S

SVM

A synthetic dataset generation and detection pipeline for counting coins and identifying those showing "tails" in images. It combines YOLOv26m for detection with a CoinCLIP-based SVM classifier, and includes a custom NMS post-processing step to improve counting accuracy.

A modular Clinical NLP Pipeline built to process and analyze unstructured medical text using both traditional machine learning and transformer-based approaches.

The project combines multiple components including OCR, text preprocessing, feature engineering, classification, named entity recognition, and visualization into a single end-to-end pipeline. It supports extracting clinical insights from raw documents and predicting medical categories using both TF-IDF + SVM and BERT-based models.

The system was designed and implemented as a structured Python project, with each stage separated into independent modules for scalability and maintainability.

Key Highlights

Built an end-to-end NLP pipeline for clinical text processing. Implemented SVM (≈51% accuracy) and BERT (≈77% accuracy) models. Integrated OCR for extracting text from medical documents. Performed Named Entity Recognition (NER) on clinical data. Designed modular architecture (src/) for clean code organization. Exported outputs for visualization and dashboard integration.

HOG/LBP + SVM classifier🖼️, A comparative study of traditional computer vision features (HOG vs LBP) with SVM classifier for image classification.

Códigos de diseño de clasificadores SVM y MLP para la identificación de vocalizaciones de perros y ovejas en entornos rurales, junto con algoritmos de optimización de arquitecturas MLP mediante algoritmos evolutivos

This project aims to develop a robust and interpretable machine learning model for early detection of cyberattacks within network systems. By analyzing network traffic data and identifying patterns associated with malicious activities, the model strives to improve network security and prevent potential cyber threats.

This project addresses a real-world challenge involving the control of a robot tasked with trajectory tracking and navigating an assembly line. The objective is to efficiently connect various components along the line while adhering to Linear Temporal Logic (LTL) constraints. The ultimate goal is to seamlessly integrate elements of the assembly line by ensuring the robot's trajectory adheres to specified conditions and constraints.

LS SVM Classification and Regression

Small (Python) project to predict facial expressions using a SVM and facial landmarks. Contains a notebook with commented code + additional code snippets.

Multi-agent system that can be used to predict movie score using contents of its review.

Python and C++ classes for SVM classification

implementing sentiment analysis using svm and lexicon based and hybrid methods. the crawler folder is the code for scraping comments in mobile.ir website. the goal of project is to determining the most popular mobile brand based on user's opinions.

Final Project that used SVM classfication to judge cheer leading motions.