scholarly journals Employee Performance Prediction Using EPP Framework

2020 ◽  
pp. 297-302
Author(s):  
M. Karthika ◽  
T. Meyyappan
Keyword(s):  
Data Mining ◽  
Performance Prediction ◽  
Employee Performance ◽  
Research Work ◽  
Prediction Methods ◽  
Internal Factors ◽  
Using Data ◽  
Proper Solutions

In the today's industrial world, every company’s growth is depends on their employees. The company achievements are completely based on the employees in the organization. The employees’ performances are measured by the targets and achievements. But some external and internal factors affect the employees’ goals and achievements. Hence, the company has to find the performance of every employee and make proper solutions to improve the performance. This research work proposes a fully automated framework which can perform deep analysis of employees’ performance and job fitness using data mining and prediction methods.

USING DATA MINING ALGORITHMS IN WEB PERFORMANCE PREDICTION

2009 ◽  
Vol 40 (2) ◽  
pp. 176-187 ◽  
Author(s):  
Leszek Borzemski ◽  
Marta Kliber ◽  
Ziemowit Nowak
Keyword(s):  
Data Mining ◽  
Performance Prediction ◽  
Web Performance ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms

scholarly journals Disease Prediction and Drug Recommendation Android Application using Data Mining (Virtual Doctor)

2019 ◽  
Vol 8 (3) ◽  
pp. 6996-7001
Keyword(s):  
Data Mining ◽  
Research Work ◽  
Application Programming Interface ◽  
Machine Learning Algorithms ◽  
Useful Knowledge ◽  
Medical Databases ◽  
Application Programming ◽  
Mining Procedure ◽  
Using Data ◽  
Programming Interface

Data Mining is a method that requires analyzing and exploring large blocks of data to glean meaningful trends and patterns. In today’s period, every person on earth relies on allopathic treatments and medicines. Data mining techniques can be applied to medical databases that have a vast scope of opportunity for textual as well as visual data. In medical services, there are myriad obscure data that needs to be scrutinized and data mining is the key to gain useful knowledge from these data. This paper provides an application programming interface to recommend drugs to users suffering from a particular disease which would also be diagnosed by the framework through analyzing the user's symptoms by the means of machine learning algorithms. We utilize some insightful information here related to mining procedure to figure out most precise sickness that can be related with symptoms. The patient can without much of a stretch recognize the diseases. The patients can undoubtedly recognize the disease by simply ascribing their issues and the application interface produces what malady the user might be tainted with. The framework will demonstrate complaisant in critical situations where the patient can't achieve a doctor's facility or when there are situations, when professional are accessible in the territory. Predictive analysis would be performed on the disease that would result in recommending drugs to the user by taking into account various features in the database. The experimental results can also be used in further research work and for Healthcare tools.

scholarly journals Diagnosis of Various Thyroid Ailments using Data Mining Classification Techniques

2019 ◽  
pp. 131-136
Author(s):  
Umar Sidiq ◽  
Syed Mutahar Aaqib ◽  
Rafi Ahmad Khan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Research Work ◽  
Support Vector ◽  
Data Sets ◽  
Data Mining Technique ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Classification Techniques ◽  
Using Data

Classification is one of the most considerable supervised learning data mining technique used to classify predefined data sets the classification is mainly used in healthcare sectors for making decisions, diagnosis system and giving better treatment to the patients. In this work, the data set used is taken from one of recognized lab of Kashmir. The entire research work is to be carried out with ANACONDA3-5.2.0 an open source platform under Windows 10 environment. An experimental study is to be carried out using classification techniques such as k nearest neighbors, Support vector machine, Decision tree and Naïve bayes. The Decision Tree obtained highest accuracy of 98.89% over other classification techniques.

scholarly journals A Review on Soil Property Detection using Machine Learning Approach

2018 ◽  
Vol 4 (8) ◽  
pp. 6
Author(s):  
Smriti Singhatiya Dr. Shivnath Ghosh
Keyword(s):  
Data Mining ◽  
Performance Prediction ◽  
Agricultural Sector ◽  
Crop Productivity ◽  
Indian Economy ◽  
Soil Database ◽  
Significant Relationships ◽  
Machine Learning Approach ◽  
Key Factor ◽  
Using Data

The agricultural sector is the backbone of the Indian economy. Although focused on industrialization, agriculture remains an important sector of the Indian economy, both in terms of contribution to gross domestic product (GDP) and jobs for millions of people across the country. One of the key factor for productive agriculture is soil. The purpose of the work is to predict the type of terrain using data mining classification methods. Agricultural properties and soil ownership play a crucial role in agricultural decision-making. This research sought to evaluate various mining association techniques and apply them to a soil database to determine if significant relationships could be created. Performance prediction is one of the applications that uses the concept of data mining to increase crop productivity. This makes the problem of crop productive performance is an interesting challenge. An earlier performance prediction was made taking into account the cultivator's experience with a particular crop and culture. This work introduces a system that uses data mining techniques to predict the category of analyzed soil datasets.

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