Browsing by Author "Seyon, S"

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Smart Monitor for Tracking Child's Brain Development
(researchgate.net, 2019-03) Anparasanesan, T; Mathangi, K; Seyon, S; Kobikanth, S; Gamage, A
This paper provides a way to track the brain development of children and improving it via gamification. Machine Learning and Gamification are the key technologies used here. As the population rises, the demand for cost-effective methods to reduce the rate of cognitive decline becomes higher. A mobile application is developed to track and develop the brain development of children. In the mobile application, the child initially undergoes an evaluation phase to determine the current level of the cognitive skills of the child. Milestones particular to that age category are also tracked in this evaluation phase. The results of this evaluation phase are analyzed by the machine learning model and suitable brain games are suggested. K-means algorithm is used to develop the model which is an unsupervised learning algorithm. The dataset is prepared by storing the results of each game category in the evaluation phase. Data preprocessing is done to clean up the dataset. During this period, data undergoes a series of steps. The dataset is divided into 80% and 20%. 80% of the dataset is used as the training dataset and the remaining 20% as the test dataset. The accuracy of the model is checked several times against the test data. Model accuracy is improved through model training and finally, the model got an accuracy of 88.49%. For the child, proper training is given to improve his cognitive skills and thus the brain development using Gamification. Games are developed using the UNITY game engine. The system generates a report and notifies parents about their child's statistics periodically. This paper elaborates the procedure of model development, model training, model testing and development of suitable brain games in details. The results of the research work and future works are also discussed in the following sections.
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Tamil Grammarly – A Typing Assistant for Tamil Language using Natural Language Processing
(IEEE, 2023-06-12) Mahadevan, P; Srihari, P; Seyon, S; Vasavan, P; Panchendrarajan, R
Tamil is one of the ancient and most convoluted languages in the world. Although it is being the official language of many Asian countries, even native speakers tend to find difficulties in writing Tamil due to its morphologically rich nature. While there are various studies focusing on automatically identifying and correcting a specific typing error, very limited effort has been made to develop a comprehensive solution to assist the native and non-native writers of Tamil. In this paper, we propose a typing assistant tool Tamil Grammarly using Natural Language Processing (NLP) techniques. Specifically, the tool aims to aid the user to fix grammatical errors and spelling errors and recommend the next words and synonyms of the current word in real-time while typing. The NLP-based typing assistant functions of Tamil Grammarly were developed using a transformer-based model, LSTM model, and Word2Vec model. Extensive evaluation performed shows that our tool can assist the users in real-time with an accuracy of 73% - 93% within 0.4 to 5.3 seconds.