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A report provides organized information on a topic to inform and help decision making. It outlines the purpose, methodology, findings, and conclusions in a structured format. Key aspects of a report include an abstract, introduction, body, and references. Reports supply reliable data that can be useful for work or research, and help address future problems.
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Prd 1099
This document discusses data mining and its applications. It begins by defining data mining as extracting vital information from large datasets. It then outlines the steps of data mining including data preparation, mining, and presentation. It discusses major data mining functionalities like classification, clustering, and association analysis. Applications discussed include healthcare, e-commerce, and marketing. Finally, it outlines challenges of data mining like dealing with large datasets and needing technical knowledge.
STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUE
The document discusses a proposed students' performance prediction system using multi-agent data mining techniques. It aims to predict student performance with high accuracy and help low-performing students. The system uses ensemble classifiers like Adaboost.M1 and LogitBoost and compares their prediction accuracy to the single classifier C4.5 decision tree. Experimental results showed SAMME boosting improved prediction accuracy over C4.5 and LogitBoost.
Vertical intent prediction approach based on Doc2vec and convolutional neural...
Vertical selection is the task of selecting the most relevant verticals to a given query in order to improve the diversity and quality of web search results. This task requires not only predicting relevant verticals but also these verticals must be those the user expects to be relevant for his particular information need. Most existing works focused on using traditional machine learning techniques to combine multiple types of features for selecting several relevant verticals. Although these techniques are very efficient, handling vertical selection with high accuracy is still a challenging research task. In this paper, we propose an approach for improving vertical selection in order to satisfy the user vertical intent and reduce user’s browsing time and efforts. First, it generates query embeddings vectors using the doc2vec algorithm that preserves syntactic and semantic information within each query. Secondly, this vector will be used as input to a convolutional neural network model for increasing the representation of the query with multiple levels of abstraction including rich semantic information and then creating a global summarization of the query features. We demonstrate the effectiveness of our approach through comprehensive experimentation using various datasets. Our experimental findings show that our system achieves significant accuracy. Further, it realizes accurate predictions on new unseen data.
STUDENT PERFORMANCE ANALYSIS USING DECISION TREE
This document describes a student performance analysis project that uses decision trees. It introduces decision trees and their use for classification problems. The project aims to use decision tree methodology to analyze student performance data, including attendance, test scores, seminar marks, and assignment marks to predict exam performance. It discusses the existing manual system and proposes a computerized system using decision tree induction. The key modules described are the calling class for data insertion, binary nodes to represent attribute values, and the decision tree module to build the tree from training data and classify new data.
Data mining to predict academic performance.
This document proposes using data warehousing and data mining techniques to predict student academic performance in schools. It describes collecting student data like scores, attendance, discipline, and assignments into a data warehouse. Data mining methods are then used to analyze the student data and identify relationships between variables to predict performance, such as whether students are progressing, being retained, or conditionally progressing. The results could help schools identify students at risk of failing and take actions to help them succeed.
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The document presents research on using machine learning algorithms to predict student performance in courses. It tested eight algorithms on data from Bradley University and evaluated their predictive accuracy. Based on the results, it makes recommendations on selecting and using ML algorithms for predictive analytics in STEM education. It also summarizes student feedback from surveys on using ML-based predictive analytics. The proposed system aims to address the lack of a system in Malaysia to analyze student data and monitor progress. It reviews literature on predicting student performance with machine learning techniques and identifies the most important attributes to improve student achievement and success. The system architecture requires a computer with at least 4GB RAM, 100GB disk, and the ability to run Python programs in an IDE like PyCharm.
WEB-BASED DATA MINING TOOLS : PERFORMING FEEDBACK ANALYSIS AND ASSOCIATION RU...
This document describes web-based data mining tools for performing association rule mining and feedback analysis on educational data. It presents two tools - one developed using ASP.NET for association rule mining to analyze elective course combinations, and one developed using PHP to collect and analyze student feedback on faculty performance and institutional infrastructure. The tools are intended to help educational institutions improve decision making, teaching effectiveness, and student outcomes by analyzing patterns in student feedback and course selection data. Sample outputs from applying the tools to student data demonstrate their ability to discover useful associations and evaluate performance.
Predicting students' performance using id3 and c4.5 classification algorithms
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.
Recommended
Prd 1099
This document discusses data mining and its applications. It begins by defining data mining as extracting vital information from large datasets. It then outlines the steps of data mining including data preparation, mining, and presentation. It discusses major data mining functionalities like classification, clustering, and association analysis. Applications discussed include healthcare, e-commerce, and marketing. Finally, it outlines challenges of data mining like dealing with large datasets and needing technical knowledge.
STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUE
The document discusses a proposed students' performance prediction system using multi-agent data mining techniques. It aims to predict student performance with high accuracy and help low-performing students. The system uses ensemble classifiers like Adaboost.M1 and LogitBoost and compares their prediction accuracy to the single classifier C4.5 decision tree. Experimental results showed SAMME boosting improved prediction accuracy over C4.5 and LogitBoost.
Vertical intent prediction approach based on Doc2vec and convolutional neural...
Vertical selection is the task of selecting the most relevant verticals to a given query in order to improve the diversity and quality of web search results. This task requires not only predicting relevant verticals but also these verticals must be those the user expects to be relevant for his particular information need. Most existing works focused on using traditional machine learning techniques to combine multiple types of features for selecting several relevant verticals. Although these techniques are very efficient, handling vertical selection with high accuracy is still a challenging research task. In this paper, we propose an approach for improving vertical selection in order to satisfy the user vertical intent and reduce user’s browsing time and efforts. First, it generates query embeddings vectors using the doc2vec algorithm that preserves syntactic and semantic information within each query. Secondly, this vector will be used as input to a convolutional neural network model for increasing the representation of the query with multiple levels of abstraction including rich semantic information and then creating a global summarization of the query features. We demonstrate the effectiveness of our approach through comprehensive experimentation using various datasets. Our experimental findings show that our system achieves significant accuracy. Further, it realizes accurate predictions on new unseen data.
STUDENT PERFORMANCE ANALYSIS USING DECISION TREE
This document describes a student performance analysis project that uses decision trees. It introduces decision trees and their use for classification problems. The project aims to use decision tree methodology to analyze student performance data, including attendance, test scores, seminar marks, and assignment marks to predict exam performance. It discusses the existing manual system and proposes a computerized system using decision tree induction. The key modules described are the calling class for data insertion, binary nodes to represent attribute values, and the decision tree module to build the tree from training data and classify new data.
Data mining to predict academic performance.
This document proposes using data warehousing and data mining techniques to predict student academic performance in schools. It describes collecting student data like scores, attendance, discipline, and assignments into a data warehouse. Data mining methods are then used to analyze the student data and identify relationships between variables to predict performance, such as whether students are progressing, being retained, or conditionally progressing. The results could help schools identify students at risk of failing and take actions to help them succeed.
machine learning based predictive analytics of student academic performance i...
The document presents research on using machine learning algorithms to predict student performance in courses. It tested eight algorithms on data from Bradley University and evaluated their predictive accuracy. Based on the results, it makes recommendations on selecting and using ML algorithms for predictive analytics in STEM education. It also summarizes student feedback from surveys on using ML-based predictive analytics. The proposed system aims to address the lack of a system in Malaysia to analyze student data and monitor progress. It reviews literature on predicting student performance with machine learning techniques and identifies the most important attributes to improve student achievement and success. The system architecture requires a computer with at least 4GB RAM, 100GB disk, and the ability to run Python programs in an IDE like PyCharm.
WEB-BASED DATA MINING TOOLS : PERFORMING FEEDBACK ANALYSIS AND ASSOCIATION RU...
This document describes web-based data mining tools for performing association rule mining and feedback analysis on educational data. It presents two tools - one developed using ASP.NET for association rule mining to analyze elective course combinations, and one developed using PHP to collect and analyze student feedback on faculty performance and institutional infrastructure. The tools are intended to help educational institutions improve decision making, teaching effectiveness, and student outcomes by analyzing patterns in student feedback and course selection data. Sample outputs from applying the tools to student data demonstrate their ability to discover useful associations and evaluate performance.
Predicting students' performance using id3 and c4.5 classification algorithms
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.
slides
The document presents an adaptive machine learning framework for ontology matching that uses semi-supervised learning with user interaction to reduce the cost of manual annotation. The framework initializes with a pre-alignment and then iterates between training multiple learners and getting user feedback to label additional samples. Experiments on matching directories show the framework requires fewer labeled samples than supervised learning alone but achieves comparable performance to other matching systems.
Graph based forcasting for social network
This document presents a critical review report on a course about algorithm design and analysis. It summarizes a paper that studied forecasting techniques based on directed weighted social graphs. The paper generated a social graph from user actions and predicted future activities. It captured user data to create the graph and used rule-based mining for forecasting. Results found the method was more efficient than Apriori and predictions were over 80% accurate. The review critiqued strengths like clear methodology but noted weaknesses such as the title being too broad and lack of recommendations.
Predicting students performance in final examination
The document discusses predicting student performance in final examinations. It examines using linear regression and multilayer perceptron algorithms on attributes of student postings in discussion forums and attendance scores. The case study involved 50 students, and the multilayer perceptron model produced slightly more accurate results based on correlation coefficients and error rates. Specifically, the multilayer perceptron model had a higher correlation coefficient of 0.84 compared to 0.82 for linear regression, and lower mean absolute and root mean squared errors.
DATA MINING METHODOLOGIES TO STUDY STUDENT'S ACADEMIC PERFORMANCE USING THE...
