Combining Data Mining Techniques for Evolutionary Analysis of Programming Languages

2019 ◽  
Author(s):  
Rafael Jose de Alencar Almeida ◽  
Vinicius Humberto Serapilha Durelli ◽  
Igor Campos Moraes ◽  
Matheus Carvalho Viana ◽  
Elverton Carvalho Fazzion ◽  
...  
Keyword(s):  
Data Mining ◽  
Programming Languages ◽  
Evolutionary Analysis ◽  
Data Mining Techniques ◽  
Combining Data

scholarly journals Combining Data Mining Techniques to Analyse Factors Associated with Allocation of Socioeconomic Resources at IFMG

2021 ◽  
Author(s):  
Eduardo Melo ◽  
Elisa Tuler ◽  
Leonardo Rocha
Keyword(s):  
Data Mining ◽  
Social Vulnerability ◽  
Machine Learning Algorithms ◽  
Data Mining Techniques ◽  
Factors Associated ◽  
Socioeconomic Resources ◽  
Selection Processes ◽  
Combining Data ◽  
Feature Selection Techniques ◽  
To Receive

The granting of socioeconomic assistance to students from Federal Education Institutions is one of the ways found to provide finantial support during their studies, focusing primarily on those who are more socially vulnerable. Institutions carry out selection processes to identify students with a profile of demand and appropriately distribute the grants according to the budget available for this purpose. This article applied Data Mining techniques to a set of information from students who applied to receive scholarships at IFMG - Campus Bambuí, seeking to identify the attributes associated with the distribution of benefits and analyzing the adequacy of the current indicator used by the institution to classify the level of social vulnerability of students. The proposed methodology involved combining different machine learning algorithms, such as data classification and feature selection techniques. In addition to identifying the degree of importance of each attribute in the constructed model, the differential of this article is to present well-founded suggestions for new attributes that could be able to improve the index used by the institution and, consequently, optimize the workload of those involved with the analysis of selective processes. The composition of the institution's index with five new attributes resulted in a gain of around 10% in rating performance.

scholarly journals Survival Analysis of Python and R within the Job Market Trend

2020 ◽  
Vol 1 (1) ◽  
pp. 31-40
Author(s):  
Hina Afzal ◽  
Arisha Kamran ◽  
Asifa Noreen
Keyword(s):  
Data Mining ◽  
Survival Analysis ◽  
Programming Languages ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Mining Techniques ◽  
R Programming Language ◽  
R Programming ◽  
High Level

The market nowadays, due to the rapid changes happening in the technologies requires a high level of interaction between the educators and the fresher coming to going the market. The demand for IT-related jobs in the market is higher than all other fields, In this paper, we are going to discuss the survival analysis in the market of parallel two programming languages Python and R . Data sets are growing large and the traditional methods are not capable enough of handling the large data sets, therefore, we tried to use the latest data mining techniques through python and R programming language. It took several months of effort to gather such an amount of data and process it with the data mining techniques using python and R but the results showed that both languages have the same rate of growth over the past years.

Combining Data Mining Techniques to Enhance Cardiac Arrhythmia Detection

2018 ◽  
pp. 321-333
Author(s):  
Christian Gomes ◽  
Alan Cardoso ◽  
Thiago Silveira ◽  
Diego Dias ◽  
Elisa Tuler ◽  
...  
Keyword(s):  
Data Mining ◽  
Cardiac Arrhythmia ◽  
Arrhythmia Detection ◽  
Data Mining Techniques ◽  
Combining Data

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.

How Sweet and Ripe are the Fruits? Data Mining Techniques for Classifying and Predicting ‘Quick-Wins’ Direct Capital Investment in Indonesia as One Approach to Business intelligence Orientation and Knowledge Management Scenarios of Indonesian Enterprises

2019 ◽  
Vol 1 (1) ◽  
pp. 121-131
Author(s):  
Ali Fauzi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Direct Investment ◽  
Capital Investment ◽  
Export Market ◽  
Added Value ◽  
Knowledge Generation ◽  
Management Scenarios ◽  
Data Mining Techniques ◽  
Knowledge Based Economy

The existence of big data of Indonesian FDI (foreign direct investment)/ CDI (capital direct investment) has not been exploited somehow to give further ideas and decision making basis. Example of data exploitation by data mining techniques are for clustering/labeling using K-Mean and classification/prediction using Naïve Bayesian of such DCI categories. One of DCI form is the ‘Quick-Wins’, a.k.a. ‘Low-Hanging-Fruits’ Direct Capital Investment (DCI), or named shortly as QWDI. Despite its mentioned unfavorable factors, i.e. exploitation of natural resources, low added-value creation, low skill-low wages employment, environmental impacts, etc., QWDI , to have great contribution for quick and high job creation, export market penetration and advancement of technology potential. By using some basic data mining techniques as complements to usual statistical/query analysis, or analysis by similar studies or researches, this study has been intended to enable government planners, starting-up companies or financial institutions for further CDI development. The idea of business intelligence orientation and knowledge generation scenarios is also one of precious basis. At its turn, Information and Communication Technology (ICT)’s enablement will have strategic role for Indonesian enterprises growth and as a fundamental for ‘knowledge based economy’ in Indonesia.

Literature Study –Data Mining Techniques on Detecting Fradulent Activities in Credit Card

2017 ◽  
Vol 6 (10) ◽  
pp. 60
Author(s):  
S. K. Saravanan ◽  
G. N. K. Suresh Babu
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Data Transfer ◽  
Study Data ◽  
Time Requirement ◽  
Literature Study ◽  
Data Mining Techniques ◽  
Online Transactions ◽  
Secured Data ◽  
Credit Card Usage

In contemporary days the more secured data transfer occurs almost through internet. At same duration the risk also augments in secure data transfer. Having the rise and also light progressiveness in e – commerce, the usage of credit card (CC) online transactions has been also dramatically augmenting. The CC (credit card) usage for a safety balance transfer has been a time requirement. Credit-card fraud finding is the most significant thing like fraudsters that are augmenting every day. The intention of this survey has been assaying regarding the issues associated with credit card deception behavior utilizing data-mining methodologies. Data mining has been a clear procedure which takes data like input and also proffers throughput in the models forms or patterns forms. This investigation is very beneficial for any credit card supplier for choosing a suitable solution for their issue and for the researchers for having a comprehensive assessment of the literature in this field.

Survey of Data Mining Techniques Used for Real Time Churn Prediction

2017 ◽  
Vol 7 (3) ◽  
pp. 219-222 ◽  
Author(s):  
Jean Claude Turiho ◽  
◽  
Wilson Cheruiyot ◽  
Anne Kibe ◽  
Irénée Mungwarakarama ◽  
...  
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Churn Prediction ◽  
Data Mining Techniques

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Data Mining Techniques for Diagnosis of Diabetes: A Review

2018 ◽  
Vol 6 (9) ◽  
pp. 212
Author(s):  
Tushar Deshmukh ◽  
H. S. Fadewar
Keyword(s):  
Data Mining ◽  
Blood Glucose ◽  
The Body ◽  
Data Mining Techniques ◽  
Early Stages ◽  
Historic Data ◽  
Prediction Of Diabetes

This Diabetes is such a common dieses found all over the globe, in which blood glucose or in normal terminology the sugar level in blood is increased. It is the condition of the body in which the insulin which is required for the metabolism of the food is not created or body cannot use the insulin produced properly. Doctors say that diabetes can be controlled if it is detected in its early stages. Data mining is the process in which the data can be used for the prediction based on historic data. The intention here is to analysis how various researchers have used the data mining for better prediction of diabetes so that it could be controlled and possible even cured.

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