2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)最新文献_第6页

Predicting the level of generalized anxiety disorder of the coronavirus pandemic among college age students using artificial intelligence technology 利用人工智能技术预测新冠肺炎大流行期间大学生广泛性焦虑障碍水平

2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2020-10-01 DOI: 10.1109/DCABES50732.2020.00064

H. Alharthi

{"title":"Predicting the level of generalized anxiety disorder of the coronavirus pandemic among college age students using artificial intelligence technology","authors":"H. Alharthi","doi":"10.1109/DCABES50732.2020.00064","DOIUrl":"https://doi.org/10.1109/DCABES50732.2020.00064","url":null,"abstract":"Introduction: Emerging reports indicate heightened anxiety among university students during the Corona pandemic. Implications of which can impact their academic performance. Artificial intelligence (AI) through machine learning can be used to predict which students are more susceptible to anxiety which can inform closer monitoring and early intervention. To date, there are no studies that have explored the efficacy of AI to predict anxiety among college students. Objective: to develop the best fit model to predict anxiety and to rank the most important factors affecting anxiety. Method: Data was collected using an online survey that included general information; Covid-19 stressors and (GAD-7). This scale categorizes level of anxiety to none, mild, moderate, and severe. We received 917 survey answers. Several machine learning classifiers were used to develop the best fit model to predict student level of anxiety. Results: the best performance based on AUC is AdaBoost (0.943) followed by neural network (0.936). Highest accuracy and F1 were for neural network (0.754) and (0.749) respectively, then neural network selected to be the best fit model. The three scoring methods revealed that the top three features that predicted anxiety to be gender; sufficient support from family and friends; and fixed family income. Conclusion: Neural network model can assist college counselors to predict which students are going through anxiety and revealed the top three features for heightened student anxiety to be gender, a support system, and family fixed income. This information can alter college councilors for early mental intervention.","PeriodicalId":351404,"journal":{"name":"2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130990757","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: 15

Research on Multi-Center Route Planning based on improved Ant Colony algorithm 基于改进蚁群算法的多中心路线规划研究

2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2020-10-01 DOI: 10.1109/dcabes50732.2020.00016

Zhao Chen

引用次数: 2

Some New Attempts to Process Biological Data 处理生物数据的一些新尝试

2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2020-10-01 DOI: 10.1109/DCABES50732.2020.00084

Shuxun Yang, Mingpu Li, Jun Luo, Yupeng Lu, Chao Yan, Xu-Qing Tang

引用次数: 1

Preface DCABES 2020

2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2020-10-01 DOI: 10.1109/dcabes50732.2020.00005

Dcabes

引用次数: 0

Dueling Double Q-learning based Real-time Energy Dispatch in Grid-connected Microgrids 基于双q学习的并网微电网实时能源调度

2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2020-10-01 DOI: 10.1109/DCABES50732.2020.00020

Yuankai Shu, Wenzheng Bi, Wei Dong, Qiang Yang

引用次数: 3

Potential Benefits and Challenges Associated with the Adoption of Mobile health (m-health) in Kingdom of Saudi Arabia 沙特阿拉伯王国采用移动医疗(移动医疗)的潜在好处和挑战

2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2020-10-01 DOI: 10.1109/DCABES50732.2020.00089

Fahad M. Al-Anezi

{"title":"Potential Benefits and Challenges Associated with the Adoption of Mobile health (m-health) in Kingdom of Saudi Arabia","authors":"Fahad M. Al-Anezi","doi":"10.1109/DCABES50732.2020.00089","DOIUrl":"https://doi.org/10.1109/DCABES50732.2020.00089","url":null,"abstract":"The healthcare system in the Kingdom of Saudi Arabia (KSA) is going through transformative changes as part of the Kingdom's 2030 vision. One of the main pillars of this vision is to improve the healthcare and wellbeing of Saudi citizen. The area of mobile health (m-health) focuses on the use of mobile and Internet technologies for improving healthcare delivery. From the healthcare perspective, the introduction of new and innovative approaches of mobile and digital health solutions in the KSA are timely to enable efficient and effective healthcare delivery services that are vital for the realization of the country's 2030 vision in the healthcare and wellbeing areas. The KSA has made noteworthy strides in adopting Health IT and e-health technologies into their healthcare system, and recently a national e-health strategy was drafted and adopted by the ministry of health. However, from the m-health and digital health perspectives there are no national strategy in the KSA in these important areas. The increasing patient population in chronic diseases, combined with spiraling healthcare costs and pressure on healthcare services makes it timely and important to introduce these concepts into the wider healthcare system. These are considered pivotal drivers in the country's healthcare and patient care transformation process. Furthermore, the unprecedented penetration and usage of smart phones and internet access by the KSA population can drive this change towards accelerated and wider adoption of m-health and digital health services.","PeriodicalId":351404,"journal":{"name":"2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132264643","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

Classification of Hyperspectral image based on superpixel segmentation and DPC algorithm 基于超像素分割和DPC算法的高光谱图像分类

2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2020-10-01 DOI: 10.1109/DCABES50732.2020.00044

Nian Chen, Hao Zhou

引用次数: 0

Research on GAN-based Container Code Images Generation Method 基于gan的容器代码图像生成方法研究

2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2020-10-01 DOI: 10.1109/DCABES50732.2020.00059

Yan-Cheng Liang, Hanbing Yao

{"title":"Research on GAN-based Container Code Images Generation Method","authors":"Yan-Cheng Liang, Hanbing Yao","doi":"10.1109/DCABES50732.2020.00059","DOIUrl":"https://doi.org/10.1109/DCABES50732.2020.00059","url":null,"abstract":"Recognizing images based on deep learning algorithms requires sufficient samples as a training dataset. In the port field, there is also a lack of container image datasets for deep learning research. This paper proposes a model based on GAN's container box character sample extended dataset (C-SAGAN), and addresses the problems of container box code character defaced and corrupt caused by the port environment, the generative adversarial network is trained with a small amount of real images to generate container character samples. The C-SAGAN model introduces class tags and self-attention in the generator and discriminator. The class tags can control the image generation process. The self-attention mechanism can extract image features based on global information and generate image samples with clear details. The experimental results show that the quality of the samples generated by the generative adversarial network model proposed in this paper is excellent. The samples are used in the CRNN model as the training dataset and the real images are used as the test sets, won the high recognition rate.","PeriodicalId":351404,"journal":{"name":"2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134583563","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

Aspect sentiment analysis based on gating convolutional network and attention weighting mechanism 基于门控卷积网络和注意力加权机制的方面情感分析

2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2020-10-01 DOI: 10.1109/DCABES50732.2020.00023

Fan Xu, Xuezhong Qian

引用次数: 0

Multi-label Garbage Image Classification Based on Deep Learning 基于深度学习的多标签垃圾图像分类

2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) Pub Date : 2020-10-01 DOI: 10.1109/DCABES50732.2020.00047

Kang Yan, Wenyu Si, Jin Hang, Hong Zhou, Quanyin Zhu

引用次数: 4