firaseltlili
About Candidate
Education
Graduated with Honors (GPA : 3.6 ) Fields Of Studies : Advanced mathematics, Numerical analysis,Linear Algebra, Probability and statistics, Algorithms and programming, Object-oriented programming, Database management system, Sequential control of systems, Process analysis, Communication techniques,Advanced programming,Operations research and optimization,Data acquisition and transmission,Artificial intelligence,Image processing and vision industrial,Architecture and programming of embedded systems,Connected objects and communication protocols,Real-time systems,Machine learning,Entrepreneurship,Medical robotics.
Graduated with Honors (GPA : 3.7 ) Fields Of Studies : Full-Stack Web Development , Data Base Management Systems , Object-Oriented Programming , Python Programming , Mathematics , Physics , Image Editing .
Work & Experience
Soccer Video Summarization Using Deep Learning: Successfully trained a custom deep learning model using YOLOv7 to detect soccer highlights. Created a Python script that effectively extracted highlights from videos. Developed a Django Web Application to deploy the model and build a user-friendly interface for easy access using HTML , CSS , JavaScript and Bootstrap . Optimized the deep learning model to improve its accuracy to achieve 90 %. Collaborated with a team to ensure the project was completed on time and within budget. Conducted extensive testing and debugging to ensure the model was functioning correctly. Documented the project thoroughly, including technical specifications and user manuals, for future reference. News Video Segmentation Web Application using Deep Learning: Developed a video analysis application utilizing object detection, face detection, SVM classifier, and YOLOv7 algorithms to identify key features and automatically segment video reports Successfully built and deployed the application's backend using Python and Django, resulting in efficient and robust data processing and storage. Designed and implemented a responsive and visually appealing user interface for the application's frontend using HTML5, CSS3, JS, Bootstrap, and ReactJS technologies. Led the project's development from inception to delivery, ensuring that it met client requirements and delivered exceptional value. Demonstrated a strong understanding of computer vision and machine learning concepts, resulting in the application's accurate and reliable performance
CHD Prediction Using Machine Learning Obtained the BioLINCC heart disease dataset from the ioLINCC website for analysis. Cleaned and preprocessed the Cleveland heart disease dataset using Data Engineering processes and Pandas Conducted exploratory data analysis using Seaborn and Matplotlib to visualize the relationships between patient features and the occurrence of CHD. Developed an understanding of the importance of factors such as the number of major vessels, chest pain type, and maximum heart rate achieved in predicting CHD. Built a Decision Tree Classifier model using the machine learning framework Scikit-learn (sklearn) and achieved an accuracy of 90% on the test set Develop the frontend of the application using HTML , CSS3 ,React and bootstrap and the backend using Django Grocery Store Management System : Firas Tlilifiraseltlili@gmail.com +21651616965 Tunis, TunisiaCollaborated with a team of three developers to develop a grocery store management system using HTML, CSS, JavaScript, and Bootstrap for the front-end. Implemented a Python back-end to handle logic and data processing. Utilized MySQL as a database to store and retrieve data. Utilized Flask as a web server framework to serve HTTP requests from the front-end to the back-end. Utilized sqlalchemy module to interact with the MySQL database from the Python back-end. Employed Github as a version control system to maintain the codebase and track changes. Helped HayNour Grocery Store automate their order and inventory management processes, resulting in a cost savings of $1000 within the first 3 months of use. Worked in a group of three team members and used JIRA for Scrum project management. Deep Learning for Strawberry Disease Detection : Implemented Data Engineering processes to clean the dataset Developed an IoT project to detect strawberry disease using deep learning techniques. Trained a custom deep learning model, YOLO v5, to detect strawberry disease by using synthetic and real data obtained from strawberry disease detection experiments. Deployed the trained model to detect strawberry diseases on plants in farms, allowing farmers to check their crops for diseases before harvesting them. Implemented the project using Python and Django for the backend and HTML 5,CSS3,bootstrap, for the frontend. Deploy the model in a web application using Azure DevOps Build a deep learning model for lip reading: Implemented Data Engineering processes to clean the dataset Implemented a deep learning model for lip reading using Keras, based on the 'LipNet: End-to-End Sentencelevel Lipreading' paper by Yannis M. Assael, Brendan Shillingford, Shimon Whiteson, and Nando de Freitas. Developed the model to accurately transform lip motions into text output, which could be used to improve speech recognition technology for individuals with speech impairments or in noisy environments. Used Python, Django and React to build the web application and deployed the model to a server to allow users to upload videos of lip motions to be analyzed and transcribed. Tested the model's performance and provided documentation for users on how to use the web application and interpret the model's output.