In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing
one single patient’s information gathered from a large volume of data over a long period of time. The
main objective of this paper is to represent our ontology-based information retrieval approach for
clinical Information System. We have performed a Case Study in the real life hospital settings. The results
obtained illustrate the feasibility of the proposed approach which significantly improved the information
retrieval process on a large volume of data over a long period of time from August 2011 until January
2012
TWO LEVEL SELF-SUPERVISED RELATION EXTRACTION FROM MEDLINE USING UMLSIJDKP
The biomedical research literature is one among many other domains that hides a precious knowledge, and
the biomedical community made an extensive use of this scientific literature to discover the facts of
biomedical entities, such as disease, drugs,etc.MEDLINE is a huge database of biomedical research
papers which remain a significantly underutilized source of biological information. Discovering the useful
knowledge from such huge corpus leads to various problems related to the type of information such as the
concepts related to the domain of texts and the semantic relationship associated with them. In this paper,
we propose a Two-level model for Self-supervised relation extraction from MEDLINE using Unified
Medical Language System (UMLS) Knowledge base. The model uses a Self-supervised Approach for
Relation Extraction (RE) by constructing enhanced training examples using information from UMLS. The
model shows a better result in comparison with current state of the art and naïve approaches
A Semantic Retrieval System for Extracting Relationships from Biological Corpusijcsit
The World Wide Web holds a large size of different information. Sometimes while searching the World Wide Web, users always do not gain the type of information they expect. In the subject of information extraction, extracting semantic relationships between terms from documents become a challenge. This
paper proposes a system helps in retrieving documents based on the query expansion and tackles the extracting of semantic relationships from biological documents. This system retrieved documents that are relevant to the input terms then it extracts the existence of a relationship. In this system, we use Boolean
model and the pattern recognition which helps in determining the relevant documents and determining the place of the relationship in the biological document. The system constructs a term-relation table that accelerates the relation extracting part. The proposed method offers another usage of the system so the
researchers can use it to figure out the relationship between two biological terms through the available information in the biological documents. Also for the retrieved documents, the system measures the percentage of the precision and recall.
Evaluating the efficiency of rule techniques for file classificationeSAT Journals
Abstract Text mining refers to the process of deriving high quality information from text. It is also known as knowledge discovery from text (KDT), deals with the machine supported analysis of text. It is used in various areas such as information retrieval, marketing, information extraction, natural language processing, document similarity, and so on. Document Similarity is one of the important techniques in text mining. In document similarity, the first and foremost step is to classify the files based on their category. In this research work, various classification rule techniques are used to classify the computer files based on their extensions. For example, the extension of computer files may be pdf, doc, ppt, xls, and so on. There are several algorithms for rule classifier such as decision table, JRip, Ridor, DTNB, NNge, PART, OneR and ZeroR. In this research work, three classification algorithms namely decision table, DTNB and OneR classifiers are used for performing classification of computer files based on their extension. The results produced by these algorithms are analyzed by using the performance factors classification accuracy and error rate. From the experimental results, DTNB proves to be more efficient than other two techniques. Index Terms: Data mining, Text mining, Classification, Decision table, DTNB, OneR
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 Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Predicting students' performance using id3 and c4.5 classification algorithmsIJDKP
An educational institution needs to have an approximate prior knowledge of enrolled students to predict
their performance in future academics. This helps them to identify promising students and also provides
them an opportunity to pay attention to and improve those who would probably get lower grades. As a
solution, we have developed a system which can predict the performance of students from their previous
performances using concepts of data mining techniques under Classification. We have analyzed the data
set containing information about students, such as gender, marks scored in the board examinations of
classes X and XII, marks and rank in entrance examinations and results in first year of the previous batch
of students. By applying the ID3 (Iterative Dichotomiser 3) and C4.5 classification algorithms on this data,
we have predicted the general and individual performance of freshly admitted students in future
examinations.
The classical or traditional information system provides answer after a user submits a complete query. It is even
noticed that presently, almost all the relational database systems rely on the query which has syntax and semantics
defined completely to access data. But often it is the case that we are willing to use vague terms in our query. The main
objective of database management system is to provide an environment that is both convenient and efficient for people
to use in storing and retrieving information. A recent trend of supporting auto complete is a first step to cope up with
this problem. We can have design of both classical and fuzzy database and can use effectively fuzzy queries on these
databases. Fuzzy databases are developed to manipulate the incomplete, unclear and vague data such as low, fast, very
high, about etc. The primary focus of fuzzy logic is on the natural language. This Paper provides the users the flexibility
or freedom to query database using natural language. Here this paper implements “interactive fuzzy search”. This
framework for interactive fuzzy search permits the user to explore the data as they type even in the presence of some
minor errors. This paper applies fuzzy queries on relational database so that it is possible to have the precise result as
well as the output for the uncertain terms we generally use based on some membership function
TWO LEVEL SELF-SUPERVISED RELATION EXTRACTION FROM MEDLINE USING UMLSIJDKP
The biomedical research literature is one among many other domains that hides a precious knowledge, and
the biomedical community made an extensive use of this scientific literature to discover the facts of
biomedical entities, such as disease, drugs,etc.MEDLINE is a huge database of biomedical research
papers which remain a significantly underutilized source of biological information. Discovering the useful
knowledge from such huge corpus leads to various problems related to the type of information such as the
concepts related to the domain of texts and the semantic relationship associated with them. In this paper,
we propose a Two-level model for Self-supervised relation extraction from MEDLINE using Unified
Medical Language System (UMLS) Knowledge base. The model uses a Self-supervised Approach for
Relation Extraction (RE) by constructing enhanced training examples using information from UMLS. The
model shows a better result in comparison with current state of the art and naïve approaches
A Semantic Retrieval System for Extracting Relationships from Biological Corpusijcsit
The World Wide Web holds a large size of different information. Sometimes while searching the World Wide Web, users always do not gain the type of information they expect. In the subject of information extraction, extracting semantic relationships between terms from documents become a challenge. This
paper proposes a system helps in retrieving documents based on the query expansion and tackles the extracting of semantic relationships from biological documents. This system retrieved documents that are relevant to the input terms then it extracts the existence of a relationship. In this system, we use Boolean
model and the pattern recognition which helps in determining the relevant documents and determining the place of the relationship in the biological document. The system constructs a term-relation table that accelerates the relation extracting part. The proposed method offers another usage of the system so the
researchers can use it to figure out the relationship between two biological terms through the available information in the biological documents. Also for the retrieved documents, the system measures the percentage of the precision and recall.
