scholarly journals A PREDICTION SYSTEM FOR FARMERS TO ENHANCE THE AGRICULTURE YIELD USING COGNITIVE DATA SCIENCE

2018 ◽  
Vol 9 (2) ◽  
pp. 780-784
Author(s):  
N. Muthurasu ◽  
Keyword(s):  
Data Science ◽  
Prediction System ◽  
Cognitive Data

scholarly journals Cognitive data science methods and models for engineering applications

2019 ◽  
Vol 23 (19) ◽  
pp. 9045-9048 ◽  
Author(s):  
Arun Kumar Sangaiah ◽  
Hoang Pham ◽  
Mu-Yen Chen ◽  
Huimin Lu ◽  
Francesco Mercaldo
Keyword(s):  
Data Science ◽  
Science Methods ◽  
Engineering Applications ◽  
Cognitive Data

scholarly journals Salary Estimator using Data Science

2020 ◽  
Vol 6 (12) ◽  
pp. 319-322
Author(s):  
Winner Walecha and Dr. Bhoomi Gupta
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Data Science ◽  
The Internet ◽  
Prediction System ◽  
Lasso Regression ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Number Of Factors ◽  
Using Data

This paper presents a salary prediction system using the job listings from an employment website, in this case Glassdoor.com. A data mining technique is used to generate a model which will scrape number of jobs from the employment website, clean it on the basis of number of factors including the rival companies, revenue and skill required thereby predicting the salary to be expected when applying for a data science job. Techniques like linear regression, lasso regression, random forest regressors are optimised using GridsearchCV to reach the best model. The model can be further extended to build a flask API thus can be deployed on the internet for public usage.

scholarly journals Applying Government Schemes in Rural Sectors Prediction System for Evaluation of Data Science Algorithm

2021 ◽  
Vol 9 (5) ◽  
pp. 70-79
Author(s):  
S. Maheswari ◽  
S. Kalaiselvi ◽  
D.Thamarai Selvi ◽  
M. Manochitra
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Environmental Factors ◽  
Data Science ◽  
Naive Bayes ◽  
Support Vector ◽  
Prediction System ◽  
Government Assistance ◽  
The Government ◽  
Evaluation Of Data

The administration dispatches different aggressive projects attempting to make the nation more prosperous, yet what they bomb is in fruitful execution and coming to recipients. The fundamental explanation for this issue is the absence of mindfulness among rustic individuals. This paper is to give an answer for this uninformed circumstance. Through this framework the rustic understudies will be instructed such that they can become acquainted with about what are the different plans that are outfitted by the administration and what are the plans they are qualified for. On the off chance that the country understudies came to know and get mindful of the apparent multitude of legislative plans gave by the Government of India for the government assistance of the provincial understudies, at that point their life would venture into next level. At first this framework will investigate the accessible government plans in the instructive for the government assistance of country understudies. Next, the understudy's information ((i.e.) name, age, station, occupation, annualincome.etc) are accumulated. At that point; both the datasets are brought into the Anaconda Navigator. At that point, investigation and grouping dependent on networks (SC, ST, BC and MBC) of the understudies and the plans are performed. At that point utilizing the forecast calculations (Naïve Bayes, Random Forest and Support Vector Machine (SVM)) what are generally the plans the specific understudy is qualified for are anticipated. An investigation is made on the proficiency of the three calculations. The exactness of the three calculations is broke down and the effective calculation which creates the outcome with most elevated precision is at last used to play out the forecast of the plans that a specific understudy is qualified for. At long last, the anticipated plans anticipated utilizing the most elevated effective calculation among the three calculations will be gotten back to the understudies. Hence, through this undertaking the rustic understudies will come to think about different recipient plans gave by government and they can use those plans for the improvement of the country environmental factors

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification

Application of the Boundary Element Method and Modal Analysis to Tire Acoustics Problems

1993 ◽  
Vol 21 (2) ◽  
pp. 66-90 ◽  
Author(s):  
Y. Nakajima ◽  
Y. Inoue ◽  
H. Ogawa
Keyword(s):  
Finite Element ◽  
Boundary Element Method ◽  
Boundary Element ◽  
Acoustic Radiation ◽  
Traffic Noise ◽  
Noise Prediction ◽  
Prediction System ◽  
Tire Noise ◽  
Acoustic Contribution ◽  
Element Method

Abstract Road traffic noise needs to be reduced, because traffic volume is increasing every year. The noise generated from a tire is becoming one of the dominant sources in the total traffic noise because the engine noise is constantly being reduced by the vehicle manufacturers. Although the acoustic intensity measurement technology has been enhanced by the recent developments in digital measurement techniques, repetitive measurements are necessary to find effective ways for noise control. Hence, a simulation method to predict generated noise is required to replace the time-consuming experiments. The boundary element method (BEM) is applied to predict the acoustic radiation caused by the vibration of a tire sidewall and a tire noise prediction system is developed. The BEM requires the geometry and the modal characteristics of a tire which are provided by an experiment or the finite element method (FEM). Since the finite element procedure is applied to the prediction of modal characteristics in a tire noise prediction system, the acoustic pressure can be predicted without any measurements. Furthermore, the acoustic contribution analysis obtained from the post-processing of the predicted results is very helpful to know where and how the design change affects the acoustic radiation. The predictability of this system is verified by measurements and the acoustic contribution analysis is applied to tire noise control.

Sign in / Sign up

Export Citation Format

Share Document