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.
Machine learning in Data Science
The document summarizes a presentation on machine learning in data science. It discusses how machine learning is a subfield of computer science that uses algorithms to learn from examples and improve performance on tasks. Popular machine learning tools used in data science include regression models, artificial neural networks, decision trees, and support vector machines. The presentation also outlines applications of machine learning and data science in fields like ecommerce, banking, media, and bioinformatics. It concludes that deep learning techniques are increasingly important and there will be growing demand for data scientists.
Lec1-Into
This document provides an overview of the CSE 591: Machine Learning and Applications course taught by Dr. Jieping Ye at Arizona State University. The following key points are discussed:
- Course information including instructor, time/location, prerequisites, objectives to provide an understanding of machine learning methods and applications.
- Topics covered include clustering, classification, dimensionality reduction, semi-supervised learning, and kernel learning.
- The grading breakdown includes homework, a group project, and an exam. Students are required to participate in class discussions.
- An introduction to machine learning is provided including definitions of supervised vs. unsupervised learning and applications in domains like bioinformatics.
Unsupervised Feature Selection Based on the Distribution of Features Attribut...
Since dealing with high dimensional data is computationally complex and sometimes even intractable, recently several feature reductions methods have been developed to reduce the dimensionality of the data in order to simplify the calculation analysis in various applications such as text categorization, signal processing, image retrieval, gene expressions and etc. Among feature reduction techniques, feature selection is one the most popular methods due to the preservation of the original features. However, most of the current feature selection methods do not have a good performance when fed on imbalanced data sets which are pervasive in real world applications. In this paper, we propose a new unsupervised feature selection method attributed to imbalanced data sets, which will remove redundant features from the original feature space based on the distribution of features. To show the effectiveness of the proposed method, popular feature selection methods have been implemented and compared. Experimental results on the several imbalanced data sets, derived from UCI repository database, illustrate the effectiveness of our proposed methods in comparison with the other compared methods in terms of both accuracy and the number of selected features.
Machine learning for Data Science
A Brief Introduction to Machine Learning techniques applied in data science. Definitions and applications of machine learning algorithms. Classification and Regression Techniques.
An efficient information retrieval ontology system based indexing for context
Abstract Many of the research or development projects are constructed and vast type of artifacts are released such as article, patent, report of research, conference papers, journal papers, experimental data and so on. The searching of the particular context through the keywords from the repository is not an easy task because the earliest system the problem of huge recalls with low precision. This paper challenges to construct a search algorithm based on the ontology to retrieve the relevant contexts. Ontology's are great knowledge of retrieving the context. In this paper, we utilize the WordNet ontology to retrieve the relevant contexts from the document repository. It is very difficult to retrieve the relevant context in its original format since we use the pre-processing step, which helps to retrieve context. The pre-processing step includes two major steps first one is stop word removal and the second one is stemming process. The outcome of the pre-processing step is indexing consist of important keywords and their corresponding keywords. When the user enter the keyword to the system, the ontology makes the several steps to make the refine keywords. Finally, the refine keywords are matched with index and relevant contexts are retrieved. The experimentation process is carried out with the help of different set of contexts to achieve the results and the performance analysis of the proposed approach is estimated by the evaluation metrics like precision, recall and F-measure. Keywords— Ontologies; WordNet; contexts; stemming; indexing.
A Survey on the Classification Techniques In Educational Data Mining
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.
L016136369
This document analyzes and compares the performance of various classification algorithms (J48, Random Forest, Multilayer Perceptron, IB1, Decision Table) in predicting student performance using data from 260 students. Random Forest performed the best with 89.23% accuracy, taking the least time to build the model and having the lowest error rates compared to the other algorithms. Attributes like attendance, economic status, and parental education were found to be most important factors influencing student results. The analysis provides insight into how different factors impact student performance.
IRJET- Student Placement Prediction using Machine Learning
This document describes a study that uses machine learning algorithms to predict whether students will be placed in jobs after graduating. Specifically, it uses Naive Bayes and K-Nearest Neighbors classifiers to analyze historical student data and predict placements. The algorithms consider parameters like academic results, skills, and previous placement data to make predictions. This system aims to help institutions increase placement percentages by identifying students' strengths and areas for improvement. It is intended to benefit both students in preparing for careers and placement cells in targeting support. Accurately predicting placements could boost a school's reputation by demonstrating career outcomes.
pentaho_usability_presentation
Three participants tested the reporting feature Pentaho in CareLogic's electronic health record system. Issues identified included unclear data field names and organization, difficulty finding features, and lack of guidance. Recommendations to improve usability included adding search functionality for data fields, defaulting field headings to collapsed, uniquely naming each field, auto-refreshing by default, improving help tips, and simplifying menu access. The highest priority recommendations were to make data field search and organization clearer for users.
