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A Hybrid Model for denoising in Data Mining and Exploration

Cookey I.B.1 , Bennett E.O.2 , Anireh V.I.E.3 , Matthias D.4

  1. Dept. of Computer Science, Faculty of Science, Rivers State University, Port-Harcourt, Nigeria.
  2. Dept. of Computer Science, Faculty of Science, Rivers State University, Port-Harcourt, Nigeria.
  3. Dept. of Computer Science, Faculty of Science, Rivers State University, Port-Harcourt, Nigeria.
  4. Dept. of Computer Science, Faculty of Science, Rivers State University, Port-Harcourt, Nigeria.

Section:Research Paper, Product Type: Journal Paper
Volume-12 , Issue-6 , Page no. 1-12, Jun-2024

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v12i6.112

Online published on Jun 30, 2024

Copyright © Cookey I.B., Bennett E.O., Anireh V.I.E., Matthias D. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Cookey I.B., Bennett E.O., Anireh V.I.E., Matthias D., “A Hybrid Model for denoising in Data Mining and Exploration,” International Journal of Computer Sciences and Engineering, Vol.12, Issue.6, pp.1-12, 2024.

MLA Style Citation: Cookey I.B., Bennett E.O., Anireh V.I.E., Matthias D. "A Hybrid Model for denoising in Data Mining and Exploration." International Journal of Computer Sciences and Engineering 12.6 (2024): 1-12.

APA Style Citation: Cookey I.B., Bennett E.O., Anireh V.I.E., Matthias D., (2024). A Hybrid Model for denoising in Data Mining and Exploration. International Journal of Computer Sciences and Engineering, 12(6), 1-12.

BibTex Style Citation:
@article{I.B._2024,
author = {Cookey I.B., Bennett E.O., Anireh V.I.E., Matthias D.},
title = {A Hybrid Model for denoising in Data Mining and Exploration},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2024},
volume = {12},
Issue = {6},
month = {6},
year = {2024},
issn = {2347-2693},
pages = {1-12},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5697},
doi = {https://doi.org/10.26438/ijcse/v12i6.112}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v12i6.112}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5697
TI - A Hybrid Model for denoising in Data Mining and Exploration
T2 - International Journal of Computer Sciences and Engineering
AU - Cookey I.B., Bennett E.O., Anireh V.I.E., Matthias D.
PY - 2024
DA - 2024/06/30
PB - IJCSE, Indore, INDIA
SP - 1-12
IS - 6
VL - 12
SN - 2347-2693
ER -

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Abstract

Image denoising is essential in digital image processing to improve the visual quality of images corrupted by noise. This study introduces a hybrid model combining fuzzy logic`s adaptive capabilities with genetic algorithms` optimization power for effective denoising. The model leverages fuzzy logic to handle uncertainty and genetic algorithms to optimize denoising parameters. The hybrid model processes images in stages: a fuzzy logic-based filter preprocesses the noise-affected image, guided by fuzzy rules from a knowledge base. Concurrently, a genetic algorithm optimizes the filter`s parameters through evolutionary techniques like crossover, mutation, and selection. The fuzzy logic and genetic algorithm components work together, with the fuzzy logic module using a Mamdani inference system and the genetic algorithm refining the denoised image Experimental results show the hybrid model outperforms traditional methods and standalone fuzzy logic or genetic algorithm approaches. Its adaptability allows it to handle varying noise levels and image content effectively, demonstrating robustness against different noise distributions. This makes it suitable for diverse denoising scenarios. The hybrid model represents a significant advancement in image denoising, highlighting the synergy between fuzzy logic and genetic algorithms. Future work will focus on further optimizations and extensions to improve applicability in real-world scenarios. Overall, this approach enhances noise reduction performance, image quality, and the preservation of important image features.

Key-Words / Index Term

Image Denoising, Fuzzy Logic, Genetic Algorithm, Datamining and Peak Signal to Noise Ratio.

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