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All Journal Journal of Applied Data Sciences
Priscilla, G. Maria
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Deep Wiener Deconvolution Denoising Sparse Autoencoder Model for Pre-processing High-resolution Satellite Images Kiruthika, S.; Priscilla, G. Maria; Vijendran, Anna Saro; Batumalay, M.; Xu, Zhengrui
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.357

Abstract

The detection of geospatial objects in surveillance applications faces significant challenges due to the misclassification of object boundaries in noisy and blurry satellite images, which complicates the detection model's computational complexity, uncertainty, and bias. To address these issues and improve object detection accuracy, this paper introduces the Deep Wiener Deconvolution Denoising Sparse Autoencoder (DWDDSAE) model, a novel hybrid approach that integrates deep learning with Wiener deconvolution and Denoising Sparse Autoencoder (DSAE) techniques. The DWDDSAE model enhances image quality by extracting deep features and mitigating adversarial noise, ultimately leading to improved detection outcomes. Evaluations conducted on the NWPU VHR-10 and DOTA datasets demonstrate the effectiveness of the DWDDSAE model, achieving notable performance metrics: 96.32% accuracy, 86.88 edge similarity, 75.47 BRISQUE, 28.05 IQI, 38.08 PSNR (dB), 0.883 SSIM, 98.25 MSE, and 0.099 RMSE. The proposed model outperforms existing methods, offering superior noise and blur removal capabilities and contributing to Sustainable Development Goals (SDGs) such as SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). This research highlights the model's potential for inclusive innovation in object detection applications, showcasing its contributions and novel approach to addressing existing limitations.
Trust Aware Congestion Control Mechanism for Wireless Sensor Network Priscilla, G. Maria; Kumar, B.L. Shiva; Maidin, Siti Sarah; Attarbashi, Zainab S.
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.564

Abstract

Congestion in wireless sensor networks (WSNs) can occur from various factors, including resource limitations and the transmission of packets surpassing the capacity of receiving nodes. This congestion may arise from natural causes or be exacerbated by self-serving nodes. Furthermore, malicious sensor nodes within WSNs have the capability to instigate congestion-like scenarios by either flooding the network with redundant fake packets or maliciously discarding genuine data packets. Relying solely on conventional congestion control techniques proves inadequate for ensuring fair delivery, necessitating a proactive approach to prevent such adversities by segregating these nodes from the network. Existing congestion control strategies often make the unrealistic assumption that all nodes are authentic and behave appropriately. To address these challenges, a proposed Genetic Algorithm based Trust-Aware Congestion Control (GA-TACC) not only manages congestion under natural circumstances but also considers scenarios where hostile nodes deliberately improve packet delivery. The GA evaluates the credibility score (CS), contributing to enhanced performance, and GA-TACC demonstrates superiority over existing state-of-the-art techniques for wireless sensor network.
Co-Authors Attarbashi, Zainab S. Batumalay, M. Kiruthika, S. Kumar, B.L. Shiva Maidin, Siti Sarah Vijendran, Anna Saro Xu, Zhengrui