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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Yasmin Makki Mohialden
Mustansiriyah University
Electrical & Electronics Engineering

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

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Efficient method for breast cancer classification based on ensemble hoffeding tree and naïve Bayes Royida A. Ibrahem Alhayali; Munef Abdullah Ahmed; Yasmin Makki Mohialden; Ahmed H. Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 2: May 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i2.pp1074-1080

Abstract

The most dangerous type of cancer suffered by women above 35 years of age is breast cancer. Breast Cancer datasets are normally characterized by missing data, high dimensionality, non-normal distribution, class imbalance, noisy, and inconsistency. Classification is a machine learning (ML) process which has a significant role in the prediction of outcomes, and one of the outstanding supervised classification methods in data mining is Naives Bayess Classification (NBC). Naïve Bayes Classifications is good at predicting outcomes and often outperforms other classifications techniques. Ones of the reasons behind this strong performance of NBC is the assumptions of conditional Independences among the initial parameters and the predictors. However, this assumption is not always true and can cause loss of accuracy. Hoeffding trees assume the suitability of using a small sample to select the optimal splitting attribute. This study proposes a new method for improving accuracy of classification of breast cancer datasets. The method proposes the use of Hoeffding trees for normal classification and naïve Bayes for reducing data dimensionality.
A smart gas leakage monitoring system for use in hospitals Nadia Mahmood Hussien; Yasmin Makki Mohialden; Nada Thanoon Ahmed; Mostafa Abdulghafoor Mohammed; Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 2: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i2.pp1048-1054

Abstract

A gas leaks lead to personal and financial damage. Much effort has been dedicated to preventing such leaks and developing reliable techniques for leak detection and leakage localization using sensors. These sensors usually sound an alarm after detecting a dangerous gas in its vicinity. This paper describes a system for detecting a gas leakage from cylinders which notifies the user via the GSM network. The system consists of an LPG gas leakage detector which sends a warning signal to Arduino Uno Microcontroller. The system uses the GSM network to send notifications, a liquid crystal display (LCD) monitor to display the warning message and buzzer to sound the alert.
COVID-19 knowledge-based system for diagnosis in Iraq using IoT environment Bashar M. Nema; Yasmin Makki Mohialden; Nadia Mahmood Hussien; Nael Ali Hussein
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp328-337

Abstract

The importance and benefits of healthcare mobile applications is increasing rapidly, especially when such applications are connected to the internet of things (IoT). This paper describes a smart knowledge-based system (KBS) that helps patients showing symptoms of Influenza verify being infected with Coronavirus, commonly known as COVID-19. In addition to the systems’ diagnostic functionality, it helps these patients get medical assistance fast by notifying medical authorities using the IoT. This system displays patient’s location, phone number, date and time of examination. During the applications’ development, the developers used Twilio, short message service (SMS), WhatsApp, and Google map applications.
Co-Authors Ahmed H. Ali Ahmed Hussein Ali Bashar M. Nema Mostafa Abdulghafoor Mohammed Mostafa Abdulghafoor Mohammed Munef Abdullah Ahmed Nada Thanoon Ahmed Nada Yousif Hashim Nadia Mahmood Hussien Nael Ali Hussein Nashwan Dheyaa Zaki Royida A. Ibrahem Alhayali Tole Sutikno