scholarly journals Utilization of Machine Learning in Brain Tumor Classification

2019 ◽  
pp. 325-330
Author(s):  
Sanjeet Pandey ◽  
Brijesh Bharadwaj ◽  
Himanshu Pandey ◽  
Vineet Kr. Singh
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Early Stage ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Mining Technique ◽  
Diabetes Prediction ◽  
System Data ◽  
Health System Data ◽  
Prediction Of Diabetes

Since past few years data mining lot of attention related to knowledge like extracting methods in health care system like diabetes, cancer, CVS etc. There are lot of technique of data mining like decision tree, Naive base, KNN; J48 etc. are being used for prediction of diabetes. Diabetes is metabolic disorder related to poor absorption of insulin into body mussels or poor lowered secretion of insulin from pancreases. As this disease, this is main death causes disease in the world. So, prediction of these diseases with the help of data mining technique may help to protect many lives. In this study, we have to discuss various data mining technique, types of diabetes, application of these data mining technique. Prediction of diabetes or any other disease could play a significant role in health system. Data mining are very useful in the scenario. These techniques help in selection, understanding and designing of large size data to analysis the chances of diseases occurrence. Recently who has announced diseases a major cause of death worldwide. The prediction and identification early stage of diabetes can play major role to treat this disease significantly. Various data mining techniques like KNN, Decision tree, Naïve Bays etc. would be a significant asset for the researcher for gaining various data about diabetes, its causes, symptoms and possible treatment that have been using in the past and currently used by various physician. In this study we have briefly discussed various data mining techniques/models. Which have been currently used for diabetes prediction? Along with this discussion, we have also focused on performance and short coming of existing models/techniques time to time evaluated by researchers.

scholarly journals Impact of Data Mining Technique in Education Institutions

2015 ◽  
Vol 4 (02) ◽  
pp. 01-07
Author(s):  
Mr. Bhushan Bandre, Ms. Rashmi Khalatkar
Keyword(s):  
Data Mining ◽  
Educational Institution ◽  
Educational Institutions ◽  
Data Mining Technique ◽  
Admission Process ◽  
Data Mining Techniques ◽  
Mining Technique ◽  
Major Parameter ◽  
Using Data ◽  
Major Decision

Major decision making process using large amount of data can be done by various techniques using data mining. In education sectors various data mining techniques are implemented to analyze the student’s data from the admission process itself. Due to large number of educational institution in India, excellence becomes a major parameter for the institutions to grow and with stand. Nowadays education institutions use data mining techniques to show their excellence. The main objective of this work to present an analysis of individual semester wise results of engineering college students using different techniques of data mining. Here we used different classification algorithms like decision tree, rule based, function based and Bayesian algorithms to analyze the semester results and comparison is made by considering parameters like accuracy and error rate. Our output shows the most suited algorithm for analyzing data in educational institutions.

Application of Data Mining Technology in Sports Teaching Quality Evaluation

2014 ◽  
Vol 543-547 ◽  
pp. 4694-4697
Author(s):  
Li Min Zhou
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Scientific Method ◽  
Quality Evaluation ◽  
Teaching Quality ◽  
Evaluation Index ◽  
Data Mining Technique ◽  
Mining Technology ◽  
Mining Technique ◽  
Sign System

The unreasonable phenomenon caused by the lack of effective scientific method, The essay attempts to carry on related analysis and research of combining the data mining technique with sport teaching quality evaluation by mining valid sport teaching quality evaluation index sign system, making full use of the decision tree technique to solve the unreasonableness of the sports teaching quality evaluation and putting forward technical method for sports teaching quality evaluation based on the decision tree, aimed at making it fair, just, reasonable and efficient.

scholarly journals Efficient Data Mining Techniques to Improve Academic Performance of Students

2021 ◽  
Vol 13 (SUP 1) ◽  
pp. 81-84
Author(s):  
Rashmi V. Varade ◽  
Blessy Thankanchan
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Large Volume ◽  
Educational Institutions ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Mining Technique ◽  
Efficient Data

Predicting the academic performance of students is very challenging due to large volume of data in the educational institutions database. Data mining techniques are implemented to predict students' academic performance in many institutions. Because of predicting students' performance, it will help teachers and institutions to decide strategies to teach to the students who are weak in studies and also they can define different strategies who are good in studies so that these students can perform better, So, aim of this paper is to study such a data mining technique which will help us to predict students' academic performance in advance.

Knowledge Structure and Data Mining Techniques

2011 ◽  
pp. 874-882
Author(s):  
Rick L. Wilson ◽  
Peter A. Rosen ◽  
Mohammad Saad Al-Ahmadi
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Inductive Learning ◽  
Knowledge Structure ◽  
Statistical Techniques ◽  
Data Sets ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Mining Technique ◽  
Learning Techniques

Considerable research has been done in the recent past that compares the performance of different data mining techniques on various data sets (e.g., Lim, Low, & Shih, 2000). The goal of these studies is to try to determine which data mining technique performs best under what circumstances. Results are often conflicting—for instance, some articles find that neural networks (NN) outperform both traditional statistical techniques and inductive learning techniques, but then the opposite is found with other datasets (Sen & Gibbs, 1994; Sung, Chang, & Lee, 1999: Spangler, May, & Vargas, 1999). Most of these studies use publicly available datasets in their analysis, and because they are not artificially created, it is difficult to control for possible data characteristics in the analysis. Another drawback of these datasets is that they are usually very small.

scholarly journals A Student Performance Prediction Model Using Data Mining Technique

2018 ◽  
Vol 7 (2.15) ◽  
pp. 61
Author(s):  
Rohaila Abdul Razak ◽  
Mazni Omar ◽  
Mazida Ahmad
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Decision Tree ◽  
Student Performance ◽  
Prediction Accuracy ◽  
Significant Variable ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Predicting Performance ◽  
Using Data

Predicting performance is very significant in the education world nowadays. This paper will describe the process of doing a prediction of student performance by using data mining technique. 257 data sets were taken from the student of semester 6 KPTM that involved four (4) academic programs which are Diploma in Computer System and Networking, Diploma in Information Technology, Diploma in Business Management and Diploma in Accountancy. Knowledge Discovery in Database (KDD) was used as a guide to the process of finding and extracting a knowledge from the dataset. A decision tree and linear regression were used to analyze the dataset based on variables selected. The variables used are Gender, Financing, SPM, GPASem1, GPASem2, GPASem3, GPASem4, GPASem5 and CGPA as a dependent variable. The result from this indicate the significant variable that contribute most to the students’ performance. Based on the analysis, the decision tree shows that GPASem1 has a strong significant to the CGPA final semester of the student and the prediction accuracy is 82%. The linear regression shows that the GPA for each semester has a highly significant with the dependent variable with 96.2% prediction accuracy. By having this information, the management of KPTM can make a plan to ensure that the student can maintain a good result and at the same time to make a strategic plans for those without a good result.  

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