Prediction of Millage Per Gallon with ML and using User Interface
Authors: K.Venkateswara Rao, Bharathi S, Nasreen Sulthana Sk, Bhargavi S, Sri Venkata AvinashT
Abstract
Now a days there is a huge competition in the automobile industries and every leading company want to provide their customers with the efficient products which meets their requirements. The basic requirement for any automobile starts with MPG, Price, Model etc. Further in the manufacturing of the vehicle or the product one need to check the MPG and it is mostly done manually. However, in this paper a model is implemented with machine learning approach which is used to calculate the fuel efficiency automatically by considering the basic parameters. As all the parameters are not going to affect the final outcome some selected parameters are used like displacement, number of cylinders, horsepower, weight, acceleration etc. The MPG and also termed as fuel efficiency will be calculated using XGBOOST with GridSearchCV and linear regression algorithms and improved accuracy up to 90%..
An Intelligent Animal Care and Management System Based on the Internet of Things and AI
Authors: S.SOUNDAR SAI, DANIEL NAVEENKUMAR S, GOPI K
Abstract
Now a days there is a huge competition in the automobile industries and every leading company want to provide their customers with the efficient products which meets their requirements. The basic requirement for any automobile starts with MPG, Price, Model etc. Further in the manufacturing of the vehicle or the product one need to check the MPG and it is mostly done manually. However, in this paper a model is implemented with machine learning approach which is used to calculate the fuel efficiency automatically by considering the basic parameters. As all the parameters are not going to affect the final outcome some selected parameters are used like displacement, number of cylinders, horsepower, weight, acceleration etc. The MPG and also termed as fuel efficiency will be calculated using XGBOOST with GridSearchCV and linear regression algorithms and improved accuracy up to 90%..
A NOTE ON sg* CONTINUOUS MAPPINGS IN SOFT TOPOLOGICAL SPACES
Authors: V. Ramya, R. Asokan
Abstract
Now a days there is a huge competition in the automobile industries and every leading company want to provide their customers with the efficient products which meets their requirements. The basic requirement for any automobile starts with MPG, Price, Model etc. Further in the manufacturing of the vehicle or the product one need to check the MPG and it is mostly done manually. However, in this paper a model is implemented with machine learning approach which is used to calculate the fuel efficiency automatically by considering the basic parameters. As all the parameters are not going to affect the final outcome some selected parameters are used like displacement, number of cylinders, horsepower, weight, acceleration etc. The MPG and also termed as fuel efficiency will be calculated using XGBOOST with GridSearchCV and linear regression algorithms and improved accuracy up to 90%..
Effect of different water flow gap distance in thermal performance of solar photovoltaic panel
Authors: Anand Kumar, Mr.Tejkaran Naroliya, Dr. Vinay Kumar Yadav
Abstract
Now a days there is a huge competition in the automobile industries and every leading company want to provide their customers with the efficient products which meets their requirements. The basic requirement for any automobile starts with MPG, Price, Model etc. Further in the manufacturing of the vehicle or the product one need to check the MPG and it is mostly done manually. However, in this paper a model is implemented with machine learning approach which is used to calculate the fuel efficiency automatically by considering the basic parameters. As all the parameters are not going to affect the final outcome some selected parameters are used like displacement, number of cylinders, horsepower, weight, acceleration etc. The MPG and also termed as fuel efficiency will be calculated using XGBOOST with GridSearchCV and linear regression algorithms and improved accuracy up to 90%..