◾ Utilized Generative Adversarial Networks (GANs) for medical image-to-image translation, achieving an accuracy rate of 86%; Work was done by Pix2Pix modified model and this accuracy improved around 4% than previous model in the company.◾ Annotate more than 2000 images for dental teeth detection (YOLO) and decay classification in coordination with specialists.◾ Developed a CT-scan image segmentation solution and implemented a user-friendly web-face interface using FastAPI.◾ Signal… Show more ◾ Utilized Generative Adversarial Networks (GANs) for medical image-to-image translation, achieving an accuracy rate of 86%; Work was done by Pix2Pix modified model and this accuracy improved around 4% than previous model in the company.◾ Annotate more than 2000 images for dental teeth detection (YOLO) and decay classification in coordination with specialists.◾ Developed a CT-scan image segmentation solution and implemented a user-friendly web-face interface using FastAPI.◾ Signal Processing for EEG signal analysis and achieving 96% accuracy in the DEAP dataset. Using pre-processing methods and combining ideas belong to model development.◾ Carried out an ensemble learning approach to achieve 87% accuracy in segmenting Macular images.◾ Maintain an annotation tool for specific object detection with Qt framework; This toolkit can be used as a faster object detection annotation. Show less

◾ Enhanced the Yolov7 algorithm by integrating an attention module (CBAM), resulting in a significant 5% accuracy boost in detecting smoke within natural environments.◾ Achieved a remarkable 91% accuracy in solving the face verification challenge and use optimization solutions to implement it edge devices like raspberry-pi.◾ A multi-camera ball detection in volleyball videos with accuracy of 96% and detect players in each video; In this work a new detection algorithm designed to get a… Show more ◾ Enhanced the Yolov7 algorithm by integrating an attention module (CBAM), resulting in a significant 5% accuracy boost in detecting smoke within natural environments.◾ Achieved a remarkable 91% accuracy in solving the face verification challenge and use optimization solutions to implement it edge devices like raspberry-pi.◾ A multi-camera ball detection in volleyball videos with accuracy of 96% and detect players in each video; In this work a new detection algorithm designed to get a faster model as 50 FPS.◾ Achieved 96% accuracy in ball detection for volleyball videos; in this work used an innovative detection algorithm for a faster processing speed of 50 FPS.◾ Progressed and adopted a highly efficient depth estimation framework for car lighting nuances and shape analysis, achieving an impressive speed of 20 FPS. Show less

- Digital Electronics Teaching Assistant (Winter2020)