Author: Mahmoud Ghorbel

Mahmoud Ghorbel
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Mahmoud is a PhD researcher in machine learning. He also holds a bachelor's degree in physical science and a master's degree in telecommunications and networking systems. His current areas of research concern computer vision, stock market prediction and deep learning. He produced several scientific articles about person re- identification and the study of the robustness and stability of deep networks.

Enhancing Underwater Image Segmentation with Deep Learning: A Novel Approach to Dataset Expansion and Preprocessing Techniques

Underwater image processing combined with machine learning offers significant potential for enhancing the capabilities of underwater robots across various marine exploration tasks. Image segmentation,...

This AI Paper Explains the Effect of Data Augmentation on Deep-Learning-based Segmentation of Long-Axis Cine-MRI

Cardiac Magnetic Resonance Imaging (CMRI) segmentation plays a crucial role in diagnosing cardiovascular diseases, particularly ischemic heart conditions, which are a leading cause of...

Balancing Privacy and Performance: This Paper Introduces a Dual-Stage Deep Learning Framework for Privacy-Preserving Re-Identification

Person Re-identification (Person Re-ID) in Machine Learning uses deep learning models like convolutional neural networks to recognize and track individuals across different camera views,...

A New Saudi AI Research Initiative Integrating Supervised and Unsupervised Machine Learning Models to Combat $500 Billion Global Tax Fraud Issue

Tax fraud, characterized by the deliberate manipulation of information in tax returns to reduce tax liabilities, poses a substantial challenge for governments globally. The...

This Paper Unveils How Machine Learning Revolutionizes Wild Primate Behavior Analysis with DeepLabCut

Studying animal behavior is crucial for understanding how different species and individuals interact with their surroundings. Video coding is preferred for collecting detailed behavioral...

Researchers from the University of Oxford Developed a Deep Learning-Based Software for Precision Tracking of Fish Movement in Complex Environments

Automated animal tracking software has revolutionized behavioral studies, particularly in monitoring laboratory creatures like aquarium fish, which is pivotal across neuroscience, medicine, and biomechanics....

Researchers from the National University of Singapore Developed a Groundbreaking RMIA (Robust Membership Inference Attack) Technique for Enhanced Privacy Risk Analysis in Machine Learning

Privacy in machine learning models has become a critical concern owing to Membership Inference Attacks (MIA). These attacks gauge whether specific data points were...

Revolutionizing Agriculture with AI: A Deep Dive into Machine Learning for Leaf Disease Classification and Smart Farming

Agriculture stands as the bedrock of humanity's sustenance. In this critical realm, the transformative power of machine learning is reshaping the landscape. Specifically in...

This AI Paper Reveals the Cybersecurity Implications of Generative AI Models – Risks, Opportunities, and Ethical Challenges

Generative AI (GenAI) models, such as ChatGPT, Google Bard, and Microsoft's GPT, have revolutionized AI interaction. They reshape multiple domains by creating diverse content...

UC Berkeley Researchers Develop ALIA: A Breakthrough in Automated Language-Guided Image Augmentation for Fine-Grained Classification Tasks

Fine-grained image classification is a computer vision task aiming to classify images into subcategories within a larger category. It involves the intricate identification of...

Can AI Truly Understand Our Emotions? This AI Paper Explores Advanced Facial Emotion Recognition with Vision Transformer Models

FER is pivotal in human-computer interaction, sentiment analysis, affective computing, and virtual reality. It helps machines understand and respond to human emotions. Methodologies have...

This AI Paper Proposes a Novel Pre-Training Strategy Called Privacy-Preserving MAE-Align’ to Effectively Combine Synthetic Data and Human-Removed Real Data

Action recognition, the task of identifying and classifying human actions from video sequences, is a crucial field within computer vision. However, its reliance on...

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