Evaluating the efficiency of rule techniques for file classificationeSAT Journals
Abstract Text mining refers to the process of deriving high quality information from text. It is also known as knowledge discovery from text (KDT), deals with the machine supported analysis of text. It is used in various areas such as information retrieval, marketing, information extraction, natural language processing, document similarity, and so on. Document Similarity is one of the important techniques in text mining. In document similarity, the first and foremost step is to classify the files based on their category. In this research work, various classification rule techniques are used to classify the computer files based on their extensions. For example, the extension of computer files may be pdf, doc, ppt, xls, and so on. There are several algorithms for rule classifier such as decision table, JRip, Ridor, DTNB, NNge, PART, OneR and ZeroR. In this research work, three classification algorithms namely decision table, DTNB and OneR classifiers are used for performing classification of computer files based on their extension. The results produced by these algorithms are analyzed by using the performance factors classification accuracy and error rate. From the experimental results, DTNB proves to be more efficient than other two techniques. Index Terms: Data mining, Text mining, Classification, Decision table, DTNB, OneR
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 Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Predicting students' performance using id3 and c4.5 classification algorithmsIJDKP
An educational institution needs to have an approximate prior knowledge of enrolled students to predict
their performance in future academics. This helps them to identify promising students and also provides
them an opportunity to pay attention to and improve those who would probably get lower grades. As a
solution, we have developed a system which can predict the performance of students from their previous
performances using concepts of data mining techniques under Classification. We have analyzed the data
set containing information about students, such as gender, marks scored in the board examinations of
classes X and XII, marks and rank in entrance examinations and results in first year of the previous batch
of students. By applying the ID3 (Iterative Dichotomiser 3) and C4.5 classification algorithms on this data,
we have predicted the general and individual performance of freshly admitted students in future
examinations.
The classical or traditional information system provides answer after a user submits a complete query. It is even
noticed that presently, almost all the relational database systems rely on the query which has syntax and semantics
defined completely to access data. But often it is the case that we are willing to use vague terms in our query. The main
objective of database management system is to provide an environment that is both convenient and efficient for people
to use in storing and retrieving information. A recent trend of supporting auto complete is a first step to cope up with
this problem. We can have design of both classical and fuzzy database and can use effectively fuzzy queries on these
databases. Fuzzy databases are developed to manipulate the incomplete, unclear and vague data such as low, fast, very
high, about etc. The primary focus of fuzzy logic is on the natural language. This Paper provides the users the flexibility
or freedom to query database using natural language. Here this paper implements “interactive fuzzy search”. This
framework for interactive fuzzy search permits the user to explore the data as they type even in the presence of some
minor errors. This paper applies fuzzy queries on relational database so that it is possible to have the precise result as
well as the output for the uncertain terms we generally use based on some membership function
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
Domain ontology development for communicable diseasescsandit
Web has become the very first resource to search for any kind of information. With the
emergence of semantic web, our search queries have started generating more informed results.
Ontologies are at the core of any semantic web application. They help in rapid development of
distributed systems by providing information on the fly. This key feature of distribution and
sharing of information has made ontologies as a new knowledge representation mechanism. A
mechanism which is strongly backed by a sound inference system. In this paper, we shall discuss
the development, verification and validation of an ontology in a health domain.
DOMAIN ONTOLOGY DEVELOPMENT FOR COMMUNICABLE DISEASEScscpconf
Web has become the very first resource to search for any kind of information. With the emergence of semantic web, our search queries have started generating more informed results.Ontologies are at the core of any semantic web application. They help in rapid development of
distributed systems by providing information on the fly. This key feature of distribution and
sharing of information has made ontologies as a new knowledge representation mechanism. A
mechanism which is strongly backed by a sound inference system. In this paper, we shall discuss the development, verification and validation of an ontology in a health domain.
Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Predictionijtsrd
Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Data mining technology which can related to this student grade well and we also used classification algorithms prediction. In this paper, we used educational data mining to predict students final grade based on their performance. We proposed student data classification using ID3 Iterative Dichotomiser 3 Decision Tree Algorithm Khin Khin Lay | San San Nwe "Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26545.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26545/using-id3-decision-tree-algorithm-to-the-student-grade-analysis-and-prediction/khin-khin-lay
Front End Data Cleaning And Transformation In Standard Printed Form Using Neu...ijcsa
Front end of data collection and loading into database manually may cause potential errors in data sets and a very time consuming process. Scanning of a data document in the form of an image and recognition of corresponding information in that image can be considered as a possible solution of this challenge. This paper presents an automated solution for the problem of data cleansing and recognition of user written data to transform into standard printed format with the help of artificial neural networks. Three different neural models namely direct, correlation based and hierarchical have been developed to handle this issue. In a very hostile input environment, the solution is developed to justify the proposed logic.
DATA MINING METHODOLOGIES TO STUDY STUDENT'S ACADEMIC PERFORMANCE USING THE...ijcsa
The study placed a particular emphasis on the so ca
lled data mining algorithms, but focuses the bulk o
f
attention on the C4.5 algorithm. Each educational i
nstitution, in general, aims to present a high qual
ity of
education. This depends upon predicting the student
s with poor results prior they entering in to final
examination. Data mining techniques give many tasks
that could be used to investigate the students'
performance. The main objective of this paper is to
build a classification model that can be used to i
mprove
the students' academic records in Faculty of Mathem
atical Science and Statistics. This model has been
done using the C4.5 algorithm as it is a well-known
, commonly used data mining technique. The
importance of this study is that predicting student
performance is useful in many different settings.
