology is speedy performance and accurate results
.Though there are variety of ways a survey can be taken manually and have it assessed manually but here
we are introducing the survey site concept where the assessment of the survey responses are created
automatically by applying certain statistics. In this paper we propose the idea of automated approach to
the analysis of the survey where the results would be evident graphically to take a wise decision. Additional
to graphical view we aim on forming a platform to view complex, tedious data in simple, understandable,
interactive and visualized form.
Text mining is the process of extracting interesting and non-trivial knowledge or information from unstructured text data. Text mining is the multidisciplinary field which draws on data mining, machine learning, information retrieval, computational linguistics and statistics. Important text mining processes are information extraction, information retrieval, natural language processing, text classification, content analysis and text clustering. All these processes are required to complete the preprocessing step before doing their intended task. Pre-processing significantly reduces the size of the input text documents and the actions involved in this step are sentence boundary determination, natural language specific stop-word elimination, tokenization and stemming. Among this, the most essential and important action is the tokenization. Tokenization helps to divide the textual information into individual words. For performing tokenization process, there are many open source tools are available. The main objective of this work is to analyze the performance of the seven open source tokenization tools. For this comparative analysis, we have taken Nlpdotnet Tokenizer, Mila Tokenizer, NLTK Word Tokenize, TextBlob Word Tokenize, MBSP Word Tokenize, Pattern Word Tokenize and Word Tokenization with Python NLTK. Based on the results, we observed that the Nlpdotnet Tokenizer tool performance is better than other tools.
An Intelligent Method for Accelerating the Convergence of Different Versions ...aciijournal
In a wide range of applications, solving the linear
system of equations Ax = b is appeared. One of the
best
methods to solve the large sparse asymmetric linear
systems is the simplified generalized minimal resi
dual
(SGMRES(m)) method. Also, some improved versions of
SGMRES(m) exist: SGMRES-E(m, k) and
SGMRES-DR(m, k). In this paper, an intelligent heur
istic method for accelerating the convergence of th
ree
methods SGMRES(m), SGMRES-E(m, k), and SGMRES-DR(m,
k) is proposed. The numerical results
obtained from implementation of the proposed approa
ch on several University of Florida standard
matrixes confirm the efficiency of the proposed met
hod.
Blood groups matcher frame work based on three level rules in myanmaraciijournal
Today Blood donation is a global interest for world to be survival lives when people are in trouble because of natural disaster. The system provides the ability how to decide to donate the blood according to the rules for blood donation not to meet the physicians. In this system, there are three main parts to accept blood from donors when they want to donate according to the features like personal health. The application facilitates to negotiate between blood donors and patients who need to get blood seriously on page without going to Blood Banks and waiting time in queue there.
HYBRID DATA CLUSTERING APPROACH USING K-MEANS AND FLOWER POLLINATION ALGORITHMaciijournal
Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other. K-Means, is one of the familiar center based clustering algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental results, FPAKM is better than FPA and K-Means.
Prediction of lung cancer is most challenging problem due to structure of cancer cell, where most of the cells are overlapped each other. The image processing techniques are mostly used for prediction of lung cancer and also for early detection and treatment to prevent the lung cancer. To predict the lung cancer various features are extracted from the images therefore, pattern recognition based approaches are useful to predict the lung cancer. Here, a comprehensive review for the prediction of lung cancer by previous researcher using image processing techniques is presented. The summary for the prediction of lung cancer by previous researcher using image processing techniques is also presented.
Predicting Students Performance using K-Median ClusteringIIRindia
The main objective of education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge of students in a particular course. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. In this paper, the K-Median method in clustering technique is used to evaluate students performance. By this task the extracted knowledge that describes students performance in end semester examination. It helps earlier in identifying the students who need special attention and allow the teacher to provide appropriate advising and coaching.
A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
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different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
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issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
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Study of Clustering of Data Base in Education Sector Using Data MiningIJSRD
Data mining is a technology used in different disciplines to search for significant relationships among variables in n number of data sets. Data mining is frequently used in all types’ areas as well as applications. In this paper the application of data mining is attached with the field of education. The relationship between student’s university entrance examination results and their success was studied using cluster analysis and k-means algorithm techniques.
Study of Clustering of Data Base in Education Sector Using Data MiningIJSRD
Data mining is a technology used in different disciplines to search for significant relationships among variables in n number of data sets. Data mining is frequently used in all types’ areas as well as applications. In this paper the application of data mining is attached with the field of education. The relationship between student’s university entrance examination results and their success was studied using cluster analysis and k-means algorithm techniques.
