scholarly journals Analysis of Bayes, Neural Network and Tree Classifier of Classification Technique in Data Mining using WEKA

2012 ◽  
Author(s):  
Yugal kumar
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Classification Technique ◽  
Tree Classifier

scholarly journals Detection of dengue disease using artificial neural network based classification technique

2017 ◽  
Vol 7 (1.3) ◽  
pp. 13 ◽  
Author(s):  
K. Balasaravanan ◽  
M. Prakash
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Data Mining Technique ◽  
Human Environment ◽  
Dengue Disease ◽  
Mining Technique ◽  
Classification Technique ◽  
Vector Borne ◽  
Cart Algorithm

The information about the patients can be maintained with clinical documents. By keeping huge volume of clinical documents we can easily predict the occurrence of any disease in the patients. Dengue is considered to be one of the vital disease which are spreading in more than 110 countries. It is a vector borne disease caused by the mosquito’s of female Aedes Albopictus and Aedes Aegypti which are well suited human environment. We have implemented a data mining technique called ANN which is a well-known technique for classification of data used here to classify diseases. We have analyzed the patients’ dataset for the occurrence of dengue and experimented with Weka and Netbeans IDE and the result is proved to be more accurate than the CART algorithm. 

scholarly journals Two-Pass Technique for Clone Detection and Type Classification Using Tree-Based Convolution Neural Network

2021 ◽  
Vol 11 (14) ◽  
pp. 6613
Author(s):  
Young-Bin Jo ◽  
Jihyun Lee ◽  
Cheol-Jung Yoo
Keyword(s):  
Neural Network ◽  
Average Rate ◽  
Convolution Neural Network ◽  
Clone Detection ◽  
Code Clones ◽  
Classification Technique ◽  
Development Costs ◽  
Code Quality ◽  
Type Information ◽  
Type Classification

Appropriate reliance on code clones significantly reduces development costs and hastens the development process. Reckless cloning, in contrast, reduces code quality and ultimately adds costs and time. To avoid this scenario, many researchers have proposed methods for clone detection and refactoring. The developed techniques, however, are only reliably capable of detecting clones that are either entirely identical or that only use modified identifiers, and do not provide clone-type information. This paper proposes a two-pass clone classification technique that uses a tree-based convolution neural network (TBCNN) to detect multiple clone types, including clones that are not wholly identical or to which only small changes have been made, and automatically classify them by type. Our method was validated with BigCloneBench, a well-known and wildly used dataset of cloned code. Our experimental results validate that our technique detected clones with an average rate of 96% recall and precision, and classified clones with an average rate of 78% recall and precision.

scholarly journals Student Academic Performance Prediction using Supervised Learning Techniques

2019 ◽  
Vol 14 (14) ◽  
pp. 92 ◽  
Author(s):  
Muhammad Imran ◽  
Shahzad Latif ◽  
Danish Mehmood ◽  
Muhammad Saqlain Shah
Keyword(s):  
Data Mining ◽  
Supervised Learning ◽  
Student Performance ◽  
Performance Prediction ◽  
Class Imbalance ◽  
Ensemble Methods ◽  
Fine Tuning ◽  
Classification Error ◽  
Decision Tree Classifier ◽  
Tree Classifier

Automatic Student performance prediction is a crucial job due to the large volume of data in educational databases. This job is being addressed by educational data mining (EDM). EDM develop methods for discovering data that is derived from educational environment. These methods are used for understanding student and their learning environment. The educational institutions are often curious that how many students will be pass/fail for necessary arrangements. In previous studies, it has been observed that many researchers have intension on the selection of appropriate algorithm for just classification and ignores the solutions of the problems which comes during data mining phases such as data high dimensionality ,class imbalance and classification error etc. Such types of problems reduced the accuracy of the model. Several well-known classification algorithms are applied in this domain but this paper proposed a student performance prediction model based on supervised learning decision tree classifier. In addition, an ensemble method is applied to improve the performance of the classifier. Ensemble methods approach is designed to solve classification, predictions problems. This study proves the importance of data preprocessing and algorithms fine-tuning tasks to resolve the data quality issues. The experimental dataset used in this work belongs to Alentejo region of Portugal which is obtained from UCI Machine Learning Repository. Three supervised learning algorithms (J48, NNge and MLP) are employed in this study for experimental purposes. The results showed that J48 achieved highest accuracy 95.78% among others.

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