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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Galib, Syed Md.
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Computer Science & IT Electrical & Electronics Engineering

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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.
Automation of 5G network slicing security using intent-based networking Islam, Md. Zahirul; Galib, Syed Md.; Kabir, Md. Humaun
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp401-413

Abstract

Network slicing is a fundamental technological advancement that facilitates the provision of novel services and solutions within the realm of 5G and the forthcoming 6G communications. Numerous challenges emerge when implementing network slicing on a large-scale commercial level since it necessitates comprehensive control and automation of the entire network. Cyberattacks, such as distributed denial of service (DDoS) and address resolution protocol (ARP) spoofing, can significantly disrupt the performance and accessibility of slices inside a multi-tenant virtualized networking infrastructure due to the shared utilization of physical resources. This article employs intent-based networking (IBN) to identify and address diverse threats through automated methods. A conceptual framework is presented in which the IBN manager is integrated into the network-slicing architecture to facilitate the implementation of automated security controls. The proposed work is assessed using an experimental test bed. The study's findings indicate that the network slice's performance exhibits improvement when successful detection and mitigation measures are implemented. This improvement is observed in various metrics: availability, packet loss, response time, central processing unit (CPU) and memory utilization.
A novel approach for generating physiological interpretations through machine learning Islam, Md. Jahirul; Adnan, Md. Nasim; Siddique, Md. Moradul; Ema, Romana Rahman; Hossain, Md. Alam; Galib, Syed Md.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1339-1352

Abstract

Predicting blood glucose trends and implementing suitable interventions are crucial for managing diabetes. Modern sensor technologies enable the collection of continuous glucose monitoring (CGM) data along with diet and activity records. However, machine learning (ML) techniques are often used for glucose level predictions without explicit physiological interpretation. This study introduces a method to extract physiological insights from ML-based glucose forecasts using constrained programming. A feed-forward neural network (FFNN) is trained for glucose prediction using CGM data, diet, and activity logs. Additionally, a physiological model of glucose dynamics is optimized in tandem with FFNN forecasts using sequential quadratic programming and individualized constraints. Comparisons between the constrained response and ML predictions show higher root mean square error (RMSE) in certain intervals for the constrained approach. Nevertheless, Clarke error grid (CEG) analysis indicates acceptable accuracy for the constrained method. This combined approach merges the generalization capabilities of ML with physiological insights through constrained optimization.
Co-Authors Adnan, Md. Nasim Ema, Romana Rahman Hosen, Md. Apu Hossain, Md. Alam Islam, Md. Zahirul Kabir, Md. Humaun Kabir, Sk. Shalauddin Md. Jahirul Islam, Md. Jahirul Moz, Shahadat Hoshen Siddique, Md. Moradul