Qualification: 
M.E
Email: 
p_siniraj@cb.amrita.edu

Sini Raj P. currently serves as Assistant Professor at Department of Computer Science and Engineering, School of Engineering, Coimbatore Campus. Her areas of research include Data Mining and Data Structures.

Publications

Publication Type: Journal Article

Year of Publication Publication Type Title

2017

Journal Article

B. Sri Datta, Ganapathy, R., Sini Raj P., Dr. Shriram K Vasudevan, and SN, A., “An Inventive and Innovative Alternate for Legacy Chain Pulling System through Internet of Things”, Indonesian Journal of Electrical Engineering and Computer Science, vol. 6, no. 3, p. pp. 688 ~ 694, 2017.[Abstract]


Indian Railways made a move in replacing the chain pulling system with the new mobile based communication system where the loco pilot’s mobile phone number shall be shared with all the passengers through a message. Despite the efforts taken to remove the chains from the train, there is a great probability of misuse through the mobile phone number provided. We aim to build a more efficient and secure solution based on the Internet of Things, which is a buzz word in the market. Our proposed system provides an alarm fixed at specific locations in the compartments of a train. In case of emergency, the passenger has to press the alarms’ button, which captures the scene using a built-in digital camera. The system also alarms the Loco-Pilot, Travelling Ticket Examiner, as well as the compartment so as to provide immediate possible help through the fellow co-passengers. The pilot slows down the train to an optimum speed whilst the travelling ticket examiner checks and confirms the pilot whether he has to stop the train. The entire system can be controlled through an authenticated mobile application provided to the ticket examiner. The pictures captured by the camera and other crucial details are uploaded to a cloud-based real-time database. Thus, saving time and taxpayers’ money as well as helping the railways to perform analytics and come up with feasible solutions to the problems of the passengers. The system has the potential to deal with the most prevailing cargo theft like that of coal, by alerting the staff without having to stop the train, making the convicts’ escapes impossible. This also definitely avoids the improper pulling of chains and thereby not causing any hassle to the passengers as well as diminishes the economic loss to the government by reducing the time delay.

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2015

Journal Article

C. Suresh, B. Yedugiri, K., Sini Raj P., and Sreedhar, S., “Inspecting the proficiency of novel algorithms on sparse data domains for efficient recommendation-a glance”, Research Journal of Applied Sciences, Engineering and Technology, vol. 10, pp. 812-815, 2015.[Abstract]


Recommender systems have played a major role in almost all the domains today where human interaction happens with system. Depending on the user's choice, a recommender system presents some promising suggestions by observing all the activities of the user on the web and thus, helps to find out similar users and interested products. All the ratings provided by the user is stored in a rating matrix. Sometimes it so happens that the users may view the item, but not always rate it; which makes the dataset sparse. Performing operations on such sparse datasets by recommender engines may not give precise suggestions to the user. This study aims to make such sparse datasets denser by applying the two novel methods, FST and UTAOS; and thereby implementing any of the collaborative filtering techniques upon it to showcase the efficiency in recommendation. Results reveal that FST outperforms the UTAOS approach in terms of sparse datasets which paves the way for an efficient recommendation. © Maxwell Scientific Organization, 2015.

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Faculty Research Interest: 
207
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OFFERED
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AMRITA
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15
CONSTITUENT
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GRADE BY
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9th
RANK(INDIA):
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150+
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