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Learning strategy with groups on page based students' profiles
1. Advanced Computational Intelligence: An International Journal (ACII), Vol.2, No.1, January 2015
1
LEARNING STRATEGY WITH GROUPS ON PAGE
BASED STUDENTS' PROFILES
San San Tint1
and Ei Ei Nyunt2
1
Department of Research and Development II, University of Computer Studies,
Mandalay, Myanmar
2
Master of Computer Science, University of computer Studies Mandalay, Myanmar
Abstract
Most of students desire to know about their knowledge level to perfect their exams. In learning environment
the fields of study overwhelm on page with collaboration or cooperation. Students can do their exercises
either individually or collaboratively with their peers. The system provides the guidelines for students'
learning system about interest fields as Java in this system. Especially the system feedbacks information
about exam to know their grades without teachers. The participants who answered the exam can discuss
with each others because of sharing e mail and list of them.
Keywords
Collaboration, Grade, Learning, Profiles, Feedback
1. INTRODUCTION
Thinking of participants is the first step of collaborative learning system. In this step the system
used the student attributes or properties from their profiles. The properties and the values of the
properties are specified by the system. The think step and pair step of the Think-Pair-Share (TPS)
Strategy are measured by the clustering method such as K-means method [1-5].
Data clustering is a data exploration technique that allows objects with similar characteristics to
be grouped together in order to facilitate their further processing. In the system clustering method
is used to produce the groups of student participants according to their properties as shown in
Table 1. The objects are the students and the initial number of cluster is specified with the random
number of students [4], [6].
Cluster analysis is a formal study of methods for understanding and algorithm for learning. K-
mean is the first choice for clustering with an initial number of clusters. K-mean algorithm is
most widely used algorithm in data mining applications. It is a simple, scalable, easily
understandable and can be adopted to deal with high dimensional data. A distance measuring
function is used to measure the similarity among objects, in such a way that more similar objects
have lower dissimilarity value. Several distance measures can be employed for clustering tasks.
The K-Mean algorithm finds partitions with distance measuring function in the cluster which is
minimized [7-9].
2. Advanced Computational Intelligence: An International Journal (ACII), Vol.2, No.1, January 2015
2
2. BACKGROUND THEORY
2.1. Think-Pair-Share (TPS) Strategy
Think-Pair-Share is a relatively low-risk and short collaborative learning technique, and is suited
for instructors and students who are new to collaborative learning.
Think-Pair-Share technique in education is also about:
Think about your answer individually.
Pair with a partner and see your answers.
Share you or your partner’s answer, when called upon.
The purposes of this technique are to process information, having a communication and develop
thinking among students. This strategy helps students become active participants in learning and
can include writing as a way of organizing thoughts generated from discussions [6].
2.2. Think-Pair-Share Technique Role
The teacher decides upon the text to be read and develops the set of questions or prompts that
target key content concepts. The teacher then describes the purpose of the strategy and provides
guidelines for discussions. As with all strategy instruction, teachers should model the procedure
to ensure that students understand how to use the strategy. Teachers should monitor and support
students as they work. In the system the Think-Pair-Share technique will be applied as following:
Thinking: each student thinking of his/her profile attributes and answering the given questions.
Pair: Pair the students’ grade and the K-means clustering result which is used students’ profile.
The students of grades (A-C) will be specified by using his/her exam result.
Share: Share the students’ list that contains not only Grade A and Grade B with their results but
also their email and original group number to communicate each other.
When the system basic level step is finished, both thinking and first part of pairing of the students
who get other Grade can retry the questions next time [3].
2.3. The Main properties of Objects (7 attributes)
The objects of the system are students’ data which are specified by the system. The main
attributes of student information are specified as objects’ properties to calculate the similarity
among objects. The attributes are specified by the collaborative learning system and are
calculated as k-means objects to determine the group of the participants. The attributes and values
of the clustering categories are specified as following Table 1. These all attributes are belonging
to the student objects which are specified by the students’ data of their profile. The system is
defined the students to fill their data which is belong to the following properties and values. The
students’ information and exam results are calculated to apply the TPS theories in this system.
3. Advanced Computational Intelligence: An International Journal (ACII), Vol.2, No.1, January 2015
3
Table .1 Attributes and Values for Student Objects
Attribute Name Value 1 Value 2 Value 3
Education Information Technology E.P Engineering Computer Technology
Occupation Student Graduate Post Graduate
Math Skill Normal Grade Distinction
Physic Skill Normal Grade Distinction
Programming
Skill
Learner Developer Expert
10th
standard
Passed Year
2006-2013(after) 1998-2005 1990-1997
Interest in
subject
OOP Networking Web Development
These attributes are specified for the calculation of clustering of the student objects’. There are
three or five clusters to group the students. The students’ attributes are calculated by the
Euclidean distance function to determine the groups of students [11].
