Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning最新文献_第9页

A U-Net based Self-Supervised Image Generation Model Applying PCA using Small Datasets 基于U-Net的小数据集PCA自监督图像生成模型

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590086

Sanghun Han, Asim Niaz, K. Choi

引用次数: 0

A Modified Fuzzy K-nearest Neighbor Using the Improved Sparrow Search Algorithm for Two-classes and Multi-classes Datasets 基于改进麻雀搜索算法的两类和多类数据集改进模糊k近邻

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590042

Chengfeng Zheng, Mohd Shareduwan Mohd Kasihmuddin, Yuan Gao, Ju Chen, M. Mansor

引用次数: 0

Adaptive model fusion algorithm for decision trees and association rules 决策树与关联规则的自适应模型融合算法

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590006

Zhang Hui, Zhiling Nie, Hongwei Xiao

引用次数: 0

An Adaptive Gradient Privacy-Preserving Algorithm for Federated XGBoost 联邦XGBoost的自适应梯度隐私保护算法

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590051

Hongyi Cai, Jianping Cai, Lan Sun

引用次数: 0

Research on Constant Perturbation Strategy for Deep Reinforcement Learning 深度强化学习的常摄动策略研究

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590101

Jiamin Shen, Li Xu, Xu Wan, Jixuan Chai, Chunlong Fan

引用次数: 0

Helmet wear detection based on YOLOV5 基于YOLOV5的头盔磨损检测

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590017

Jun Liu, Jiacheng Cao, Changlong Zhou

引用次数: 0

Generate earthquake catalog using the VAE method 使用VAE方法生成地震目录

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590052

Zhangyu Wang, J. Zhang

{"title":"Generate earthquake catalog using the VAE method","authors":"Zhangyu Wang, J. Zhang","doi":"10.1145/3590003.3590052","DOIUrl":"https://doi.org/10.1145/3590003.3590052","url":null,"abstract":"The earthquake catalog is essential for seismic activity analysis and earthquake forecasting. Researchers would like to use a complete catalog for further study. In this study, we use a machine learning method to derive a double-variable model to learn the latent rules of catalogs and generate the synthetic ones from a historical catalog. In the first step, we obtain an individual cluster from the catalog by the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Then we take the envelope of the magnitude-time curve of the clusters. In the end, we apply the Variational AutoEncoder (VAE) method to learn the inherent feature and produce the latent magnitude-time curves. We use the earthquakes in Southern California from 2016 January 1 to 2022 December 18 to train the VAE model. After training, the model can generate abundant magnitude-time curves and the result shows that the magnitude-time curves during this period can be divided into single-peak, double-peak, and treble-peak patterns. Furthermore, we can use this method to generate more clusters for swarm identification and analysis of regional seismic activity.","PeriodicalId":340225,"journal":{"name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123215636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Ensemble Model using Face and Pose Tracking for Engagement Detection in Game-based Rehabilitation 一种基于人脸和姿态跟踪的集成模型用于基于游戏的康复中参与检测

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590085

Xujie Lin, Siqi Cai, P. Chan, Longhan Xie

引用次数: 0

Deep Learning AD Detection Model based on a Two-Layer Ensemble Module with Data Augmentation and Contrastive Learning 基于数据增强和对比学习的双层集成模块深度学习AD检测模型

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590061

Weicheng Wang

{"title":"Deep Learning AD Detection Model based on a Two-Layer Ensemble Module with Data Augmentation and Contrastive Learning","authors":"Weicheng Wang","doi":"10.1145/3590003.3590061","DOIUrl":"https://doi.org/10.1145/3590003.3590061","url":null,"abstract":"Abstract—Alzheimer's Disease (AD) is a long-term disease that gradually decreases cognitive functioning, such as thinking, memory and behavior. In 2015, 29.8 million AD cases were recorded and 1.9 million AD-related deaths were reported worldwide. Early detection and intervention are critical for such a deadly and costly disease. I present to tackle the detection of AD and its severity using a deep-learning architecture that consists of a two-stage ensemble system with contrastive learning and data augmentation. I evaluated it on the ADReSS Challenge's dataset, which is subject-independent and balanced in terms of age and gender. When compared against a one-stage ensemble baseline approach, my two-stage ensemble system was able to achieve better results, with a F1-score of 95.7% in the AD classification task, and an RMSE score of 5.432. Moreover, I found that the data augmentation can effectively improve the robustness of the AD detection performance, particularly when there are sensor noises in the training and test data. Besides data augmentation, I also explored whether contrastive loss can further boost the robustness, and the results showed that contrastive learning might not be necessary when we have data augmentation.","PeriodicalId":340225,"journal":{"name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128415507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MCSC-UTNet: Honeycomb lung segmentation algorithm based on Separable Vision Transformer and context feature fusion MCSC-UTNet:基于可分离视觉转换器和上下文特征融合的蜂窝肺分割算法

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590093

Wei Jianjian, Gang Li, Kan He, Pengbo Li, Ling Zhang, Ronghua Wang

{"title":"MCSC-UTNet: Honeycomb lung segmentation algorithm based on Separable Vision Transformer and context feature fusion","authors":"Wei Jianjian, Gang Li, Kan He, Pengbo Li, Ling Zhang, Ronghua Wang","doi":"10.1145/3590003.3590093","DOIUrl":"https://doi.org/10.1145/3590003.3590093","url":null,"abstract":"Abstract: Due to the problems of more noise and lower contrast in X-ray tomography images of the honeycomb lung, and the poor generalization of current medical segmentation algorithms, the segmentation results are unsatisfactory. We propose an automatic segmentation algorithm MCSC-UTNet based on SepViT with contextual feature fusion for honeycomb lung lesions to address these problems. Firstly, a Multi-scale Channel Shuffle Convolution (MCSC) module is constructed to enhance the interaction between different image channels and extract the local lesion feature at different scales. Then, a Separable Vision Transformer (SepViT) module is introduced at the bottleneck layer of the network to enhance the representation of the global information of the lesion. Finally, we add a context-aware fusion module to relearn the encoder feature and strengthen the contextual relevance of the encoder and decoder. In comparison experiments with eight prevalent segmentation models on the honeycomb lung dataset, the segmentation metrics of this method, Jaccard coefficient, mIoU, and DSC are 90.85%, 95.32%, and 95.07%, with Jaccard coefficient improving by 3.56% compared with that before. Compared with medical segmentation models such as TransUNet, Sharp U-Net, and SETR, this paper's method has improved results and segmentation performance.","PeriodicalId":340225,"journal":{"name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128421035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0