KRISHNAKUMARI K
@avccengg.net
ASSOCIATE PROFESSOR and CSE
A.V.C.COLLEGE OF ENGINEERING
Dr. K. Krishnakumari, an Associate Professor with 20 years of academic experience, holds qualifications in B.E., M.E., M.B.A., and Ph.D. Her expertise lies in text mining, sentiment analysis, and automata theory. She has contributed significantly to academia with 16 international conference publications, 16 international journal papers, and 18 national journal papers. Dr. Krishnakumari has receive NPTEL discipline star and participated in various ATAL FDPs. She received funding from prestigious organizations like the Computer Society of India and TNSCST for her projects. Recognized with awards, she actively engages in various professional affiliations and serves in multiple roles within her institution and as a resource person in esteemed universities and colleges.
EDUCATION
B.E, M.E., Ph.D., Computer Science
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Krishnakumari K, Padmapriya S, Abirami N, Kanaghadhara N E, and Arul P
IEEE
The ability of computers to accurately determine the human state of mind can be improved with the help of the recognition of human facial expressions that reflect emotions. This will make it possible for workplace interactions to be more tailored to the individual. We will be able to recognize the emotions by paying attention to the placement of the brows and eyes, as well as the position of the mouth and the various changes that occur in the facial features. Using a deep learning Convolutional Neural Network (CNN)-INCEPTION and RESNET50 network model, we examine the recognition of human face expressions. The system utilizes a labeled data set that has around 32,298 photos with a variety of facial expressions. This data set is used for both training and testing purposes. A CNN model is trained using the dataset’s grayscale photos to categorize facial expressions into one of five categories: happy, sad, neutral, fearful, or furious. The use of batch normalization and dropout helps to increase the accuracy of the model while also preventing it from becoming overfit.
K. Krishnakumari and E. Sivasankar
Inderscience Publishers
E. Sivasankar, K. Krishnakumari, and P. Balasubramanian
Springer Science and Business Media LLC
K. Krishnakumari, E. Sivasankar, and Sam Radhakrishnan
Springer Science and Business Media LLC
Kalyanasundaram Krishnakumari and Elango Sivasankar
Springer Singapore
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
Publications
I REFEREED JOURNALS
1. E. Sivasankar, K. Krishnakumari, P. Balasubramanian (2021) An Enhanced Sentiment Dictionaryfor Domain Adaptation with multi-domain dataset in Tamil language (ESD-DA). Soft Computing (SCIE, IF: 3.643, pp:3697-3711) (DOI:.
2. K. Krishnakumari, E. Sivasankar, Sam Radhakrishnan (2020) Hyperparameter tuning in convolutional neural networks for domain adaptation in sentiment classification (HTCNN-DASC). Soft Computing (SCIE, IF: 3.643, pp:3511-3527) (DOI:.
3. Krishnakumari K., Sivasankar E.,(2018), Aspect based Summarization in the Big Data Environment, International Journal of Advanced Intelligence Paradigms (IJAIP), (SCOPUS Indexed IF: 0.8) InderScience Publishers (Article in Press) (DOI: 10.1504/
4. Thilagavathi, R., & Krishnakumari, K. (2016). Tamil english language sentiment analysis system. International Journal of Engineering Research and Technology, 4, 114-118.
II CONFERENCES
1. K. Krishnakumari, E. Sivasankar (2018) Scalable Aspect-Based Summarization in the Hadoop Environment. In: Aggarwal V., Bhatnagar V., Mishra D. (eds) Big Data Analytics. Advances in Intelligent Systems and Computing, vol 654. Springer, Singapore, 439-449.
2. K. Krishnakumari, E. Sivasankar (2017) A Deep Learning approach for Sentiment Analysis using Convolution Neural Networks, International Conference on National Development through Innovations in Management, Science