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All Journal EMITTER International Journal of Engineering Technology Journal of Electronics, Electromedical Engineering, and Medical Informatics
Chhabra, Prachi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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KFREAIN: Design of A Kernel-Level Forensic Layer for Improving Real-Time Evidence Analysis Performance in IoT Networks Shukla, Seema; Mangesh, Sangeeta; Chhabra, Prachi
EMITTER International Journal of Engineering Technology Vol 11 No 2 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i2.804

Abstract

An exponential increase in number of attacks in IoT Networks makes it essential to formulate attack-level mitigation strategies. This paper proposes design of a scalable Kernel-level Forensic layer that assists in improving real-time evidence analysis performance to assist in efficient pattern analysis of the collected data samples. It has an inbuilt Temporal Blockchain Cache (TBC), which is refreshed after analysis of every set of evidences. The model uses a multidomain feature extraction engine that combines lightweight Fourier, Wavelet, Convolutional, Gabor, and Cosine feature sets that are selected by a stochastic Bacterial Foraging Optimizer (BFO) for identification of high variance features. The selected features are processed by an ensemble learning (EL) classifier that use low complexity classifiers reducing the energy consumption during analysis by 8.3% when compared with application-level forensic models. The model also showcased 3.5% higher accuracy, 4.9% higher precision, and 4.3% higher recall of attack-event identification when compared with standard forensic techniques. Due to kernel-level integration, the model is also able to reduce the delay needed for forensic analysis on different network types by 9.5%, thus making it useful for real-time & heterogenous network scenarios.
COV-TViT: An Improved Diagnostic System for COVID Pneumonitis Utilizing Transfer Learning and Vision Transformer on X-Ray Images Kumar, Sunil; Yadav, Amar Pal; Nandal, Neha; Awasthi, Vishal; Sapra, Luxmi; Chhabra, Prachi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 4 (2025): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i4.1037

Abstract

COVID is a contagious lung ailment that continues to be a world curse, and it remains a highly infectious respiratory disease with global health implications. Traditional diagnostic methods, such as RT-PCR, though widely used, are often constrained by high costs, limited accessibility, and delayed results. In contrast, radiology for lung disease detection has been proven advantageous for identifying deformities, and chest X-rays are the most preferred radiological method due to their non-invasive nature. To address these limitations, this study aims to develop an efficient, automated diagnostic system leveraging radiological imaging, specifically X-rays, which are cost-effective and widely available. The primary contribution of this research is the introduction of COV-TViT, a novel deep learning framework that integrates transfer learning with Vision Transformer (ViT) architecture for the accurate detection of COVID pneumonitis. The proposed method is evaluated using the COVID-QU-Ex dataset, which comprises a balanced set of X-ray images from COVID positive and healthy individuals. Methodologically, the system employs pre-trained convolutional neural networks (CNNs), specifically VGG16 and VGG19 (Visual Geometry Group), for transfer learning, followed by fine tuning to enhance feature extraction. The ViT model, known for its self-attention mechanism, is then applied to capture complex spatial dependencies in the X-ray images, enabling robust classification. Experimental results demonstrate that COV-TViT achieves a classification accuracy of 98.96% and an F1 score of 96.21%, outperforming traditional CNN based transfer learning models in several scenarios. These findings underscore the model’s potential for high-precision COVID pneumonitis detection. The proposed approach significantly transforms classification tasks using self-attention mechanisms to extract features and learn representations. Overall, the proposed diagnostic system COV-TViT can be advantageous in the fundamental identification of COVID pneumonitis.
Co-Authors Awasthi, Vishal Mangesh, Sangeeta Nandal, Neha Sapra, Luxmi Shukla, Seema Sunil Kumar, Sunil Yadav, Amar Pal