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Permanent URI for this collectionhttp://192.168.24.11:4000/handle/123456789/237

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    Machine Learning: navigating promises and pitfalls of AI
    (Shipra Publication New Delhi, 2025) Dhriti Tiwari, Sangeeta Suman
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    Research on Coding and Decoding Scheme for 5G Terminal Protocol Conformance Test Based on TTCN-3
    (Springer, Singapore, 2023) Asif Khan, Sharmin Ansar; Cao Jingyao, Amit Yadav
    Protocol conformance testing is an indispensable part of the marketization of commercial terminals. This paper elaborates and studies the architecture, test model, and design scheme of the 5G terminal protocol conformance testing system and proposes a 5G terminal protocol based on TTCN-3 Conformance test codec solution. The outcome of research shows that this solution has a positive role in promoting the industrialization of 5G.
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    Mask Wearing Detection System for Epidemic Control Based on STM32
    (Springer, Singapore, 2023) Luoli, Amit Yadav, Asif Khan, Naushad Varish, Priyanka Singh & Hiren Kumar Thakkar
    This paper designs an epidemic prevention and control mask wearing detection system based on STM32, which is used to monitor the situation of people wearing masks. Tiny-YOLO detection algorithm is adopted in the system, combined with image recognition technology, and two kinds of image data with and without masks are used for network training. Then, the trained model can be used to carry out real-time automatic supervision on the wearing of masks in the surveillance video. When the wrong wearing or not wearing masks are detected, the buzzer will send an alarm, so as to effectively monitor the wearing of masks and remind relevant personnel to wear masks correctly.
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    Detection of Rheumatoid Arthritis Using CNN by Transfer Learning
    (Springer, Singapore, 2024) Afroj Alam, Muhammad Kalamuddin Ahamad, K. O. Mohammed Aarif , Taushif Anwar
    Rheumatoid arthritis (RA) represents a long-term autoimmune condition marked by joint inflammation, resulting in discomfort and distortions of the joint structure. Early and accurate detection of RA plays a pivotal role in managing the disease and preventing irreversible joint damage. In this study, we propose a novel approach for detecting Rheumatoid Arthritis through the utilization of Convolutional Neural Networks (CNN) employing transfer learning. Our methodology leverages pre-trained CNN architectures to extract hierarchical features from medical images, particularly X-ray images of affected joints. Transfer learning enables the model to capitalize on the knowledge learned from large and diverse datasets, enhancing its ability to discriminate between healthy and RA-affected joints even with limited labeled data. Fine-tuning of the pre-trained model is performed to adapt the network to the specific characteristics of RA-related features. We present a comprehensive evaluation of our approach using a dataset consisting of X-ray images from both RA patients and healthy individuals. The experimental results showcase the effectiveness of our CNN-based transfer learning method in accurately detecting RA. The outcomes of this research have significant implications for early diagnosis and management of RA. By harnessing the power of deep learning and transfer learning, our approach contributes to the development of a non-invasive and efficient tool for RA detection, which can aid healthcare practitioners in making informed decisions and improving patient outcomes.