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Mirsaeid Abolghasemi San Jose State University CMPE-297 Sec 49 - Advanced Deep Learning - Short story assignment Fall 2020
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Deep Neural Networks in Text Classification using Active Learning
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journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals, yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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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.
IJET-V2I6P32
IJET-V2I6P32
IJET - International Journal of Engineering and Techniques
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
Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction
Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction
ijtsrd
Application and Implementation of different deep learning based on "A Survey of Deep Learning for Scientific Discovery".
Application and Implementation of different deep learning
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Feature selection is considered as a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data. However, identification of useful features from hundreds or even thousands of related features is not an easy task. Selecting relevant genes from microarray data becomes even more challenging owing to the high dimensionality of features, multiclass categories involved and the usually small sample size. In order to improve the prediction accuracy and to avoid incomprehensibility due to the number of features different feature selection techniques can be implemented. This survey classifies and analyzes different approaches, aiming to not only provide a comprehensive presentation but also discuss challenges and various performance parameters. The techniques are generally classified into three; filter, wrapper and hybrid.
A Survey and Comparative Study of Filter and Wrapper Feature Selection Techni...
A Survey and Comparative Study of Filter and Wrapper Feature Selection Techni...
theijes
Empfohlen
Mirsaeid Abolghasemi San Jose State University CMPE-297 Sec 49 - Advanced Deep Learning - Short story assignment Fall 2020
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Deep Neural Networks in Text Classification using Active Learning
Mirsaeid Abolghasemi
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals, yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
IJERD Editor
Prof. Eric Nyberg (CMU) poster for IBM Yorktown Cognitive Systems Institute (Oct 30, 2014)
Csi poster
Csi poster
ISSIP
Language Models for Information Retrieval
Language Models for Information Retrieval
Dustin Smith
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.
IJET-V2I6P32
IJET-V2I6P32
IJET - International Journal of Engineering and Techniques
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
Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction
Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction
ijtsrd
Application and Implementation of different deep learning based on "A Survey of Deep Learning for Scientific Discovery".
Application and Implementation of different deep learning
Application and Implementation of different deep learning
JIEJackyZOUChou
Feature selection is considered as a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data. However, identification of useful features from hundreds or even thousands of related features is not an easy task. Selecting relevant genes from microarray data becomes even more challenging owing to the high dimensionality of features, multiclass categories involved and the usually small sample size. In order to improve the prediction accuracy and to avoid incomprehensibility due to the number of features different feature selection techniques can be implemented. This survey classifies and analyzes different approaches, aiming to not only provide a comprehensive presentation but also discuss challenges and various performance parameters. The techniques are generally classified into three; filter, wrapper and hybrid.
A Survey and Comparative Study of Filter and Wrapper Feature Selection Techni...
A Survey and Comparative Study of Filter and Wrapper Feature Selection Techni...
theijes
FEW SHOT LEARNING/ONE SHOT LEARNING/ N SHOT LEARNING/ INFORMATIVE RETRIEVE LENS/ MACHINE LEARNING/
Few shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learning
ﺁﺻﻒ ﻋﻠﯽ ﻣﯿﺮ
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
MACHINE LEARNING TOOLBOX
MACHINE LEARNING TOOLBOX
mlaij
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
Evaluating the efficiency of rule techniques for file classification
Evaluating the efficiency of rule techniques for file classification
eSAT Journals
Data are any facts, numbers, or text that can be processed by a computer. Data Mining is an analytic process which designed to explore data usually large amounts of data. Data Mining is often considered to be \"a blend of statistics. In this paper we have used two data mining techniques for discovering classification rules and generating a decision tree. These techniques are J48 and JRIP. Data mining tools WEKA is used in this paper.
J48 and JRIP Rules for E-Governance Data
J48 and JRIP Rules for E-Governance Data
CSCJournals
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.
Evaluating the efficiency of rule techniques for file
Evaluating the efficiency of rule techniques for file
eSAT Publishing House
Abstract Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. Proposed work presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Keywords:-Text mining, text classification, pattern mining, pattern evolving, information filtering.
Using data mining methods knowledge discovery for text mining
Using data mining methods knowledge discovery for text mining
eSAT Journals
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.
Predicting students' performance using id3 and c4.5 classification algorithms
Predicting students' performance using id3 and c4.5 classification algorithms
IJDKP
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
Using data mining methods knowledge discovery for text mining
Using data mining methods knowledge discovery for text mining
eSAT Publishing House
Course Syllabus
Course Syllabus
butest
Much prior work has shown the practical value of modeling random variables as IID in order to simplify statistical inference, yet prior work has also shown this assumption to be suboptimal in terms of model performance. Occam’s razor prompts us to simplify explanations, and this talk will present how a very simple transform has been leveraged to improve performance of both generative and discriminative learners, as well as unsupervised learning, in a number of application domains including differentially private community discovery.
