Browsing by Author "Kumar, Mohit"

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    CoviDetector: A transfer learning-based semi supervised approach to detect Covid-19 using CXR images
    (ELSEVIER, 2023) Chowdhury, Deepraj; Das, Anik; Dey, Ajoy; Banerjee, Soham; Golec, Muhammed; Kollias, Dimitrios; Kumar, Mohit; Kaur, Guneet; Kaur, Rupinder; Arya, Rajesh Chand; Wander, Gurleen; Wander, Praneet; Wander, Gurpreet Singh; Parlikad, Ajith Kumar; Gill, Sukhpal Singh; Uhlig, Steve; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Golec, Muhammed
    COVID-19 was one of the deadliest and most infectious illnesses of this century. Research has been done to decrease pandemic deaths and slow down its spread. COVID-19 detection investigations have utilised Chest X-ray (CXR) images with deep learning techniques with its sensitivity in identifying pneumonic alterations. However, CXR images are not publicly available due to users’ privacy concerns, resulting in a challenge to train a highly accurate deep learning model from scratch. Therefore, we proposed CoviDetector, a new semisupervised approach based on transfer learning and clustering, which displays improved performance and requires less training data. CXR images are given as input to this model, and individuals are categorised into three classes: (1) COVID-19 positive; (2) Viral pneumonia; and (3) Normal. The performance of CoviDetector has been evaluated on four different datasets, achieving over 99% accuracy on them. Additionally, we generate heatmaps utilising Grad-CAM and overlay them on the CXR images to present the highlighted areas that were deciding factors in detecting COVID-19. Finally, we developed an Android app to offer a user-friendly interface. We release the code, datasets and results’ scripts of CoviDetector for reproducibility purposes; they are available at: https://github.com/dasanik2001/CoviDetector
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    Edge AI: A Taxonomy, Systematic Review and Future Directions
    (SPRINGER, 2024) Gill, Sukhpal Singh; Golec, Muhammed; Hu, Jianmin; Xu, Minxian; Du, Junhui; Wu, Huaming; Walia, Guneet Kaur; Murugesan, Subramaniam Subramanian; Ali, Babar; Kumar, Mohit; Ye, Kejiang; Verma, Prabal; Kumar, Surendra; Cuadrado, Felix; Uhlig, Steve; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Golec, Muhammed
    Edge Artificial Intelligence (AI) incorporates a network of interconnected systems and devices that receive, cache, process, and analyse data in close communication with the location where the data is captured with AI technology. Recent advancements in AI efficiency, the widespread use of Internet of Things (IoT) devices, and the emergence of edge computing have unlocked the enormous scope of Edge AI. The goal of Edge AI is to optimize data processing efficiency and velocity while ensuring data confidentiality and integrity. Despite being a relatively new field of research, spanning from 2014 to the present, it has shown significant and rapid development over the last five years. In this article, we present a systematic literature review for Edge AI to discuss the existing research, recent advancements, and future research directions. We created a collaborative edge AI learning system for cloud and edge computing analysis, including an in-depth study of the architectures that facilitate this mechanism. The taxonomy for Edge AI facilitates the classification and configuration of Edge AI systems while also examining its potential influence across many fields through compassing infrastructure, cloud computing, fog computing, services, use cases, ML and deep learning, and resource management. This study highlights the significance of Edge AI in processing real-time data at the edge of the network. Additionally, it emphasizes the research challenges encountered by Edge AI systems, including constraints on resources, vulnerabilities to security threats, and problems with scalability. Finally, this study highlights the potential future research directions that aim to address the current limitations of Edge AI by providing innovative solutions.