BS
B.V Seshu Kumari
VNRVJIET, JNTUCEH
Research areas: Computer Science · Artificial Intelligence · Syntax, Semantics, Linguistic Variation · Natural Language Processing Techniques · Speech and dialogue systems
Employment
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VNRVJIET Associate Professor2017 - Present
Education
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JNTUCEH Ph.D2011 - 2015
Projects & Funding
Projects & funding information is unavailable.
Publications (29)
- Artificial intelligence framework for multi-stage lung disease detection with audio signals Save
- Machine learning-driven prognostics for lithium-ion batteries: enhancing RUL prediction and performance in smart energy storage systems Save
- A Scalable Federated Learning Framework for Efficient Brain Tumor Segmentation With Advanced Data Privacy Save
- Artificial intelligence-based multi-key security for protected and transparent medical cloud storage Save
- Islanding detection of integrated DG system using rate of change of frequency over reactive power Save
- LVDC Grid Side Performance of Solar PV and Wind Plant Using Hybrid Controller Save
- Injury Prediction in Sports using Artificial Intelligence Applications: A Brief Review Save
- Power Quality Improvement of PV fed Grid Connected System using ANN Controlled Shunt Active Power Filter Save
- Efficient Energy Trading and Charging System: Electric Vehicles with Blockchain Save
- Realization of Small Scale Models of FCATS Devices and its Optimization in Power Systems using Stability Indices and Particle Swarm Optimization Save
- Suppression of Ferranti Effect of Transmission System Using Closed Loop Control of FACTS Devices Save
- Exploring Different Approaches for Parsing Telugu Save
- Optimized task scheduling for cloud computing using PSO and GA Save
- Telugu dependency parsing using different statistical parsers Save
- Two Approaches for Incorporating Linguistic Constraints to Improve the Usability of Telugu Dependency Parser Save
- Exploring different statistical Parsers for parsing Telugu Save
- Exploring transition-based and graph-based parsers for Telugu Dependency Parsing Save
- Improving Telugu dependency parsing using Combinatory Categorial Grammar supertags Save
- Combining Malt & MST for Telugu dependency parsing Save
- Improving the usability of Statistical Parsers by incorporating linguistic constraints Save