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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) International Journal of Applied Research and Sustainable Sciences (IJARSS)
Jumaat, Abdul Kadir
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Religion Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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Partitioning intensity inhomogeneity colour images via Saliency-based active contour Syukri Mazlin, Muhammad; Jumaat, Abdul Kadir; Embong, Rohana
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp337-346

Abstract

Partitioning or segmenting intensity inhomogeneity colour images is a challenging problem in computer vision and image shape analysis. Given an input image, the active contour model (ACM) which is formulated in variational framework is regularly used to partition objects in the image. A selective type of variational ACM approach is better than a global approach for segmenting specific target objects, which is useful for applications such as tumor segmentation or tissue classification in medical imaging. However, the existing selective ACMs yield unsatisfactory outcomes when performing the segmentation for colour (vector-valued) with intensity variations. Therefore, our new approach incorporates both local image fitting and saliency maps into a new variational selective ACM to tackle the problem. The euler-lagrange (EL) equations were presented to solve the proposed model. Thirty combinations of synthetic and medical images were tested. The visual observation and quantitative results show that the proposed model outshines the other existing models by average, with the accuracy of 2.23% more than the compared model and the Dice and Jaccard coefficients which were around 12.78% and 19.53% higher, respectively, than the compared model.
Hybrid deep learning and active contour for segmenting hazy images Ahmad Khairul Anuar, Firhan Azri; Jone, Jenevy; Raja Azhar, Raja Farhatul Aiesya; Jumaat, Abdul Kadir
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i3.pp429-437

Abstract

Image segmentation seeks to distinguish the foreground from the background for further analysis. A recent study presented a new active contour model (ACM) for image segmentation, termed Gaussian regularization selective segmentation (GRSS). This interactive ACM is effective for segmenting certain objects in images. However, a weakness of the GRSS model becomes apparent when utilized on hazy images, as it is not intended for such conditions and produces inadequate outcomes. This paper introduces a new ACM for segmenting hazy images that hybridizes a pretrained deep learning model, namely DehazeNet, with the GRSS model. Specifically, the haze-free images are estimated using DehazeNet, which fuses the information with the GRSS model. The new formulation, designated as GRSS with DehazeNet (GDN), is addressed via the calculus of variations and executed in MATLAB software. The segmentation accuracy was evaluated by calculating Error, Jaccard, and Dice metrics, while efficiency was determined by measuring processing time. Despite the increased processing time, numerical experiments demonstrated that the GDN model achieved higher accuracy, as indicated by the lower error and higher Jaccard and Dice than the GRSS model. The GDN model can potentially be formulated in the vector-valued image domain in the future.
The Utilization of a Combination of Heatsink Material And A Water Cooler Block As An Effort To Reduce Heat From Solar Panels Abdullah; Putri, Maharani; Syahruddin, M.; Silitonga, Arridina Susan; Dharma, Surya; Jumaat, Abdul Kadir; Ridzuan, Abdul Rahim; Aritonang, Gideon
International Journal of Applied Research and Sustainable Sciences Vol. 2 No. 5 (2024): May 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijarss.v2i5.1806

Abstract

Solar panels are an important utilization in generating renewable energy, but high temperatures can reduce energy conversion efficiency and shorten the lifetime of solar panels. This research aims to explore and evaluate the utilization of a combination of heatsink material and cooling water block as a solution to reduce the heat generated by solar panels. The research method includes the design and manufacture of a prototype consisting of solar panels mounted on a combination of heatsink material and water block. Experiments were conducted by varying the use of passive cooling and the absence of cooling on solar panels according to environmental conditions to evaluate the effectiveness of the system in reducing the temperature of solar panels. A power increase of 43.3% was achieved by comparing the system without cooling design with the passive cooling design system. The results showed that the use of a combination of heatsink material and cooling water block significantly reduced the solar panel temperature under various operational conditions. In addition, data analysis showed an increase in the energy conversion efficiency of the solar panel after the implementation of the cooling system. This research makes an important contribution to the development of more efficient and durable solar panel technology by addressing the problem of heat generation.
Co-Authors Abdullah Ahmad Khairul Anuar, Firhan Azri Aritonang, Gideon Arridina Susan Silitonga - Embong, Rohana Jone, Jenevy Putri, Maharani Raja Azhar, Raja Farhatul Aiesya Ridzuan, Abdul Rahim Surya Dharma Syahruddin, M. Syukri Mazlin, Muhammad