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Xu, Zhengrui
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Combined Fire Fly – Support Vector Machine Digital Radiography Classification (FF-SVM-DRC) Model for Inferior Alveolar Nerve Injury (IANI) Identification Manikandaprabhu, P.; Thirumoorthi, C.; 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.v5i3.356

Abstract

Inferior Alveolar Nerve Injury (IANI) is a severe complication in oral surgery that can significantly affect a patient's quality of life. Accurate diagnosis is crucial for effective management, and digital radiography has become an essential tool in this regard. This study proposes a novel feature selection-based classification algorithm to enhance the diagnostic precision of digital radiographs (DRs) for IANI detection. The objective is to improve classification accuracy by selecting the most relevant features using a Firefly algorithm-based method. Our approach identifies optimal features that preserve critical information from the dataset, enabling more accurate predictions by machine learning models. The proposed method was tested using a dataset of 140 DRs and achieved a classification accuracy of 97.4%, with a sensitivity of 80.9% and a specificity of 94.8%. These results demonstrate that the Firefly algorithm-based feature selection significantly outperforms traditional methods in diagnosing IANI. The novelty of this research lies in its integration of advanced feature selection techniques with support vector machines, offering a robust tool for improving diagnostic accuracy in dental imaging. This work contributes to enhanced clinical decision-making and could be valuable for broader applications in healthcare systems.
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.
IoT based Intrusion Detection for Edge Devices using Augmented System Nagarajan, R.; 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.v5i3.358

Abstract

The Edge Computing (EC) paradigm is gaining popularity among users due to its inherent characteristics and expeditious delivery approach. Users may get information from the network's edge thanks to this feature of network architecture. The security of this edge network design, however, is a major issue. Through the Internet and in a shared setting, users can access all EC services. Intrusion detection is a method of network security that searches for threats. It is ineffective to monitor real-time network data, and current detection techniques are unable to identify known dangers. To address this problem, a technique known as augmentation oversampling is proposed, which incorporates the minority classes in the dataset. Our Sort-Augment-Combine (SAC) approach divides the dataset into subsets of the class labels, from which synthetic data is generated for each group. The developed synthetic data was then used to oversample the minority classes. After the oversampling process was complete, the distinct classes were combined to provide improved training data for model fitting. When compared to the original dataset, the models trained using the enhanced datasets perform better in terms of accuracy, recall (sensitivity), and true positives (specificity). SAC fared best in a UNSW-NB15 dataset when compared to the Synthetic Minority Oversampling Technique (SMOTE) and Generative Adversarial Network-Data Augmentation (GAN-DA). Additionally, SAC points to improvements in general sensitivity, specificity, and accuracy. SMOTE, datasets with ROSE enhancements, and Random Over-Sampling Examples for process innovation.
Co-Authors Batumalay, M. Kiruthika, S. Manikandaprabhu, P. Nagarajan, R. Priscilla, G. Maria Thirumoorthi, C. Vijendran, Anna Saro