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    • An innovative method based on Shannon energy envelope and summit navigation for detecting R peaks of noise stress test signals
    Authors: Hoang, Van Manh;  Advisor: Nguyen, Ngoc-Viet; Pham, Manh Thang;  Co-Author: - (2021)

    In recent decades, there has been an increased demand for the processing of electrocardiogram (ECG) signals because of its significant role in diagnosing cardiac diseases. The QRS complex is the dominant feature of the ECG signal. The detection of QRS complexes is thus an essential part of almost any ECG signal processing systems. This paper presents a developed QRS complex detection method using dominant peak extraction and Shannon energy envelope for useful ECG signal analysis. The algorithm is divided into three main stages: pre-processing, searching for dominant peaks, and removing false R peaks. The proposed algorithm is validated in static ECG recordings from the MIT-BIH Arrhythmia Database (MITDB) and noise-contaminated ECG stress tests from the Glasgow University Database (G...
    • Enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification
    Authors: Bui, Thi Hanh; Hoang, Van Manh; Nguyen, Ngoc Viet;  Advisor: -;  Co-Author: - (2022)

    Plant-leaf diseases have become a significant threat to food security due to reducing the quantity and quality of agricultural products. Plant disease detection methods are commonly based on experience through manual observations of leaves. Developing fast, accurate, and automated techniques for identifying of crop diseases using computer vision and artificial intelligence (AI) can help overcome human shortcomings. In the current study, the EfficientNet architectures with pre-trained Noisy-Student weights were implemented using the transfer learning approach to classify leaf image-based healthy and diseased plant groups. The deep learning models were performed on the extended and enhanced PlantVillage datasets, consisting of leaf images of 14 different plant species, with background...
    • Enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification
    Authors: Bui, Thi Hanh; Hoang, Van Manh; Ngoc-Viet Nguyen;  Advisor: -;  Co-Author: - (2022)

    Plant-leaf diseases have become a significant threat to food security due to reducing the quantity and quality of agricultural products. Plant disease detection methods are commonly based on experience through manual observations of leaves. Developing fast, accurate, and automated techniques for identifying of crop diseases using computer vision and artificial intelligence (AI) can help overcome human shortcomings. In the current study, the EfficientNet architectures with pre-trained Noisy-Student weights were implemented using the transfer learning approach to classify leaf image-based healthy and diseased plant groups. The deep learning models were performed on the extended and enhanced PlantVillage datasets, consisting of leaf images of 14 different plant species, with background...