AmirhosseinHonardoust

AmirhosseinHonardoust

👤 Developer

7 repositories on SrcLog

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7 Repos
176 Stars
2 Forks
176 Watchers

Repositories (7)

Stock-LSTM-Forecasting AmirhosseinHonardoust/Stock-LSTM-Forecasting Python

Predict stock prices using LSTM networks in PyTorch. This project covers data preprocessing, sliding window creation, model training with early stopping, and evaluation with RMSE/MAE/MAPE. Includes visualizations of training loss, predicted vs actual prices, and short-horizon forecasts.

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Sentiment-Analysis-BERT AmirhosseinHonardoust/Sentiment-Analysis-BERT Python

End-to-end sentiment analysis of tweets using BERT. Includes preprocessing, training, and evaluation with classification reports, confusion matrices, ROC curves, and word clouds. Demonstrates fine-tuning of transformer models for text classification with modular, reproducible code.

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Smart-Contract-Risk-Analyzer AmirhosseinHonardoust/Smart-Contract-Risk-Analyzer Solidity

A lightweight static analysis engine for Solidity smart contracts. Extracts code features, detects dangerous patterns (delegatecall, tx.origin, call.value), computes heuristic risk scores, and classifies contracts into Low/Medium/High risk levels. Includes multiple example vulnerabilities and a clean CLI for rapid security assessment.

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Measuring-The-Soul-of-Data AmirhosseinHonardoust/Measuring-The-Soul-of-Data

A narrative and technical exploration of data authenticity through the four pillars of synthetic data realism, Fidelity, Coverage, Privacy, and Utility. This thought-leadership piece combines storytelling, mathematics, and code to explain how these metrics define the ethical and functional “soul” of data in AI systems.

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Python-Solidity-Feature-Engineering AmirhosseinHonardoust/Python-Solidity-Feature-Engineering

A practical, research-friendly toolkit demonstrating how Python can read, parse, and analyze Solidity smart contracts using feature-engineering techniques. Extracts structural and security-relevant signals from Solidity code, detects risky patterns, builds interpretable features, and forms the basis for heuristic or ML-driven security analysis.

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Autocurator-Synthetic-Data-Benchmark AmirhosseinHonardoust/Autocurator-Synthetic-Data-Benchmark Python

Autocurator is a comprehensive benchmarking toolkit for evaluating synthetic tabular data. It measures fidelity, coverage, privacy, and utility through quantitative metrics, visual reports, and PCA/correlation diagnostics. Ideal for validating VAE, GAN, Copula, or Diffusion-generated datasets.

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amirhosseinhonardoust.github.io AmirhosseinHonardoust/amirhosseinhonardoust.github.io HTML

Personal website and portfolio built with plain HTML and CSS. Showcases selected machine learning projects, applied data work, and short notes on evaluation, forecasting, and building practical ML systems. Hosted with GitHub Pages.

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