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• Conducted advanced research on enhancing IoT security using Deep Learning models (BiLSTM, LSTM, GRU, CNN, BiGRU) and feature selection techniques (Recursive Feature Elimination, XGBoost).• Developed a highly accurate cyber-attack detection model for IoT networks, integrating feature selection (REFCV) and ensemble learning (LSTM, Bi-LSTM, GRU, Bi-GRU), achieving 96.86% accuracy on the CIC IoT2023 dataset.• Focused on improving the detection of malicious traffic in IoT-based smart healthcare systems, contributing to more secure IoT environments.• Primary author of a research paper published in the 4th International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2024 (Paper ID: 183).

• Utilized Python and scikit-learn to analyze a comprehensive clinical and biopsy dataset, focusing on key features relevant to early breast cancer detection.• Designed and implemented machine learning algorithms, including Logistic Regression and Random Forest, tailored to the medical dataset's specific characteristics.• Successfully achieved a 95% accuracy rate in early breast cancer detection, showcasing the effectiveness of the developed models.• The highly accurate model significantly improved early detection capabilities, leading to better patient care and outcomes.