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Nireeksha Naresh

Nireeksha Naresh

AI & Data Labeling Specialist | Expert in Vision and Sensor Data Annotation

India flagkarnataka, India
$130.00/hrIntermediateProdigyOther

Key Skills

Software

ProdigyProdigy
Other

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor
ImageImage
TextText

Top Task Types

Classification
Object Detection
Prompt Response Writing SFT

Freelancer Overview

I have hands-on experience working with AI training data and annotation across image, text, and sensor domains. My journey includes projects like bone fracture detection using ResNet50, IoT-based ambulance and E-Challan systems, and wearable biomedical devices that capture and process real-time health data. These experiences helped me develop a deep understanding of how clean, well-labeled data directly impacts model performance and decision accuracy. With a strong foundation in Artificial Intelligence, Data Science, and IoT, I bring both technical skill and practical insight to every project. I enjoy transforming raw, unstructured data into meaningful patterns that train smarter AI systems. My goal is to create high-quality datasets that contribute to building reliable, ethical, and impactful AI solutions across healthcare, transportation, and automation.

IntermediateHindiArabicEnglishKannada

Labeling Experience

Bone Fracture Detection Dataset Preparation and Annotation

OtherImageClassification
This project involved creating and labeling a high-quality dataset of radiograph images for training a deep learning model to detect bone fractures using ResNet50. I was responsible for organizing, cleaning, and annotating medical images by identifying fractured and non-fractured regions to support supervised training. The dataset was prepared with strict consistency and accuracy standards, ensuring each labeled sample enhanced the model’s diagnostic reliability. Quality checks and cross-verifications were performed to maintain over 95% labeling accuracy.

This project involved creating and labeling a high-quality dataset of radiograph images for training a deep learning model to detect bone fractures using ResNet50. I was responsible for organizing, cleaning, and annotating medical images by identifying fractured and non-fractured regions to support supervised training. The dataset was prepared with strict consistency and accuracy standards, ensuring each labeled sample enhanced the model’s diagnostic reliability. Quality checks and cross-verifications were performed to maintain over 95% labeling accuracy.

2025

Ambulance Detection and Traffic Data Labeling

Other3D SensorObject Detection
This project involved creating and annotating datasets for a smart ambulance detection system using camera and sensor data. I labeled vehicles, ambulances, and road objects, and annotated sensor signals from ESP32/ESP8266 modules to train AI models for traffic monitoring and emergency response prioritization. The dataset included hundreds of video frames and sensor sequences, with rigorous quality control to ensure accurate object identification and consistent labeling across frames.

This project involved creating and annotating datasets for a smart ambulance detection system using camera and sensor data. I labeled vehicles, ambulances, and road objects, and annotated sensor signals from ESP32/ESP8266 modules to train AI models for traffic monitoring and emergency response prioritization. The dataset included hundreds of video frames and sensor sequences, with rigorous quality control to ensure accurate object identification and consistent labeling across frames.

2024

Biosensor Wearable Data Labeling for Health Monitoring

Other3D SensorTrackingDiagnosis
I worked on annotating multi-sensor data collected from wearable devices measuring heartbeat, muscle activity (EMG), body temperature, and other physiological signals. The project involved labeling patterns corresponding to different health conditions, muscle contractions, and abnormal readings to train AI models for early detection and monitoring. The dataset encompassed thousands of data points, annotated with high precision and cross-verified for consistency to ensure reliable model training.

I worked on annotating multi-sensor data collected from wearable devices measuring heartbeat, muscle activity (EMG), body temperature, and other physiological signals. The project involved labeling patterns corresponding to different health conditions, muscle contractions, and abnormal readings to train AI models for early detection and monitoring. The dataset encompassed thousands of data points, annotated with high precision and cross-verified for consistency to ensure reliable model training.

2023

Education

M

Mahatma Gandhi Memorial College

Pre-university, PCMCS

Pre-university
2020 - 2022
N

Navodaya Kishore Kendra

10th, General

10th
2019 - 2020

Work History

M

Manipal Payment and Identity Solutions

Image Processing Intern

Bengaluru
2025 - Present
S

SystemTron

Web Development Intern

N/A
2025 - 2025