SVM

This project explores the application of Support Vector Machines (SVM) in classifying human cell records to predict the malignancy of breast cancer samples. By training an SVM model on a dataset of breast cancer features, the goal is to accurately differentiate between benign and malignant cases. This research aims to contribute to the development of early detection and diagnosis methods for breast cancer.

Pulmo is a web-based application that provides an easy-to-use interface for users to input their medical history and other relevant data to get a prediction of their risk of developing lung cancer. The project has also highlighted the importance of data preprocessing and feature selection in improving the performance of machine learning algorithms. Exploratory data analysis was also useful in identifying the most relevant features for predicting lung cancer.

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.