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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Fahmid Al Farid
Multimedia University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Hajj pilgrimage video analytics using CNN Md Roman Bhuiyan; Junaidi Abdullah; Noramiza Hashim; Fahmid Al Farid; Mohd Ali Samsudin; Norra Abdullah; Jia Uddin
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.2361

Abstract

This paper advances video analytics with a focus on crowd analysis for Hajj and Umrah pilgrimages. In recent years, there has been an increased interest in the advancement of video analytics and visible surveillance to improve the safety and security of pilgrims during their stay in Makkah. It is mainly because Hajj is an entirely special event that involve hundreds of thousands of people being clustered in a small area. This paper proposed a convolutional neural network (CNN) system for performing multitude analysis, in particular for crowd counting. In addition, it also proposes a new algorithm for applications in Hajj and Umrah. We create a new dataset based on the Hajj pilgrimage scenario in order to address this challenge. The proposed algorithm outperforms the state-of-the-art approach with a significant reduction of the mean absolute error (MAE) result: 240.0 (177.5 improvement) and the mean square error (MSE) result: 260.5 (280.1 improvement) when used with the latest dataset (HAJJ-Crowd dataset). We present density map and prediction of traditional approach in our novel HAJJ-crowd dataset for the purpose of evaluation with our proposed method.
Accurate detection of Alzheimer’s disease using machine learning model on magnetic resonance imaging data Rahman Md Mojnur; Md. Tanjil Sarker; Hezerul Abdul Karim; Fahmid Al Farid; Aziah Ali; Wan Noorshahida Mohd Isa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2357-2368

Abstract

The rapid identification and diagnosis of Alzheimer's disease (AD) is essential for initiating early intervention and effective treatment planning. Magnetic resonance imaging (MRI) provides valuable structural insights into the pathological alterations in the brain associated with AD. Early and accurate detection of AD is critical for initiating timely interventions. This study presents a classical machine learning (ML) approach for detecting AD using structured features extracted from MRI metadata, such as mini-mental state examination (MMSE) scores, brain volume metrics, and cognitive attributes. Unlike deep learning models that rely on raw imaging data, the interpretable framework offers reduced computational complexity and better alignment with real-world clinical constraints. Models such as random forest (RF) and extreme gradient boosting (XGBoost) achieved up to 85% accuracy, showing strong potential for deployment in resource-limited environments. The results demonstrate the potential of classical ML in supporting early AD diagnosis, particularly in low-resource clinical settings. Moreover, the proposed approach offers a computationally efficient and interpretable alternative to deep learning models, facilitating adoption in real-world healthcare environments.
Co-Authors Aziah Ali Hezerul Abdul Karim Jia Uddin Junaidi Abdullah Md Roman Bhuiyan Md. Tanjil Sarker Mohd Ali Samsudin Noramiza Hashim Norra Abdullah Rahman Md Mojnur Wan Noorshahida Mohd Isa