Systems Registry

A comprehensive archive of engineering architectures, production pipelines, continuous-time networks, and competitive research validations.

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NODE // OFFLINE // REGISTRY

Simia-MatchMaking

NODE // AGENT

The AI-Driven Therapist Matchmaking Engine is designed to solve the problem of connecting patients seeking mental health care with the most clinically compatible, logistically available, and relationally aligned therapist.

This system resolves these inefficiencies by executing an automated, multi-layered pipeline ( 6 layers). It instantly translates unstructured patient narratives into structured categorical profiles, runs lightning-fast deterministic exclusion queries, and ranks providers using a hybrid approach of explicit algorithmic clinical scoring and optimized classical machine learning for interpersonal alignment.

#RecommenderSystem

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NODE // OFFLINE // REGISTRY

Tehran Apartment Price Prediction

NODE // GITHUB

A data-driven machine learning project for estimating the value of apartments located in Tehran, Iran.

All necessary data was collected from Divar — a major Iranian classified ads platform — using a custom web scraper. The dataset underwent thorough cleaning and preprocessing before being used to train regression models that predict apartment prices based on features such as area, number of rooms, floor, and neighborhood.

The project covers the full ML pipeline: data collection via Divar API, data cleaning, exploratory data analysis (EDA), feature engineering, model training, and evaluation.

#Python#Machine Learning#Scikit-learn#Pandas#EDA#Real Estate#Data Science

Source Code→

NODE // OFFLINE // REGISTRY

Multi-Agent Travel Planner

NODE // AGENT

A practical multi-agent system that helps travelers choose when to travel, where to stay, and which flight to book by evaluating complete itineraries instead of optimizing each part in isolation.

Rather than picking the cheapest flight or the best hotel separately, the system scores full trip plans using weather conditions, prices, availability, and personal preferences — then recommends the most reasonable options with clear explanations.

The request flows through a set of specialized agents:

• TravelProfileAssistant — understands traveler habits, budget, and preferences

• WeatherAnalysisAgent — evaluates which dates have better conditions

• FlightOptimizationAgent — finds efficient and affordable flights

• HotelRecommendationAgent — selects suitable accommodations

• Aggregator (Decision Engine) — combines all signals, builds full trip options, scores and ranks the best matches

Instead of guessing, the system compares trade-offs and explains why a certain plan is better.

#Python#Multi-Agent#LLM#FastAPI#Travel#AI

Source Code→

AzkiVam FinTech Hackathon

Synapse//December 2025

NODE // HACKATHONS

The AzkiVam FinTech Hackathon (December 2025) was a prominent industry event focused on developing innovative and intelligent solutions within the financial ecosystem.

The intensive competition aimed to optimize LendTech services and leverage emerging technologies to resolve complex market challenges.

The submitted project presented an innovative decentralized finance (DeFi) solution specifically tailored for crowdfunding.

The architecture established a transparent and efficient framework for smart capital acquisition and management, supported by a functional Minimum Viable Product (MVP).

Following rigorous evaluation among numerous specialized teams, the project successfully secured third place in the competition.

#FinTech#DeFi#crowdfunding

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NODE // OFFLINE // REGISTRY

Cooking with RAG — A Retrieval-Augmented Recipe Assistant

NODE // KAGGLE

This notebook builds an intelligent, conversational culinary companion using Retrieval-Augmented Generation (RAG). By coupling a dense vector database of diverse cooking recipes with a Large Language Model (LLM), the project demonstrates how to overcome standard model hallucinations and knowledge cutoff limitations. It implements efficient semantic search to query recipes by title, preparation method, or "what's in my fridge" ingredients, providing accurate, highly relevant recipes and contextual substitution advice in real-time.

#RAG#LLM#FAISS#semantic-search#Python#Kaggle

Kaggle Project↗

NODE // OFFLINE // REGISTRY

Natural Language Processing with Disaster Tweets

NODE // KAGGLE

This notebook implements a high-scoring text classification model for a competitive Kaggle challenge. It utilizes a specific compressed, lightweight variant of the BERT architecture (bert_en_uncased_L-10_H-128_A-2) with 10 layers, a hidden size of 128, and 2 attention heads. The project focuses on fine-tuning this compact transformer model to achieve strong competitive performance ( Public Score 0.79711 ) while keeping resource consumption and training time minimal.

#NLP#Transformer#BERT#PyTorch#text-classification#Kaggle

Kaggle Project↗