End-to-end deep learning framework combining a Vision Transformer generator (252M parameters) with an XceptionNet detector for facial manipulation synthesis and detection. Validated on FaceForensics++ with adversarial optimization and high-fidelity identity transfer capabilities.

Deep learning-based virtual try-on system using multi-modal feature fusion (41 channels) and GANs to generate photorealistic garment transfer. Published research on Zenodo with comprehensive implementation.

Hybrid deep learning system combining Convolutional Autoencoders and Transformer Encoders to detect AI-generated music with 95% accuracy, 100% recall for authentic music, and perfect protection for human-created content.

Production-ready multi-agent OSINT system that autonomously monitors global intelligence sources 24/7, processing 1000+ articles/hour through Kafka streams. Features temporal knowledge graph (Neo4j), NLI-based contradiction detection, credibility scoring, and real-time analytics dashboard.

Cutting-edge autonomous system that transforms entire codebases across frameworks (TensorFlow→PyTorch) with zero logical drift. Features AST-driven semantic understanding, 5-agent orchestration, LLM-powered transformations, and automated verification proving behavioral equivalence.

Comprehensive ensemble-based text classification system achieving 99.59% F1-score in distinguishing human-written from AI-generated text using 6 diverse classifiers and 4 advanced ensemble techniques.

Production-ready ASL recognition system achieving 99.60% accuracy with real-time performance (25-30 FPS) using ResNet18 and MediaPipe hand detection on consumer hardware.

Intelligent system combining GraphRAG (Graph-based Retrieval Augmented Generation) with multi-agent orchestration to automatically generate comprehensive, fact-checked Wikipedia-style articles with 83.3% verification rate.

Domain-specific fine-tuned PubMedBERT model optimized for generating high-quality medical text embeddings using contrastive learning on 1,918 PubMed Central articles, achieving 0.78+ similarity scores.

Advanced AI system that clones conversational personas using RAG, OCEAN personality profiling, and LLM fine-tuning. Analyzes chat histories to generate authentic responses mimicking communication style and personality.

Vision-language model combining CLIP's visual encoder with GPT-2 for automatic image captioning, achieving 97.8% training loss reduction with efficient transfer learning on Flickr8k dataset.

Dual-objective research study evaluating TimeGAN vs Diffusion Models for synthetic data generation AND forecasting performance across 11 financial assets. TimeGAN achieves 54% better generation quality; ARIMA dominates forecasting with 97.51% accuracy.

Implementation of CycleGAN for unpaired image-to-image translation between face sketches and photographs, featuring advanced Flask web application with 7-feature automatic input detection and real-time camera support.

End-to-end Retrieval-Augmented Generation pipeline processing 505 chunks from PDFs with hybrid Sentence-BERT + CLIP embeddings. Achieved MAP 0.253, Precision@1 62.5%, integrated LLaMA 3.2 via Ollama for local inference with 2.1s average response time.

Production-grade AI/NLP system for financial document analysis using Retrieval-Augmented Generation (RAG). Features LangChain integration, FastAPI REST API, multi-LLM support (Groq, OpenAI, Anthropic, Google, Cohere), and comprehensive testing with 45+ automated tests.

State-of-the-art fine-grained visual classification using ConvNeXt V2 Base (88M parameters) with progressive training methodology, achieving 92.45% validation accuracy on 120 dog breeds from Stanford Dogs Dataset.