As people's awareness of ecological protection increases, bird sound monitoring has received more and more attention. Among them, using bird sound monitoring as part of audio recognition has become a hot research ...
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Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to...
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Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, the spatiotemporal fusion module incorporates a spatiotemporal graph convolutional network to jointly model temporal and spatial features, uncovering complex dependencies within the Electrocardiogram data and improving the model’s ability to represent complex patterns. In experiments conducted on the MIT-BIH arrhythmia dataset, the model achieved 99.95% accuracy, 99.80% recall, and a 99.78% F1 score. The model was further validated for generalization using the clinical INCART arrhythmia dataset, and the results demonstrated its effectiveness in terms of both generalization and robustness.
Fostering students' science identity through games and immersive technology is one approach to encouraging student interest in STEM subjects and careers. In this study, we used the Projective Reflection framework ...
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ISBN:
(纸本)9783031804748;9783031804755
Fostering students' science identity through games and immersive technology is one approach to encouraging student interest in STEM subjects and careers. In this study, we used the Projective Reflection framework to analyze WaterWays, highlighting features that facilitate science identity exploration. Features including augmented reality, interactive mini-games, automated text read-aloud, and a chat function exemplify aspects of this framework. We examined qualitative interview data obtained from teachers who piloted WaterWays in their classrooms to clarify how students responded to these features in classroom settings. Through this analysis, we discuss how the design features available on digital platforms can promote students' identity exploration. This study contributes to the growing body of literature on designing and evaluating interactive game-based learning environments guided by the Projective Reflection framework.
The emergence of quantum computing poses a significant threat to the security of traditional blockchain systems, which rely heavily on classical cryptographic algorithms. To safeguard the integrity and reliability of ...
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A number of industries are benefiting from the introduction of 5G technology, including eHealth, automation, robots, and smart infrastructure. The fast expansion of 5G has presented substantial problems in terms of ne...
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Influence maximization (IM) is the task of selecting the most influential nodes in the network. IM achieves the goal of spreading information, influencing behaviour, or promoting sales of products. Existing studies in...
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Endpoint detection in speech recognition identifies when a natural sentence stops during *** research on integrating endpoint detection and speech recognition into a single streaming ASR model is limited, with past me...
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The grading of fruits relies on inspections, experiences, and observations, with a proposed system integrating machine learning techniques to assess fruit freshness. By analyzing 2D fruit portrayals based on shape and...
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Motor imagery electroencephalogram recognition is a key area in brain-computer interfaces, with applications in human-computer interaction, rehabilitation, and virtual reality. Traditional methods often overlook the b...
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This paper describes a novel virtual platform for university teaching, which in particular allows the creation and use of complex IT infrastructures even for non-experts. Until now, complex network infrastructures in ...
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