Data
from the previous students' academic records in the
faculty have been used to illustrate the considere
d
algorithm in order to build our classification mode
l.
CLUSTERING DICHOTOMOUS DATA FOR HEALTH CAREijistjournal
Dichotomous data is a type of categorical data, which is binary with categories zero and one. Health care data is one of the heavily used categorical data. Binary data are the simplest form of data used for heath care databases in which close ended questions can be used; it is very efficient based on computational efficiency and memory capacity to represent categorical type data. Clustering health care or medical data is very tedious due to its complex data representation models, high dimensionality and data sparsity. In this paper, clustering is performed after transforming the dichotomous data into real by wiener transformation. The proposed algorithm can be usable for determining the correlation of the health disorders and symptoms observed in large medical and health binary databases. Computational results show that the clustering based on Wiener transformation is very efficient in terms of objectivity and subjectivity.
Classification of data is a data mining technique based on machine learning is used to classification of each item set in as a set of dataset into a set of predefined labelled as classes or groups. Classification is tasks for different application such as text classification, image classification, class’s predictions, data Classification etc. In this paper, we presenting the major classification techniques used for prediction of classes using supervised learning dataset. Several major types of classification method including Random Forest, Naive Bayes, Support Vector Machine (SVM) techniques. The goal of this review paper is to provide a review, accuracy and comparative between different classification techniques in data mining.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
The Statement of Conjunctive and Disjunctive Queries in Object Oriented Datab...Editor IJCATR
Entrance of object orienting concept in database caused the relation database gradually to replace with object oriented
database in various fields. On the other hand for solving the problem of real world uncertain data, several methods were presented.
One of these methods for modeling database is an approach wich couples object-oriented database modeling with fuzzy logic. Many
queries that users to pose are expressed on the basis of linguistic variables. Because of classical databases are not able to support these
variables, leads to fuzzy approaches are considered. We investigate databases queries in this study both simple and complex ways. In
the complex way, we use conjunctive and disjunctive queries. In the following, we use the XML labels to express inqueries into fuzzy.
We can also communicate with other sections of software by entering into XML world as the most reliable opportunity. Also we want
to correct conjunctive and disjunctive queries related to fuzzy object oriented database using the concept of dependency measure and
weight, and weight be assigned to different phrases of a query based on user emphasis. The other aim of this research is mapping fuzzy
queries to fuzzy-XML. It is expected to be simple implement of query, and output of execution of queries be greatly closer to users'
needs and fulfill her expect. The results show that the proposed method explains the possible conjunctive and disjunctive queries the
database in the form of Fuzzy-XML.
Novel Database-Centric Framework for Incremental Information Extractionijsrd.com
Information extraction (IE) has been an active research area that seeks techniques to uncover information from a large collection of text. IE is the task of automatically extracting structured information from unstructured and/or semi structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in document processing like automatic annotation and content extraction could be seen as information extraction. Many applications call for methods to enable automatic extraction of structured information from unstructured natural language text. Due to the inherent challenges of natural language processing, most of the existing methods for information extraction from text tend to be domain specific. In this project a new paradigm for information extraction. In this extraction framework, intermediate output of each text processing component is stored so that only the improved component has to be deployed to the entire corpus. Extraction is then performed on both the previously processed data from the unchanged components as well as the updated data generated by the improved component. Performing such kind of incremental extraction can result in a tremendous reduction of processing time and there is a mechanism to generate extraction queries from both labeled and unlabeled data. Query generation is critical so that casual users can specify their information needs without learning the query language.
Flight Information Display System Case Study - Doha International AirportZaid Haque
This is a case study of my work at Doha International Airport on the Flight Information Display System redesign, where I was the Lead Interaction Designer.
The main goal of this presentation is to delivering recommendations on Infosys’s IS strategy,
Considering the Design Thinking as a Strategy for Innovation within Infosys.
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
Domain ontology development for communicable diseasescsandit
Web has become the very first resource to search for any kind of information. With the
emergence of semantic web, our search queries have started generating more informed results.
Ontologies are at the core of any semantic web application. They help in rapid development of
distributed systems by providing information on the fly. This key feature of distribution and
sharing of information has made ontologies as a new knowledge representation mechanism. A
mechanism which is strongly backed by a sound inference system. In this paper, we shall discuss
the development, verification and validation of an ontology in a health domain.
DOMAIN ONTOLOGY DEVELOPMENT FOR COMMUNICABLE DISEASEScscpconf
Web has become the very first resource to search for any kind of information. With the emergence of semantic web, our search queries have started generating more informed results.Ontologies are at the core of any semantic web application. They help in rapid development of
distributed systems by providing information on the fly. This key feature of distribution and
sharing of information has made ontologies as a new knowledge representation mechanism. A
mechanism which is strongly backed by a sound inference system. In this paper, we shall discuss the development, verification and validation of an ontology in a health domain.
Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Predictionijtsrd
Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Data mining technology which can related to this student grade well and we also used classification algorithms prediction. In this paper, we used educational data mining to predict students final grade based on their performance. We proposed student data classification using ID3 Iterative Dichotomiser 3 Decision Tree Algorithm Khin Khin Lay | San San Nwe "Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26545.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26545/using-id3-decision-tree-algorithm-to-the-student-grade-analysis-and-prediction/khin-khin-lay
Front End Data Cleaning And Transformation In Standard Printed Form Using Neu...ijcsa
Front end of data collection and loading into database manually may cause potential errors in data sets and a very time consuming process. Scanning of a data document in the form of an image and recognition of corresponding information in that image can be considered as a possible solution of this challenge. This paper presents an automated solution for the problem of data cleansing and recognition of user written data to transform into standard printed format with the help of artificial neural networks. Three different neural models namely direct, correlation based and hierarchical have been developed to handle this issue. In a very hostile input environment, the solution is developed to justify the proposed logic.
DATA MINING METHODOLOGIES TO STUDY STUDENT'S ACADEMIC PERFORMANCE USING THE...ijcsa
The study placed a particular emphasis on the so ca
lled data mining algorithms, but focuses the bulk o
f
attention on the C4.5 algorithm. Each educational i
nstitution, in general, aims to present a high qual
ity of
education. This depends upon predicting the student
s with poor results prior they entering in to final
examination. Data mining techniques give many tasks
that could be used to investigate the students'
performance. The main objective of this paper is to
build a classification model that can be used to i
mprove
the students' academic records in Faculty of Mathem
atical Science and Statistics. This model has been
done using the C4.5 algorithm as it is a well-known
, commonly used data mining technique. The
importance of this study is that predicting student
performance is useful in many different settings.
Data
from the previous students' academic records in the
faculty have been used to illustrate the considere
d
algorithm in order to build our classification mode
l.
CLUSTERING DICHOTOMOUS DATA FOR HEALTH CAREijistjournal
Dichotomous data is a type of categorical data, which is binary with categories zero and one. Health care data is one of the heavily used categorical data. Binary data are the simplest form of data used for heath care databases in which close ended questions can be used; it is very efficient based on computational efficiency and memory capacity to represent categorical type data. Clustering health care or medical data is very tedious due to its complex data representation models, high dimensionality and data sparsity. In this paper, clustering is performed after transforming the dichotomous data into real by wiener transformation. The proposed algorithm can be usable for determining the correlation of the health disorders and symptoms observed in large medical and health binary databases. Computational results show that the clustering based on Wiener transformation is very efficient in terms of objectivity and subjectivity.
Classification of data is a data mining technique based on machine learning is used to classification of each item set in as a set of dataset into a set of predefined labelled as classes or groups. Classification is tasks for different application such as text classification, image classification, class’s predictions, data Classification etc. In this paper, we presenting the major classification techniques used for prediction of classes using supervised learning dataset. Several major types of classification method including Random Forest, Naive Bayes, Support Vector Machine (SVM) techniques. The goal of this review paper is to provide a review, accuracy and comparative between different classification techniques in data mining.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
The Statement of Conjunctive and Disjunctive Queries in Object Oriented Datab...Editor IJCATR
Entrance of object orienting concept in database caused the relation database gradually to replace with object oriented
database in various fields. On the other hand for solving the problem of real world uncertain data, several methods were presented.
One of these methods for modeling database is an approach wich couples object-oriented database modeling with fuzzy logic. Many
queries that users to pose are expressed on the basis of linguistic variables. Because of classical databases are not able to support these
variables, leads to fuzzy approaches are considered. We investigate databases queries in this study both simple and complex ways. In
the complex way, we use conjunctive and disjunctive queries. In the following, we use the XML labels to express inqueries into fuzzy.
We can also communicate with other sections of software by entering into XML world as the most reliable opportunity. Also we want
to correct conjunctive and disjunctive queries related to fuzzy object oriented database using the concept of dependency measure and
weight, and weight be assigned to different phrases of a query based on user emphasis. The other aim of this research is mapping fuzzy
queries to fuzzy-XML. It is expected to be simple implement of query, and output of execution of queries be greatly closer to users'
needs and fulfill her expect. The results show that the proposed method explains the possible conjunctive and disjunctive queries the
database in the form of Fuzzy-XML.
Novel Database-Centric Framework for Incremental Information Extractionijsrd.com
Information extraction (IE) has been an active research area that seeks techniques to uncover information from a large collection of text. IE is the task of automatically extracting structured information from unstructured and/or semi structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in document processing like automatic annotation and content extraction could be seen as information extraction. Many applications call for methods to enable automatic extraction of structured information from unstructured natural language text. Due to the inherent challenges of natural language processing, most of the existing methods for information extraction from text tend to be domain specific. In this project a new paradigm for information extraction. In this extraction framework, intermediate output of each text processing component is stored so that only the improved component has to be deployed to the entire corpus. Extraction is then performed on both the previously processed data from the unchanged components as well as the updated data generated by the improved component. Performing such kind of incremental extraction can result in a tremendous reduction of processing time and there is a mechanism to generate extraction queries from both labeled and unlabeled data. Query generation is critical so that casual users can specify their information needs without learning the query language.
Flight Information Display System Case Study - Doha International AirportZaid Haque
This is a case study of my work at Doha International Airport on the Flight Information Display System redesign, where I was the Lead Interaction Designer.
The main goal of this presentation is to delivering recommendations on Infosys’s IS strategy,
Considering the Design Thinking as a Strategy for Innovation within Infosys.
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : A C...IJNSA Journal
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing one single patient’s information gathered from a large volume of data over a long period of time. The main objective of this paper is to represent our ontology-based information retrieval approach for clinical Information System. We have performed a Case Study in the real life hospital settings. The results obtained illustrate the feasibility of the proposed approach which significantly improved the information retrieval process on a large volume of data over a long period of time from August 2011 until January 2012.
Great model a model for the automatic generation of semantic relations betwee...ijcsity
The
large
a
v
ailable
am
ou
n
t
of
non
-
structured
texts
that
b
e
-
long
to
differe
n
t
domains
su
c
h
as
healthcare
(e.g.
medical
records),
justice
(e.g.
l
a
ws,
declarations),
insurance
(e.g.
declarations),
etc. increases
the
effort
required
for
the
analysis
of
information
in
a
decision making
pro
-
cess.
Differe
n
t
pr
o
jects
and t
o
ols
h
av
e
pro
p
osed
strategies
to
reduce
this
complexi
t
y
b
y
classifying,
summarizing
or
annotating
the
texts.
P
artic
-
ularl
y
,
text
summary
strategies
h
av
e
pr
ov
en
to
b
e
v
ery
useful
to
pr
o
vide
a
compact
view
of
an
original
text.
