Projects

Optimizing Feature Representation of Deep Neural Networks for Enhanced Deepfake Detection [Project]

This project focuses on detecting deepfake images using the 140k Real and Fake Faces dataset from Kaggle. The VGG16 model, known for its deep architecture, was employed as the primary feature extractor. To enhance performance, a channel attention mechanism was introduced, allowing the model to prioritize relevant feature channels while reducing the impact of less useful ones. This resulted in a significant improvement in classification accuracy. Additionally, an ablation study was conducted using ResNet50, demonstrating how attention mechanisms improve feature representation. The final model achieved a high accuracy of 99.80% with VGG16 and channel attention, making it an effective solution for detecting deepfake images.

Fake News Detection System [Project]

Developed a deepfake detection system to classify video frames as real or fake using GloVe embeddings and an LSTM-based neural network in TensorFlow. Preprocessed data through feature extraction, including frame-wise analysis to capture temporal features across videos. Integrated GloVe embeddings for improved semantic understanding of input text data. Designed the LSTM network to capture sequential dependencies and optimize feature representation. Evaluated the model using various metrics, including accuracy, confusion matrix, precision, recall, F1-score, and ROC curve, ensuring robust performance analysis.

Cardiotocogram Data Analysis for Fetal Health Classification Using Machine Learning [Project]

This project aims to classify fetal health status using machine learning models applied to Cardiotocogram (CTG) data. The dataset consists of 2,126 records with 21 features extracted from CTG exams, categorized into three classes: Normal, Suspect, and Pathological. Models like Random Forest, K-Nearest Neighbors, and Gradient Boosting were trained on the preprocessed dataset. Feature selection, data standardization, and SMOTE were employed to improve model performance. Random Forest achieved the highest accuracy of 98.47%. This project showcases how machine learning can assist in automating and improving fetal health assessment.

Maternal and Child Health Care [Project]

This initiative involved creating a comprehensive Maternal and Child Health Care website designed to support expecting mothers with tools for due date calculation and immunization schedules. The site features personalized SMS and email notifications, as well as a query posting function to facilitate communication with healthcare specialists. Additionally, a mobile app was developed using Android Studio and Firebase, mirroring the website’s features. This project aims to enhance prenatal and postnatal care by providing a supportive online platform for mothers.