Tvgeetha

Personal Details
Name:
Designation: BSR Faculty Fellow (UGC) Senior Professor (Retired),
Department of Computer Science and Engineering,
CEG, Anna University
Former Dean, (June 2017-May 2020) CEG, Anna University
Address: GA, Abirami Struthilaya,
42, 28th Cross Street,
Indira Nagar,
Chennai-600020
Mobile no: 9445040791
drtvgeetha@, tv_g@

EDUCATION

B.E. (Electronics and Communication Engineering)
M.E ( Computer Science and Engineering)
Ph.D (Computer Science and Engineering

RESEARCH INTERESTS

Artificial Intelligence, Machine Learning, Natural Language Processing, Text Analytics, Web Search, Biomedical Data Mining

FUTURE PROJECTS

Scopus Publications

Genetically and semantically aware homogeneous network for prediction and scoring of comorbidities
Karpaga Priyaa Kartheeswaran, Arockia Xavier Annie Rayan, Geetha Thekkumpurath Varrieth
Computers in Biology and Medicine, 2024
Retraction Note: Knowledge-enhanced temporal word embedding for diachronic semantic change estimation (Soft Computing, (2020), 24, 17, (12901-12918), 10.1007/s00500-020-04714-0)
J. Vijayarani, T. V. Geetha
Soft Computing, 2024
DRADTiP: Drug repurposing for aging disease through drug-target interaction prediction
Saranya Muniyappan, Arockia Xavier Annie Rayan, Geetha Thekkumpurath Varrieth
Computers in Biology and Medicine, 2024
Learning Joint Topic Representation for Detecting Drift in Social Media Text
J. Vijayarani, T.V. Geetha
International Journal of Uncertainty Fuzziness and Knowledge Based Systems, 2024
Social media texts like tweets and blogs are collaboratively created by human interaction. Rapidly changing trends are leading to topic drift in the social media text. This drift is usually associated with words and hashtags. However, geotags play an essential part in determining topic distribution with location context. The rate of change in the distribution of words, hashtags and geotags cannot be considered uniform and must be handled accordingly. This paper builds a topic model that associates the topic with a mixture of distributions of words, hashtags and geotags. Stochastic gradient Langevin dynamic model with varying mini-batch sizes is used to capture the changes due to the asynchronous distribution of words and tags. Topic representations with co-occurrence and location contexts are specified as hashtag context vector and geotag context vector respectively. These two vectors are jointly learned to yield topical word embedding vectors over time conditioned on hashtags and geotags that can predict location-based topical variations effectively. When evaluated with Chennai and UK geolocated Twitter data, the proposed joint topical word embedding model enhanced by the social tags context, outperforms other methods.
EGeRepDR: An enhanced genetic-based representation learning for drug repurposing using multiple biomedical sources
Saranya Muniyappan, Arockia Xavier Annie Rayan, Geetha Thekkumpurath Varrieth
Journal of Biomedical Informatics, 2023
Poetic and Semantic Features for Lyricist Identification from Tamil Film Lyrics
Karthika Ranganathan, T. V. Geetha
SN Computer Science, 2023
DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network
Saranya Muniyappan, Arockia Xavier Annie Rayan, Geetha Thekkumpurath Varrieth
Mathematical Biosciences and Engineering, 2023
<abstract> <p>Motivation: In vitro experiment-based drug-target interaction (DTI) exploration demands more human, financial and data resources. In silico approaches have been recommended for predicting DTIs to reduce time and cost. During the drug development process, one can analyze the therapeutic effect of the drug for a particular disease by identifying how the drug binds to the target for treating that disease. Hence, DTI plays a major role in drug discovery. Many computational methods have been developed for DTI prediction. However, the existing methods have limitations in terms of capturing the interactions via multiple semantics between drug and target nodes in a heterogeneous biological network (HBN). Methods: In this paper, we propose a DTiGNN framework for identifying unknown drug-target pairs. The DTiGNN first calculates the similarity between the drug and target from multiple perspectives. Then, the features of drugs and targets from each perspective are learned separately by using a novel method termed an information entropy-based random walk. Next, all of the learned features from different perspectives are integrated into a single drug and target similarity network by using a multi-view convolutional neural network. Using the integrated similarity networks, drug interactions, drug-disease associations, protein interactions and protein-disease association, the HBN is constructed. Next, a novel embedding algorithm called a meta-graph guided graph neural network is used to learn the embedding of drugs and targets. Then, a convolutional neural network is employed to infer new DTIs after balancing the sample using oversampling techniques. Results: The DTiGNN is applied to various datasets, and the result shows better performance in terms of the area under receiver operating characteristic curve (AUC) and area under precision-recall curve (AUPR), with scores of 0.98 and 0.99, respectively. There are 23,739 newly predicted DTI pairs in total.</p> </abstract>