H
ow
e
v
er,
the
a
v
ailable
strategies
to
generate
these
summaries
do
not
fit
v
ery
w
ell
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Recruitment Based On Ontology with Enhanced Security Featurestheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Biomedical indexing and retrieval system based on language modeling approachijseajournal
In the medical field, scientific articles represent a very important source of knowledge for researchers of
this domain. But due to the large volume of scientific articles published on the web, an efficient detection
and use of this knowledge is quite a difficult task.
In this paper, we propose our contribution for conceptual indexing of medical articles by using the MeSH
(Medical Subject Headings) thesaurus. With this in mind, we propose a tool for indexing medical articles
called BIOINSY (BIOmedical Indexing SYstem) which uses a language model for selecting the best
representative descriptors for each document.
The proposed indexing approach was evaluated by intensive experiments. These experiments were
conducted on document test collections of real world clinical extracted from scientific collections, namely
PUBMED and CLEF. The results generated by these experiments demonstrate the effectiveness of our
indexing approach
Sentimental classification analysis of polarity multi-view textual data using...IJECEIAES
The data and information available in most community environments is complex in nature. Sentimental data resources may possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical models. These types of data resource characteristics can form multi-view polarity textual data. However, knowledge creation from this type of sentimental textual data requires considerable analytical efforts and capabilities. In particular, data mining practices can provide exceptional results in handling textual data formats. Besides, in the case of the textual data exists as multi-view or unstructured data formats, the hybrid and integrated analysis efforts of text data mining algorithms are vital to get helpful results. The objective of this research is to enhance the knowledge discovery from sentimental multi-view textual data which can be considered as unstructured data format to classify the polarity information documents in the form of two different categories or types of useful information. A proposed framework with integrated data mining algorithms has been discussed in this paper, which is achieved through the application of X-means algorithm for clustering and HotSpot algorithm of association rules. The analysis results have shown improved accuracies of classifying the sentimental multi-view textual data into two categories through the application of the proposed framework on online polarity user-reviews dataset upon a given topics.
Applying Soft Computing Techniques in Information RetrievalIJAEMSJORNAL
There is plethora of information available over the internet on daily basis and to retrieve meaningful effective information using usual IR methods is becoming a cumbersome task. Hence this paper summarizes the different soft computing techniques available that can be applied to information retrieval systems to improve its efficiency in acquiring knowledge related to a user’s query.
Blended intelligence of FCA with FLC for knowledge representation from cluste...IJECEIAES
Formal concept analysis is the process of data analysis mechanism with emergent attractiveness across various fields such as data mining, robotics, medical, big data and so on. FCA is helpful to generate the new learning ontology based techniques. In medical field, some growing kids are facing the problem of representing their knowledge from their gathered prior data which is in the form of unordered and insufficient clustered data which is not supporting them to take the right decision on right time for solving the uncertainty based questionnaires. In the approach of decision theory, many mathematical replicas such as probability-allocation, crisp set, and fuzzy based set theory were designed to deals with knowledge representation based difficulties along with their characteristic. This paper is proposing new ideological blended approach of FCA with FLC and described with major objectives: primarily the FCA analyzes the data based on relationships between the set of objects of prior-attributes and the set of attributes based prior-data, which the data is framed with data-units implicated composition which are formal statements of idea of human thinking with conversion of significant intelligible explanation. Suitable rules are generated to explore the relationship among the attributes and used the formal concept analysis from these suitable rules to explore better knowledge and most important factors affecting the decision making. Secondly how the FLC derive the fuzzification, rule-construction and defuzzification methods implicated for representing the accurate knowledge for uncertainty based questionnaires. Here the FCA is projected to expand the FCA based conception with help of the objective based item set notions considered as the target which is implicated with the expanded cardinalities along with its weights which is associated through the fuzzy based inference decision rules. This approach is more helpful for medical experts for knowing the range of patient’s memory deficiency also for people whose are facing knowledge explorer deficiency.
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTIONIJDKP
Developing predictive modelling solutions for risk estimation is extremely challenging in health-care
informatics. Risk estimation involves integration of heterogeneous clinical sources having different
representation from different health-care provider making the task increasingly complex. Such sources are
typically voluminous, diverse, and significantly change over the time. Therefore, distributed and parallel
computing tools collectively termed big data tools are in need which can synthesize and assist the physician
to make right clinical decisions. In this work we propose multi-model predictive architecture, a novel
approach for combining the predictive ability of multiple models for better prediction accuracy. We
demonstrate the effectiveness and efficiency of the proposed work on data from Framingham Heart study.
Results show that the proposed multi-model predictive architecture is able to provide better accuracy than
best model approach. By modelling the error of predictive models we are able to choose sub set of models
which yields accurate results. More information was modelled into system by multi-level mining which has
resulted in enhanced predictive accuracy.
Using a Bag of Words for Automatic Medical Image Annotation with a Latent Sem...ijaia
We present in this paper a new approach for the automatic annotation of medical images, using the
approach of "bag-of-words" to represent the visual content of the medical image combined with text
descriptors based approach tf.idf and reduced by latent semantic to extract the co-occurrence between
terms and visual terms. A medical report is composed of a text describing a medical image. First, we are
interested to index the text and extract all relevant terms using a thesaurus containing MeSH medical
concepts. In a second phase, the medical image is indexed while recovering areas of interest which are
invariant to change in scale, light and tilt. To annotate a new medical image, we use the approach of "bagof-
words" to recover the feature vector. Indeed, we use the vector space model to retrieve similar medical
image from the database training. The calculation of the relevance value of an image to the query image is
based on the cosine function. We conclude with an experiment carried out on five types of radiological
imaging to evaluate the performance of our system of medical annotation. The results showed that our
approach works better with more images from the radiology of the skull.
SEGMENTATION OF THE GASTROINTESTINAL TRACT MRI USING DEEP LEARNINGijaia
This paper proposes a deep learning-based model to segment gastrointestinal tract (GI) magnetic
resonance images (MRI). The application of this model will be useful in potentially accelerating treatment
times and possibly improve the quality of the treatments for the patients who must undergo radiation
treatments in cancer centers. The proposed model employs the U-net architecture, which provides
outstanding overall performance in medical image segmentation tasks. The model that was developed
through this project has a score of 81.86% using a combination of the dice coefficient and the Hausdorff
distance measures, rendering it highly accurate in segmenting and contouring organs in the
gastrointestinal system.
