Proceedings of the 2020 4th International Conference on Deep Learning Technologies最新文献_第3页

Applying Artificial Intelligence to Survival Prediction of Hepatocellular Carcinoma Patients 人工智能在肝癌患者生存预测中的应用

Proceedings of the 2020 4th International Conference on Deep Learning Technologies Pub Date : 2020-07-10 DOI: 10.1145/3417188.3417197

Kun-Huang Chen, Hui-Wu Wang, Chung-Ming Liu

{"title":"Applying Artificial Intelligence to Survival Prediction of Hepatocellular Carcinoma Patients","authors":"Kun-Huang Chen, Hui-Wu Wang, Chung-Ming Liu","doi":"10.1145/3417188.3417197","DOIUrl":"https://doi.org/10.1145/3417188.3417197","url":null,"abstract":"Cancer, a disease that has gradually attracted attention in the world now, it has even become one of the main causes of death. Among them, liver cancer occurs in the liver or deadly tumors starting from the liver. According to the World Cancer Report (2014), the liver cancer is primary the cancer that the occurrence of 6% ranked second highest, and the death rate is 9% ranked sixth. So the liver cancer has become the target of academic research and discussion. If we can find out the key factors that affect the death of liver cancer when identifying liver cancer, it can improve the survival prediction of patients with liver cancer, and it will bring more effective treatment and confidence to the disease. In this paper, we chose a data that a real clinically diagnosed HCC patient was collected from a University of Coimbra and Coimbra Hospital in Portugal, and we separated the data into testing and training to predict the death of HCC and find out the key factors from the prediction model. The prediction model includes Decision Tree (DT), Support vector machine (SVM), and Logistic Regression (LR). The results of this paper showed that the G-means of the three modeling methods are 0.76 (LR), 0.72 (DT), and 0.68 (SVM). The best performance is logistic regression (LR), and find out the key factors that affect the survival rate of HCC include Aspartate transaminase (U / L), Age at diagnosis, and Alkaline phosphatase (U / L).","PeriodicalId":373913,"journal":{"name":"Proceedings of the 2020 4th International Conference on Deep Learning Technologies","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125879292","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: 3

The Development Trend of Design Methodology under the Influence of Artificial Intelligence and Big Data 人工智能和大数据影响下设计方法论的发展趋势

Proceedings of the 2020 4th International Conference on Deep Learning Technologies Pub Date : 2020-07-10 DOI: 10.1145/3417188.3417214

W. Shu, Fuliang Sun, Yueen Li

引用次数: 0

An Empirical Analysis of the Influential Factors of College Students' Academic Performance 大学生学业成绩影响因素的实证分析

Proceedings of the 2020 4th International Conference on Deep Learning Technologies Pub Date : 2020-07-10 DOI: 10.1145/3417188.3417212

Yansha Guo

{"title":"An Empirical Analysis of the Influential Factors of College Students' Academic Performance","authors":"Yansha Guo","doi":"10.1145/3417188.3417212","DOIUrl":"https://doi.org/10.1145/3417188.3417212","url":null,"abstract":"Based on the scores of selected undergraduates with two kinds of educational system in an ordinary university, with the help of variance analysis and multiple regression, the effects of different factors and their interaction on the total scores and the scores of moral, intellectual and physical courses are studied by using the ideas of discovering (the difference of students' scores in different categories), verifying (the effect of influential factors and their interaction on students' scores) and analyzing (trends and causes of scores changed based on different factors ). The key results are as follows: (1) College students' scores are significantly correlated with time, gender, birthplace, starting point for admission and their interaction, but the influence of different factors on each course is diverse. (2) The scores of female students are higher than those of male students, especially public courses; in addition to public courses, the scores of technical-school-source students are higher than those of high-school-source students, especially moral courses; students with high entrance score remain excellent in college, especially specialized courses. (3) The students in the 1st and 2nd years have the lowest academic performance, the reason of which are that many students failed; the grades of specialized course determine the total grades. (4) Curriculum classification plays a key role in studying influential factors of college students' scores. The research can provide reference for practical teaching work.","PeriodicalId":373913,"journal":{"name":"Proceedings of the 2020 4th International Conference on Deep Learning Technologies","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114479004","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

Design and Practice of Online Teaching Based on "Smart Classroom": Take the "Production and Operations Management" Course as an Example 基于“智能课堂”的在线教学设计与实践——以《生产与经营管理》课程为例

Proceedings of the 2020 4th International Conference on Deep Learning Technologies Pub Date : 2020-07-10 DOI: 10.1145/3417188.3417202

Huaimin Li, Ying Yang, Huaming Liu

引用次数: 0

Incomplete Information Competition Strategy Based on Improved Asynchronous Advantage Actor Critical Model 基于改进异步优势参与者关键模型的不完全信息竞争策略

Proceedings of the 2020 4th International Conference on Deep Learning Technologies Pub Date : 2020-07-10 DOI: 10.1145/3417188.3417189

Cong Zhao, Bing Xiao, Lin Zha

{"title":"Incomplete Information Competition Strategy Based on Improved Asynchronous Advantage Actor Critical Model","authors":"Cong Zhao, Bing Xiao, Lin Zha","doi":"10.1145/3417188.3417189","DOIUrl":"https://doi.org/10.1145/3417188.3417189","url":null,"abstract":"In recent years, game theory has been widely used in the field of deep learning, mainly including intelligent competition strategies of complete information games and incomplete information games. This paper focuses on incomplete information games, and proposes a low-dimensional semantic feature based on category coding and an incomplete information competition strategy based on the improved Asynchronous Advantage Actor-Critic (A3C) network model. First, the A3C network model in deep reinforcement learning is adopted in the competition strategy, and its network structure is improved according to the semantic features based on category coding. The improved A3C model is implemented in parallel by a series of \"workers\". The \"workers\" is a new deep learning model structure proposed in this paper. Secondly, this article combines supervised learning and Deep Reinforcement Learning (DRL) to propose a new competitive strategy. Through conducting a large number of real-time experiments with human players on online competitive websites, the comparison with the existing methods in terms of the ratio of winning and losing and the ranking rate, the experimental results indicate the superiority of the new competitive strategy.","PeriodicalId":373913,"journal":{"name":"Proceedings of the 2020 4th International Conference on Deep Learning Technologies","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134406767","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

Construction of Practice Teaching Evaluation System for Undergraduate Introduction to Logistics Based on AHP Method and Gagne's Learning Theory 基于层次分析法和Gagne学习理论的本科物流导论实践教学评价体系构建

Proceedings of the 2020 4th International Conference on Deep Learning Technologies Pub Date : 2020-07-10 DOI: 10.1145/3417188.3417208

Rong Zhou, Cuiling Guan

引用次数: 0