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.

Using Data Mining Approaches for Force Prediction of a Dynamically Loaded Flexible Structure

2014 ◽  
Author(s):  
Meik Schlechtingen ◽  
Sofiane Achiche ◽  
Tiago Lourenco Costa ◽  
Maxime Raison ◽  
Ilmar Santos
Keyword(s):  
Data Mining ◽  
Research Work ◽  
Model Performance ◽  
Random Excitation ◽  
Force Transducer ◽  
Flexible Structure ◽  
Adaptive Models ◽  
Dynamically Loaded ◽  
Using Data ◽  
Selection Of

This paper presents the results obtained from a research work investigating the performance of different Adaptive models developed to predict excitation forces on a dynamically loaded flexible structure. For this purpose, a flexible structure is equipped with acceleration transducers at each degree of freedom and a force transducer for validation and training. The models are trained using data obtained from applying a random excitation force on the flexible structure. The performance of the developed models is evaluated by analyzing the prediction capabilities based on a normalized prediction error. The frequency domain is considered to analyze the similarity of the frequencies in the predicted and the original force signal. For a selection of the best models, a more advanced performance analysis is carried out. This includes application of the trained models to deterministic and non-deterministic excitation forces with different excitation frequencies and amplitudes. Additionally, the influence of the sampling frequency and sensor location on the model performance is investigated. The results obtained in this paper show that most data mining approaches can be used, when a certain degree of inaccuracy is accepted. Furthermore, the comparison study points out that the transducer location is crucial for the model performance. However, there exists no general solution for the final selection of models.

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