Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Hosen, Md. Apu
Unknown Affiliation
Computer Science & IT Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
MPCNN: a novel approach for detecting human Monkeypox from skin lesion images leveraging deep neural network Kabir, Sk. Shalauddin; Hosen, Md. Apu; Moz, Shahadat Hoshen; Galib, Syed Md.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1573-1582

Abstract

The global healthcare scenario encounters a substantial challenge caused by the widespread outbreak of Monkeypox affecting over 65 countries. Limited availability of polymerase chain reaction (PCR) tests and biochemical assays necessitates alternative strategies. This study explores the viability of computer-aided identification of Monkeypox through the analysis of skin lesion images, offering a potential solution, particularly in resource-constrained settings. Employing data augmentation techniques, we augment the dataset to enhance its robustness. Subsequently, we utilize various pre-trained deep learning models, including EfficientNetB3, VGG16, ResNet50, AlexNet, and EfficientNet for classification tasks related to Monkeypox and other diseases. The achieved accuracies for these models are 98.48%, 69.19%, 91.41%, 78.38%, and 94.44%, respectively. We introduce a novel modified convolutional neural network (CNN) architecture named MPCNN to further improve performance. Our proposed MPCNN model demonstrates exceptional accuracy, precisely identifying Monkeypox patients with a remarkable precision of 99.49%. This technological advancement in disease identification holds significant promise for enhancing healthcare strategies and response mechanisms in the context of global health concerns.
Quality and shelf-life prediction of cauliflower using machine learning under vacuum and modified atmosphere packaging Hosen, Md. Apu; Md. Galib, Dr. Syed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp907-916

Abstract

Ensuring the freshness and quality of cauliflower during storage and transportation is essential due to its high perishability. This study harnesses the power of machine learning to predict the quality and shelf-life of cauliflower under cost-effective vacuum and modified atmosphere packaging (MAP) techniques. By investigating key parameters such as total soluble solids (TSS), pH, weight loss, and color change, a significant impact on post-packaging quality was identified. To address the challenge of accurate color change measurement, an innovative method utilizing a bilateral filter for noise reduction and particle swarm optimization (PSO) with Markov random field (MRF) segmentation was developed. TSS, weight loss, and color change were identified as key parameters, and leveraging these parameters, artificial neural networks (ANN) were employed to create highly precise predictive models, achieving R-squared values of 0.952 for TSS, 0.992 for weight loss, and 0.981 for color change. This approach not only enhances the efficiency and sustainability of food production and distribution but also minimizes food waste and maximizes profitability for cauliflower in global markets through the use of cost-effective packaging solutions.
Co-Authors Galib, Syed Md. Kabir, Sk. Shalauddin Md. Galib, Dr. Syed Moz, Shahadat Hoshen