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Intelligent visual question answering in TCM education: An innovative application of IoT and multimodal fusion


This paper proposes an innovative Traditional Chinese Medicine Ancient Text Education Intelligent Visual Question Answering System (TCM-VQA IoTNet), which integrates Internet of Things (IoT) technology with multimodal learning to achieve a deep understanding and intelligent question answering of both the images and textual content of traditional Chinese medicine ancient texts. The system utilizes the VisualBERT model for multimodal feature extraction, combined with Gated Recurrent Units (GRU) to process time-series data from IoT sensors, and employs an attention mechanism to optimize feature fusion, dynamically adjusting the question answering strategy.

Experimental evaluations on standard datasets such as VQA v2.0, CMRC 2018, and the Chinese Traditional Medicine Dataset demonstrate that TCM-VQA IoTNet achieves accuracy rates of 72.7%, 69.%, and 75.4% respectively, with F1-scores of 70.3%, 67.5%, and 73.9%, significantly outperforming existing mainstream models. Furthermore, TCM-VQA IoTNet has shown excellent performance in practical applications of traditional Chinese medicine education, significantly enhancing the precision and interactivity of intelligent education. Future research will focus on improving the model’s generalization ability and computational efficiency, further expanding its application potential in traditional Chinese medicine diagnosis and education.

The TCM-VQA IoTNet model, as introduced in this study, integrates VisualBERT, GRU units, and attention mechanisms to facilitate multimodal visual question–answering within the domain of TCM classical texts. This model’s primary contribution is its pioneering approach to multimodal data fusion and its dynamic IoT feedback system, which bolsters the system’s comprehension of visual and textual elements from TCM literature and enables tailored educational experiences through real-time learner state monitoring. The TCM-VQA IoTNet model has demonstrated notable strengths in managing intricate visual and textual elements of TCM classics, offering innovative avenues for advancing and digitizing TCM educational practices.

Future research will focus on optimizing several key aspects of the TCM-VQA IoTNet model. First, we aim to enhance the model’s stability and accuracy in real-world educational settings and explore its extension to broader fields, such as Chinese medicine diagnosis and treatment recommendation. Second, we will focus on improving the model’s generalization ability and computational efficiency to ensure it can respond quickly in diverse learning tasks and large-scale data processing scenarios. Additionally, computational simulation will play a critical role in future research.

computer vision, deep learning, object detection, image recognition, facial recognition, neural networks, machine learning, image segmentation, pattern recognition, intelligent imaging, visual analytics, smart surveillance, augmented reality, autonomous systems, real-time tracking, image processing, vision-based AI, sensor fusion, feature extraction, AI vision systems

#ComputerVision, #DeepLearning, #ObjectDetection, #ImageRecognition, #FacialRecognition, #NeuralNetworks, #MachineLearning, #ImageSegmentation, #PatternRecognition, #IntelligentImaging, #VisualAnalytics, #SmartSurveillance, #AugmentedReality, #AutonomousSystems, #RealTimeTracking, #ImageProcessing, #VisionAI, #SensorFusion, #FeatureExtraction, #AIVision




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