Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Ahmad Yahya Dawod
Chiang Mai University
Computer Science & IT Electrical & Electronics Engineering Engineering

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A hybrid method for traumatic brain injury lesion segmentation Ahmad Yahya Dawod; Aniwat Phaphuangwittayakul; Salita Angkurawaranon
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1437-1448

Abstract

Traumatic brain injuries are significant effects of disability and loss of life. Physicians employ computed tomography (CT) images to observe the trauma and measure its severity for diagnosis and treatment. Due to the overlap of hemorrhage and normal brain tissues, segmentation methods sometimes lead to false results. The study is more challenging to unitize the AI field to collect brain hemorrhage by involving patient datasets employing CT scans images. We propose a novel technique free-form object model for brain injury CT image segmentation based on superpixel image processing that uses CT to analyzing brain injuries, quite challenging to create a high outstanding simple linear iterative clustering (SLIC) method. The maintains a strategic distance of the segmentation image to reduced intensity boundaries. The segmentation image contains marked red hemorrhage to modify the free-form object model. The contour labelled by the red mark is the output from our free-form object model. We proposed a hybrid image segmentation approach based on the combined edge detection and dilation technique features. The approach diminishes computational costs, and the show accomplished 96.68% accuracy. The segmenting brain hemorrhage images are achieved in the clustered region to construct a free-form object model. The study also presents further directions on future research in this domain.
Assessing mangrove deforestation using pixel-based image: a machine learning approach Ahmad Yahya Dawod; Mohammed Ali Sharafuddin
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3199

Abstract

Mangrove is one of the most productive global forest ecosystems and unique in linking terrestrial and marine environment. This study aims to clarify and understand artificial intelligence (AI) adoption in remote sensing mangrove forests. The performance of machine learning algorithms such as random forest (RF), support vector machine (SVM), decision tree (DT), and object-based nearest neighbors (NN) algorithms were used in this study to automatically classify mangrove forests using orthophotography and applying an object-based approach to examine three features (tree cover loss, above-ground carbon dioxide (CO2) emissions, and above-ground biomass loss). SVM with a radial basis function was used to classify the remainder of the images, resulting in an overall accuracy of 96.83%. Precision and recall reached 93.33 and 96%, respectively. RF performed better than other algorithms where there is no orthophotography. 
Simulated trial and error experiments on productivity Karn Thamprasert; Ahmad Yahya Dawod; Nopasit Chakpitak
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Trial and error experiments in socioeconomics were proved to be beneficial by Nobel prize laureates. However, replication is challenging and costly in term of time and money. The approach required interventions on human society, and moral issues have to be carefully considered in research designs. This work tried to make the approach more feasible by developing virtual economic environment to allow simulated trial and error experiments to take place. This research demonstrated the framework using 19 macroeconomic indicators in 6 interested categories to study the effect on productivity if each indicator value grew by 5 percent for each of 65 countries. Seven predictive models including some machine learning (ML) models were compared. Neural network dominated in accurateness and was selected as the core of the simulator. Experimented results are in full of surprises, and the framework acted as expected to be a data-driven guide toward country-specific policy making.
Co-Authors Aniwat Phaphuangwittayakul Karn Thamprasert Mohammed Ali Sharafuddin Nopasit Chakpitak Salita Angkurawaranon