2.4. Distance Measuring Function for K-Means Clustering
The system specifies the random objects or students by using Rnd function on attributes. And get
top three or five objects to specify the centroids of initial clusters. Then the system calculates the
members of clusters by using the Euclidean distance function which is shown as following:
(1)
The mean for a cluster is:
∑∈
=
ji Cx
i
j
j x
C
m
1
(2)
Where, ),...,,( 112111 nxxxX =
),...,,( 222212 nxxxX =
jC = number of data points in cluster jC
2.4.1. K-Means Clustering Algorithm for the System
Input:
Let k=5 is the number of clusters to partition.
D is a database containing n objects:(n is the number of students’ attributes from profile).
Output:
A set of k clusters: ( including class of attributes with their member’s objects.)
Method:
arbitrarily choose k objects from D as the initial cluster centers;
Let Ct =new centroids ( t=1,2,3,…,k)
4. Advanced Computational Intelligence: An International Journal (ACII), Vol.2, No.1, January 2015
4
xi=arbitrarily students’ attributes(i=1,2,3,…,k)
xj=all students’ attributes (j=1,2,3,…,n)
Dt =data set of distance values (t=1,2,3,…,k)
Initialize xi=1 , xj=1;
3) repeat j
repeat i
d(xi ,xj) = || xi – xj ||2
increase i
until i <= k
choose minimum distance value and assign to Dt.
increase t
increase j
until j <= n
Compare Dt with minimize pair value and reassign each object to the cluster to which the object
is the most similar based on the means value of the objects in the cluster (Ct, where t=1, 2,
3,…,k).Update the cluster means; i.e., calculate the mean value of the objects for each cluster
until no change.
2.4.2. Roles of Student Profiles
In the clustering method of the system, the role of students’ profiles is to calculate the groups of
students. The groups of students are specified by the clustering method. Then the students answer
the exam questions which are become the input of next step of the collaborative learning of the
system. The next step is the calculation of paring students according to the result of their exam
marks and their grades. The inputs of the first step are as shown in the following Table 2.
Table 2. Example profile table of the Students
Stude
nt
Obj
Education Occupation Math
Skill
Physic
Skill
Program
Skill
10th
Std
Passed
Year
Interested
in Subject
a1 1 2 1 2 2 2 3
a2 3 1 2 1 1 1 1
a3 2 1 2 2 1 1 1
a4 1 1 1 1 1 1 2
b1 1 3 2 1 2 2 3
b2 2 1 2 1 1 1 1
b3 3 2 2 1 3 2 3
c1 2 3 1 3 1 3 2
c2 1 1 3 1 2 1 2
c3 3 3 3 2 3 3 2
Education 1 = IT 2 = EP 3 = Computer
Occupation 1 = Student 2 = Graduate 3 = Post Graduate
Math Skill 1 = Normal 2 = Grade 3 = Distinction
Physic Skill 1 = Normal 2 = Grade 3 = Distinction
Program Skill 1 = Learner 2 = Developer 3 = Expert
10th
Standard Passed Year 1 = 2006-2013 2 = 1998-2005 3= 1990-1997
later
Interested in Subject 1 = OOP 2 = Networking 3=Web
5. Advanced Computational Intelligence: An International Journal (ACII), Vol.2, No.1, January 2015
5
In K-means method, the number of clusters must be specified as the initial clusters’ centroid.
Therefore the example objects are specified from the student profile table as the initial clusters’
centroid are as shown in the following Table 3. The initial cluster centroids are calculated with
other objects in the student profile table by substitution of the Euclidean distance function.
Table 3. Objects from the Profile as initial Cluster
Obj Education Occupation Math
Skill
Physic
Skill
Program
Skill
10th
Std
Passed
Year
Interested
in Subject
a1 1 2 1 2 2 2 3
b2 2 1 2 1 1 1 1
c3 3 3 3 2 3 3 2
Table 4. Centroids for Second Iteration
Student
Obj
Education Occupation Math
Skill
Physic
Skill
Program
Skill
10th
Std
Passed
Year
Interested
in Subject
a1 , b1, c1 1.3 2.7 1.3 2 1.7 2.3 2.7
a2 ,a3 ,a4
,b2 ,c2
1.8 1 2 1.2 1.2 1 1.4
b3 , c3 3 2.5 2.5 1.5 3 2.5 2.5
Step 6
The centroid from second iteration is no change. The k-means algorithm is terminated. The
cluster is illustrated with set graph as shown in fig.1.
