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20232026
most citedFaster, Lighter, More Accurate: A Deep Learning Ensemble for Content Moderation

1 citations · 3 across the 8 of their papers we have counts for

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cs.CV2026

Evolving CNN Architectures: From Custom Designs to Deep Residual Models for Diverse Image Classification and Detection Tasks

Mahmudul Hasan, Mabsur Fatin Bin Hossain

This paper presents a comparative study of a custom convolutional neural network (CNN) architecture against widely used pretrained and transfer learning CNN models across five real…

cs.CV2025

Unmasking Deep Fakes: Leveraging Deep Learning for Video Authenticity Detection

Mahmudul Hasan, Sadia Ruhama, Sabrina Tajnim Sithi +2

Deepfake videos, produced through advanced artificial intelligence methods now a days, pose a new challenge to the truthfulness of the digital media. As Deepfake becomes more convi…

cs.CV2025

Parkinson's Disease Freezing of Gait (FoG) Symptom Detection Using Machine Learning from Wearable Sensor Data

Mahmudul Hasan

Freezing of gait (FoG) is a special symptom found in patients with Parkinson's disease (PD). Patients who have FoG abruptly lose the capacity to walk as they normally would. Accele…

cs.CV2025★ 1 cited

Automatic Vehicle Detection using DETR: A Transformer-Based Approach for Navigating Treacherous Roads

Istiaq Ahmed Fahad, Abdullah Ibne Hanif Arean, Nazmus Sakib Ahmed +1

Automatic Vehicle Detection (AVD) in diverse driving environments presents unique challenges due to varying lighting conditions, road types, and vehicle types. Traditional methods,…

cs.CV2024

Vision Eagle Attention: a new lens for advancing image classification

Mahmudul Hasan

In computer vision tasks, the ability to focus on relevant regions within an image is crucial for improving model performance, particularly when key features are small, subtle, or…

cs.CV2023★ 1 cited

Faster, Lighter, More Accurate: A Deep Learning Ensemble for Content Moderation

Mohammad Hosseini, Mahmudul Hasan

To address the increasing need for efficient and accurate content moderation, we propose an efficient and lightweight deep classification ensemble structure. Our approach is based…