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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Zainal Hisham Che Soh
Universiti Teknologi MARA

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Cyclic voltammetry characterization analysis on the cu/flame retardant 4 fabricated biosensor Irni Hamiza Hamzah; Azman Ab Malik; Aida Zulia Zulhanip; Zainal Hisham Che Soh; Alhan Farhanah Abd Rahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i3.pp1199-1206

Abstract

Silicon, glass and ceramic are commonly base substrate used in DNA biosensor fabrication due to its biocompatible, expensive, hard and brittle. However due to the difficulty for drilling and dicing, these materials required expensive equipments and complex methodology of fabrication. Large gap of thermal expansion coefficient (TEC) between silicon/glass and film caused microcracks. Hence, the aim of this work is to investigate the suitability and the application of a non-biocompatible material, flame retardant 4 (FR4) as a base substrate for a label free DNA biosensor. Cyclic voltammetry (CV) reversible method has been implemented to test the fabricated Cu/Au on the FR4. It is found that the fabrication of Au has been made possible by the used of oxide-free Cu as an adhesion layer on the FR4 substrate. The area size of counter electrode (CE), working electrode (WE) and reference electrode (RE) are found to be 6.25 mm2, 0.581 mm2 and 1.04 mm2, respectively, in order to achieve the unity reversible redox relationship and to ensure the sensor’s reliability for 10 mM K3Fe(CN)6 solution in 0.1 M KCl. Thus it can be concluded that the proposed FR4-based substrate fulfilled the CV reversible process characterization.
Enhancing lung lesion localization in CT-scans: a novel approach using FE_CXY and statistical analysis Nurul Najiha Jafery; Siti Noraini Sulaiman; Muhammad Khusairi Osman; Noor Khairiah A. Karim; Mohd Firdaus Abdullah; Iza Sazanita Isa; Zainal Hisham Che Soh
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp913-925

Abstract

Intelligence algorithm systems rely on a large dataset to effectively extract significant features that can recognize patterns for classification purposes and extensively utilized to assist the physicians in diagnosis of lung cancer. Extracting valuable features from the available dataset is crucial, especially in cases where additional real data may not be readily accessible. In this context, we propose a novel method called feature extraction based on centroid (FE_CXY) for lesion localization, utilizing a statistical approach. The approach begins with a segmentation process that employs image processing techniques to extract features of interest which is data centroid. This extracted data is then used to compute statistical measurements, revealing hidden patterns that contribute to distinguishing between lesion and non-lesion locations. The method’s efficiency is reflected in the development of robust models with improved performance in localizing lung lesions. The study’s statistical findings strongly indicate that FE_CXY plays a crucial role as an important feature for detecting lesion localization supported by a student’s t-test, which identifies a statistically significant difference in the patterns between lesion and non-lesion localization (p<0.05). By incorporating this method into lung cancer detection systems, we anticipate improved accuracy and efficacy, thereby benefiting early diagnosis and treatment planning.
Co-Authors Aida Zulia Zulhanip Alhan Farhanah Abd Rahim Azman Ab Malik Irni Hamiza Hamzah Iza Sazanita Isa Mohd Firdaus Abdullah Muhammad Khusairi Osman Noor Khairiah A. Karim Nurul Najiha Jafery Siti Noraini Sulaiman