Dogga Pavan Sekhar

Dogga Pavan Sekhar

B.Tech CSE Student specializing in Artificial Intelligence & Machine Learning

🌍 My Mission

Leveraging AI to solve real-world challenges in healthcare and beyond.

As a Computer Science student specializing in AI & Machine Learning, my mission is to build intelligent systems that create real-world impact. I’m passionate about uncovering hidden insights from complex datasets, particularly within the healthcare sector.

From developing deep learning models for non-invasive anemia detection to leading a team of ML interns, I focus on transforming ambitious ideas into impactful solutions. With strong skills in Python, data structures, and software development, I strive to build technology that is powerful, purposeful, and accessible.

πŸ’‘ My Expertise

Machine Learning

Building predictive models and deep learning solutions for complex problems.

Healthcare AI

Applying AI to medical diagnostics, including sepsis and osteoporosis detection.

Programming

Proficient in Python, Java, C++, and web development technologies.

Problem Solving

Strong analytical thinking and a passion for innovative solutions.

πŸ’Ό My Experience

Nov 2025 - Present

Machine Learning Intern

Arinova Studio

Designing and developing intelligent chatbots with advanced NLP capabilities. Working on web-scraping pipelines integrated with neural networks for automated data extraction and analysis.

Chatbot Development Web Scraping Deep Learning

Sept 2025 - Present

Deep Learning Intern (Team Lead)

Centre for Health and Innovations, MRIIRS

Leading 8 ML interns on the AnemoScan project. Developed deep learning models for non-invasive Anemia detection.

Team Leadership TensorFlow CNNs

May 2025 - July 2025

GUNI Summer Research Program Intern

Ganpat University (Remote)

Applied deep learning and computer vision to estimate age from skeletal X-rays for forensic and medical applications.

Computer Vision PyTorch X-ray Analysis

Feb 2025 - Apr 2025

Project Associate Intern

Centre of Health and Innovations

Developed models for sepsis detection and knee osteoporosis prediction, leading to a successful journal research paper.

Scikit-learn Pandas Research

Sept 2024 - Nov 2024

Graphic Designer Intern

Manav Rachna University, CST Department

Conceptualized and created branding materials including newsletters, flyers, and logos for the department.

Figma Canva Branding

June 2024 - July 2024

Machine Learning Intern

Center of Health and Innovations

Built cough classification models using MFCCs audio features and developed a Django web interface.

Django Librosa Audio Processing

πŸš€ Featured Projects

AI Powered Travel Planner

A web app using Streamlit and Gemini AI to generate personalized travel itineraries based on user preferences.

Live App View on GitHub

Food Freshness Detection

A deep learning system using models like AlexNet to classify fruit freshness from images, achieving 84% accuracy.

View on GitHub

Osteoporosis Detection

A system to classify bone X-rays into Normal, Osteopenia, and Osteoporosis with a custom CNN model achieving 89% accuracy.

View on GitHub

Ongoing Research

In Progress

Non-Invasive Anemia Detection

Developing models to detect anemia from images of the eye conjunctiva, palm, and nail beds.

TECH STACK:

TensorFlow OpenCV CNNs
Data Collection

Respiratory Conditions Detection

Classifying conditions like wheezing and crackles from audio signals using the ICBHI dataset.

TECH STACK:

PyTorch Librosa Audio AI
Pre-processing

Skin Lesion Classification

Building models for Melanoma detection and classification from skin lesion images.

TECH STACK:

Fast.ai Vision Transformer

Publications

Heart Attack Prediction System using Machine Learning Algorithm

Published in IEEE Xplore | Dec 23, 2024

Read Publication

Diabetics and Risk Prediction Using XGBoost - A Scalable and Interactive Model

Conference: SocProS 2025

A scalable and interactive model for predicting diabetes risk using the XGBoost algorithm.

Deep Learning-Based Multiclass Detection of Osteoporosis from Knee Radiographs

Ongoing Work

Status: Manuscript Submitted

A deep learning approach for detecting multiple classes of osteoporosis from knee X-rays.

πŸ“© Get In Touch

I'm actively looking for new opportunities and collaborations. Let’s build something amazing together!

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