xsux86 (xsux86)

2023: we have a variety of projects spanning across various fields such as data analysis, software development, graphic design, and content writing. Each project we undertake reflects our dedication to delivering the best results to our clients. With extensive experience and solid technical skills, we believe that our portfolio is an accurate representation of our abilities and work quality. We continuously strive to improve ourselves and deliver creative and innovative solutions to every challenge we encounter.

2023: "Introducing our revolutionary website, a dynamic platform developed using the latest Laravel framework along with PHP and several other web languages. Our website serves as a symbol of progress in the fight against tuberculosis, aiming to track changes in TB infection, depict the journey towards better health outcomes. Through meticulous data analysis and user-friendly design, we empower communities to stay informed and vigilant."

2023: "Introducing our revolutionary website, a dynamic platform developed using the latest Laravel framework along with PHP and several other web languages. Our website serves as a symbol of progress in the fight against tuberculosis, aiming to track changes in TB infection, depict the journey towards better health outcomes. Through meticulous data analysis and user-friendly design, we empower communities to stay informed and vigilant."

2024: This project is an implementation of a classification system for diagnosing tuberculosis (TB) using GLCM (Gray Level Co-occurrence Matrix) feature extraction and SVM (Support Vector Machine) model in the Python programming environment. GLCM is a commonly used method in image processing for extracting texture. SVM model is an effective machine learning algorithm for handling binary classification problems.

The steps taken in this project include:

Data Collection: TB patient data including radiographic images are required to train and test the classification model.
Data Preprocessing: Radiographic image preprocessing is performed to remove noise, enhance contrast, and improve image quality.
GLCM Feature Extraction: Texture features are extracted from radiographic images using GLCM. These features may include contrast, energy, homogeneity, and direction.
SVM Model Learning: SVM model is trained using the extracted features to classify radiographic images as TB positive or negative.
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