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All Journal International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Scientific Journal of Engineering Research Scientific Journal of Computer Science
Rahman, Md Owahedur
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Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Effectiveness of Fourier, Wiener, Bilateral, and CLAHE Denoising Methods for CT Scan Image Noise Reduction Kobra, Mst Jannatul; Nakib, Arman Mohammad; Mweetwa, Peter; Rahman, Md Owahedur
Scientific Journal of Engineering Research Vol. 1 No. 3 (2025): September
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i3.2025.27

Abstract

The proper reduction of noise inside CTscan Images remains crucial to achieve both better diagnosis results and clinical choices. This research analyzes through quantitative metrics the effectiveness of four popular noise reduction methods which include Fourier-based denoising and Wiener filtering as well as bilateral filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE) applied to more than 500 CTscan Images. The investigated methods were assessed quantitatively through Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) while Mean Squared Error (MSE) served as the additional metric for evaluation. The evaluated denoising methods revealed bilateral filtering as the best technique based on its 50.37 dB PSNR and 0.9940 SSIM together with its 0.5967 MSE. Denoising with Fourier-based methods succeeded in removing high-frequency noise however it produced PSNR of 25.89 dB along with SSIM of 0.8138 while maintaining MSE at 167.4976 indicating lost crucial Image information. The performance balance of Wiener filtering resulted in 40.87 dB PSNR and 0.9809 SSIM and 5.3270 MSE that outperformed Fourier denoising in SSIM yet demonstrated higher MSE. CLAHE produces poor denoising outcomes because it achieves the lowest PSNR of 21.51 dB together with SSIM of 0.5707, and the maximum MSE of 459.1894 while creating undesirable artifacts. This research stands out through a full evaluation of four denoising techniques on a big dataset to create more precise analysis than prior research. The research results show bilateral filtering to be the most reliable technique for CTscan Image noise reduction when maintaining picture quality and thus represents a suitable choice for clinical use. This research adds new information to medical imaging research about quality enhancement which directly benefits clinical diagnostics and therapeutic planning.
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): December
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.
Exploring Raised Cosine Filtering for Signal Integrity and ISI Mitigation in Digital Communication Systems’ Kobra, Mst Jannatul; Rahman, Md Owahedur
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 7 No. 1 (2026): INJIISCOM: VOLUME 7, ISSUE 1, JUNE 2026 (ONLINE FIRST)
Publisher : Universitas Komputer Indonesia

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Abstract

In high-speed networks such as 4G/5G, bandwidth constraints are a major challenge to digital communication systems, where Inter-Symbol Interference (ISI) is a major problem. The present paper addresses the application of raised cosine filtering to eliminate ISI and improve signal integrity in the context of several modulation methods (BPSK, QPSK and M-PSK). By using MATLAB simulations, a series of roll-off values (0.1 to 0.9) were studied, with the performance measured in terms of Bit Error rate (BER), peak-to-average power ratio (PAPR) and signal quality, represented by eye diagrams. The work concludes that a higher roll-off factor has led to a better reduction of ISI and signal clearance although there is an expense of using more bandwidth. The findings point out to a trade-off between bandwidth efficiency and ISI mitigation which is useful in the design of optimized filters in contemporary high-speed communication systems. Such results can be applied to the creation of more efficient filtering methods to realistic networks such as 4G/5G and Wi-Fi, which can provide useful information to the designers of the communication systems.
A GAT-Assisted Hybrid Reinforcement Learning and Swarm Intelligence Framework for Autonomous UAV Coordination Kobra, Mst Jannatul; Rahman, Md Owahedur; Rashid, Mizanur
Scientific Journal of Computer Science Vol. 1 No. 2 (2025): December
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i2.2025.316

Abstract

The autonomous UAV swarms have fundamental issues with strong coordination that arise under delays in communication, dynamic obstacles and noisy sensing environments, and the existing centralized or heuristic-based solutions are insufficient in addressing such issues. To cover this gap, this paper proposes a Graph Attention Network (GAT)-based Hybrid Reinforcement Learning and Swarm Intelligence Framework that can enable the communication-aware decentralized cooperation of UAVs. It is a multi-agent reinforcement learning and PSO, ACO, Differential Evolution, flocking behavior and Control Barrier Function-based safety correction, and GAT-inspired adaptive graph communication encoding. The results of the simulation of 18 episodes with 24 UAVs demonstrate that the reward, coverage, and collision were demonstrated to be improved by 32%, 27%, and 40% respectively as compared to a classical greedy baseline. The findings confirm the fact that the proposed hybrid GAT-RL architecture enables to promote significantly more scalability, safety, and real-time responsiveness of UAV swarms, which is a possibility on the path to large-scale autonomous aerial coordination.
Co-Authors Kobra, Mst Jannatul Mweetwa, Peter Nakib, Arman Mohammad Rashid, Mizanur