FEDY BEN HASSOUNA

◦ Built an augmented diagnosis agent that validates doctor diagnoses against patient statements, suggests other conditions and symptoms, and calculates severity levels, improving diagnostic consistency by 85%. ◦ Designed and implemented a custom knowledge graph (Statements-Diagnosis-Symptoms) to ground the agent’s reasoning and integrated it into a multi-agent system, reducing researchers’ manual verification workload by 40%. ◦ Expanded alternative diagnosis coverage by 25%, enabling explainable diagnostic support for medical staff

◦ Built an AI-powered analytics agent for retail company , enabling data and business analysts to query KPIs (sales, promotions, inventory) in natural language; reduced manual SQL/reporting effort by 80% . ◦ Delivered 7 interactive analytics modules (promotion impact, stock turnover, basket analysis, forecasting,unsold stock detection) used daily by 20+ analysts and managers, accelerating decision-making

• Developed an autonomous garbage collection robot using an ESP32 microcontroller, integrating YOLOv8-based object detection (fine-tuned on a custom obstacle dataset) via a laptop webcam to identify and collect trash items such as bottles and papers, achieving 90% detection accuracy on common trash types. • Implemented real-time navigation and obstacle avoidance using ultrasonic sensors and DC motors, reducing collision incidents by 95% in dynamic environments. • Practical Workshops: Led interactive workshops to guide students through building and programming their own autonomous robots, focusing on automation, AI, and hardware integration.

• Worked on deploying and integrating AI models on a cloud platform (Azure AI Studio) , ensuring they align with the current billing framework and do not require external payment setups. • Compared two models, GPT-4o and Mixtral, to manage the billing framework within Azure, ensuring operational efficiency and cost-effectiveness

Developed a machine learning model to detect defects in wind turbines by analyzing sensor data and performing data preprocessing. The objective of this project is : - to identify early signs of failure, enabling predictive maintenance. - to reduce downtime, lower maintenance costs - to extend turbine lifespan, ensuring optimal performance.

◦ Developed a deep learning system using DeepForest to detect cork oak trees in aerial imagery, achieving 84.31% accuracy and 91.49% F1 score on validation data. ◦ Created a custom pipeline for biomass estimation based on detected canopy areas and allometric equations, supporting forest inventory and management. ◦ Annotatedhigh-resolution images using VGG Image Annotator and converted them to DeepForest-compatible formats, enhancing model training efficiency

Developed a sentiment analysis tool to determine the sentiment (positive, negative, neutral) of Twitter tweets. Utilizing sentiment analysis libraries like TextBlob, I implemented a solution to: - Accurately analyze tweet data and gauge public opinion. - Enhance understanding of social media trends and user sentiment. - Support data-driven decision-making by providing clear insights into online conversations.