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2021 2nd International Conference on Computing and Data Science (CDS)最新文献

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Review on Data Science and Prediction 数据科学与预测综述
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00100
Abdulwahab Alazeb, Mohammed S. Alshehri, Sultan Almakdi
{"title":"Review on Data Science and Prediction","authors":"Abdulwahab Alazeb, Mohammed S. Alshehri, Sultan Almakdi","doi":"10.1109/CDS52072.2021.00100","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00100","url":null,"abstract":"The exponential growth of data over the years has brought into focus the increasingly-important task of managing all of it. This need has been driven by advancements in technology that have created large increases in data volume. Due to the societal importance of many newly-developed forms of technology, management of data is no longer just a matter of storage, but security and availability as well. In the business world, the management of data affects the delivery of services and overall productivity. This paper seeks to explain the different aspects of data science related to modern needs and future significance. Data science is explored in detail with emphasis on the background, history, and concepts of data management. Various sources from existing literature and academia are thoroughly discussed regarding the growing importance of data management and techniques involved. An evaluation of these techniques is also presented.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128287202","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
Research on Hepatitis Auxiliary Diagnosis Based on Random Forest and Support Vector Machine 基于随机森林和支持向量机的肝炎辅助诊断研究
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00041
Jizhuo Du
{"title":"Research on Hepatitis Auxiliary Diagnosis Based on Random Forest and Support Vector Machine","authors":"Jizhuo Du","doi":"10.1109/CDS52072.2021.00041","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00041","url":null,"abstract":"Hepatitis not only endangers the health and life of patients, but also causes a heavy burden for their family on the society. It has become an important disease with serious social and public health problems. In this study, we studied a large sample of people and obtained biochemical data of patients related to the hospital, including a series of continuous and discrete data, such as age, bilirubin, alk_phosphate, Sgot, Albumin, etc. Then support vector machine (SVM) and random forest model were constructed to assist hepatitis. The SVM with RBF kernel is the best experimental model, which has good performance in the evaluation of accuracy and ROC. Next, we can provide reference and help for clinicians to make clinical decisions based on the results of the experiment, so as to improve the diagnostic accuracy of the non-invasive diagnosis.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"1 6448 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123363289","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
The Combination of CNN, RNN, and DNN for Relation Extraction 结合CNN、RNN和DNN进行关系提取
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00106
Yunzhou Li
{"title":"The Combination of CNN, RNN, and DNN for Relation Extraction","authors":"Yunzhou Li","doi":"10.1109/CDS52072.2021.00106","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00106","url":null,"abstract":"Relation extraction, which is a subtask of NLP (natural language processing) field, its target is to identify the entities in texts and extract the relation between entities. Previous works prove that neural networks are feasible for relation extraction. CNN (convolutional neural networks) and LSTM (long short-term memory) are two majority models used in relation extraction. Further research shows that the combination of CNN and LSTM has a better performance. Inspired by the solution of LVCSR (Large-Vocabulary-Continuous-Speech-Recognition), another task in the NLP field, we propose adding DNN after the combination of CNN and LSTM. This model achieves a better effect on the precision-recall curve than the previous model.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116100092","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
The Application of Machine Learning in League Index Prediction 机器学习在联赛指数预测中的应用
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00060
Yi-liu Liu
{"title":"The Application of Machine Learning in League Index Prediction","authors":"Yi-liu Liu","doi":"10.1109/CDS52072.2021.00060","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00060","url":null,"abstract":"Along with the technological developments and the commercial utilization of 4G, electronic sports and live broadcasting institutions have got huge progresses. Thus, it is vital and meaningful to optimize the gaming circumstances and maximize the gaming experiences of players and this is why exploring the main contributing factors or players' league indexes as well as constructing predictive models are so necessary and pragmatic. It is the experiences and the observations that the traditional methods applied for judgments, which cannot handle massive data and will end up with low-accuracy outcomes, whereas the situations can be processed effectively, precisely and objectively via machine learning. In this paper, via processing over 3338 data of 19 variables extracted from real game players, descriptive statistical analysis has been firstly processed for identifying the real factors that influence the players' league indexes, then, six well-known machine learning models are used to build the prediction models. We have discovered that, during the tests, Artificial Neural Network model offers the best prediction and correctly predicts over 45.4% of the testing data.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133053320","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
Chinese Medical Named Entity Recognition using CRF-MT-Adapt and NER-MRC 基于CRF-MT-Adapt和NER-MRC的中医命名实体识别
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00068
Hengyi Zheng, Bin Qin, Ming Xu
{"title":"Chinese Medical Named Entity Recognition using CRF-MT-Adapt and NER-MRC","authors":"Hengyi Zheng, Bin Qin, Ming Xu","doi":"10.1109/CDS52072.2021.00068","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00068","url":null,"abstract":"Medical Named Entity Recognition is a fundamental component of understanding the medical free-text notes in Electronic Health Records, and it has become a popular research topic in both academia and industry. The China Conference on Knowledge Graph and Semantic Computing (CCKS) organizes a challenge for Medical Named Entity Recognition, aiming at extracting medical entity mentions and categorizing them into pre-defined classes. We propose a Multi-Task sequence labeling model with Adaptive Loss Weighting (CRF-MT-Adapt) to address the issue of low recall and a Named Entity Recognition model based on Machine Reading Comprehension (NER-MRC) to address the issue of long-span entity mentions. We experimentally demonstrate the state-of-the-art performance of the two proposed models and the ensemble even surpasses the strong baselines by at least 2% F-score. On the official test set, our best submission achieves an F-score of 90.51% and 95.96% under strict and relaxed criteria respectively.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115519714","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: 7