Gene Selection Based on Rough Set Applications of Rough Set on Computational ...
Rough Set Theory is an effective mathematical tool for selecting informative genes from microarray data. The paper discusses using Rough Set Theory for gene selection, which is the process of choosing the most informative genes from a whole gene database. It describes the basics of Rough Set Theory and proposes using a Quick-Reduct based Genetic Algorithm for Rough Set attribute reduction to select informative genes from microarray data.
A Study on Machine Learning and Its Working
Machine learning (ML) is widely popular these days and used in wide variety of domains for prediction of
outcomes. In machine learning lot of algorithms exists for predicting the outcomes. But choosing the right
algorithm according to the domain plays a very important in deciding the performance of the algorithm. This
paper consists of five sections is organized in following way first section deals about collection of data from
various resources, second section deals about data cleaning, third part deals about choosing the correct ML
algorithm, fourth part deals about gaining knowledge from models and final part deals about data
visualization
A survey of memory based methods for collaborative filtering based techniques
This document summarizes a study that evaluates three collaborative filtering techniques - Euclidean distance, Pearson correlation, and cosine similarity. The study uses a movie rating dataset to calculate similarities between users and generate predictions for unseen movie ratings. The techniques are evaluated by comparing actual ratings to predicted ratings on a test set of 120 movie ratings. The mean absolute error and number of predictions within certain deviation thresholds are used to assess accuracy. The process is repeated three times to arrive at a conclusion on the relative performance of the three similarity measures for collaborative filtering.
Shape based plagiarism detection for flowchart figures in texts
Plagiarism detection is well known phenomenon in the academic arena. Copying other people is
considered as serious offence that needs to be checked. There are many plagiarism detection systems such
as turn-it-in that has been developed to provide this checks. Most, if not all, discard the figures and charts
before checking for plagiarism. Discarding the figures and charts results in look holes that people can take
advantage. That means people can plagiarized figures and charts easily without the current plagiarism
systems detecting it. There are very few papers which talks about flowcharts plagiarism detection.
Therefore, there is a need to develop a system that will detect plagiarism in figures and charts. This paper
presents a method for detecting flow chart figure plagiarism based on shape-based image processing and
multimedia retrieval. The method managed to retrieve flowcharts with ranked similarity according to
different matching sets.
Dissertation data analysis in management science tutors india.com for my man...
Management Science is very much crucial in management decision making. The primary purpose of decision-making is for effective and efficient utilization of scarce or the limited resources for which there are both private and public sectors of that economy. The present article helps the USA, the UK, Europe and the Australian students pursuing their master’s degree to identify the best data analysis, which is usually considered to be challenging. Tutors India offers UK dissertation in various Domains.
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Detailed structure applicable to hybrid recommendation technique
This document discusses a hybrid recommendation technique that combines content-based filtering, collaborative filtering, and demographic techniques to provide personalized point of interest (POI) recommendations for tourists. The technique uses a three-part process: 1) querying tourists for preferences and trip constraints, 2) using the tourist information to score POIs from each recommendation technique, and 3) clustering the top POIs to find the optimal trip area. The proposed hybrid approach aims to automatically select the most personalized POIs for tourists based on their initial information.
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...
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
published article
Health Informatics: An International Journal (HIIJ)
ISSN : 2319 - 2046 (Online); 2319 - 3190 (Print)
https://airccse.org/journal/hiij/index.html
Volume 10, Number 4
Towards a Standard of Modelling Annotations in the E-Health Domain
For more info
https://airccse.org/journal/hiij/Current2021.html
TOWARDS A STANDARD OF MODELLING ANNOTATIONS IN THE E-HEALTH DOMAIN
A large number of annotation systems in e-health domain have been implemented in the literature. Several factors distinguish these systems from one another. In fact, each of these systems is based on a separate paradigm, resulting in a disorganized and unstructured vision. As part of our research, we attempted to categorize them based on the functionalities provided by each system, and we also proposed a model of annotations that integrates both the health professional and the patient in the process of annotating the medical file.
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slides
The document presents an adaptive machine learning framework for ontology matching that uses semi-supervised learning with user interaction to reduce the cost of manual annotation. The framework initializes with a pre-alignment and then iterates between training multiple learners and getting user feedback to label additional samples. Experiments on matching directories show the framework requires fewer labeled samples than supervised learning alone but achieves comparable performance to other matching systems.
Graph based forcasting for social network
This document presents a critical review report on a course about algorithm design and analysis. It summarizes a paper that studied forecasting techniques based on directed weighted social graphs. The paper generated a social graph from user actions and predicted future activities. It captured user data to create the graph and used rule-based mining for forecasting. Results found the method was more efficient than Apriori and predictions were over 80% accurate. The review critiqued strengths like clear methodology but noted weaknesses such as the title being too broad and lack of recommendations.
