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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Sayeed, Md Shohel
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Computer Science & IT Electrical & Electronics Engineering

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Covid-19 forecasting model based on machine learning approaches: a review Sayeed, Md Shohel; Hishamuddin, Siti Najihah; Song, Ong Thian
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7304

Abstract

As coronavirus disease (Covid-19) it is a contagious disease that is spread by the SARS-CoV-2 virus, one of the most common causes of disease in humans. The disease was initially discovered in Wuhan, China, in 2019, and has now spread throughout the world, including Malaysia. A large number of people have lost their life partners and families because of this disease. Thus, in order for us to stop this epidemic spread, we have to implement social distance. The Covid-19 infection displays this type of behavior, which necessitates the development of mathematical and predictive modeling techniques capable of predicting possible disease patterns or trends, in order to assist the government and health authorities in predicting and preparing for potential outbreaks. The purpose of this paper is to provide an in-depth critique and analysis of the machine-learning approaches that have been implemented by researchers to predict Covid-19, based on existing research. As a result, future researchers will be able to use this paper as a valuable resource for their research related to the Covid-19 forecasting model.
Clinical named entity extraction for extracting information from medical data Kuttaiyapillai, Dhanasekaran; Madasamy, Anand; Ayyavu, Shobanadevi; Sayeed, Md Shohel
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1722-1731

Abstract

Clinical named entity extraction (NER) based on deep learning gained much attention among researchers and data analysts. This paper proposes a NER approach to extract valuable Parkinson’s disease-related information. To develop an effective NER method and to handle problems in disease data analytics, a unique NER technique applies a “recognize-map-extract (RME)” mechanism and aims to deal with complex relationships present in the data. Due to the fast-growing medical data, there is a challenge in the development of suitable deep-learning methods for NER. Furthermore, the traditional machine learning approaches rely on the time-consuming process of creating corpora and cannot extract information for specific needs and locations in certain situations. This paper presents a clinical NER approach based on a convolutional neural network (CNN) for better use of specific features around medical entities and analyzes the performance of the proposed approach through fine-tuning NER with effective pre-training on the BC5CDR dataset. The proposed method uses annotation of entities for various medical concepts. The second stage develops a clinically NER method. This proposed method shows interesting results on the performance measures achieving a precision of 92.57%, recall of 92.22%, and F1- measure of 91.6%.
Machine learning approaches to cybersecurity in the industrial internet of things: a review Heier, Melanie; Prasad, Penatiyana W. Chandana; Sayeed, Md Shohel
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3851-3866

Abstract

The industrial internet of things (IIoT) is increasingly used within various sectors to provide innovative business solutions. These technological innovations come with additional cybersecurity risks, and machine learning (ML) is an emerging technology that has been studied as a solution to these complex security challenges. At time of writing, to the author’s knowledge, a review of recent studies on this topic had not been undertaken. This review therefore aims to provide a comprehensive picture of the current state of ML solutions for IIoT cybersecurity with insights into what works to inform future research or real-world solutions. A literary search found twelve papers to review published in 2021 or later that proposed ML solutions to IIoT cybersecurity concerns. This review found that federated learning and semi-supervised learning in particular are promising ML techniques being proposed to combat the concerns around IIoT cybersecurity. Artificial neural network approaches are also commonly proposed in various combinations with other techniques to ensure fast and accurate cybersecurity solutions. While there is not currently a consensus on the best ML techniques to apply to IIoT cybersecurity, these findings offer insight into those approaches currently being utilized along with gaps where further examination is required.
Co-Authors Ayyavu, Shobanadevi Heier, Melanie Hishamuddin, Siti Najihah Kuttaiyapillai, Dhanasekaran Madasamy, Anand Prasad, Penatiyana W. Chandana Song, Ong Thian