SEGMENTATION OF THE GASTROINTESTINAL TRACT MRI USING DEEP LEARNINGgerogepatton
This paper proposes a deep learning-based model to segment gastrointestinal tract (GI) magnetic
resonance images (MRI). The application of this model will be useful in potentially accelerating treatment
times and possibly improve the quality of the treatments for the patients who must undergo radiation
treatments in cancer centers. The proposed model employs the U-net architecture, which provides
outstanding overall performance in medical image segmentation tasks. The model that was developed
through this project has a score of 81.86% using a combination of the dice coefficient and the Hausdorff
distance measures, rendering it highly accurate in segmenting and contouring organs in the
gastrointestinal system
SEGMENTATION OF THE GASTROINTESTINAL TRACT MRI USING DEEP LEARNINGgerogepatton
This paper proposes a deep learning-based model to segment gastrointestinal tract (GI) magnetic
resonance images (MRI). The application of this model will be useful in potentially accelerating treatment
times and possibly improve the quality of the treatments for the patients who must undergo radiation
treatments in cancer centers. The proposed model employs the U-net architecture, which provides
outstanding overall performance in medical image segmentation tasks. The model that was developed
through this project has a score of 81.86% using a combination of the dice coefficient and the Hausdorff
distance measures, rendering it highly accurate in segmenting and contouring organs in the
gastrointestinal system.
A HUMAN-CENTRIC APPROACH TO GROUP-BASED CONTEXT-AWARENESSIJNSA Journal
The emerging need for qualitative approaches in context-aware information processing calls for proper modelling of context information and efficient handling of its inherent uncertainty resulted from human interpretation and usage. Many of the current approaches to context-awareness either lack a solid theoretical basis for modelling or ignore important requirements such as modularity, high-order uncertainty management and group-based context-awareness. Therefore, their real-world application and extendibility remains limited. In this paper, we present f-Context as a service-based contextawareness framework, based on language-action perspective (LAP) theory for modelling. Then we identify some of the complex, informational parts of context which contain high-order uncertainties due to differences between members of the group in defining them. An agent-based perceptual computer architecture is proposed for implementing f-Context that uses computing with words (CWW) for handling uncertainty. The feasibility of f-Context is analyzed using a realistic scenario involving a group of mobile users. We believe that the proposed approach can open the door to future research on context-awareness by offering a theoretical foundation based on human communication, and a service-based layered architecture which exploits CWW for context-aware, group-based and platform-independent access to information systems.
A Proposed Security Architecture for Establishing Privacy Domains in Systems ...IJERA Editor
Information and communication technology (ICT) are becoming a natural part in healthcare. Instead of keeping patient information inside a written file, you can find all information stored in an organized database as well defined files using a specific system in almost every hospital. But those files sometimes got lost or information was split up in files in different hospitals or different departments so no one could see the whole picture from this point we come up with our idea. One of this paper targets is to keep that information available on the cloud so doctors and nurses can have an access to patient record everywhere, so patient history will be clear which helps doctors in giving the right decision. We present security architecture for establishing privacy domains in e-Health bases. In this case, we will improve the availability of medical data and provide the ability for patients to moderate their medical data. Moreover, e-Health system in cloud computing has more than one component to be attacked. The other target of this paper is to distinguish between different kinds of attackers and we point out several shortcomings of current e-Health solutions and standards, particularly they do not address the client platform security, which is a crucial aspect for the overall security of systems in cloud. To fill this gap, we present security architecture for establishing privacy domains in e-Health infrastructures. Our solution provides client platform security and appropriately combines this with network security concepts.
With the surge in modern research focus towards Pervasive Computing, lot of techniques and challenges
needs to be addressed so as to effectively create smart spaces and achieve miniaturization. In the process of
scaling down to compact devices, the real things to ponder upon are the Information Retrieval challenges.
In this work, we discuss the aspects of multimedia which makes information access challenging. An
Example Pattern Recognition scenario is presented and the mathematical techniques that can be used to
model uncertainty are also presented for developing a system that can sense, compute and communicate in
a way that can make human life easy with smart objects assisting from around his surroundings.
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
Ontology oriented concept based clusteringeSAT Journals
Abstract Worldwide health centre scientists, physicians and other patients are accessing, analyzing, integrating and storing massive amounts of digital medical data in different database. The potential for retrieval of information is vast and daunting. The objective of our approach is to differentiate relevant information from irrelevant through user friendly and efficient search algorithms. The traditional solution employs keyword based search without the semantic consideration. So the keyword retrieval may return inaccurate and incomplete results. In order to overcome the problem of information retrieval from this huge amount of database, there is a need for concept based clustering method in ontology. In the proposed method, WorldNet is integrated in order to match the synonyms for the identified keywords so as to obtain the accurate information and it presents the concept based clustering developed using k-means algorithm in accordance with the principles of ontology so that the importance of words of a cluster can be identified. Keywords: Ontology, Concept based clustering, K-means algorithm and information retrieval.
The World Wide Web holds a large size of different information. Sometimes while searching the World Wide Web, users always do not gain the type of information they expect. In the subject of information extraction, extracting semantic relationships between terms from documents become a challenge. This paper proposes a system helps in retrieving documents based on the query expansion and tackles the extracting of semantic relationships from biological documents. This system retrieved documents that are relevant to the input terms then it extracts the existence of a relationship. In this system, we use Boolean model and the pattern recognition which helps in determining the relevant documents and determining the place of the relationship in the biological document. The system constructs a term-relation table that accelerates the relation extracting part. The proposed method offers another usage of the system so the researchers can use it to figure out the relationship between two biological terms through the available information in the biological documents. Also for the retrieved documents, the system measures the percentage of the precision and recall.