Predicting students performance in final examination
The document discusses predicting student performance in final examinations. It examines using linear regression and multilayer perceptron algorithms on attributes of student postings in discussion forums and attendance scores. The case study involved 50 students, and the multilayer perceptron model produced slightly more accurate results based on correlation coefficients and error rates. Specifically, the multilayer perceptron model had a higher correlation coefficient of 0.84 compared to 0.82 for linear regression, and lower mean absolute and root mean squared errors.
DATA MINING METHODOLOGIES TO STUDY STUDENT'S ACADEMIC PERFORMANCE USING THE...
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.
Machine learning in Data Science
The document summarizes a presentation on machine learning in data science. It discusses how machine learning is a subfield of computer science that uses algorithms to learn from examples and improve performance on tasks. Popular machine learning tools used in data science include regression models, artificial neural networks, decision trees, and support vector machines. The presentation also outlines applications of machine learning and data science in fields like ecommerce, banking, media, and bioinformatics. It concludes that deep learning techniques are increasingly important and there will be growing demand for data scientists.
Lec1-Into
This document provides an overview of the CSE 591: Machine Learning and Applications course taught by Dr. Jieping Ye at Arizona State University. The following key points are discussed:
- Course information including instructor, time/location, prerequisites, objectives to provide an understanding of machine learning methods and applications.
- Topics covered include clustering, classification, dimensionality reduction, semi-supervised learning, and kernel learning.
- The grading breakdown includes homework, a group project, and an exam. Students are required to participate in class discussions.
- An introduction to machine learning is provided including definitions of supervised vs. unsupervised learning and applications in domains like bioinformatics.
Unsupervised Feature Selection Based on the Distribution of Features Attribut...
Since dealing with high dimensional data is computationally complex and sometimes even intractable, recently several feature reductions methods have been developed to reduce the dimensionality of the data in order to simplify the calculation analysis in various applications such as text categorization, signal processing, image retrieval, gene expressions and etc. Among feature reduction techniques, feature selection is one the most popular methods due to the preservation of the original features. However, most of the current feature selection methods do not have a good performance when fed on imbalanced data sets which are pervasive in real world applications. In this paper, we propose a new unsupervised feature selection method attributed to imbalanced data sets, which will remove redundant features from the original feature space based on the distribution of features. To show the effectiveness of the proposed method, popular feature selection methods have been implemented and compared. Experimental results on the several imbalanced data sets, derived from UCI repository database, illustrate the effectiveness of our proposed methods in comparison with the other compared methods in terms of both accuracy and the number of selected features.
Machine learning for Data Science
A Brief Introduction to Machine Learning techniques applied in data science. Definitions and applications of machine learning algorithms. Classification and Regression Techniques.
An efficient information retrieval ontology system based indexing for context
Abstract Many of the research or development projects are constructed and vast type of artifacts are released such as article, patent, report of research, conference papers, journal papers, experimental data and so on. The searching of the particular context through the keywords from the repository is not an easy task because the earliest system the problem of huge recalls with low precision. This paper challenges to construct a search algorithm based on the ontology to retrieve the relevant contexts. Ontology's are great knowledge of retrieving the context. In this paper, we utilize the WordNet ontology to retrieve the relevant contexts from the document repository. It is very difficult to retrieve the relevant context in its original format since we use the pre-processing step, which helps to retrieve context. The pre-processing step includes two major steps first one is stop word removal and the second one is stemming process. The outcome of the pre-processing step is indexing consist of important keywords and their corresponding keywords. When the user enter the keyword to the system, the ontology makes the several steps to make the refine keywords. Finally, the refine keywords are matched with index and relevant contexts are retrieved. The experimentation process is carried out with the help of different set of contexts to achieve the results and the performance analysis of the proposed approach is estimated by the evaluation metrics like precision, recall and F-measure. Keywords— Ontologies; WordNet; contexts; stemming; indexing.
A Survey on the Classification Techniques In Educational Data Mining
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.
L016136369
This document analyzes and compares the performance of various classification algorithms (J48, Random Forest, Multilayer Perceptron, IB1, Decision Table) in predicting student performance using data from 260 students. Random Forest performed the best with 89.23% accuracy, taking the least time to build the model and having the lowest error rates compared to the other algorithms. Attributes like attendance, economic status, and parental education were found to be most important factors influencing student results. The analysis provides insight into how different factors impact student performance.
IRJET- Student Placement Prediction using Machine Learning
This document describes a study that uses machine learning algorithms to predict whether students will be placed in jobs after graduating. Specifically, it uses Naive Bayes and K-Nearest Neighbors classifiers to analyze historical student data and predict placements. The algorithms consider parameters like academic results, skills, and previous placement data to make predictions. This system aims to help institutions increase placement percentages by identifying students' strengths and areas for improvement. It is intended to benefit both students in preparing for careers and placement cells in targeting support. Accurately predicting placements could boost a school's reputation by demonstrating career outcomes.
pentaho_usability_presentation
Three participants tested the reporting feature Pentaho in CareLogic's electronic health record system. Issues identified included unclear data field names and organization, difficulty finding features, and lack of guidance. Recommendations to improve usability included adding search functionality for data fields, defaulting field headings to collapsed, uniquely naming each field, auto-refreshing by default, improving help tips, and simplifying menu access. The highest priority recommendations were to make data field search and organization clearer for users.
