A. Agnes Lydia

Assistant Professor with 3 years of experience successfully contributing to Computer Science and Engineering curriculum development and delivery. Driven to contribute to program outcomes by facilitating engagement and supporting learning objectives. Enthusiastic professional with background in academic advisement.

Certificates:

• Government —Qualified TAMILNADU STATE ELIGIBILITY TEST (TNSET 2016)
• LATEX for Everyone and Everything” offered by Udemy in 2019
• LinkedIn Learning MySQL Installation and Configuration, October 2022
• LinkedIn Learning Learning R, July 2022
• LinkedIn Learning Python: Working with Predictive Analytics, April 2022
• LinkedIn Learning TensorFlow: Working with NLP, March 2022
• LinkedIn Learning GitHub Essential Training, February 2022
• LinkedIn Learning Machine Learning with Python: Foundations, January 2022
• Coursera: Diabetic Retinopathy Detection with Artificial Intelligence, September 2022.

FELLOWSHIPS

• Recipient of Maulana Azad National Fellowship (MANF) for Ph.D., from 2016 - 2021

PATENT:

• Multi Stage Bagging Pruning-Based Ensemble Classification Approach in Machine Learning over Electronic Health Records (EHR), Application No.: 202241029653.

1. Faculty Development Programme on "Current Trends in Information Technology", organized by School of Computational Sciences and CANNY - Consortium of Colleges, Nehru Arts and Science College (Autonomous), Coimbatore, 2nd February, 2022 to 9th February, 2022.
2. Faculty Development Programme on "Amazon Web Services", organized by Rajiv Gandhi Arts and Science College, Puducherry in collaboration with Brainovision Solutions India Pvt.Ltd & All India Council For Technical Education - AICTE. Pondicherry, 22nd August, 2022 to 27th August, 2022.
3. Faculty Development Programme on "Emerging Trends and Technologies in Cyber Security", organized by Madanapalle Institute of Technology and Science, Andhra Pradesh, 14th November, 2022 to 18th November, 2022.
4. Faculty Development Programme on "Recent Advances in Machine Learning Algorithms and Applications", organized by NITTE Meenakshi Institute of Technology, Bengaluru, 16th January, 2023 to 20th January, 2023.
5. Faculty Development Programme on "Recent Advances and Future Trends in Artificial Intelligence, Computer Vision and NLP", organized by Vellore Institute of Technology, Chennai, 26th to 27th March, 2023 & 1st April to 3rd April, 2023.

6. Faculty Development Programme on "Research Insights on Recent Revolutions and Evolutions in Information Technology", organized by Madanapalle Institute of Technology and Science, Andhra Pradesh, 12th June to 17th June, 2023.

1. Lydia, A. Agnes, and F. Sagayaraj Francis. "Convolutional neural network with an optimized backpropagation technique." 2019 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, (2019): 1-5. 

2. Lydia, A. Agnes, and F. Sagayaraj Francis. "A Survey of optimization techniques for deep learning networks." International Journal for Research in Engineering Application and Management (IJREAM). 5.2 (2019): 601-604. (UGC Journal) 

3. Lydia, A. Agnes, and F. Sagayaraj Francis. "Adagrad { An optimizer for stochastic gradient descent." International Journal of Information and Computing Science (IJICS). 6.5 (2019): 566-568. (UGC Journal) 

4. Lydia, A. Agnes, and F. Sagayaraj Francis. "Learning Rate Scheduling Policies." International Journal of Innovative Technology and Exploring Engineering (IJITEE). 9.1 (2019): 3641-3644. (Scopus Indexed) 

5. Lydia, Lydia, A. Agnes, and F. Sagayaraj Francis. "Multi-label Classification using Deep Convolutional Neural Network." 2020 International Conference on Innovative Trends in Information Technology (ICITIIT), IEEE, (2020): 1-6. (Scopus Indexed) 

6. Lydia, A. Agnes, and F. Sagayaraj Francis. "Optimistic Approach to Initialize Learning Rate." 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). IEEE, (2020): 1-7. (Scopus Indexed) (Best Paper Award) 

7. Lydia, A. Agnes, and Sheela Chandrasekar. "A Comparative Study on Regularization Techniques in Convolutional Neural Networks." International Journal of Research in Engineering and Science (IJRES). Volume 10 Issue 7 (2022): 784-793.