Author: 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.

Paper Summary: Efficient Deep Learning Approach to Recognize Person Attributes by Using Hybrid Transformers for Surveillance Scenarios

AI Paper Summary
Person Re-Id Attributes or Person Attribute Recognition (PAR) identifies and classifies attributes of people in images or videos. PAR is important in video surveillance,...

This AI Research Shows How ChatGPT Can Be Used For Finance Research

AI Paper Summary
Artificial intelligence language models are computer programs that use machine learning algorithms to analyze and understand human language. These models are designed to automatically...

The Concept of Data Generation

Uncategorized
Data generation (DG) refers to creating or producing new data. This can be done through various means, such as collecting data from sources, conducting...

Time Series Forecasting

Editors Pick
In today's world, where data plays an increasingly crucial role in decision-making, businesses must invest in the necessary resources and expertise to meet the...

This AI Paper Raises a Rarely Studied Privacy Risk of the Training Data of Person Re-Identification

AI Paper Summary
Person re-identification (Re-ID) is an image retrieval task that identifies a specific person in different images or video sequences. However, leaking information from the...

An Enhanced Joint Generative And Contrastive Learning (GCL+) Framework For Unsupervised Person Re-Identification (ReID)

AI Paper Summary
Unsupervised representation learning in person re-identification (ReID) is a task in computer vision that aims to identify a specific person across different camera views...

A New Game-based Framework to Systematize the Body of Knowledge on Privacy Inference Risks in Machine Learning

AI Paper Summary
Machine learning (ML) models have become increasingly popular, but with this popularity comes a growing concern about the leakage of information about training data....

New Individualized PATE Versions Support the Training of Machine Learning Models with Individualized Privacy Guarantees

AI Paper Summary
Differential privacy is a technique for protecting the privacy of individuals when their data, such as personal information or medical records, is used for...

A New Method to Evaluate the Performance of Models Trained with Synthetic Data When They are Applied to Real-World Data

AI Paper Summary
Credit scoring models are crucial in assessing and managing credit risk within financial institutions. However, it is limited due to challenges in obtaining data...

This AI Paper Introduces a New Attack on Machine Learning Where an Adversary Poisons a Training Set to Harm the Privacy of Other Users’...

AI Paper Summary
Machine learning models are used in various applications such as image and speech recognition, natural language processing, and predictive modeling. However, the security and...

This AI Paper Proposes A Privacy-Preserving Face Recognition Method Using Differential Privacy In The Frequency Domain

AI Paper Summary
Deep learning has significantly advanced face recognition models based on convolutional neural networks. These models have a high accuracy rate and are used in...

Latest Artificial Intelligence (AI) Research From CMU And Meta Demonstrates How Machine Learning’s Potential Can Be Leveraged To Identify Low Energy Adsorbate-Surface Configurations More...

AI Shorts
Over the last decade, computational catalysis has emerged as one of the most active research areas, and it is currently a vital tool for...
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