Artificial Intelligence and Cloud Computing Conference最新文献

Modelling Flight Delays in the Presence of Class Imbalance 存在舱位不平衡的航班延误建模

Artificial Intelligence and Cloud Computing Conference Pub Date : 2018-12-21 DOI: 10.1145/3299819.3299847

Y. E. Tan, Kai Sheng Teong, Mehlam Shabbir, Lee Kien Foo, Sook-Ling Chua

引用次数: 1

A Fault Diagnosis and Maintenance Decision System for Production Line Based on Human-machine Multi- Information Fusion 基于人机多信息融合的生产线故障诊断与维修决策系统

Artificial Intelligence and Cloud Computing Conference Pub Date : 2018-12-21 DOI: 10.1145/3299819.3299824

Zhao-Hui Sun, Renjun Liu, X. Ming

引用次数: 6

An Android Malware Detection Method Based on Deep AutoEncoder 基于深度自动编码器的Android恶意软件检测方法

Artificial Intelligence and Cloud Computing Conference Pub Date : 2018-12-21 DOI: 10.1145/3299819.3299834

Nengqiang He, Tianqi Wang, Pingyang Chen, Hanbing Yan, Z. Jin

引用次数: 13

Early Abnormal Heartbeat Multistage Classification by using Decision Tree and K-Nearest Neighbor 基于决策树和k近邻的早期异常心跳多阶段分类

Artificial Intelligence and Cloud Computing Conference Pub Date : 2018-12-21 DOI: 10.1145/3299819.3299848

Mohamad Sabri bin Sinal, E. Kamioka

{"title":"Early Abnormal Heartbeat Multistage Classification by using Decision Tree and K-Nearest Neighbor","authors":"Mohamad Sabri bin Sinal, E. Kamioka","doi":"10.1145/3299819.3299848","DOIUrl":"https://doi.org/10.1145/3299819.3299848","url":null,"abstract":"Heart diseases contribute to the highest cause of death around the world particularly for middle aged and elderly people. There are various types of heart disease symptoms. One of the most common types is Arrhythmia which is considered as a dangerous heart condition since the symptom itself may initiate more chronic heart diseases and result in death if it is not treated earlier. However, the detection of Arrhythmia by humans is regarded as a challenging task because the natures of the symptom appear at random times. Therefore, an automatic detection method of abnormal heartbeat in ECG (electrocardiogram) data is needed to overcome the issue. In this paper, a novel multistage classification approach using K-Nearest Neighbor and decision tree of the 3 segments in the ECG cycle is proposed to detect Arrhythmia heartbeat from the early minute of ECG data. Specific attributes based on feature extraction in each heartbeat are used to classify the Normal Sinus Rhythm and Arrhythmia. The experimental result shows that the proposed multistage classification approach is able to detect the Arrhythmia heartbeat with 90.6% accuracy for the P and the Q peak segments, 91.1% accuracy for the Q, R and S peak segments and lastly, 97.7% accuracy for the S and the T peak segments, outperforming the other data mining techniques.","PeriodicalId":119217,"journal":{"name":"Artificial Intelligence and Cloud Computing Conference","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125268625","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

Microblog Mood Predicts the Box Office Performance 微博情绪预测票房

Artificial Intelligence and Cloud Computing Conference Pub Date : 2018-12-21 DOI: 10.1145/3299819.3299839

Xiaoyang Qiu, T. Tang

引用次数: 1

Smart Mirror Design Powered by Raspberry PI 智能镜子设计由树莓派供电

Artificial Intelligence and Cloud Computing Conference Pub Date : 2018-12-21 DOI: 10.1145/3299819.3299840

A. Mohamed, M. Wahab, S. S. Suhaily, Darshan Babu L. Arasu

引用次数: 11

An Orchestration Framework for a Global Multi-Cloud 面向全局多云的编排框架

Artificial Intelligence and Cloud Computing Conference Pub Date : 2018-12-21 DOI: 10.1145/3299819.3299823

Ming Lu, Lijuan Wang, Youyan Wang, Zhicheng Fan, Yatong Feng, Xiaodong Liu, Xiaofang Zhao

{"title":"An Orchestration Framework for a Global Multi-Cloud","authors":"Ming Lu, Lijuan Wang, Youyan Wang, Zhicheng Fan, Yatong Feng, Xiaodong Liu, Xiaofang Zhao","doi":"10.1145/3299819.3299823","DOIUrl":"https://doi.org/10.1145/3299819.3299823","url":null,"abstract":"Orchestration management in a global multi-cloud environment encounters many challenges, such as the centralized management of global cloud computing and application resources, more diverse cloud platforms and APIs, differentiated service catalogs. Network latency and instability between cloud platforms in various countries and accessibility between data centers of different security levels also makes orchestration not easy to manage. Orchestration tools, such as Ansible[1], has high requirements for many server ports and network quality. In a complex network environment, SaltStack[2] or Puppet[3], cannot deal with the multi-cloud management of large-scale computing and storage resource nodes. Apache Ambari[4], for applications that run on different cloud computing service providers, it lacks effective management capabilities. Therefore, it is difficult for common orchestration management tools to overcome these problems. In this paper, we propose a global multi-cloud orchestration framework (MCOF), which converts the orchestration instructions initiated from the MCOF master into a standardized orchestration definition model that is distributed to the MCOF workers inside each data center through the message queue. Then the MCOF workers perform the orchestration activities suitable for the corresponding cloud service provider behind the data center firewall to adapt to the complex cloud platform operating environment, and achieve standardization, efficiency, quality, reliability, and traceable orchestration management.","PeriodicalId":119217,"journal":{"name":"Artificial Intelligence and Cloud Computing Conference","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128289242","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