Enhanced disease-disease association with information enriched disease representation
Karpaga Priyaa Kartheeswaran, Arockia Xavier Annie Rayan, Geetha Thekkumpurath Varrieth
Mathematical Biosciences and Engineering, 2023
<abstract> <p>Objective: Quantification of disease-disease association (DDA) enables the understanding of disease relationships for discovering disease progression and finding comorbidity. For effective DDA strength calculation, there is a need to address the main challenge of integration of various biomedical aspects of DDA is to obtain an information rich disease representation. Materials and Methods: An enhanced and integrated DDA framework is developed that integrates enriched literature-based with concept-based DDA representation. The literature component of the proposed framework uses PubMed abstracts and consists of improved neural network model that classifies DDAs for an enhanced literaturebased DDA representation. Similarly, an ontology-based joint multi-source association embedding model is proposed in the ontology component using Disease Ontology (DO), UMLS, claims insurance, clinical notes etc. Results and Discussion: The obtained information rich disease representation is evaluated on different aspects of DDA datasets such as Gene, Variant, Gene Ontology (GO) and a human rated benchmark dataset. The DDA scores calculated using the proposed method achieved a high correlation mainly in gene-based dataset. The quantified scores also shown better correlation of 0.821, when evaluated on human rated 213 disease pairs. In addition, the generated disease representation is proved to have substantial effect on correlation of DDA scores for different categories of disease pairs. Conclusion: The enhanced context and semantic DDA framework provides an enriched disease representation, resulting in high correlated results with different DDA datasets. We have also presented the biological interpretation of disease pairs. The developed framework can also be used for deriving the strength of other biomedical associations.</p> </abstract>
Building and Analysis of Tamil Lyric Corpus with Semantic Representation
Amta 2022 15th Conference of the Association for Machine Translation in the Americas Proceedings Workshop on Empirical Translation Process Research, 2022
Machine Learning: Concepts, Techniques and Applications
T V Geetha, S Sendhilkumar
Machine Learning Concepts Techniques and Applications, 2022
Joint learning of author and citation contexts for computing drift in scholarly documents
J. Vijayarani, T. V. Geetha
International Journal of Machine Learning and Cybernetics, 2021
Pattern-based bootstrapping framework for biomedical relation extraction
S.S. Deepika, T.V. Geetha
Engineering Applications of Artificial Intelligence, 2021
Concept map information content enhancement using joint word embedding and latent document structure
Kodaikkaavirinaadan Urkalan, Geetha T. V.
International Journal on Semantic Web and Information Systems, 2020
Knowledge-enhanced temporal word embedding for diachronic semantic change estimation
J. Vijayarani, T. V. Geetha
Soft Computing, 2020
A neural network framework for predicting dynamic variations in heterogeneous social networks
Mathiarasi Balakrishnan, Geetha T. V.
Plos One, 2020
Phrase Extraction Using Pattern-Based Bootstrapping Approach
R. Hema, T. V. Geetha
Lecture Notes in Networks and Systems, 2020
Influential Researcher Identification in Academic Network Using Rough Set Based Selection of Time- Weighted Academic and Social Network Features
Manju G., Kavitha V., Geetha T.V.
Cognitive Analytics Concepts Methodologies Tools and Applications, 2020
Entity Resolution and Blocking: A Review
K.A. Vidhya, T.V. Geetha
Proceedings of the 2019 IEEE 9th International Conference on Advanced Computing Iacc 2019, 2019
Advances in neuroevolution through augmenting topologies - A case study
Mohamed Yilmaz Ibrahim, Rangarajan Sridhar, T.V. Geetha, S.S. Deepika
Proceedings of the 11th International Conference on Advanced Computing Icoac 2019, 2019
New crossover operators using dominance and co-dominance principles for faster convergence of genetic algorithms
G. Pavai, T. V. Geetha
Soft Computing, 2019
A new dynamic neighbourhood- based semantic dissimilarity measure for ontology
Sathiya Balasubramanian, Geetha T. V.
International Journal of Intelligent Information Technologies, 2019
A review on semantic similarity measures for ontology
B. Sathiya, T.V. Geetha
Journal of Intelligent and Fuzzy Systems, 2019
Bootstrapping of semantic relation extraction for a morphologically rich language: Semi-supervised learning of semantic relations
Balaji Jagan, Ranjani Parthasarathi, T V Geetha
International Journal on Semantic Web and Information Systems, 2019
Cross-Corpus Training with CNN to Classify Imbalanced Biomedical Relation Data
S. S. Deepika, M. Saranya, T. V. Geetha
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019
A meta-learning framework using representation learning to predict drug-drug interaction
S.S. Deepika, T.V. Geetha
Journal of Biomedical Informatics, 2018

