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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Kabir, Sk. Shalauddin
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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.
Development and evaluation of a generalized ontology framework for software requirement specification Kundu, Sourav; Das, Soumay Kanti; Md Jamil, Abu Rafe; Islam, Md Kamrul; Kabir, SK. Shalauddin; Akhond, Mostafijur Rahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1050-1064

Abstract

This paper presents an ontology developed to address challenges such as com munication gaps, risks of errors, and inconsistencies during the manual process of creating software requirement specifications (SRS). The proposed ontology offers a systematic and formal depiction of the requirements, enhancing consis tency and communication among stakeholders. The ontology has been devel oped from the software requirements documents to facilitate the development process. This paper discusses the process of creating the ontology and demon strates using Pellet Reasoner for inference and Prot´eg´e for ontology construction to save and reuse information. The ontology seems to be efficient in manag ing complex software projects, enabling accurate requirement retrieval through SPARQL queries. This study emphasizes how incorporating ontologies into re quirement engineering can significantly enhance the quality and reliability of SRS.
Co-Authors Akhond, Mostafijur Rahman Das, Soumay Kanti Galib, Syed Md. Hosen, Md. Apu Islam, Md Kamrul Kundu, Sourav Md Jamil, Abu Rafe Moz, Shahadat Hoshen