Ms. Sumalatha U.
Educational Qualification
BE – Computer Science and Engineering
M.Tech – Computer Science and Engineering
PhD (Ongoing)
Total Experience
07 years
Areas of Interest
- Data Mining
- Machine Learning
- Data Science
Professional Memberships
ISTE
Subjects Handled
- Problem-solving skills using C, Data Structures, Java Programming, Python Programming, dot NET using C#
- Discrete Mathematical Structures, Design and Analysis of Algorithms
- Software Engineering, Software Testing
- Data Mining and Data Warehousing, Management & Entrepreneurship
Research Interests
● Cryptography and Network Security
● Computer Networks
● Machine Learning
● Deep Learning
Scholarly Profiles
- ORCiD: [0000-0002-5914-7053]
- Scopus: [https://www.scopus.com/authid/detail.uri?authorId=59263909500]
- Google Scholar: [https://scholar.google.com/citations?user=Vm-QJYAAAAAJ&hl=en]
Workshops / FDPs / SDPs / STTPs Attended/Conducted
13
Conferences
1. Presented and published paper titled “Analysis of Classification Algorithms for predicting Parkinson’s Disease and applications in the field of Cybersecurity” on 30-31st December, 2022, at the International Conference on Applications and Techniques in Information Security 2022.
2. Presented paper titled “Design and Implementation of an Efficient and Secure Fingerprint Biometric Authentication System using Deep Learning Autoencoders and Classifiers” in online mode on 3rd October, 2023, at International Conference on Computational Methods in Engineering and Health Sciences, Malaysia.
Journals
1. A Comprehensive Review of Unimodal and Multimodal Fingerprint Biometric Authentication Systems: Fusion, Attacks, and Template Protection, in IEEE Access, vol. 12, pp. 64300-64334, 2024.
2. Deep Learning Applications in ECG Analysis and Disease Detection: Recent Advances in IEEE Access.
3. Enhancing Finger Vein Recognition with Image Preprocessing Techniques and Deep Learning Models in IEEE Access.
4.From Geometry to Deep Learning: AnOverview of Finger Knuckle Biometrics Recognition Methods and Their Applications in IEEE Access.
5.Multimodal Biometric Authentication: A Novel Deep Learning Framework Integrating ECG, Fingerprint, and Finger Knuckle Print for High-Security Applications in Engineering Research Express, IOPscience.
6.Privacy-Preserving Techniques in Biometric Systems: Approaches and Challenges in IEEE Access.
7. Touch of Privacy: A Homomorphic Encryption-Powered Deep Learning Framework for Fingerprint Authentication in IEEE Access.
8. Secure Knuckle Print Authentication: Template Protection and Attack Analysis in IEEE Access.