Image Classification for Vehicle Type Dataset Using State-of-the-art Convolutional Neural Network Architecture 基于卷积神经网络架构的车辆类型数据集图像分类

Artificial Intelligence and Cloud Computing Conference Pub Date : 2018-12-21 DOI: 10.1145/3299819.3299822

Yian Seo, K. Shin

{"title":"Image Classification for Vehicle Type Dataset Using State-of-the-art Convolutional Neural Network Architecture","authors":"Yian Seo, K. Shin","doi":"10.1145/3299819.3299822","DOIUrl":"https://doi.org/10.1145/3299819.3299822","url":null,"abstract":"Fast development in Deep Learning and its hybrid methodologies has led diverse applications in different domains. For image classification tasks in vehicle related fields, Convolutional Neural Network (CNN) is mostly chosen for recent usages. To train the CNN classifier, various vehicle image datasets are used, however, most of previous studies have learned features from datasets with a single form of images taken in the controlled condition such as surveillance camera vehicle image dataset from the same road, which results the classifier cannot guarantee the generalization of the model onto different forms of vehicle images. In addition, most of researches using CNN have used LeNet, GoogLeNet, or VGGNet for their main architecture. In this study, we perform vehicle type (convertible, coupe, crossover, sedan, SUV, truck, and van) classification and we use our own collected dataset with vehicle images taken in different angles and backgrounds to ensure the generalization and adaptability of proposed classifier. Moreover, we use the state-of-the-art CNN architecture, NASNet, which is a hybrid CNN architecture having Recurrent Neural Network structure trained by Reinforcement Learning to find optimal architecture. After 10 folded experiments, the average final test accuracy points 83%, and on the additional evaluation with random query images, the proposed model achieves accurate classification.","PeriodicalId":119217,"journal":{"name":"Artificial Intelligence and Cloud Computing Conference","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121255626","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

Risk Assessment for Big Data in Cloud: Security, Privacy and Trust 云环境下大数据的风险评估:安全、隐私与信任

Artificial Intelligence and Cloud Computing Conference Pub Date : 2018-12-21 DOI: 10.1145/3299819.3299841

Hazirah Bee bt Yusof Ali, Lili Marziana Abdullah, M. Kartiwi, Azlin Nordin

引用次数: 2

A Platform for Dynamic Optimal Nurse Scheduling Based on Integer Linear Programming along with Multiple Criteria Constraints 基于多准则约束的整数线性规划动态优化护士调度平台

Artificial Intelligence and Cloud Computing Conference Pub Date : 2018-12-21 DOI: 10.1145/3299819.3299825

Te-Wei Ho, Jia-Sheng Yao, Yao-Ting Chang, F. Lai, Jui-Fen Lai, Sue-Min Chu, Wan-Chung Liao, Han-Mo Chiu

{"title":"A Platform for Dynamic Optimal Nurse Scheduling Based on Integer Linear Programming along with Multiple Criteria Constraints","authors":"Te-Wei Ho, Jia-Sheng Yao, Yao-Ting Chang, F. Lai, Jui-Fen Lai, Sue-Min Chu, Wan-Chung Liao, Han-Mo Chiu","doi":"10.1145/3299819.3299825","DOIUrl":"https://doi.org/10.1145/3299819.3299825","url":null,"abstract":"Nurse rostering is a critical issue in hospitals around the world. With multiple constraints that must be considered to ensure job satisfaction, nurse scheduling usually poses a heavy financial burden on human resources with limited available staff resources. Managers also need to reproduce the roster of duties for the nursing staff. In addition, the staff allocation should be based on the visit number of patients. Hence, to address this issue, we implemented an automatic mechanism of nurse scheduling based on integer linear programing, along with multiple criteria constraints, which are suitable for real-world practice, and users can configure conditions for tasks and nurses as constraints in the integer linear programing. Finally, the platform could assign 36 staff members to 23 stations based on the proposed dynamic optimal algorithm following 20 stringent constraints in 0.5 second. Moreover, the specific manipulation shifts of scheduling on the platform is easy and can be automatically computed in minimum time. Compared with the manual scheduling, the proposed automatic mechanism could perform the scheduling task quickly and fairly. Most importantly, the platform is adequately reliable to decrease the burden for scheduling.","PeriodicalId":119217,"journal":{"name":"Artificial Intelligence and Cloud Computing Conference","volume":"10 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130160189","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