Gene Selection Based on Rough Set Applications of Rough Set on Computational ...
Rough Set Theory is an effective mathematical tool for selecting informative genes from microarray data. The paper discusses using Rough Set Theory for gene selection, which is the process of choosing the most informative genes from a whole gene database. It describes the basics of Rough Set Theory and proposes using a Quick-Reduct based Genetic Algorithm for Rough Set attribute reduction to select informative genes from microarray data.
A Study on Machine Learning and Its Working
Machine learning (ML) is widely popular these days and used in wide variety of domains for prediction of
outcomes. In machine learning lot of algorithms exists for predicting the outcomes. But choosing the right
algorithm according to the domain plays a very important in deciding the performance of the algorithm. This
paper consists of five sections is organized in following way first section deals about collection of data from
various resources, second section deals about data cleaning, third part deals about choosing the correct ML
algorithm, fourth part deals about gaining knowledge from models and final part deals about data
visualization
A survey of memory based methods for collaborative filtering based techniques
This document summarizes a study that evaluates three collaborative filtering techniques - Euclidean distance, Pearson correlation, and cosine similarity. The study uses a movie rating dataset to calculate similarities between users and generate predictions for unseen movie ratings. The techniques are evaluated by comparing actual ratings to predicted ratings on a test set of 120 movie ratings. The mean absolute error and number of predictions within certain deviation thresholds are used to assess accuracy. The process is repeated three times to arrive at a conclusion on the relative performance of the three similarity measures for collaborative filtering.
Shape based plagiarism detection for flowchart figures in texts
Plagiarism detection is well known phenomenon in the academic arena. Copying other people is
considered as serious offence that needs to be checked. There are many plagiarism detection systems such
as turn-it-in that has been developed to provide this checks. Most, if not all, discard the figures and charts
before checking for plagiarism. Discarding the figures and charts results in look holes that people can take
advantage. That means people can plagiarized figures and charts easily without the current plagiarism
systems detecting it. There are very few papers which talks about flowcharts plagiarism detection.
Therefore, there is a need to develop a system that will detect plagiarism in figures and charts. This paper
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Dissertation data analysis in management science tutors india.com for my man...
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Detailed structure applicable to hybrid recommendation technique
This document discusses a hybrid recommendation technique that combines content-based filtering, collaborative filtering, and demographic techniques to provide personalized point of interest (POI) recommendations for tourists. The technique uses a three-part process: 1) querying tourists for preferences and trip constraints, 2) using the tourist information to score POIs from each recommendation technique, and 3) clustering the top POIs to find the optimal trip area. The proposed hybrid approach aims to automatically select the most personalized POIs for tourists based on their initial information.
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...
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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published article
Health Informatics: An International Journal (HIIJ)
ISSN : 2319 - 2046 (Online); 2319 - 3190 (Print)
https://airccse.org/journal/hiij/index.html
Volume 10, Number 4
Towards a Standard of Modelling Annotations in the E-Health Domain
For more info
https://airccse.org/journal/hiij/Current2021.html
TOWARDS A STANDARD OF MODELLING ANNOTATIONS IN THE E-HEALTH DOMAIN
A large number of annotation systems in e-health domain have been implemented in the literature. Several factors distinguish these systems from one another. In fact, each of these systems is based on a separate paradigm, resulting in a disorganized and unstructured vision. As part of our research, we attempted to categorize them based on the functionalities provided by each system, and we also proposed a model of annotations that integrates both the health professional and the patient in the process of annotating the medical file.
Towards a Standard of Modelling Annotations in the E-Health Domain
A large number of annotation systems in e-health domain have been implemented in the literature. Several factors distinguish these systems from one another. In fact, each of these systems is based on a separate paradigm, resulting in a disorganized and unstructured vision. As part of our research, we attempted to categorize them based on the functionalities provided by each system, and we also proposed a model of annotations that integrates both the health professional and the patient in the process of annotating the medical file.
Sub1557
This document summarizes a research paper on opinion mining from Twitter data. It discusses the challenges of sentiment analysis on short Twitter posts, including named entity recognition, anaphora resolution, parsing, and detecting sarcasm. It also reviews several papers on related topics, such as frameworks for Twitter opinion mining using classification techniques, using Twitter as a corpus for sentiment analysis, and analyzing opinions during the 2012 Korean presidential election on Twitter. Overall, it covers key techniques in opinion mining like identifying opinion targets and orientation. It proposes future work to develop a web application to compare Twitter opinion mining performance and use supervised learning to improve accuracy.
