scholarly journals Using Text Mining and Data Mining Techniques for Applied Learning Assessment

2019 ◽  
Vol 2 (1) ◽  
pp. 60-79 ◽  
Author(s):  
Jessica Cook ◽  
Cuixian Chen ◽  
Angelia Griffin
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Work Experience ◽  
Decision Fusion ◽  
Student Evaluations ◽  
Learning Assessment ◽  
Support Vector ◽  
Applied Learning ◽  
Data Mining Techniques ◽  
The Impact

In a society where first hand work experience is greatly valued many universities or institutions of higher education have designed their Quality enhancement plan (QEP) to address student applied learning. This paper is the results of a university’s QEP plan, called Experiencing Transformative Education Through Applied Learning or ETEAL.  This paper will highlight the research that was conducted using text mining and data mining techniques to analyze a dataset of 672 student evaluations collected from 40 different applied learning courses from fall 2013 to spring 2015, in order to evaluate the impact on instructional practice and student learning. Text mining techniques are applied through the NVivo text mining software to find the 100 most frequent terms to create a document-term matrix in Excel. Then, the document-term matrix is merged with the manual interpretation scores received to create the applied learning assessment data. Lastly, data mining techniques are applied to evaluate the performance, including Random Forest, K-nearest neighbors, Support Vector Machines (with linear and radial kernel), and 5-fold cross-validation. Our results show that the proposed text mining and data mining approach can provide prediction rates of around 67% to 85%, while the decision fusion approach can provide an improvement of 69% to 86%. Our study demonstrates that automatic quantitative analysis of student evaluations can be an effective approach to applied learning assessment.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 72
Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu
Keyword(s):  
Data Mining ◽  
Kernel Functions ◽  
Support Vector ◽  
Ann Model ◽  
Data Mining Techniques ◽  
Yarn Quality ◽  
Yarn Properties ◽  
Svm Model ◽  
Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

scholarly journals Plagiarism Detection Process using Data Mining Techniques

2017 ◽  
Vol 5 (4) ◽  
pp. 68
Author(s):  
Mahwish Abid ◽  
Muhammad Usman ◽  
Muhammad Waleed Ashraf
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Computer Systems ◽  
Plagiarism Detection ◽  
Data Mining Techniques ◽  
Detection Process ◽  
Using Data ◽  
Day By Day

<strong>As the technology is growing very fast and usage of computer systems is increased  as compared to the old times, plagiarism is the phenomenon which is increasing day by day. Wrongful appropriation of someone else’s work is known as plagiarism. Manually detection of plagiarism is difficult so this process should be automated. There are various tools which can be used for plagiarism detection. Some works on intrinsic plagiarism while other work on extrinsic plagiarism. Data mining the field which can help in detecting the plagiarism as well as can help to improve the efficiency of the process. Different data mining techniques can be used to detect plagiarism. Text mining, clustering, bi-gram, tri-grams, n-grams are the techniques which can help in this process</strong>

Analysis of flight delays in aviation system using different classification algorithms and feature selection methods

2019 ◽  
Vol 123 (1267) ◽  
pp. 1415-1436 ◽  
Author(s):  
A. B. A. Anderson ◽  
A. J. Sanjeev Kumar ◽  
A. B. Arockia Christopher
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Classification Model ◽  
System Level ◽  
Support Vector ◽  
Flight Delays ◽  
Data Mining Techniques ◽  
Mining Methods ◽  
Artificial Neural Network Ann ◽  
Aircraft System

ABSTRACTData mining is a process of finding correlations and collecting and analysing a huge amount of data in a database to discover patterns or relationships. Flight delay creates significant problems in the present aviation system. Data mining techniques are desired for analysing the performance in which micro-level causes propagate to make system-level patterns of delay. Analysing flight delays is very difficult – both when looking from a historical view as well as when estimating delays with forecast demand. This paper proposes using Decision Tree (DT), Support Vector Machine (SVM), Naive Bayesian (NB), K-nearest neighbour (KNN) and Artificial Neural Network (ANN) to study and analyse delays among aircrafts. The performance of different data mining methods is found in the different regions of the updated datasets on these classifiers. Finally, the result shows a significant variation in the performance of different data mining methods and feature selection for this problem. This paper aims to deal with how data mining techniques can be used to understand difficult aircraft system delays in aviation. Our aim is to develop a classification model for studying and reducing delay using different data mining methods and, in this manner, to show that DT has a greater classification accuracy. The different feature selectors are used in this study in order to reduce the number of initial attributes. Our results clearly demonstrate the value of DT for analysing and visualising how system-level effects happen from subsystem-level causes.