Figure 1. Clusters of Students with similar properties
2.5. Distance Measuring for New Student
The distance Measure for new student is calculated with Table 5 cetroids which are derived from
k-mean distance measuring function. The derivation of new student’s group is as shown in the
following Table 6.
6. Advanced Computational Intelligence: An International Journal (ACII), Vol.2, No.1, January 2015
6
Table 5. New Student’s Profile
( ),111 dcbaDist = 3.967
( ),22432 dcbaaaDist = 1.5
),( 33 dcbDist = 3.354
Figure 2. Cluster for new Student with Similar Properties
2.6. Pair and Sharing of Participants
The pair and sharing of participants is the final step of the system. The system thinks the
participants’ or students’ skills both calculating the k-mean clustering method and examination
method. The system decides the pair of participants with the grade of advance level students’
marks. Then the system shares the similar upper levels of students’ skill. The students will answer
two steps of exam with their groups. The finally the students are shared among the upper two
levels of groups with their examination information.
Table 6. Student Grade Table
Class of Grade Marks
Grade A >=8
Grade B 7 4
Grade C < 4
In the sharing stage of students, the system gives the students’ information such as email
addresses each other. The students will go on sharing and learning the sense or assuming on the
relative subject with their mail addresses.
7. Advanced Computational Intelligence: An International Journal (ACII), Vol.2, No.1, January 2015
7
3. IMPLEMENTATION OF THE SYSTEM
The two main parts of the implementation system have explained as shown in the following
figures. The figures demonstrate the collaborative system how to cluster the profiles and hold the
examinations.
3.1. Main Page for Administrator
Figure 3. Main Page for Administrator
The Figure 3 is Main Page for Administrator page. In this page, there are two links such as
Administrator Login and About System. Moreover the introduction of collaborative system is
described on this page.
3.2. Admin Login Page
Figure 4. Admin Login Page
The Figure 4 is the Login Page for Administrator. In this page, the admin can enter into the
system by using his/her name and password. There are two links such as Home and About
System to join the home page and About System page.
8. Advanced Computational Intelligence: An International Journal (ACII), Vol.2, No.1, January 2015
8
3.3. Admin Role Page
Figure 5. Admin Role Page
The Admin Role Page, Figure 5 consists of seven links such as Arrange Exam Date, Arrange
Groups, Insert Questions, View Marks and View Students. Log Out! and Home links are used to
connect with Admin Login Page and Admin Home Page.
3.4. Arrange Exam Date
Figure 6. Arrange Exam Date with Exam ID
Figure 7. Error Page for Arrange Exam Date
9. Advanced Computational Intelligence: An International Journal (ACII), Vol.2, No.1, January 2015
9
Figure 8. The New Arrange Exam Date
Above Figure 6, 7, and 8 illustrate the specification of New Exam Date. According to Figure 6
the admin needs to type Exam ID and Exam ID fields. And then press Add Date button. In Figure
7 the system replies that the exam date is pass over or impossible date to specify for the
examination. In Figure 8 the Exam Date is successfully specified by the administrator and then
the system displays the new Exam Date with old list.
3.5. Arrange Groups
Figure 9. Arrange Group with Initial Group Numbers
10. Advanced Computational Intelligence: An International Journal (ACII), Vol.2, No.1, January 2015
10
Figure 10. Arrange Groups with Initial Cluster Number
Figure 11. Arrange Groups with Random Students’ profile
Figure 12. Arrange Groups with Students’ Profiles List
11. Advanced Computational Intelligence: An International Journal (ACII), Vol.2, No.1, January 2015
11
Figure 13. Error Page for Arrange Groups
From Figure 6 to 12 are shown the Arrange Groups. The system shows the arrange groups
numbers for initial clusters as shown in Figure 10. Figure 11 and 12 show the three random
students’ profiles to calculate the groups of students who are signed up the system. If the exam is
hold on today, then the administrator cannot specify the groups. Figure 12 shows the students’
profile records with their groups. The error message as in Figure 13 will be shown.
3.6. Insert Question Page
Figure 14. Question Entry Form
12. Advanced Computational Intelligence: An International Journal (ACII), Vol.2, No.1, January 2015
12
The admin can add question by using Question Entry Form as shown in Figure 14 and the
administrator fills this form and press Add button. Then the system saves the question to the
database.