Target localization method based on virtual anchor nodes of one single unmanned aerial vehicle 基于单架无人机虚拟锚节点的目标定位方法
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00063
R. Chen
{"title":"Target localization method based on virtual anchor nodes of one single unmanned aerial vehicle","authors":"R. Chen","doi":"10.1109/CDS52072.2021.00063","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00063","url":null,"abstract":"Aiming at the GNSS rejection environment, or even the situation where GNSS is not available, this paper proposes a guided relative positioning method. This method takes advantage of the GNSS information from the known target to obtain the position formation of the intermediate target, and then calculate the final localization by the measurement information derived from the relative position information of the pending target. The simulation results indicate that the localizing accuracy of the guided relative positioning achieves the desired requirements, and the number of anchor nodes has a certain influence on the precision of the guided relative positioning method.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"234 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121420760","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 of House Price Index Based on Machine Learning Methods 基于机器学习方法的房价指数预测
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00087
Ze Li
{"title":"Prediction of House Price Index Based on Machine Learning Methods","authors":"Ze Li","doi":"10.1109/CDS52072.2021.00087","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00087","url":null,"abstract":"A house price index (HPI) is significant for people to receive accurate information such as banks, financial departments, real estate industry investors, and home owners. Data from Kaggle website by using neural network and regression models, such as linear, ridge Lasso regression. There are 99326 samples. We explore the relationships between factors of frequency, HPI flavor, HPI type, HPI index, level, period, place id, place name, and the year of houses sold or rent. In terms of the accuracy of the prediction, the accuracy of BP neural network is slightly better since the value is smaller than other two regression prediction both on the training set and testing set. However, better model like XGBoost could be chosen to improve the prediction result. Since an international concern about house prices raises recently, the precise calculation of house prices is important as well.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125767311","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
Reinforcement Learning for Multi-Robot System: A Review 多机器人系统的强化学习研究综述
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00043
Xudong Yang
{"title":"Reinforcement Learning for Multi-Robot System: A Review","authors":"Xudong Yang","doi":"10.1109/CDS52072.2021.00043","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00043","url":null,"abstract":"The optimization control of multi-robot systems based on Reinforcement Learning is the frontier field of Robotics and distributed Artificial Intelligence in recent years. Multi-robot systems have the characteristics of distribution, heterogeneity, and high-dimensional spatial continuity, which makes the research of reinforcement learning for multi-robot systems face a series of challenges. This paper reviews the challenges in four practical problems of the multi-robot system which are distributed collaborative driving of multiple vehicles, mobile sensing robot team, multi-robot collaborative monitoring, and multi-UAV cooperative task planning and the latest solutions of them. Methods based on Deep Reinforcement Learning and Multi-Agent Reinforcement Learning are also described. This review may be useful to guide researchers and technologists from the industry in their choice of better cope with the multi-robot system's problems.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130224312","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
Input High-Dimensional Expansion Convolution: Convolution Optimization for Spatially Varying Convolution 输入高维展开卷积:空间变化卷积的卷积优化
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00036
Jiahao Yu
{"title":"Input High-Dimensional Expansion Convolution: Convolution Optimization for Spatially Varying Convolution","authors":"Jiahao Yu","doi":"10.1109/CDS52072.2021.00036","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00036","url":null,"abstract":"In this paper, in order to improve the execution speed of complex image processing functions in convolutional neural networks, we propose an optimization algorithm for convolution. This algorithm is aimed at optimizing the special convolution calculation of complex image processing functions in image processing, in which the weights of the kernel change with the position of the convolution kernel My algorithm mainly expands the image and variable convolution kernel to higher dimensions to reduce the number of cycles through vectorization operations, and optimizes the method of image expansion to higher dimensions. The experimental results show that my algorithm fully utilizes the parallel computing power of the CPU, which is more than 20 times faster than the direct method.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127293436","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
Research Progress and Development of Deep Learning Based on Convolutional Neural Network 基于卷积神经网络的深度学习研究进展
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00052
Hao Tang
{"title":"Research Progress and Development of Deep Learning Based on Convolutional Neural Network","authors":"Hao Tang","doi":"10.1109/CDS52072.2021.00052","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00052","url":null,"abstract":"As a popular branch of the field of artificial intelligence and machine learning, deep learning has received increasing attention and continuous development. This paper first introduces the development and current situation of deep learning. Then, it introduces the component and mathematical theories of convolutional neural network (CNN). As for CNN optional variables and parameters, the optimal range of each parameter tested is explored through the training and tests on the datasets of Fashion-MNIST and CIFAR20 respectively. Finally, this paper proposes existing defects and future development of deep learning based on CNN.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121930967","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: 1
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