The World Wide Web holds a large size of different information. Sometimes while searching the World Wide Web, users always do not gain the type of information they expect. In the subject of information extraction, extracting semantic relationships between terms from documents become a challenge. This paper proposes a system helps in retrieving documents based on the query expansion and tackles the extracting of semantic relationships from biological documents. This system retrieved documents that are relevant to the input terms then it extracts the existence of a relationship. In this system, we use Boolean model and the pattern recognition which helps in determining the relevant documents and determining the place of the relationship in the biological document. The system constructs a term-relation table that accelerates the relation extracting part. The proposed method offers another usage of the system so the researchers can use it to figure out the relationship between two biological terms through the available information in the biological documents. Also for the retrieved documents, the system measures the percentage of the precision and recall.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Accelerate your Kubernetes clusters with Varnish Caching
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : A CASE STUDY
1. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.1, January 2013
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL
FOR HEALTHCARE INFORMATION SYSTEM :
A CASE STUDY
Aidarus M. Ibrahim1 and Hussein A. Hashi2 Abdullalem A. Mohamed2
1
Faculty of Computer Science and Information system, University Technology
Malaysia (UTM), Malaysia
labiile@hotmail.com
2
Faculty of Computer Science and Information system, University Technology
Malaysia (UTM), Malaysia
oday_hashi@hotmail.com
2
Faculty of Computer Science and Information system, University Technology
Malaysia (UTM), Malaysia
aaldolah@yahoo.com
ABSTRACT
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing
one single patient’s information gathered from a large volume of data over a long period of time. The
main objective of this paper is to represent our ontology-based information retrieval approach for
clinical Information System. We have performed a Case Study in the real life hospital settings. The results
obtained illustrate the feasibility of the proposed approach which significantly improved the information
retrieval process on a large volume of data over a long period of time from August 2011 until January
2012
.Keywords
Ontology, Information Retrieval, UTM Clinic, Resource Description Framework(RDF)
1. INTRODUCTION
The field of Information Retrieval (IR) was born [1] to facilitate the fast and relevant retrieval.
Fort the past forty years, the field has full-fledged significantly. Extensive users from different
locations use the IR systems for their daily activity. However, finding relevant and useful
information from large collections of data sources still poses some significant challenges. In this
context, one of the substantial opportunities is to consider the semantics of the information
using ontology. The use of ontology helps to overcome the limitations of keyword-based search
[1] and puts forward as one of the motivations of the present work. While there have been
contributions in this direction during the last few years, most achievements so far either make
partial use of the full expressive power of an ontology-based knowledge, or are based on
Boolean retrieval models, and therefore lack an appropriate ranking model [1] which is needed
for scaling up to massive information sources.
2. RELATED WORK
In the recent past, several ontology-based approaches [1][2] have been proposed. The paper [1]
illustrates reports on the methods, results and experience using a concept-based information
retrieval approach.
DOI : 10.5121/ijnsa.2013.5105 61
2. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.1, January 2013
Authors [2] evaluated the document adequacy with respect to a query using semantic
proximities between ontology concepts and aggregating models. They described that the IR
system depends on domain ontology to broaden the group of appropriate documents which are
retrieved by employing graphical rending of query results to indulge user communications.
In paper [3], authors presented method for semantic query in out-dated relational database by
creating ontological layer. A schema ontology mined from relational database schema and
modified with global domain ontology is wrapped.
The paper [4] exposes an implementation of ontology and semantic approaches for accessing
data from heterogeneous database. The main idea is to get the appropriate data and pass the
relevant information to web users.
[5]This paper reports the experiment results of the UIUC-IBM team in participating in the
medical case retrieval task of Image CLEF. It is experimented with multiple methods to
leverage medical oncology and user (physician) feedback; both have worked very well,
achieving the best retrieval performance among all the submissions.
In Article [6], described that the improvement of precision-recall of the search results is linked
to semantic based information retrieval technique which needs to understand the meaning of the
concepts in the user query. A huge amount of Relational dataset (RDB) that chains structuring
data in a syntactic base is referred as traditional web. Unfortunately, to convert the available
data stored in relational database into RDF format, which is mostly accepted standard proposed
by the World Wide Web(W3C) is monotonous and error prone.[7,9,8].The authors build a
wrapper that works as translator from semantic queries issued to the system into syntactic data
available within these databases instead of immigrating available legacy data in relational
database into RDF format based ontology. Moreover, another side effect, due to some
limitations in RDF as purely text files [7, 8, and 9]; it can be benefited from the strong
characteristics of relational model to RDF format. Although, the contemporary literature with
regard to semantic search predominantly focuses on ontology query, while a mass of data is
saved in heterogonous relational database. Hence, there is problematic issue which is how to
use sematic query in database [12].
Perez and Conrad [11], showed “Relational OWL”, which routinely fetches the semantic of
relational database and converts into RDF/OWL. This approach enables the usability of
relational database in semantic applications based on the semantic of database structure on the
database contents.
In paper [13] also presented a method to represent the schema and data stored in relational
databases can be represented semantically. This showed how to figure out data items stored in
that specific database. Although, with this approach, the schema and data items of each
relational database are ready for semantic reasoning without the intervention of any expert. The
method did not concentrate the ontological relations between tables and attributes.
The Article [14], The authors stored the RDF data in relational database to achieve the best
possible query results where the actual query is performed. Later, they performed and adequate
mapping of all RDF tipples to the relational data model. This mapping is specific to the needs of
querying normal RDF triples, stored in a special way in relational database. The paper [15],
writers introduced a working draft for an algebraic definition of RDF models. Unluckily, this
work lacks the procedures required for enquiring the RDF graph.
In this paper, we proposed ontology based information retrieval approach resulted from a case
study accomplished in University Technology Malaysia (UTM) Clinic. This paper is organized
62
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as follows: Section 2 presents the concepts and ontology based modeling for UTM Clinic.
Section 3 is engaged to show the methodology to build the ontology based information retrieval.
Section 4 demonstrates Query Execution and Information Retrieval Results. Finally, Section 5
concludes the paper.
3. Ontology based Modelling for UTM clinic
Ontology is the specification of concepts and relations. It plays a central role in semantic web
applications by providing a shared knowledge about objects in real world, which promotes
reusability among different modules. Therefore the semantic should be the first concern in any
semantic applications.
For this Case Study, we modeled UTM Clinic ontology.Figure1 shows classes as identified
during the case study. UTM Clinic and person are classes, while staff and patient are sub-class
of person. Moreover, Doctor and Assistant are also sub-classes of Staff. The interesting point
seen from the figure is patient is a sub-class of person. However, Person and Patient can be
shared for some properties such as Fname, Iname, Height and Weight.
Sub-
class Sub-class
Sub-class Sub-class
Figure 1: UTM Clinic Ontology-based Modeling
Figure 2 shows the whole idea for our Case-Study. The functionality and inter-dependency of
classes and objects is figured out here. UTM Clinic has staff. Doctor and Assistant are sub-
classes of staff. Due to the massive task of Doctor, cooperation is needed from the Assistant to
take care of the patient. The four major duties performed by the doctor to see the patient can be
either, medical checkup, consultation, diagnosis or suggestions. Patient can also be person while
person can also a patient. For example, Doctor is a person; on the other hand patient can be a
doctor.
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Figure 2: UTM Clinic Ontology Representation
4. Retrieval Mechanism
Information retrieval is used to satisfy user’s information needs. In order, to achieve this goal,
Information retrieval deals with representation, storage, organization of, and access to
information items. Therefore, the information retrieval changes the users demand for
information into machine processable query. Usually this query contains a set of terms that
describes the Information needed. As information retrieval mainly deals with natural language,
which might be semantically ambiguous, small errors concerning retrieved objects are
inconsequential. The user may rather be interested in retrieving information about a subject.
In our system as shown in figure3, the user who wants to access the system by retrieving
specific topic using the user interface through which the user can create query that brings the
relevant Information. The user interface submits user query to the wrapper ontology to access
the stored files in the database. The wrapper ontology wraps the relational database with schema
ontology. The result generated by the query is translated using wrapper ontology to bring for the
user. That is why users can issue queries on data semantics without having to use different
ontology language.
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Figure 3: Retrieval Mechanism
The method implemented below exmins that any semantic retrieval algorithm must be tough
enough to deal with the insuffeicency of KBs and Ontologies as they are not yet sufficiently
developed to depend upon absolutely, and so they propose integrating a keyword based retrieval
method in the overall system. Their semantic ranking method, brought here as an example for
such methods is usually based on two steps:
a. Explaining documents with a weighted annotation, a graphic of the importance of the
explained instance is add to the text. So, each instance is thus weighted,
Figure 4: Annotation Weighing
di - The weight of instance i in document d.
freqd, i - number of occurrences of i in d. maxk{freqd,k} - Maximum number of
occurrences of any instance in d.
N –Total numbers of documents in the search space.
ni - number of documents annotated with i. The idea is that some annotations may bring
us closer to the goal of evaluating the concepts in the document.
Figure 5: document and Query relevance
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d -The related document is weighted by the vector of annotation
q - A vector resembling the query. qi is the number of variables in the query which are
related to instance i.
b. This approach of ranking is reworking of the classic Vector-space model used for
correspondence evaluation. The final similarity result for the document is:
Where , is the ontology based on similarity index and is the keyword based
similarity indices will be available to soften the error’s implications.
4.1 Method of calculating the weight module
I. The more times the words appear in the document, more possibly it is the
characteristic words;
II. . The measurement of the words will also mark the significance of words.
Seemingly, one idea in the ontology is connected to other notion in that domain
ontology. That also means that the association between two concepts can be
dogged using the length of these two concept’s connecting path in the concept
lattice.
III. If the chances of one word are high, then the word will get additional weight.
One word may be the characteristic word even if it doesn't appear in the
document.
5. Query Execution and Information Retrieval Results
In this experiment, suppose that the user is looking to get single patient’s information .So, the
user builds the query and sends in to the system to get the exact information about the patient.
As we can see from the following figure is the data generated by the system.
The first query process depends on the design of figure1 is extracting a SQL Query. To retrieve
information a SQL statement must go through a process to extract the exact information about
the patient needed by the user. In here, the execution of an RDQL query in RAP consists of four
main steps. At first, the query string is parsed into a memory representation and subsequently
passed to the corresponding engine. In the second step, the query engine searches for triples
matching all patterns from the WHERE clause of the RDQL query and returns a set of variable
bindings. Next, this result set is filtered by evaluating Boolean expressions specified in the
AND clause. The last step is the processing of the final result.
In figure 6, illustrates the basic information related to the patient. We executed different queries
based on out interest and the information that as user we want to retrieve.
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Figure 6: Information of the patient
In figure 7, we sent a query related to the patient’s name, although same names are
already stored in the database, we can classify using their identity. As shown in the
figure, all the names of the patient that are requested by the user are generated.
Figure 7: Result of Query one
As shown in figure 8, as long as the user creates effective query the better result is
generated. The query below shows, that the user has sophisticatedly based the query on
criteria to obtain the relevant information.
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The same goes in figure 9; we generated a query related to patient’s information. Handling the
different queries for different purposes makes the system very powerful. All the queries as seen
in the figures are compiled successfully and generated the relevant results associated to the
patient’s information requested by the user.
Figure 8: Result of Query 2
Figure 9: Result of Query 3
5. In conclusion
In conclusion, the paper presents case study performed in UTM Clinic for ontology based
information retrieval approach. Web based approach was developed using PHP, RDF API PHP
(RAP). PHP is a programming language used to develop web based applications. It is powerful
language which can make the web applications portable and easily accessible. RAP is a library
for parsing, querying, manipulating, serializing and serving RDF models. It is developed to
provide API for ontology and RDF schemas the case study focused on producing more relevant
information on UTM clinic system. The main attainment of this case study was to generate a
result from single patient’s information useful for information retrieval.
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