An Assessment of the Impact of Rheological Properties on Rate of Penetration Using Data Mining Techniques

2019 ◽  
Author(s):  
Abo Taleb T. Al-Hameedi ◽  
Husam H. Alkinani ◽  
Shari Dunn-Norman ◽  
Ralph E. Flori ◽  
Mortadha T. Alsaba ◽  
...  
Keyword(s):  
Data Mining ◽  
Rheological Properties ◽  
Rate Of Penetration ◽  
Data Mining Techniques ◽  
Using Data ◽  
The Impact

An Assessment of the Impact of Rheological Properties on Rate of Penetration Using Data Mining Techniques

2019 ◽  
Author(s):  
Abo Taleb T. Al-Hameedi ◽  
Husam H. Alkinani ◽  
Shari Dunn-Norman ◽  
Ralph E. Flori ◽  
Mortadha T. Alsaba ◽  
...  
Keyword(s):  
Data Mining ◽  
Rheological Properties ◽  
Rate Of Penetration ◽  
Data Mining Techniques ◽  
Using Data ◽  
The Impact

scholarly journals Cardiovascular Disease Prediction System Using Extra Trees Classifier

2019 ◽  
Author(s):  
Rahman Shafique ◽  
Arif Mehmood ◽  
Saleem ullah ◽  
Gyu Sang Choi
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Health Care ◽  
Support Vector Machine ◽  
Prediction Models ◽  
Support Vector ◽  
Prediction System ◽  
Classification Techniques ◽  
Data Mining Techniques ◽  
Tree Classifier

Abstract Heart Disease as cardiovascular disease is the leading cause of death for both men and women. It is the major cause of morbidity and mortality in present society. Therefore, researchers are working to help health care professionals in diagnosing process by using data mining techniques. Although the health care industry is richer in the database this data is not properly mined in order to discover hidden patterns and can able to make decisions based on these patterns. The major goal of this learning refers the extraction of hidden layers by applying numerous data mining techniques that probably give remarkable results in order to ensure the presence of cardiovascular disease among peoples. Data mining classification techniques are used to discover these patterns for research in medical industry. The dataset containing 13 attributes has analyzed for prediction system. The dataset contains some commonly used medical terms like blood pressure, cholesterol level, chest pain and 11 other attributes used to predict cardiovascular disease. The most common and effective classification techniques that are used in mining process are Verdict Tree commonly known as Decision Tree, Extra Trees Classifier, Random Forest, Support Vector Machine, Naive Bays and Logistic Regression has analyzed in this paper. Diagnosing and controlling ratio of deaths from cardiovascular disease Extra classifier trees consider is the best approach. We evaluate these prediction models by using evaluation parameters which are Accuracy, Precision, Recall, and F1-score. As per our experimental results shows accuracy of Extra trees classifier, Logistic Model tree classifier, support vector machine, and naive bays classifiers are 90%, 88%, 87%, 86% respectively. So as per our experiment analysis Extra Tree classifier with highest accuracy considered best approach for predication cardiovascular disease.

scholarly journals Data Mining Techniques for Identification and Classification of Various Diseases in Plants

2019 ◽  
Vol 9 (2S) ◽  
pp. 676-680
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Nearest Neighbors ◽  
Crop Productivity ◽  
Vital Role ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbors ◽  
Data Mining Techniques

Data mining is currently being used in various applications; In research community it plays a vital role. This paper specify about data mining techniques for the preprocessing and classification of various disease in plants. Since various plants has different diseases based on that each of them has different data sets and different objectives for knowledge discovery. Data Mining Techniques applied on plants that it helps in segmentation and classification of diseased plants, it avoids Oral Inspection and helps to increase in crop productivity. This paper provides various classification techniques Such as K-Nearest Neighbors, Support Vector Machine, Principle component Analysis, Neural Network. Thus among various techniques neural network is effective for disease detection in plants.

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