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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Nursabillilah Mohd Ali
Universiti Teknikal Malaysia Melaka
Electrical & Electronics Engineering

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Sign Detection Vision Based Mobile Robot Platform Hairol Nizam Mohd Shah; Mohd Zamzuri Ab Rashid; Zalina Kamis; Mohd Shahrieel Mohd Aras; Nursabillilah Mohd Ali; Faizil Wasbari; Tengku Muhammad Mahfuz Tengku Anuar
Indonesian Journal of Electrical Engineering and Computer Science Vol 7, No 2: August 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v7.i2.pp524-532

Abstract

Vision system applied in electrical power generated mobile robot to provide a comfortable ride while providing comfort to tourist to interact with visitors. The camera is placed in front of the mobile robot to snap the images along in pathways. The system can recognized the sign which are right, left and up by using Harris corner algorithms and will be display in Graphical User Interface (GUI). A sign can be determined from the vertex coordinates according to the degree to distinguish the direction of the sign. The system will be tested in term of percentage of success in Harris point detection and availability to detect sign with different range. The result show the even though not all Harris point in an image can be detected but most of the images possible to recognise it sign direction.
Develop and Implementation of Solar Powered Ventilation System Hairol Nizam Mohd Shah; Zalina Kamis; Mohd Fairus Abdollah; Mohd Khairi Mohd Zambri; Faizil Wasbari; Nursabillilah Mohd Ali; Amirul Anwar Mat Shah
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i3.pp1211-1221

Abstract

A ventilation system comprising a ventilator or fan to inhale fresh air from environment surrounding enters the cabin of a car where at the same time exhale the hot air to the outside. This invention system is using solar power integrate with the ventilation system in order to stabilize the thermal condition inside the car during sunny day. The solar power is chosen as it is reasonable due to its limitless and environmental friendly source just like mentioned in the previous objective of the project. In addition, a rechargeable battery is used to power the ventilator in the absence of the alternative energy during cloudy day or has obstacle like being shaded by buildings, trees and others. It is just like an extra supply during emergency case. This may prevent the lack of power energy supply for the system. A heat sensor is used as to detect targeted (high temperature) and desired temperature (drop temperature) inside the car. It is operatively connected to a logic circuit to measure environmental factor, wherein the controller (PIC Microcontroller) utilizes the measured environmental condition to allow the power supply to activate or deactivate the ventilator.
Comparison of microarray breast cancer classification using support vector machine and logistic regression with LASSO and boruta feature selection Nursabillilah Mohd Ali; Nor Azlina Ab Aziz; Rosli Besar
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 2: November 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i2.pp712-719

Abstract

Breast cancer is the most frequent cancer diagnosis amongst women worldwide. Despite the advancement of medical diagnostic and prognostic tools for early detection and treatment of breast cancer patients, research on development of better and more reliable tools is still actively conducted globally. The breast cancer classification is significantly important in ensuring reliable diagnostic system. Preliminary research on the usage of machine learning classifier and feature selection method for breast cancer classification is conducted here. Two feature selection methods namely Boruta and LASSO and SVM and LR classifier are studied. A breast cancer dataset from GEO web is adopted in this study. The findings show that LASSO with LR gives the best accuracy using this dataset.
A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation Nursabillilah Mohd Ali; Rosli Besar; Nor Azlina Ab Aziz
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4838

Abstract

Breast cancer is one of the leading causes of death and most frequently diagnosed cancer amongst women. Annually, almost half a million women do not survive the disease and die from breast cancer. Machine learning is a subfield of artificial intelligence (AI) and computer science that uses data and algorithms to mimic how humans learn, and gradually improving its accuracy. In this work, simple machine learning methods are used to classify breast cancer microarray data to normal and relapse. The data is from the gene expression omnibus (GEO) website namely GSE45255 and GSE15852. These two datasets are integrated and combined to form a single dataset. The study involved three machine learning algorithms, random forest (RF), extra tree (ET), and support vector machine (SVM). Grid search cross validation (CV) is applied for hyperparameter tuning of the algorithms. The result shows that the tuned SVM is best among the tested algorithms with accuracy of 97.78%. In the future it is recommended to include feature selection method to get the optimal features and better classification accuracies.
Co-Authors Amirul Anwar Mat Shah Faizil Wasbari Faizil Wasbari Hairol Nizam Mohd Shah Mohd Fairus Abdollah Mohd Khairi Mohd Zambri Mohd Shahrieel Mohd Aras Mohd Zamzuri Ab Rashid Nor Azlina Ab Aziz Rosli Besar Tengku Muhammad Mahfuz Tengku Anuar Zalina Kamis