Summarize the following text to main points and sub-points and make detailed explain for them:

Osama Mohamed Mohamed
Email: <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
github.com/OsamaM0
LinkedIn: <PRESIDIO_ANONYMIZED_URL>
Sumusta, Bani-Suaf, Egypt
Mobile: <PRESIDIO_ANONYMIZED_PHONE_NUMBER>
Summary
• AI Engineer experience in Machine Learning, Deep Learning, NLP, LLM and Computer Vision. Skilled in building and
deploying AI-driven solutions in fields like healthcare, security, and fitness. Proficient in TensorFlow and PyTorch for
model development and AWS SageMaker for scalable deployments. Demonstrated ability to execute AI projects
end-to-end, from data preprocessing to deployment and monitoring, with a strong focus on model accuracy and efficiency.
Education
• Faculty of Computers and Artificial Intelligence, Bani-Suaf University
Bani-Suaf, Egypt
Bachelor in Computer Science; CGPA: 3.4
Sept 2020 – July 2024
Experience
• Sphinx Company
Remote
Freelance Computer Vision Engineer
Feb 2024 – Jun 2024
– Weaponized Violence Detection Model: Increased mAP from 95% to 98% by optimizing data preprocessing,
augmenting datasets, and fine-tuning CNN architectures for real-time video surveillance systems. This improved the accuracy
of violent behavior detection in live streams by 3%.
• Freelance
Remote
Python Developer & AI Engineer
Jan 2022 – Present
– Computer Vision and NLP Projects: Developed and deployed multiple deep learning models, achieving performance
boosts of up to 25% through optimization techniques and hyperparameter tuning. Managed model deployment using Flask
and FastAPI, and integrated with cloud services like AWS Lambda.
– Python Development: Created Python-based automation scripts, RESTful APIs, and desktop applications that reduced
client workload by 30%. Utilized Django and Flask for API development, supporting end-to-end machine learning pipelines.
Key Projects
• FAIT (Fitness AI Trainer) - Graduation Project: Technologies: Pose Estimation, Deep Learning, Statistical Analysis
Developed an AI-driven fitness trainer using pose estimation and deep learning to analyze workout techniques with minimal
datasets. Integrated proprietary statistical algorithms achieving an exercise evaluation accuracy of 93.5%. Currently preparing a
research paper on the methodology.
• Federated Learning System: Technologies: Federated Learning, Blockchain,
Invented a federated learning model (FedGreedy) using a blockchain-based reward/penalty system, achieving 7% higher accuracy
than FedAvg and 5% more than FedProx in distributed settings. Focused on enhancing defense against model poisoning attacks.
• Weaponized Violence Detection: Technologies: YOLOv10, Real-time Video Surveillance
Designed a YOLOv10-based solution for real-time violent activity detection in video streams with 98% detection accuracy.
Integrated into live video surveillance systems for enhanced security monitoring.
• Multilingual Educational Content Generator: Technologies: LangChain, G4FLLM, RAG, Telegram-Bot, Django
Created a multilingual content generation platform using LangChain and G4FLLM to process text from multimedia sources like
YouTube videos, documents, and audio. Generates language-specific educational content, including quizzes, summaries, and Q&A
with RAG for enhanced relevance.
• Arabic Sign Language (ArSL) Recognition: Technologies: CNN, Transformers, Real-time Video Processing
Developed a real-time ArSL recognition model optimized with custom CNN and transformer architectures, channel attention, and
causal convolutions, using our proprietary dataset for high accuracy in video inputs about F1 score reach to 92% on 40 class.
• Breast Cancer Detection: Technologies: CNN, Medical Image Processing, VGG
Built a CNN model for early-stage breast cancer detection on medical images with a 93.79% accuracy, aiding in early diagnosis and
treatment planning.
• Drug Interaction Prediction: Technologies: StanfordNLP, Deep Neural Networks, Flask API
Utilized StanfordNLP and deep neural networks to predict drug-drug interactions. Deployed the model through a Flask API,
making it accessible via a user-friendly web interface.
Certifications
• Data Science and Machine Learning Foundations (Maven Analytics):
• Python for Data Science (Maven Analytics):
• Machine Learning Specialization (DeepLearning.AI):
• Deep Learning Specialization (DeepLearning.AI):
• Data Science Track (365 Data Science):
• Project Management (PMI): Certified in Agile and Predictive project management.


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