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

Prathilothamai M. currently serves as Assistant Professor at Department of Computer Science and Engineering, School of Engineering, Coimbatore Campus. Her areas of research include Big Data and Semantic Web,

Publications

Publication Type: Journal Article

Year of Publication Publication Type Title

2016

Journal Article

Prathilothamai M., Lakshmi, A. M. Sree, and Viswanthan, D., “Cost effective road traffic prediction model using Apache spark”, Indian Journal of Science and Technology, vol. 9, 2016.[Abstract]


Objectives: We proposed a cost effective model to predict the traffic to inform the public about the current traffic condition to all persons who are entering the same lane. Analysis: In real time application like traffic monitoring, it needs to process huge volume of data in huge size. We analyzed the traffic prediction using the current technologies Apache Hadoop and Apache Spark framework. Spark is processing the 10 Terabytes of data in half-a-second. The main uniqueness from our approach is that we can predict the road traffic using Spark within half-a-second. Findings: Road traffic is predicted using Ultrasonic and PIR sensor within a half second. The proposed system uses the vehicle count and speed to predict the traffic condition. Existing system using hadoop will predict the traffic in few seconds. Whereas in the proposed system performance gets increased using Spark. Therefore, the results are more helpful in finding the road traffic condition. Improvement: The proposed system predicts it in a half a second by using Spark whereas the existing system predicted the road traffic by consuming more time.

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2016

Journal Article

Prathilothamai M., Prashant R. Nair, R. Singh, A. P., and Aditya, P. N. S., “Offline Navigation: GPS based Location Assisting System”, Indian Journal of Science and Technology, vol. 9, no. 45, pp. 1-6, 2016.[Abstract]


Objectives: We have developed an Offline Navigation Android1 application especially for the visually impaired people, the application also features a module through which the user, if lost track or has been abducted can reach back to the original location without the use of the internet.
Methods/Statistical Analysis: We have used Google’s direction API2 for the route data, using this data the application guides the user with the help of Global Positioning System and Magnetic sensor. The main uniqueness of this application is, it works without the internet and consumes less battery charge.
Findings: The developed application uses the data from the direction API (i.e., JSON format text) for navigating without displaying the map instead by reading out instructions, showing just direction and giving vibration feedback if deviating from the path. Thus the application differs from a typical navigation application. Application/Improvements: The developed application has a unique functionality for retracing a user’s path, and also consumes less space and computation compared to other navigation applications.

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2015

Journal Article

Prathilothamai M., M. Devii, M., and Viswanathan, V., “Identification of threat to public life by classifying NEWS RSS feeds”, International Journal of Applied Engineering Research, vol. 10, pp. 33859-33862, 2015.[Abstract]


Identifying incidents of threat to public life from news articles can be used in various applications like traffic management, disaster management etc. In this paper we are identifying threat to public life by processing RSS feed of news articles and we investigate the tradeoff between speed Vs accuracy of predicting the severity of the incidents which is treat to public life. It can also be used to collect information on geographical locations where threat has occurred. We have proposed a NLP-based approach that would help identify features of the data that are salient for classifying it as road incident like accident or bomb blast etc and its severity. © Research India Publications.

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Faculty Research Interest: 
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