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Project Description: Developed an end-to-end AI-powered medical intelligence platform for analyzing chest X-ray images and assisting with disease prediction. The system uses Deep Learning with DenseNet121 to classify X-ray images into five categories: Normal, Pneumonia, Atelectasis, Cardiomegaly, and Effusion. Integrated Grad-CAM Explainable AI (XAI) to generate visual heatmaps highlighting regions of the X-ray that contributed to the model's prediction. Built REST APIs using FastAPI for image upload, prediction, model information, and health monitoring. The platform also supports prediction history and is designed for integration with AI-assisted medical report generation using LLMs. Key Features 1. Medical Image Analysis – Processes chest X-ray images using deep learning. 2. ...

A comprehensive Japanese technical report on artificial intelligence now needs to read as though it were originally written in polished, publish-ready English. The current draft is packed with algorithm explanations, code snippets, architecture diagrams, and performance tables, and every element must stay exactly where it is on the page—headings, figure numbers, table captions, footnotes, everything. Only the language changes; the layout does not. Because the piece is earmarked for formal publication, I expect precise terminology, consistent tone, and zero tolerance for ambiguity or machine-style phrasing. References to frameworks, model names, or metrics (e.g., PyTorch, Transformer-based encoder accuracy, ROC-AUC) must be rendered in the same professional style used by leading AI j...

I’m ready to add an AI-powered chat experience to my custom-built website (JavaScript front end with a .NET back end). The goal is simple: guide visitors through a short conversation that captures three things—pickup address and contact details, destination and any special instructions, and the pickup time—then pass that data straight into our existing order-processing logic. You’re free to recommend whether Google Gemini, OpenAI’s ChatGPT, or another LLM is the smartest choice for this workflow; I’m interested in hearing pros, cons, and any cost or latency considerations. Whichever model we use, I’ll need the chat widget embedded without disrupting current pages or load times. Key deliverables • Conversational flow design and prompt enginee...