Neellima Rampalli

• Driven to learn quickly, advance computer proficiency and training in industry operations. Solid background in field and office settings supporting team needs. Flexible and hardworking team player focused on boosting productivity and performance with conscientious and detail-oriented approaches.
• Results-oriented software engineering professional with expertise in building secure applications. Excellent team player with positive attitude. Solid background building Web and mobile applications.

• PYTHON
• DBMS
• AI/ML
• SQL
• C
• C++
• XCODE
• JAVA

PROJECTS:

1) SIGN LANGUAGE INTERPRETER FOR DUMB AND DEAF PEOPLE

Used the Python programming language to create a novel sign language interpreter that made use of cutting-edge machine learning and artificial intelligence (AI) methods. The initiative aims to close the communication gap for deaf and mute people, allowing them to communicate and engage with others in an effective manner.

Responsibilities & Achievements :

• Using computer vision algorithms, such as Convolutional Neural Networks (CNNs) for precise gesture identification, real-time sign language recognition was designed and put into practice.
• Made a significant contribution to assistive technology and social equality through this project, which showcased considerable skill in Python programming, AI/ML techniques, computer vision, and NLP.

2) CORONA CASES PREDICTOR

Supervised the creation of an innovative COVID-19 Cases Predictor using the Python programming language and cutting-edge ML and AI techniques. By providing precise COVID-19 case forecasts, this study hoped to support policymakers, public health authorities, and medical professionals in their decision-making and strategy implementation.

Responsibilities & Achievements:

• Used machine learning techniques to create strong prediction models that could anticipate COVID-19 instances based on past and present data, including Random Forest and Long Short-Term Memory (LSTM) networks.
• Conducted cross-functional collaborations with epidemiologists, data scientists, and public health specialists to verify the efficacy of the model and synchronize forecasts with empirical observations.