3.7. View Marks Page
Figure 15. View the Student List With their exam Marks
Figure 16. Error Page for View Marks
The administrator can view the students list with their exam marks and groups as shown in the
Figure 15. When the students are grouped next time and the exam date has been specified or the
new examination is not hold yet, the error page for View Marks will be displayed as shown in
Figure 16.
13. Advanced Computational Intelligence: An International Journal (ACII), Vol.2, No.1, January 2015
13
3.8. Student Main Page
Figure 17. Student Main Page
Student Main Page as shown in Figure 17 represents the students or participant of the
Collaborative Learning System. The student can enter into the system by using the Student Login
link. About System link is used for the description bout the Collaborative Learning System.
3.9. Student Login Page
Figure 18. Student Login Page
Figure 18 shows the login page for student by using their names and passwords. If there is no
exam for the student today, the system shows Message as shown in Figure 19. Moreover, the
exam date is not specified, then the system will display the old shared groups’ list with student as
in Figure 20.
14. Advanced Computational Intelligence: An International Journal (ACII), Vol.2, No.1, January 2015
14
Figure 19. Error Page for Exam Date
Figure 20. Student Group List Page
But the system shows the past student grades with their groups list to the login student. If the
student has completed the past exam, and the grade is high then he/she can see the same students’
groups. If the student is new for this system, he/she must fill the form as shown in Figure 21. If
today is the exam date, the new student cannot enter the exam pages as in Figure 22.
15. Advanced Computational Intelligence: An International Journal (ACII), Vol.2, No.1, January 2015
15
Figure 21. Student Sign Up Page for New Student
Figure 22. Error Page for New Student’s Logo in
3.10. Grouping and Pairing Students
In this part the student can see the examination entrance page to participate the exam with their
groups. Figure 23 shows the entrance page for student to enter the examination. At first, the
student can answer basic level of exam and can send his/her answers by pressing the submit
button as in Figure 24. Then the system display basic level marks and grade of student as shown
in Figure 25. If the student's grade does not reached at specified level, the student must answers
the basic level again. If the students log out at the end of basic level, the system shows the basic
grade. When the student re-enters again to the system, the system shows the message as shown in
Figure 26. If the student's the grade reaches at specified level, the student can answer the advance
level as the next step. After finishing the exam, the student can see his/her grade with his/her
marks. If the student reached at the specified level, he/she can see the shared list with his/her
name as shown in Figure 28. But the student did not reach at specified level, his/her name does
not appear in upper list.
16. Advanced Computational Intelligence: An International Journal (ACII), Vol.2, No.1, January 2015
16
Figure 23. Entrance Page for Student Examination
Figure 24. Basic Level Examination Page
Figure 25. Basic Level of Student’s Marks, Grade and Group
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Figure 26. Student who does not answer the Advance Level yet
Figure 27. Students’ Information with Advance Grades
Figure 28. Error Entrance of Student who answered all questions
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Figure 29. Student Grade with Marks
In Figure 28, there is a Error Entrance of Student who answered all questions. If the student had
answered all questions and passed level, he/she can see the message as in Figure 28 when he/she
enters to the system again. In Figure 29, the student can see only the groups of students who get
the similar grade with him/her.
4. CONCLUSION
This system aims at the students in order to promote active learning in computer based learning
environment. A well-known collaborative learning technique, the “Think-Pair-Share” is applied
because it has simplicity and suitability to be implemented in a collaborative learning
environment. This system provides the benefits to specify the grades and group of the students by
using K-mean clustering algorithm and also improves the students' learning without difficulties to
find out their interest fields.
ACKNOWLEDGEMENTS
Our heartfelt thanks go to all people, who support us at the University of Computer Studies,
Mandalay, Myanmar. This paper is dedicated to our parents. Our special thanks go to all
respectable persons who support for valuable suggestion in this paper.
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Authors
She is Associate Professor, Head of Department of Research and Development II in
University of Computer Studies, Mandalay, Myanmar. Her research areas include
Information Retrieval, Cryptography and Network Security, Web Mining and Networking.
She received her B.Sc. (Physics), M.Sc.(Physics) from Yangon University, Myanmar and
M.A.Sc.(Computer Engineering) and Ph.D.( Information Technology) from University of
Computer Studies, Yangon, Myanmar.Author studied computer science at the University of Computer
Studies, lasho, Myanmar where she received her B.C.Sc Degree in 2011. She received B.C.Sc(Hons:) in
computer science from the University of Computer Studies, Lasho, Myanmar in 2012. Since 2012, Author
has studied computer science at the University of Computer Studies, Mandalay, Myanmar where her
primary interests include web mining, graph clustering, grouping and web log analysis.