Case Studies

Deep dives into selected projects, exploring the challenges and solutions

BrainScan AI

Problem Statement

Segmenattion of brain tumor from MRI scans using kaggle dataset

Tools & Technologies

Python TensorFlow EfficientNet Flask

Approach

1 Data Collection
2 Preprocessing
3 Model Training
4 Validation

Outcome

95% accuracy in tumor detection

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BrainScan AI Visualization

VideoGPT: Interactive Video Assistant

Problem Statement

Traditional video consumption remains passive and lacks intelligent interaction, especially for offline viewing. Users need a way to engage with video content more deeply, whether online or offline, through natural conversations

Tools & Technologies

Python Flask DeepSeek LLM Speech Recognition TTS YouTube API Multithreading PyTorch

Approach

1 Dual Processing Pipeline: Built separate handlers for local and YouTube videos
2 DeepSeek LLM Integration: Implemented contextual understanding for video content
3 Offline Support: Developed local video processing capabilities
4 Speech-to-Text: Advanced audio transcription using TTS
5 Interactive Chat: Real-time conversation with video content

Outcome

Developed a versatile video interaction system that works both online and offline, enabling users to have meaningful conversations about video content through advanced LLM integration. Supports local video files and YouTube content with equal capabilities.

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VideoGPT: Interactive Video Assistant Visualization

Self-Reconfiguring Hybrid Analog-Digital AI Chip

Problem Statement

Current AI hardware lacks dynamic adaptation capabilities during training, leading to inefficiencies in power consumption and computational performance.

Tools & Technologies

FPGA Architecture Memristor Technology ML Control Systems Analog Computing Digital Logic Design

Approach

1 Research Phase: Literature Review & State-of-the-art Analysis
2 Architecture Design & Theoretical Modeling
3 Simulation & Performance Analysis
4 Optimization Strategy Development
5 Future: Hardware Implementation

Outcome

Currently in active research phase: Developing theoretical foundations and architecture specifications for a revolutionary chip design that could transform how AI hardware adapts during training.

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Self-Reconfiguring Hybrid Analog-Digital AI Chip Visualization