RECENT SCHOLAR PUBLICATIONS

Retraction Note: Knowledge-enhanced temporal word embedding for diachronic semantic change estimation
J Vijayarani, TV Geetha
Soft Computing 28 (Suppl 1), 165-165 , 2024
2024
Learning Joint Topic Representation for Detecting Drift in Social Media Text
J Vijayarani, TV Geetha
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems … , 2024
2024
Citations: 1
Network alignment and link prediction using event-based embedding in aligned heterogeneous dynamic social networks: M. Balakrishnan and TV Geetha
M Balakrishnan, G TV
Applied Intelligence 53 (20), 24638-24654 , 2023
2023
Citations: 7
Machine learning: concepts, techniques and applications
TV Geetha, S Sendhilkumar
Chapman and Hall/CRC , 2023
2023
Citations: 55
Enhanced disease-disease association with information enriched disease representation
KP Kartheeswaran, AXA Rayan, GT Varrieth
Mathematical Biosciences and Engineering 20 (5), 8892-8932 , 2023
2023
Citations: 3
DTiGNN: learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network
S Muniyappan, AXA Rayan, GT Varrieth
Mathematical Biosciences and Engineering 20 (5), 9530-9571 , 2023
2023
Citations: 8
Poetic and Semantic Features for Lyricist Identification from Tamil Film Lyrics
K Ranganathan, TV Geetha
SN Computer Science 4 (1), 4 , 2022
2022
Building and Analysis of Tamil Lyric Corpus with Semantic Representation
K Ranganathan, TV Geetha
Proceedings of the 15th biennial conference of the Association for Machine … , 2022
2022
Citations: 1
Features of Semantic Similarity Assessment: Content-and Model-Based Perspectives
J Vijayarani, TV Geetha
Handbook of Research on Opinion Mining and Text Analytics on Literary Works … , 2022
2022
Citations: 1
Relation Extraction between Biomedical Entities from Literature using Semi-Supervised Learning Approach
M Saranya, M Saranya, R Arockia Xavier Annie, TV Geetha
CS & IT Conference Proceedings 11 (23) , 2021
2021
Phrase Extraction Using Pattern-Based Bootstrapping Approach
R Hema, TV Geetha
Intelligent Computing and Innovation on Data Science: Proceedings of ICTIDS … , 2021
2021
Comparative analysis of different deep learning techniques for relation extraction from biomedıcal literature
M Saranya, TV Geetha, RAX Annie
Sentimental Analysis and Deep Learning: Proceedings of ICSADL 2021, 423-438 , 2021
2021
Citations: 1
Joint learning of author and citation contexts for computing drift in scholarly documents
J Vijayarani, TV Geetha
International Journal of Machine Learning and Cybernetics 12 (6), 1667-1686 , 2021
2021
Citations: 1
Pattern-based bootstrapping framework for biomedical relation extraction
SS Deepika, TV Geetha
Engineering Applications of Artificial Intelligence 99, 104130 , 2021
2021
Citations: 27
Things and everything: Internet of nano-things future growth trends
T Geetha, J Balaji, M Dinesh, MS DHIVAKAR
International Journal of Computer Science Engineering Techniques, 6 (2), 1-11 , 2021
2021
Citations: 1
Joint topical word embedding for detecting drift in social media text
J Vijayarani, TV Geetha
2020
Citations: 1
Concept map information content enhancement using joint word embedding and latent document structure
K Urkalan, TV Geetha
International Journal on Semantic Web and Information Systems (IJSWIS) 16 (4 … , 2020
2020
Citations: 2
RETRACTED ARTICLE: Knowledge-enhanced temporal word embedding for diachronic semantic change estimation: J. Vijayarani, TV Geetha
J Vijayarani, TV Geetha
Soft Computing 24 (17), 12901-12918 , 2020
2020
Citations: 3
A neural network framework for predicting dynamic variations in heterogeneous social networks
M Balakrishnan, G TV
Plos one 15 (4), e0231842 , 2020
2020
Citations: 15
Lexical Syntactic Patterns and Novel Statistical Measures based Bootstrapping Approach for Evolution of Biomedical Ontologies
B Sathiya, TV Geetha
International Journal of Computer Applications 177 (39), 21-27 , 2020
2020

