fatima ezzahra elkamouny

Development of an Immersive Real Estate Platform -Technologies: Django REST Framework, Flask, Celery, MongoDB, AI, Machine Learning, Deep Learning -Contributions:Developed the backend using Django REST Framework and integrated MongoDB Designed and deployed deep learning and machine learning models to automate and optimize real estate services , Created an AI-powered intelligent chatbot capable of responding to customer inquiries in real time and Deployed and managed AI models for real estate analysis and recommendation Results: Implemented a high-performance, immersive platform integrating AI models to enhance user experience, automate client interactions, and optimize real estate data management. Home Staging – Deep Learning Model for Interior Design Technologies: Python, CNN, Deep Learning, Image Processing -Contributions: Developed a deep learning model based on convolutional neural networks (CNN),Processed and analyzed a large dataset of room images before and after staging and Improved the accuracy of home staging recommendations using AI -Results: Created an effective model capable of identifying and analyzing room transformations, helping clients visualize potential enhancements for their living spaces. Theft Detection in Supermarkets -Technologies: Python, OpenCV, Deep Learning, Computer Vision -Contributions: Developed a deep learning model using OpenCV for theft detection Integrated computer vision algorithms to identify suspicious behaviors in real time and Optimized video stream processing to ensure efficient and discreet surveillance -Results: Implemented an intelligent automated surveillance system capable of detecting theft in real time and enhancing supermarket security.

-Modeling and Prediction of Blood Components Using Infrared Spectroscopy Technologies: R, Python, Machine Learning, Deep Learning Contributions: Rabat, Morocco Applied machine learning and deep learning models to predict blood components from infrared spectroscopic data. Used models such as Support Vector Machine Regression (SVR), Multiple Linear Regression, KNeighborsClassifier, SVM Classifier, and Artificial Neural Networks for regression tasks. Results: This project led to the achievement of a patent, an award mention, and the publication of a scientific article, highlighting its impact and innovation in the field of blood analysis through spectroscopy.