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: Conference Proceedings

Year of Publication Title

2020

P. Sanjana and Prathilothamai M., “Drone Design for First Aid Kit Delivery in Emergency Situation”. IEEE, Coimbatore, India, 2020.[Abstract]


Ambulance getting stuck in traffic has resulted in countless precious lives. Statistics have shown that especially in the Indian traffic conditions the chances of such scenarios are at peak. In this paper we are providing a solution to speed up the delivery procedure of first aid kit in situations including but not limited to (i) Ambulance getting stuck in traffic, (ii) War torn regions with limited medical supply etc. From the minor ailment to the more serious injury a first aid kit can help reduce the risk of infection or the severity of the injury. With drones changing the face of human technology, it can be used in the medical field to assert timely delivery of essential first aid to people in not easily accessible regions. When the user books an ambulance for the victim, if the ambulance is stuck in traffic, Automated drones can deliver personalized first aid kit to the user location so that the victim can be diagnosed by the remedy medicines with assistance of doctor using web app till the ambulance arrives to the victim location and takes the victim to hospital. Users can also request pharmacy for immediate remedy medicines in case of emergency

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2017

Prathilothamai M. and Nair, P. S., “De-duplication of passports using Aadhaar”, 2017 International Conference on Computer Communication and Informatics, ICCCI 2017. Institute of Electrical and Electronics Engineers Inc., 2017.[Abstract]


Big data is an emerging technology that is becoming an essential part of national governance. Aadhaar is the unique identification scheme of India, handled by the Unique Identification Authority of India (UIDAI), which deals with big data. Every person above the age of 5 years has to register their demographic details (Name, Date of Birth, Address and Phone number) and biometric details (10 fingerprints and both iris) and then these details are used to verify the authenticity of the person when any services are required by him. Passport is a legal document that is carried by a person when he travels between countries, but in the case of the older passports with no biometric data, a person may have more than one legal passport with different demographic details. This paper does a case study on the existing de-duplication methods for passport enrolments and other such documents. In the case of newer passports, it takes 10 days to link with Aadhaar at the time of registration, hence the aim is to reduce the processing time of the linking and verification. String matching algorithms are used to compare the demographics, and techniques such as genetic programming and hashing are used for de-duplication. This case study also helps identify big and fast data platforms to identify such e-governance plans, by evaluating the accuracy and efficiency of existing algorithms. This system aims to predict the duplication of passports by linking Aadhaar and passport details, and to reduce the processing time of the Aadhaar database by using parallel algorithms. © 2017 IEEE.

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Publication Type: Journal Article

Year of Publication Title

2020

D. K, Ramasamy, M., Raju S, and Prathilothamai M., “A Dependency-Directed Opinion Analytics For Product Review Classification Based On Keyphrase”, International Journal of Scientific & Technology Research, vol. 9, 2020.[Abstract]


Text classification on product reviews has long been a challenging task due to the rapid growth of Web usage that has resulted in a huge volume of unstructured data. Recently, Opinion mining has been emerged as an important discipline to process the unstructured data. Although several opinion mining approaches addressed the problem of dealing with unstructured data, further research opportunities are available due to the issues like class imbalance, and complexity in text data analytics that affects the performance of opinion learning. Further, the manual text classification consumes a lot of time while identifying useful information. Also, the existing approaches for classifying texts based on majority category are not enough for realistic scenarios specifically in large scale applications. This paper proposes a prediction approach which focuses on obtaining useful information by using keyphrase and category labels. In this paper, we first investigate existing machine learning techniques to classify customer opinions with respect to multiple categories. Moreover, we propose keyphrase based multiclass text classification that finds insights from opinions of various customers on financial products and services. The result of our experiment shows that our dependency-directed opinion learning can show significant improvement over precision, recall, and F1-measure.

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2020

J. A. Basha, B, S. Balaji, Poornima, S., Prathilothamai M., and Venkatachalam, K., “Support vector machine and simple recurrent network based automatic sleep stage classification of fuzzy kernel”, Journal of Ambient Intelligence and Humanized Computing, 2020.[Abstract]


Recently, sleep disorder is taken as a serious issue in people living. Normally people cerebrum passes through variety of static physiological steps or changes for the duration of sleep. Biomedical signal such as EEG, ECG, EOG and EMG setup and signals used to recognize sleep disorders. This work proposes better technique that can be designed to discriminate the stages of sleep which can help physicians to do an analysis and examination of related sleep disorders. In order to identify a modification inside brain, EEG signal partitioned with 5 frequency bands: delta, theta, alpha, beta and gamma. After signal acquisition, Band pass filter is applied to discriminate the input EEG signal of Fpz–Cz electrodes into frequency bands. Statistical specific features are extracted from distinctiveness impression of EEG signal. Then classification is required for classifying the sleep stages automatically with fuzzy kernel support vector machine and simple recurrent network (SRN). In SRN, statistical features were extracted and allocate 30 s period to 5 possible levels in sleep; wakefulness, Non Rapid Eye Movement Sleep Stage 1 (NREMSS 1), NREMSS 2, NREMSS 3 and NREMSS 4, Rapid Eye Movement Sleep Stage (REMSS). These signal acquired from sleep-EDF repository from PhysioBank (PB) used to validate our proposed scheme. Simple recurrent network classification performance rate is found as 90.2% than that of other new classifiers such as feed forward neural network (FNN) and probabilistic neural network (PNN) next it was compared and results are experimented in proposed work.

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2016

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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2016

B. Pratheeba and Prathilothamai M., “A study of distributed systems in realtime applications”, International Journal of Control Theory and Applications, vol. 9, no. 10, pp. 4233-4240, 2016.[Abstract]


In Real-Time Applications, handling data is a challenging task due to the huge volume and high velocity of data. In order to handle Big Data in real time, Distributed Frameworks like Hadoop and Apache Spark are introduced. We investigate the tradeoffbetween speed Vs. storage of Hadoop and Apache Spark. We have proposed a system which will help public in emergency situation during travelling on the road. As a result of this survey, we have finalized Apache Spark is suitable for our system. © International Science Press.

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2016

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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2015

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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Publication Type: Book Chapter

Year of Publication Title

2016

Prathilothamai M., Marilakshmi, S., Majeed, N., and Viswanathan, V., “Timely prediction of road traffic congestion using ontology”, in Advances in Intelligent Systems and Computing, vol. 398, Springer Verlag, 2016, pp. 331-344.[Abstract]


In developing countries, traffic in a road network is a major issue. In this paper we investigate the tradeoff between speed versus accuracy of predicting the severity of road traffic congestion. The timely prediction of traffic congestion using semantic web technologies that will be helpful in various applications like better road guidance, vehicle navigation system. In the proposed work, ontology is created based on sensor and video data. By using rule inference of ontology on parallel processing of sensor and video data, our system gives the timely prediction of traffic congestion. © Springer India 2016.

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Faculty Research Interest: