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Scientific Journal of Engineering Research
ISSN : -     EISSN : 31091725     DOI : https://doi.org/10.64539/sjer
Core Subject : Engineering,
The Scientific Journal of Engineering Research (SJER) is a peer-reviewed and open-access scientific journal, managed and published by PT. Teknologi Futuristik Indonesia in collaboration with Universitas Qamarul Huda Badaruddin Bagu and Peneliti Teknologi Teknik Indonesia. The journal is committed to publishing high-quality articles in all fundamental and interdisciplinary areas of engineering, with a particular emphasis on advancements in Information Technology. It encourages submissions that explore emerging fields such as Machine Learning, Internet of Things (IoT), Deep Learning, Artificial Intelligence (AI), Blockchain, and Big Data, which are at the forefront of innovation and engineering transformation. SJER welcomes original research articles, review papers, and studies involving simulation and practical applications that contribute to advancements in engineering. It encourages research that integrates these technologies across various engineering disciplines. The scope of the journal includes, but is not limited to: Mechanical Engineering Electrical Engineering Electronic Engineering Civil Engineering Architectural Engineering Chemical Engineering Mechatronics and Robotics Computer Engineering Industrial Engineering Environmental Engineering Materials Engineering Energy Engineering All fields related to engineering By fostering innovation and bridging knowledge gaps, SJER aims to contribute to the development of sustainable and intelligent engineering systems for the modern era.
Articles 1 Documents
Search results for , issue "Vol. 1 No. 4 (2025): October Article in Process" : 1 Documents clear
Hybrid K-means, Random Forest, and Simulated Annealing for Optimizing Underwater Image Segmentation Kobra, Mst Jannatul; Rahman, Md Owahedur; Nakib, Arman Mohammad
Scientific Journal of Engineering Research Vol. 1 No. 4 (2025): October Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i4.2025.46

Abstract

The process of underwater image segmentation is also very difficult because the data collected by the underwater sensors and cameras is of very high complexity, and much data is generated and in that case, the data is not well seen, the color is distorted, and the features overlap. Current solutions, including K-means clustering and Random Forest classification, are unable to partition complex underwater images with high accuracy, or are unable to scale to large datasets, although the possibility of dynamically optimizing the number of clusters has not been fully explored. To fill these gaps, this paper advises a hybrid solution that combines K-means clustering, Random Forest classification and the Simulated Annealing optimization as a complete end to end system to maximize the efficiency and accuracy of segmentation. K-means clustering first divides images based on pixel intensity, Random Forest narrows its segmentation of images with features like texture, color and shape, and Simulated Annealing determines the desired number of clusters dynamically to segment images with minimal segmentation error. The segmentation error of the proposed method was 30 less than the baseline K-means segmentation accuracy of 65 percent and the proposed method segmentation accuracy was 95% with an optimal cluster number of 10 and a mean error of 7839.22. This hybrid system offers a large-scale, scalable system to underwater image processing that is robust and has applications in marine biology, environmental research, and autonomous underwater system exploration.

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