Skip to main content

Intelligent visual

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




For Enquiries: support@researchw.com

Get Connected Here
---------------------------------
---------------------------------

Comments

Post a Comment

Popular posts from this blog

HealthAIoT: Revolutionizing Smart Healthcare! HealthAIoT combines Artificial Intelligence and the Internet of Things to transform healthcare through real-time monitoring, predictive analytics, and personalized treatment. It enables smarter diagnostics, remote patient care, and proactive health management, enhancing efficiency and outcomes while reducing costs. HealthAIoT is the future of connected, intelligent, and patient-centric healthcare systems. What is HealthAIoT? HealthAIoT is the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) in the healthcare industry. It integrates smart devices, sensors, and wearables with AI-powered software to monitor, diagnose, and manage health conditions in real-time. This fusion is enabling a new era of smart, connected, and intelligent healthcare systems . Key Components IoT Devices in Healthcare Wearables (e.g., smartwatches, fitness trackers) Medical devices (e.g., glucose monitors, heart rate sensors) Rem...
Post a Comment
Read more
Detecting Co-Resident Attacks in 5G Clouds! Detecting co-resident attacks in 5G clouds involves identifying malicious activities where attackers share physical cloud resources with victims to steal data or disrupt services. Techniques like machine learning, behavioral analysis, and resource monitoring help detect unusual patterns, ensuring stronger security and privacy in 5G cloud environments. Detecting Co-Resident Attacks in 5G Clouds In a 5G cloud environment, many different users (including businesses and individuals) share the same physical infrastructure through virtualization technologies like Virtual Machines (VMs) and containers. Co-resident attacks occur when a malicious user manages to place their VM or container on the same physical server as a target. Once co-residency is achieved, attackers can exploit shared resources like CPU caches, memory buses, or network interfaces to gather sensitive information or launch denial-of-service (DoS) attacks. Why are Co-Resident Attack...
Post a Comment
Read more
                        Neural Networks Neural networks are computing systems inspired by the human brain, consisting of layers of interconnected nodes (neurons). They process data by learning patterns from input, enabling tasks like image recognition, language translation, and decision-making. Neural networks power many AI applications by adjusting internal weights through training with large datasets.                                                    Structure of a Neural Network Input Layer : This is where the network receives data. Each neuron in this layer represents a feature in the dataset (e.g., pixels in an image or values in a spreadsheet). Hidden Layers : These layers sit between the input and output layers. They perform calculations and learn patterns. The more hidden layers a ne...
Post a Comment
Read more