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All Journal International Journal of Electrical and Computer Engineering Journal of Future Artificial Intelligence and Technologies
Md. Abdur Rahman
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Computer Science & IT Electrical & Electronics Engineering

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Rectangular Microstrip Patch Antenna at 2GHZ on Different Dielectric Constant for Pervasive Wireless Communication Md. Maruf Ahamed; Kishore Bhowmik; Md. Shahidulla; Md. Shihabul Islam; Md. Abdur Rahman
International Journal of Electrical and Computer Engineering (IJECE) Vol 2, No 3: June 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (272.603 KB)

Abstract

In this Paper presents the result for different dielectric constant values and the result is performed by thickness of 2.88mm and resonance frequency of 2GHz where 2.32 (Duroid) are gives the best result. In the recent years the development in communication systems requires the development of low cost, minimal weight, low profile antennas   that are capable of maintaining high performance over a wide spectrum of frequencies.  This technological trend has focused much effort into the design of a Microstrip patch antenna. The proposed antenna design on different dielectric constant and analyzed result of all dielectric constant between 1 to 10, when the proposed antenna designs on Duroid substrate with dielectric constant 2.32. At 2GHz the verify and tested result on MATLAB are Radiation Efficiency=91.99%, Directivity=5.4dBi, Directive gain=4.98dBi and Half Power Beam Width-H plane=99.6123 degrees.DOI:http://dx.doi.org/10.11591/ijece.v2i3.341
Leveraging GANs for Synthetic Data Generation to Improve Intrusion Detection Systems Rahman, Md. Abdur; Francia, Guillermo A.; Shahriar, Hossain
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 4 (2025): March 2025
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-52

Abstract

This research presents a hybrid intrusion detection approach that integrates Generative Adversarial Networks (GANs) for synthetic data generation with Random Forest (RF) as the primary classifier. The study aims to improve detection performance in cybersecurity applications by enhancing dataset diversity and addressing challenges in traditional models, particularly in detecting minority attack classes often underrepresented in real-world datasets. The proposed method employs GANs to generate synthetic attack samples that mimic real-world intrusions, which are then combined with real data from the UNSW-NB15 dataset to create a more balanced training set. By leveraging synthetic data augmentation, our approach mitigates issues related to class imbalance and enhances the generalization capability of the classifier. Extensive experiments demonstrate that RF trained on the combined dataset of real and synthetic data achieves superior detection performance compared to models trained exclusively on real data. Specifically, RF trained solely on the original dataset achieves an accuracy of 97.58%, whereas integrating GAN-generated synthetic data improves accuracy to 98.27%. The proposed methodology is further evaluated through comparative analysis against alternative classifiers, including Support Vector Machine (SVM), XGBoost, Gated Recurrent Unit (GRU), and related studies in the field. Our findings indicate that GAN-augmented training significantly enhances detection rates, particularly for rare attack types, while maintaining computational efficiency. Furthermore, RF outperforms other classifiers, including deep learning models, demonstrating its effectiveness as a lightweight yet robust classification method. Integrating GANs with RF offers a scalable and adaptable framework for intrusion detection, ensuring improved resilience against evolving cyber threats.
Co-Authors Francia, Guillermo A. Kishore Bhowmik Md. Maruf Ahamed Md. Shahidulla Md. Shihabul Islam Shahriar, Hossain