Paper Reading AI Learner

Improved AdaBoost for Virtual Reality Experience Prediction Based on Long Short-Term Memory Network

2024-05-17 03:47:30
Wenhan Fan, Zhicheng Ding, Ruixin Huang, Chang Zhou, Xuyang Zhang

Abstract

A classification prediction algorithm based on Long Short-Term Memory Network (LSTM) improved AdaBoost is used to predict virtual reality (VR) user experience. The dataset is randomly divided into training and test sets in the ratio of 7:3.During the training process, the model's loss value decreases from 0.65 to 0.31, which shows that the model gradually reduces the discrepancy between the prediction results and the actual labels, and improves the accuracy and generalisation ability.The final loss value of 0.31 indicates that the model fits the training data well, and is able to make predictions and classifications more accurately. The confusion matrix for the training set shows a total of 177 correct predictions and 52 incorrect predictions, with an accuracy of 77%, precision of 88%, recall of 77% and f1 score of 82%. The confusion matrix for the test set shows a total of 167 correct and 53 incorrect predictions with 75% accuracy, 87% precision, 57% recall and 69% f1 score. In summary, the classification prediction algorithm based on LSTM with improved AdaBoost shows good prediction ability for virtual reality user experience. This study is of great significance to enhance the application of virtual reality technology in user experience. By combining LSTM and AdaBoost algorithms, significant progress has been made in user experience prediction, which not only improves the accuracy and generalisation ability of the model, but also provides useful insights for related research in the field of virtual reality. This approach can help developers better understand user requirements, optimise virtual reality product design, and enhance user satisfaction, promoting the wide application of virtual reality technology in various fields.

Abstract (translated)

基于长短期记忆网络(LSTM)的分类预测算法改进后的AdaBoost用于预测虚拟现实(VR)用户体验。数据集按7:3的比例随机分成训练和测试集。在训练过程中,模型的损失值从0.65降低到0.31,这说明模型逐渐减小预测结果与实际标签之间的差异,并提高了准确性和泛化能力。模型的最终损失值为0.31,表明模型对训练数据适应良好,能够更准确地进行预测和分类。训练集的混淆矩阵显示总共177个正确预测和52个错误预测,准确率为77%,精度为88%,召回率为77%,F1分数为82%。测试集的混淆矩阵显示总共167个正确预测和53个错误预测,准确率为75%,精度为87%,召回率为57%,F1分数为69%。总之,基于LSTM和AdaBoost的分类预测算法在预测虚拟现实用户体验方面表现出良好的预测能力。这项研究对于增强虚拟现实技术在用户体验方面的应用具有重要意义。通过结合LSTM和AdaBoost算法,在用户体验预测方面取得了显著的进展,不仅提高了模型的准确性和泛化能力,还为该领域的虚拟现实研究提供了有价值的见解。这种方法可以帮助开发人员更好地理解用户需求,优化虚拟现实产品设计,提高用户满意度,从而在各个领域广泛应用虚拟现实技术。

URL

https://arxiv.org/abs/2405.10515

PDF

https://arxiv.org/pdf/2405.10515.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot