2022 14th International Conference on Machine Learning and Computing (ICMLC)最新文献_第7页

FDA-Unet: A Feature fusional U-Net with Deep Supervision and Attention Mechanism for COVID-19 Lung Infection Segmentation from CT Images FDA-Unet:一种具有深度监督和关注机制的特征融合U-Net，用于CT图像中COVID-19肺部感染的分割

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529931

Tianshun Hong, Weitao Huang, Yuhang Bai, Tailai Zeng

引用次数: 0

Face Attribute Analysis Method Based on Self-Supervised Siamese Network 基于自监督暹罗网络的人脸属性分析方法

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529838

Huan Xiong, Shuai Dong, Kun Zou

引用次数: 0

Research on Hypoxia and Fatigue Resistant Food at Plateau Based on Big Data Technology 基于大数据技术的高原缺氧抗疲劳食品研究

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529950

Chenggong Zhai, Heng Zhang, Xiaoli Li, Shan Chen, Liyong Zhou, Rangmin Wu

{"title":"Research on Hypoxia and Fatigue Resistant Food at Plateau Based on Big Data Technology","authors":"Chenggong Zhai, Heng Zhang, Xiaoli Li, Shan Chen, Liyong Zhou, Rangmin Wu","doi":"10.1145/3529836.3529950","DOIUrl":"https://doi.org/10.1145/3529836.3529950","url":null,"abstract":"The thin air, low oxygen content, harsh environment, sparse population, desolate ground and difficult concealment in the severe cold area of the plateau have a great impact on the food security of personnel going to the severe cold area of the plateau. Researchers went to the food security in the severe cold areas of the plateau, made solid preparations for various security, and achieved results for food security at the first time, which plays an important role in building and completing the food system in China. Focusing on the impact and requirements of plateau cold areas on food security, combined with the difficulties and practical problems faced by personnel going to plateau cold areas and big data technology, this paper analyzes the overview and characteristics of big data, the needs of plateau hypoxia resistance and fatigue resistance, and the advantages of plateau hypoxia resistance and fatigue resistance food, and puts forward the plateau hypoxia resistance and fatigue resistance based on big data The conception of anti fatigue food system, and the plateau food data safety system is constructed.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121976615","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

Attention-guided Multiple Receptive Field Residual Block for Single Low-light Image Enhancement 单次弱光图像增强的注意引导多感受野残块

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529909

Wen-Zheng Xu, Haibo Wan, Xue Li

引用次数: 0

A Machine Learning-Based Approach for Cardiovascular Diseases Prediction 基于机器学习的心血管疾病预测方法

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529863

Haoran Lyu

{"title":"A Machine Learning-Based Approach for Cardiovascular Diseases Prediction","authors":"Haoran Lyu","doi":"10.1145/3529836.3529863","DOIUrl":"https://doi.org/10.1145/3529836.3529863","url":null,"abstract":"In recent decades, the development of technology and the increase of living standards have affected people's attention in healthcare. The healthcare market has expanded along with great attention from researchers, and the study of disease forecasting has become an inevitable process in future healthcare development. Cardiovascular disease is a category for the type of disease that causes a negative impact on the heart and blood vessels. As one of the most common and lethal potential diseases, predicting cardiovascular diseases helped in high-risk patients' decision-making process, which identifies the potential threats in the early stages. With the help of tremendous medical data and increased computational capabilities, machine learning has proved one of the most efficient prediction methods in cardiovascular disease forecasting. However, most of the proposed research either focuses on single algorithms with different parameter settings or large-scale selected algorithms with single criteria for modeling. This experiment aims to study the performance of small-scale selected algorithms with multiple criteria used in the modeling process. Specifically, this study used historical healthcare data with fourteen attributes selected. The experiment results show the Random Forest built by Classification and Regression Trees (CART) has dominant performance among all selected algorithms, which can help construct an alert system for cardiovascular disease prediction.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115093281","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

HSIE: Improving Named Entity Disambiguation with Hidden Semantic Information Extractor 基于隐式语义信息提取器的命名实体消歧改进

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529920

Hongbin Zhang, Quan Chen, Weiwen Zhang, Mengna Nie

{"title":"HSIE: Improving Named Entity Disambiguation with Hidden Semantic Information Extractor","authors":"Hongbin Zhang, Quan Chen, Weiwen Zhang, Mengna Nie","doi":"10.1145/3529836.3529920","DOIUrl":"https://doi.org/10.1145/3529836.3529920","url":null,"abstract":"Named Entity Disambiguation (NED) is a fundamental task in natural language processing. However, existing methods pay more attention to the acquisition of global features, but ignore hidden semantic information in local features. In this paper, we propose a Hidden Semantic Information Extractor (HSIE) to capture hidden semantic features. Specifically, the HSIE is composed of multi-layer neural networks with multi-head attention and a transition layer. Vectors of candidate entities are iteratively updated in the HSIE to capture more elaborated semantic features, which are beneficial for Neural Attention Module (NAM) to compute precise feature scores of candidate entities. An extensible vector space module aims to connect the HSIE and the NAM effectively, which enhances the performance of the HSIE. Such an HSIE can be embedded into local model effectively and extended to global and global (neighbor) models. Moreover, we develop a disambiguation system by orchestrating local, global and global (neighbor) models, each of which is equipped with their own HSIE, the extensible vector space module and the NAM respectively. Experimental results show that our disambiguation system achieves the best performance on AIDA-B, MSNBC and ACE2004 datasets.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130768982","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: 2

Deep Atrial Fibrillation Classification Based on Multi-modal Attention Network 基于多模态注意网络的深心房颤动分类

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529929

Zhen-En Shao

引用次数: 0

A Intelligence Target Situation Assessment Method for Cooperative Search by ASW Patrol Aircraft 一种反潜巡逻机协同搜索情报目标态势评估方法

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529933

Y. Sun, Junfeng Gai, Huayun Liu

{"title":"A Intelligence Target Situation Assessment Method for Cooperative Search by ASW Patrol Aircraft","authors":"Y. Sun, Junfeng Gai, Huayun Liu","doi":"10.1145/3529836.3529933","DOIUrl":"https://doi.org/10.1145/3529836.3529933","url":null,"abstract":"To handle the problem of target situation assessment with a large amount of uncertain information in naval battlefield, Bayesian network and cloud theory are introduced, and a method of situation assessment for ASW patrol aircraft cooperative search potential target in autonomous mode based on cloud Bayesian network is presented. In this method, the cooperative search schemes of autonomous mode and lead-wing mode for ASW patrol aircraft are established. And based on autonomous mode, the target situation assessment model based on cloud Bayesian network is designed, and Bayesian network structure which is the directed acyclic graph is constituted. Then, the indexes of target situation assessment are delineated. After that, the cloud model is adopted to discretize the model. And. the conditional probability table(CPT) for each node are calculated as well. At last, the target type and the other side's fighting intention are inferred, and the battlefield situation is formed. The simulation is combined with the problem analysis process, and the method is feasible from the simulation results, what's more, it will have the desired effect.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134135818","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

Prediction for Sea Surface Temperature of Submarine Thermal Wake: Combining CFD with Neural Network 海底热尾流海面温度预测:CFD与神经网络的结合

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529934

Zhiyuan Hua, Danhong Zhang, Yue Qi, Zhiwen Leng

引用次数: 0

JFMNet: Joint Fusion Multi-Networks for Image Dehazing and Denoising in The Port Environment 港口环境下图像去雾降噪的联合融合多网络

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529923

Guancheng Lin, Yijie Zheng, Zhi-Jian Xu, Tianzhi Xia, Peng Yuan

{"title":"JFMNet: Joint Fusion Multi-Networks for Image Dehazing and Denoising in The Port Environment","authors":"Guancheng Lin, Yijie Zheng, Zhi-Jian Xu, Tianzhi Xia, Peng Yuan","doi":"10.1145/3529836.3529923","DOIUrl":"https://doi.org/10.1145/3529836.3529923","url":null,"abstract":"The bad weather events, such as haze, in maritime traffic dramatically reduce the visibility, which can seriously affect the ship navigation especially in areas with intensive port traffic. Meanwhile, unwanted signals are inevitably introduced by the maritime imaging device during image capturing and transmission in hazy conditions. Therefore, the captured image is not only degraded by the haze, but also may contain unwanted noise. These low-quality images interfere with the subsequent image processing and increase the potential for maritime traffic accidents. It is therefore imperative to improve the image quality in hazy conditions. To reveal the information hidden in the haze while suppress noise, this paper proposes the joint fusion multi-networks (termed JFMNet) for Image dehazing and denoising in the port environment. The multi-networks use the dehazing module (DHNet) and the denoising module (DNNet) to suppress the noise and haze. Then use the information fusion module (FNet) to integrate the results of the DNNet and DHNet with the information of the original input images to achieve the goal of dehazing and denoising while preserving the details. The modules in multi-networks are based on an encoder-decoder structure. Experiments on a number of challenging hazy images with noise are present to reveal the efficacy of this structure. Meanwhile, experiments also show our JFMNet's superiority over several state-of-the-arts in terms of dehaze quality and efficiency.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124841501","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