Intuidex - To be or not to be iid by William M. Pottenger (NYC Machine Learni...
Intuidex - To be or not to be iid by William M. Pottenger (NYC Machine Learni...
Hakka Labs
Multi-label spatial classification based on association rules with multi objective genetic algorithms (MOGA) enriched by semi supervised learning is proposed in this paper. It is to deal with multiple class labels problem. In this paper we adapt problem transformation for the multi label classification. We use hybrid evolutionary algorithm for the optimization in the generation of spatial association rules, which addresses single label. MOGA is used to combine the single labels into multi labels with the conflicting objectives predictive accuracy and comprehensibility. Semi supervised learning is done through the process of rule cover clustering. Finally associative classifier is built with a sorting mechanism. The algorithm is simulated and the results are compared with MOGA based associative classifier, which out performs the existing
Multi Label Spatial Semi Supervised Classification using Spatial Associative ...
Multi Label Spatial Semi Supervised Classification using Spatial Associative ...
cscpconf
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
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...
IJNSA Journal
Genetic algorithms in molecular design of novel fabrics Sylvia Wower / Market Research / Philadelphia / DVIRC / Manufacturing / Philadelphia MSA
Genetic algorithms in molecular design of novel fabrics Sylvia Wower
Genetic algorithms in molecular design of novel fabrics Sylvia Wower
Sylvia Wower
Clinical Information Models (CIMs) expressed as archetypes play an essential role in the design and development of current Electronic Health Record (EHR) information structures. Although there exist many experiences about using archetypes in the literature, a comprehensive and formal methodology for archetype modeling does not exist. Having a modeling methodology is essential to develop quality archetypes, in order to guide the development of EHR systems and to allow the semantic interoperability of health data. In this work, an archetype modeling methodology is proposed. This paper describes its phases, the inputs and outputs of each phase, and the involved participants and tools. It also includes the description of the possible strategies to organize the modeling process. The proposed methodology is inspired by existing best practices of CIMs, software and ontology development. The methodology has been applied and evaluated in regional and national EHR projects. The application of the methodology provided useful feedback and improvements, and confirmed its advantages. The conclusion of this work is that having a formal methodology for archetype development facilitates the definition and adoption of interoperable archetypes, improves their quality, and facilitates their reuse among different information systems and EHR projects. Moreover, the proposed methodology can be also a reference for CIMs development using any other formalism.
Archetype Modeling Methodology
Archetype Modeling Methodology
David Moner Cano
This paper has been presented at the third workshop on intelligent textbooks (iTextbooks'2021).
Contextual Definition Generation
Contextual Definition Generation
Sergey Sosnovsky
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.
Applying Soft Computing Techniques in Information Retrieval
Applying Soft Computing Techniques in Information Retrieval
IJAEMSJORNAL
http://college.emory.edu/undergraduate-research/summer-programs/index.html
Unsupervised Main Entity Extraction from News Articles using Latent Variables
Unsupervised Main Entity Extraction from News Articles using Latent Variables
Jinho Choi
This presentation discusses about Genetic algorithms vs Traditional algorithms
Genetic algorithms vs Traditional algorithms
Genetic algorithms vs Traditional algorithms
Dr. C.V. Suresh Babu
Due to increasing interest in data mining and educational system, educational data mining is the emerging topic for research community. educational data mining means to extract the hidden knowledge from large repositories of data with the use of technique and tools. educational data mining develops new methods to discover knowledge from educational database and used for decision making in educational system. The various techniques of data mining like classification. clustering can be applied to bring out hidden knowledge from the educational data. In this paper, we focus on the educational data mining and classification techniques. In this study we analyze attributes for the prediction of student's behavior and academic performance by using WEKA open source data mining tool and various classification methods like decision trees, C4.5 algorithm, ID3 algorithm etc.
A Survey on the Classification Techniques In Educational Data Mining
A Survey on the Classification Techniques In Educational Data Mining
Editor IJCATR
Ethnograph 10 Jul07
Ethnograph 10 Jul07
Clara Kwan
Machine Learning for automated diagnosis of distributed ...AE
Machine Learning for automated diagnosis of distributed ...AE
butest
kantorNSF-NIJ-ISI-03-06-04.ppt
kantorNSF-NIJ-ISI-03-06-04.ppt
butest
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Was ist angesagt?