MOST CITED SCHOLAR PUBLICATIONS

Learning content design and learner adaptation for adaptive e-learning environment: a survey
KR Premlatha, TV Geetha
Artificial Intelligence Review 44 (4), 443-465 , 2015
2015
Citations: 147
A survey on crossover operators
G Pavai, TV Geetha
ACM Computing Surveys (CSUR) 49 (4), 1-43 , 2016
2016
Citations: 140
Raga identification of carnatic music for music information retrieval
R Sridhar, TV Geetha
International Journal of recent trends in Engineering 1 (1), 571 , 2009
2009
Citations: 115
Dynamic learner profiling and automatic learner classification for adaptive e-learning environment
KR Premlatha, B Dharani, TV Geetha
Interactive Learning Environments 24 (6), 1054-1075 , 2016
2016
Citations: 98
A meta-learning framework using representation learning to predict drug-drug interaction
SS Deepika, TV Geetha
Journal of biomedical informatics 84, 136-147 , 2018
2018
Citations: 62
Machine learning: concepts, techniques and applications
TV Geetha, S Sendhilkumar
Chapman and Hall/CRC , 2023
2023
Citations: 55
Morphological analyzer for Tamil
P Anandan, K Saravanan, R Parthasarathi, TV Geetha
International Conference on Natural language Processing 3, 12-22 , 2002
2002
Citations: 54
CRF models for Tamil part of speech tagging and chunking
SL Pandian, TV Geetha
International Conference on Computer Processing of Oriental Languages, 11-22 , 2009
2009
Citations: 45
Personalized ontology for web search personalization
S Sendhilkumar, TV Geetha
Proceedings of the 1st Bangalore annual Compute conference, 1-7 , 2008
2008
Citations: 42
Morpheme based language model for Tamil part-of-speech tagging
S Lakshmana Pandian, TV Geetha
Polibits, 19-25 , 2008
2008
Citations: 38
Tamil document summarization using semantic graph method
M Banu, C Karthika, P Sudarmani, TV Geetha
International conference on computational intelligence and multimedia … , 2007
2007
Citations: 37
Swara indentification for south indian classical music
R Sridhar, TV Geetha
9th International Conference on Information Technology (ICIT'06), 143-144 , 2006
2006
Citations: 37
Semi-supervised bootstrapping approach for named entity recognition
S Thenmalar, J Balaji, TV Geetha
arXiv preprint arXiv:1511.06833 , 2015
2015
Citations: 35
Document summarization and information extraction for generation of presentation slides
KG Prasad, H Mathivanan, TV Greetha, M Jayaprakasam
2009 International Conference on Advances in Recent Technologies in … , 2009
2009
Citations: 34
New crossover operators using dominance and co-dominance principles for faster convergence of genetic algorithms
G Pavai, TV Geetha
Soft Computing 23 (11), 3661-3686 , 2019
2019
Citations: 33
Morpho-Semantic Features for Rule-based Tamil Enconversion
J Balaji, P Ranjani, K Madhan
International Journal of Computer Applications 26 (6), 11-18 , 2011
2011
Citations: 30
Rough set theory for document clustering: A review
KA Vidhya, TV Geetha
Journal of Intelligent & Fuzzy Systems 32 (3), 2165-2185 , 2017
2017
Citations: 29
Unl deconverter for tamil
T Dhanabalan, TV Geetha
International Conference on the Convergences of Knowledge, Culture, Language … , 2003
2003
Citations: 29
Unsupervised domain ontology learning from text
SH Venu, V Mohan, K Urkalan, G Tv
International Conference on Mining Intelligence and Knowledge Exploration … , 2016
2016
Citations: 28
Automatic extractive text summarization based on fuzzy logic: a sentence oriented approach
ME Hannah, TV Geetha, S Mukherjee
International Conference on Swarm, Evolutionary, and Memetic Computing, 530-538 , 2011
2011
Citations: 28