A presentation on Applications of ICT in Research.pptx
The document summarizes the applications of information and communication technology (ICT) in research. It discusses how ICT can be used before, during, and after data analysis. Specifically, it notes that ICT allows researchers to search literature more efficiently, collect and analyze both quantitative and qualitative data using statistical software, and disseminate findings through manuscript submission tools and reference management programs. The document concludes that ICT has significantly improved various aspects of the research process and will continue transforming scientific inquiry.
IRJET- PDF Extraction using Data Mining Techniques
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International Journal of Engineering Research and Development (IJERD)
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International Journal of Engineering Research and Development (IJERD)
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yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTION
The user generated content on the web grows rapidly in this emergent information age. The evolutionary changes in technology make use of such information to capture only the user’s essence and finally the useful information are exposed to information seekers. Most of the existing research on text information processing, focuses in the factual domain rather than the opinion domain. In this paper we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes value for each word of text. The proposed approach combines K-means clustering and Support Vector Machine with PSO (SVM-PSO) classification algorithm that can be used to group the forums into two clusters forming hotspot forums and non-hotspot forums within the current time span. The proposed system accuracy is compared with the other classification algorithms such as Naïve Bayes, Decision tree and SVM. The experiment helps to identify that K-means and SVM-PSO together achieve highly consistent results.
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTION
The user generated content on the web grows rapidly in this emergent information age. The evolutionary changes in technology make use of such information to capture only the user’s essence and finally the useful information are exposed to information seekers. Most of the existing research on text information processing, focuses in the factual domain rather than the opinion domain. In this paper we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes value for each word of text. The proposed approach combines K-means clustering and Support Vector Machine with PSO (SVM-PSO) classification algorithm that can be used to group the forums into two clusters forming hotspot forums and non-hotspot forums within the current time span. The proposed system accuracy is compared with the other classification algorithms such as Naïve Bayes, Decision tree and SVM. The experiment helps to identify that K-means and SVM-PSO together achieve highly consistent results.
SOCIAL MEDIA ANALYSIS ON SUPPLY CHAIN MANAGEMENT IN FOOD INDUSTRY
This paper proposes the importance of
social media analysis in supply chain management in the
food industry. In this analysis, the social media platform
(Twitter) is used to obtain information. In this approach,
two different software (Nodexl and Nvivo) are used to
conduct data mining and text analysis. The outcome of this
analysis will help researchers to make decisions based on
customer feedback.
A Comparative Study of Recent Ontology Visualization Tools with a Case of Dia...
Ontology is a conceptualization of a domain into machine readable format. Ontologies are becoming increasingly popular modelling schemas for knowledge management services and applications. Focus on developing tools to graphically visualise ontologies is rising to aid their assessment and analysis. Graph visualisation helps to browse and comprehend the structure of ontologies. A number of ontology visualizations exist that have been embedded in ontology management tools. The primary goal of this paper is to analyze recently implemented ontology visualization tools and their contributions in the enrichment of users’ cognitive support. This work also presents the preliminary results of an evaluation of three visualization tools to determine the suitability of each method for end user applications where ontologies are used as browsing aids with a case of Diabetes data
Online Book Donation System Project Report (Android)
Daksha -online book donation system provides a platform for all the people who need books, stationery, and money for education and cannot afford it and also for people who want to give away their unused books or books that are no longer required by them. The system has the facility to donate money and stationary as well. The donation can be made to an individual or to the NGO that needs it in a huge amount.
A Study On Sentiment Analysis Methods And Tools
The document summarizes sentiment analysis methods and tools. It discusses how sentiment analysis is used to analyze opinions expressed in text sources like blogs, reviews and social media to determine whether the sentiment is positive, negative or neutral. It describes the key steps in sentiment analysis as opinion retrieval from sources, opinion classification (identifying text as expressing positive or negative sentiment) and opinion summarization. It also outlines different techniques used for sentiment analysis including supervised machine learning algorithms and lexicon-based methods.
710201947
This document presents a new algorithm for extracting and summarizing news from online newspapers. The algorithm first extracts news related to the topic using keyword matching. It then distinguishes different types of news about the same topic. A term frequency-based summarization method is used to generate summaries. Sentences are scored based on term frequency and the highest scoring sentences are selected for the summary. The algorithm was evaluated on news datasets from various newspapers and showed good performance in intrinsic evaluation metrics like precision, recall and F-score. Thus, the proposed method can effectively extract and summarize online news for a given keyword or topic.