FEW SHOT LEARNING/ONE SHOT LEARNING/ N SHOT LEARNING/ INFORMATIVE RETRIEVE LENS/ MACHINE LEARNING/
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Few shot learning/ one shot learning/ machine learning
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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
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mlaij
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
Evaluating the efficiency of rule techniques for file classification
Evaluating the efficiency of rule techniques for file classification
eSAT Journals
Data are any facts, numbers, or text that can be processed by a computer. Data Mining is an analytic process which designed to explore data usually large amounts of data. Data Mining is often considered to be \"a blend of statistics. In this paper we have used two data mining techniques for discovering classification rules and generating a decision tree. These techniques are J48 and JRIP. Data mining tools WEKA is used in this paper.
J48 and JRIP Rules for E-Governance Data
J48 and JRIP Rules for E-Governance Data
CSCJournals
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.
Evaluating the efficiency of rule techniques for file
Evaluating the efficiency of rule techniques for file
eSAT Publishing House
Abstract Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. Proposed work presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Keywords:-Text mining, text classification, pattern mining, pattern evolving, information filtering.
Using data mining methods knowledge discovery for text mining
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eSAT Journals
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.
Predicting students' performance using id3 and c4.5 classification algorithms
Predicting students' performance using id3 and c4.5 classification algorithms
IJDKP
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
Using data mining methods knowledge discovery for text mining
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Course Syllabus
Course Syllabus
butest
Much prior work has shown the practical value of modeling random variables as IID in order to simplify statistical inference, yet prior work has also shown this assumption to be suboptimal in terms of model performance. Occam’s razor prompts us to simplify explanations, and this talk will present how a very simple transform has been leveraged to improve performance of both generative and discriminative learners, as well as unsupervised learning, in a number of application domains including differentially private community discovery.
Intuidex - To be or not to be iid by William M. Pottenger (NYC Machine Learni...
Intuidex - To be or not to be iid by William M. Pottenger (NYC Machine Learni...
Hakka Labs
Multi-label spatial classification based on association rules with multi objective genetic algorithms (MOGA) enriched by semi supervised learning is proposed in this paper. It is to deal with multiple class labels problem. In this paper we adapt problem transformation for the multi label classification. We use hybrid evolutionary algorithm for the optimization in the generation of spatial association rules, which addresses single label. MOGA is used to combine the single labels into multi labels with the conflicting objectives predictive accuracy and comprehensibility. Semi supervised learning is done through the process of rule cover clustering. Finally associative classifier is built with a sorting mechanism. The algorithm is simulated and the results are compared with MOGA based associative classifier, which out performs the existing
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Multi Label Spatial Semi Supervised Classification using Spatial Associative ...
cscpconf
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
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ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...
IJNSA Journal
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Genetic algorithms in molecular design of novel fabrics Sylvia Wower
Genetic algorithms in molecular design of novel fabrics Sylvia Wower
Sylvia Wower
Clinical Information Models (CIMs) expressed as archetypes play an essential role in the design and development of current Electronic Health Record (EHR) information structures. Although there exist many experiences about using archetypes in the literature, a comprehensive and formal methodology for archetype modeling does not exist. Having a modeling methodology is essential to develop quality archetypes, in order to guide the development of EHR systems and to allow the semantic interoperability of health data. In this work, an archetype modeling methodology is proposed. This paper describes its phases, the inputs and outputs of each phase, and the involved participants and tools. It also includes the description of the possible strategies to organize the modeling process. The proposed methodology is inspired by existing best practices of CIMs, software and ontology development. The methodology has been applied and evaluated in regional and national EHR projects. The application of the methodology provided useful feedback and improvements, and confirmed its advantages. The conclusion of this work is that having a formal methodology for archetype development facilitates the definition and adoption of interoperable archetypes, improves their quality, and facilitates their reuse among different information systems and EHR projects. Moreover, the proposed methodology can be also a reference for CIMs development using any other formalism.
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Archetype Modeling Methodology
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This paper has been presented at the third workshop on intelligent textbooks (iTextbooks'2021).
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Sergey Sosnovsky
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.
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CSE 591: Machine
learning and Applications Jieping Ye Department of Computer Science & Engineering Arizona State University
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Classification Example categorical
categorical continuous class Training Set Learn Classifier Test Set Model
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Kernel Methods: Basic
ideas Original Space Feature Space
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Data integration mRNA
expression data protein-protein interaction data hydrophobicity data sequence data (gene, protein) Genome-wide data
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Intuition: how does
your brain store these pictures?
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Course schedule
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Hinweis der Redaktion
During the past decade, a heterogeneous spectrum of data became available describing the genome: - Seq. Data -> similarities between proteins / genes - mRNA expression levels associated with a gene: under different experimental conditions
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