Abinaya Mahendiran

Assistant Manager at Mphasis NextLabs
Bangalore, Karnataka, India
Tools & Libraries:
Sublime Text 3, Ubuntu, MySQL, R
International Institue of Information Technolog, Bangalore
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Language Activity
Technical Skills
Backend Development
Tools & Libraries
Sublime Text 3, Ubuntu, MySQL, R, Matlab
Work Experience
Assistant Manager
Mphasis NextLabs
Programmer Analyst Trainee
Cognizant technology solution, Coimbatore, Tamil Nadu, India
Jul 2010 - Apr 2011 (10 months)
Text Summarization Using Deep Learning Techniques
Dec 2013 - Jun 2014 (7 months)
The main aim of this thesis work is to build an automated generic summariza-tion system that will summarize legal case reports taken from the Federal Court of Australia (FCA) using deep learning techniques. Deep Belief Networks (DBN) is the architecture that is used to learn the high level abstract representation of the given case reports and these features could potentially be a part of the sum-mary. Once the features are learnt using the DBN, summary for each case report is generated (i.e. Single-document summarization) by scoring the sentences based on the combined importance of the features/words present in the sentences and the parts of speech to which they belong to. A neural network is implemented to learn the importance of the different parts of speech. Natural language processing techniques are used to perform parts of speech (POS) tagging.
SMS Spam Detection
Jul 2013 - Dec 2013 (6 months)
Implemented the machine learning algorithm called Support Vector Machine in order to classify spam SMS from ham SMS.
Skills: Python | Weka
Cross Referencing Genetic Markers for Cancer
Jan 2012 - May 2012 (5 months)
This is a GPU based project aimed at implementing a machine learning algorithm that can examine datasets and identify multiple genetic markers that are associated with the same cancer, and also identify cases where a single genetic marker is associated with multiple cancers.
Skills: C | HTML | Matlab | JSP
Cross-Referencing Cancer Data Using GPU for Multinomial Classification of Genetic Markers
CSI Journal of Computing
1 May, 2015
We propose a classification scheme for genetic markers, to associate them with a single type or multiple types of cancer. Given multiple unknown datasets with genetic markers that are associated with particular types of cancer, we classify these genetic markers into two different classes. One class comprises the single genetic markers associated with a particular type of cancer, but with the potential to cause other cancers. The other class is comprised of multiple genetic markers each associated with a particular type of cancer, which in combination also have the potential to
be associated with a different type of cancer. Use of this classification scheme would be helpful in predicting a possibility of new cancers from available data about genetic markers, and would thus have diagnostic or therapeutic value in oncology. A graphics processing unit (GPU) is used to accelerate the process of classification by making the entire classification scheme faster compared to the same process running on a CPU. A feature subset selection algorithm is used to remove irrelevant features by comparison of all the features from the dataset with the threshold file which
contains the appropriate feature that relate them with the possibility of causing cancer. This work enables the classification of all given genetic markers into two categories, of which one category in particular consists of markers whose influence in causing multiple types of cancer is otherwise unknown.

Skills: Cuda | HTML | JSP | Gpu
International Institue of Information Technolog, Bangalore
Master of Technology (MTech), Computer Science
2012 - 2014
Amrita School of Engineering
B.Tech, Information Technology
2006 - 2010
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