710201947
This document presents a new algorithm for extracting and summarizing news from online newspapers. The algorithm first extracts news related to the topic using keyword matching. It then distinguishes different types of news about the same topic. A term frequency-based summarization method is used to generate summaries. Sentences are scored based on term frequency and the highest scoring sentences are selected for the summary. The algorithm was evaluated on news datasets from various newspapers and showed good performance in intrinsic evaluation metrics like precision, recall and F-score. Thus, the proposed method can effectively extract and summarize online news for a given keyword or topic.
Dams dynamic attendance management system
This document describes a Dynamic Attendance Management System (DAMS) that was developed to more efficiently manage student and teacher attendance records using a centralized database and web interface. The system allows attendance to be taken and updated live, and personalized reports to be generated. It also functions as a student-teacher portal. Data mining techniques are applied to the attendance data to identify trends and predict student performance. Classification algorithms like ID3 and C4.5 are used to analyze factors like attendance, test scores, and past academic performance to generate performance predictions. The system aims to make attendance management simpler and more accessible through an easy-to-use interface and database backend.
INTELLIGENT AGENT FOR PUBLICATION AND SUBSCRIPTION PATTERN ANALYSIS OF NEWS W...
The rapid growth of Internet has revolutionized online news reporting. Many users tend to use online news
websites to obtain news information. When considering Sri Lanka, there are numerous news websites,
which are subscribed on a daily basis. With the rise in this number of news websites, the Sri Lankan
authorities of media face the issue of lacking a proper methodology or a tool which is capable of tracking
and regulating publications made by different disseminators of news.
INTELLIGENT AGENT FOR PUBLICATION AND SUBSCRIPTION PATTERN ANALYSIS OF NEWS W...
The rapid growth of Internet has revolutionized online news reporting. Many users tend to use online news
websites to obtain news information. When considering Sri Lanka, there are numerous news websites,
which are subscribed on a daily basis. With the rise in this number of news websites, the Sri Lankan
authorities of media face the issue of lacking a proper methodology or a tool which is capable of tracking
and regulating publications made by different disseminators of news.
This paper proposes a News Agent toolbox which periodically extracts news articles and associated
comments with the aid of a concept called Mapping Rules; to classify them into Personalized Categories
defined in terms of keywords based Category Profiles. The proposed tool also analyzes comments made by
the readers with the aid of simple statistical techniques to discover the most popular news articles and
fluctuations in popularity of news stories.
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TOWARDS A STANDARD OF MODELLING ANNOTATIONS IN THE E-HEALTH DOMAIN
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Towards a Standard of Modelling Annotations in the E-Health Domain
A presentation on Applications of ICT in Research.pptx
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IRJET- PDF Extraction using Data Mining Techniques
IRJET- PDF Extraction using Data Mining Techniques
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
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A Comparative Study of Recent Ontology Visualization Tools with a Case of Dia...
Online Book Donation System Project Report (Android)
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what is a report?
- 2. It is a type of academic writing which it is about a document that presents organized information through a structured format for a specific audience and purpose. It seeks to inform clearly and thoroughly a result, especially of a observation or an inquiry. Taken from About Education (2015).
- 3. 2. It supplies reliable data that can be so useful for those who are interested in utilize it for some work or investigation. 1. A report can provide organized and up-to- date information about any matter or subject. 3. The information of a report can be very meaningful to make decisions and develop plans to face future problems, especially in companies. According to Gaurav (2011), some advantages of a report are:
- 4. Computer systems: Scanners Abstract This report investigates the current state of scanner technology. Table of contents Abstract 1. Introduction 2. How scanners work 3. Conclusions 4. References 1. Introduction The purpose of this report is to survey the current state of scanner technology. 2. How scanners work A scanner is a device that uses a light source to electronically convert an image into binary data. 3. Conclusions This report has identified five types of scanners currently available. 4. References Blatner, D., Fleishman, G. Roth, G. (1998) Real world scanning and halftones 2nd edition, Peachpit Press, USA. Full example here
- 5. 1. Preliminaries Title page Abstract Contents 2. Main text Introduction Methodology Findings and Results Discussion Conclusion 3. End matter References Appendices These are the most important steps to make a report: Taken from EUFAP (2015).
- 6. Akrani, G. (2011, May 11). What are the Main Advantages of Report Writing ? . Retrieved from Kalyan City Life: http://kalyan- city.blogspot.com/2010/11/what-are-advantages-of-report-or-report.html Canberra, U. o. (2015, February 23). Writing a report. Retrieved from University of Canberra: http://learnonline.canberra.edu.au/mod/book/view.php?id=180631 Gillett, A. (2015, February 23). Genres in academic writing: Reports. Retrieved from UEFAP: http://www.uefap.com/writing/genre/genrefram.htm Monash. (2015, February 23). Sample report. Retrieved from Monash University: http://www.monash.edu.au/lls/llonline/writing/information- technology/report/1.3.3.xml Nordquist, R. (2015, February 23). Report. Retrieved from About Education: http://grammar.about.com/od/rs/g/reporterm.htm