Jaikumar M. Patil
@ssgmce.ac.in
Associate Professor, HOD, CSE
SSGMCE Shegaon
EDUCATION
Ph . D Computer Science and Engineering
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Artificial Intelligence, Computer Science Applications, Computer Science
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Jaikumar M. Patil and S. R. Gupta
IEEE
Over the last decade, there is a tremendous increase in online learning. The use of electronic devices has paved a path for creating a learning gateway beginning from lower classes to higher class alltogther i.e. from preschool to postgraduate level. These mechnisms are being used enormously to improve the quality of education. The online mode of education has also provided researchers with mechanism to collect data and analyze learning patterns which would to path breaking results in ananlyzing the learning habits of a child and help us provide him with personlized inputs for the best results. The educators and management would also be benifitted from the result of analytics. Everyone around the globe is willing to adopt the EDM and LA to frame strategic policies and understand student behaviours. The Educational Data Mining and Learning Analytics helps us look for the new patterns in data and helps us predict the instructional systems. The implementation of EDM and LA is largely helped by advancement in the last decade in e-learning implementations in education and training as well as of research and development work in areas such as educational data mining, web analytics and statistics. The analytical requirement would be implemented various data mining and visualization tools. Learning analytics is an emergent field of research that is growing fast.
Rohini B. Jadhao and J. M. Patil
IEEE
We proposed and developed a taxi-sharing system that accepts taxi passengers' real-time ride requests sent from smart phones and schedules proper taxis to pick up them via ride sharing, subject to time, capacity, and monetary constraints. The monetary constraints provide incentives for both passengers and taxi drivers: passengers will not pay more compared with no ridesharing and get repayment if their travel time is long or extended due to ride sharing; taxi drivers will make money for all the long way around distance due to ride sharing. While such a system is of important social and environmental benefit, e.g., saving energy consumption and satisfying people's commute, real-time taxi-sharing has not been well studied yet. To this end, we plan a mobile-cloud architecture based taxi-sharing system. Taxi riders and taxi drivers use the taxi-sharing service provided by the system via a smart phone App. The Cloud first finds candidate taxis quickly for a taxi ride request using a taxi searching algorithm supported by as patio-temporal index. A scheduling process is then performed in the cloud to select a taxi that satisfies the request with minimum increase in travel distance. We built an experimental platform using the GPS trajectories generated by over 33,000 taxis over a period of three months. A ride request generator is developed (available at http://cs.uic.edu/_sma/ridesharing) in terms of the stochastic Process modeling real ride requests learned from the data set. Tested on this platform with extensive experiments, our proposed system established its efficiency, effectiveness and scalability. For example, when the ratio of the number of ride requests to the number of taxis is 6, our proposed system serves three times as many taxi riders as that when no ridesharing is performed while saving 11 percent in total travel distance and 7 percent taxi fare per rider.
Neha Raghuvanshi and J.M. Patil
IEEE
This paper presents a brief review on sentiment analysis. To mine the opinion on the web, it is essential to perform a well defined task, which helps us to retrieve the information from the available data on the web. We have started our discussion with the introduction on sentiment analysis, which gives us a insight into sentiment analysis. The detail discussion on various methods proposed by different researchers is also presented. Different types of sentiment analysis techniques give a research direction in different directions. Finally a method is proposed based on the naìˆve Bayes classifier.
Priyanka A. Nandagawali and Jaikumar M. Patil
IEEE
The spread of technology through internet is increasing day by day. With the increasing technology, use of the recommendation systems is coming into force. The recommendation systems solve many problems of customers by providing them recommendation based on their choice of products. Many of the collaborative filtering algorithms have been used for this purpose. This paper provides a solution for the various recommender systems by using collaborative filtering algorithms with the community based user domain model. The main purpose is to satisfy the customer's product needs by providing them recommendation based on products.