Y. E. Tan, Kai Sheng Teong, Mehlam Shabbir, Lee Kien Foo, Sook-Ling Chua
Flight delay is one of the common problems faced by many air passengers. Delays in flights not only bring about inconvenience to passengers, but also cost the airlines. To streamline travel experience, airlines have been leveraging on data analytics to predict flight delays. Although many prediction models have been proposed, they perform poorly especially on data that have imbalanced class distributions. Often, these models pay less attention to the minority 'delay' class, which are usually more relevant and important. In this paper, we address the issue of imbalanced class distributions to improve the overall classification performance in predicting flight delays. We validated our approach on a public airline on-time performance dataset.
{"title":"Modelling Flight Delays in the Presence of Class Imbalance","authors":"Y. E. Tan, Kai Sheng Teong, Mehlam Shabbir, Lee Kien Foo, Sook-Ling Chua","doi":"10.1145/3299819.3299847","DOIUrl":"https://doi.org/10.1145/3299819.3299847","url":null,"abstract":"Flight delay is one of the common problems faced by many air passengers. Delays in flights not only bring about inconvenience to passengers, but also cost the airlines. To streamline travel experience, airlines have been leveraging on data analytics to predict flight delays. Although many prediction models have been proposed, they perform poorly especially on data that have imbalanced class distributions. Often, these models pay less attention to the minority 'delay' class, which are usually more relevant and important. In this paper, we address the issue of imbalanced class distributions to improve the overall classification performance in predicting flight delays. We validated our approach on a public airline on-time performance dataset.","PeriodicalId":119217,"journal":{"name":"Artificial Intelligence and Cloud Computing Conference","volume":"33 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":"127590576","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}
In this paper, we describe the importance of the operation and maintenance in manufacturing systems for Manufacturing Enterprises. Through the mining of enterprise fault detection information by data mining method, we obtain the probability of machine failure. The importance of each machine in the manufacturing system is evaluated by the FUZZY FMEA method, and the importance information of the machine is obtained. Moreover, based on the D-S evidence theory, the contradictory and conflict information is merged in this paper, and a machine fault operation and maintenance decision-making system based on human-machine multi-information fusion is constructed. The feasibility of the decision-making system is verified by industrial case.
{"title":"A Fault Diagnosis and Maintenance Decision System for Production Line Based on Human-machine Multi- Information Fusion","authors":"Zhao-Hui Sun, Renjun Liu, X. Ming","doi":"10.1145/3299819.3299824","DOIUrl":"https://doi.org/10.1145/3299819.3299824","url":null,"abstract":"In this paper, we describe the importance of the operation and maintenance in manufacturing systems for Manufacturing Enterprises. Through the mining of enterprise fault detection information by data mining method, we obtain the probability of machine failure. The importance of each machine in the manufacturing system is evaluated by the FUZZY FMEA method, and the importance information of the machine is obtained. Moreover, based on the D-S evidence theory, the contradictory and conflict information is merged in this paper, and a machine fault operation and maintenance decision-making system based on human-machine multi-information fusion is constructed. The feasibility of the decision-making system is verified by industrial case.","PeriodicalId":119217,"journal":{"name":"Artificial Intelligence and Cloud Computing Conference","volume":"4 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":"114456210","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}
Nengqiang He, Tianqi Wang, Pingyang Chen, Hanbing Yan, Z. Jin
With the emergence of various Android malwares, many detection algorithms based on machine learning have been proposed to minimize their threat. However, those still have many shortcomings for detecting the emerging Android malware, thus some deep learning algorithms have already been applied to Android malware detection, but to the best of our knowledge deep AutoEncoder has not yet. In this paper, an Android malware detection method based on deep AutoEncoder is proposed, where a specify AutoEncoder structure is designed to reduce the dimension of feature vectors which are extracted and converted from APK, and the logistic regression model is also applied to learn and classify the Android applications to be normal or not. The experimental results show the recall rate and F1 value of our proposal can respectively reach 0.93 and 0.643, which perform better than other three similar models.
{"title":"An Android Malware Detection Method Based on Deep AutoEncoder","authors":"Nengqiang He, Tianqi Wang, Pingyang Chen, Hanbing Yan, Z. Jin","doi":"10.1145/3299819.3299834","DOIUrl":"https://doi.org/10.1145/3299819.3299834","url":null,"abstract":"With the emergence of various Android malwares, many detection algorithms based on machine learning have been proposed to minimize their threat. However, those still have many shortcomings for detecting the emerging Android malware, thus some deep learning algorithms have already been applied to Android malware detection, but to the best of our knowledge deep AutoEncoder has not yet. In this paper, an Android malware detection method based on deep AutoEncoder is proposed, where a specify AutoEncoder structure is designed to reduce the dimension of feature vectors which are extracted and converted from APK, and the logistic regression model is also applied to learn and classify the Android applications to be normal or not. The experimental results show the recall rate and F1 value of our proposal can respectively reach 0.93 and 0.643, which perform better than other three similar models.","PeriodicalId":119217,"journal":{"name":"Artificial Intelligence and Cloud Computing Conference","volume":"19 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":"130528043","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}
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.
{"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}
Zhan Lang 2 (Wolf Warrior 2), an action movie directed and acted by Wu Jing, won a huge success in the Chinese film market in 2017. Its earned 5.6 billion yuan in the box office, become the biggest film of all time in China. The discussion of the movie on social media plays an indispensable role in influencing the box office performance. This study aims to predict movie box office performance based on affective computation on the related feeds on social media. Since the research on Chinese sentiment analysis is limited, and the accuracy of the analysis highly depends on the context, this study proposes to combine topic's hotness degree with emotion score to improve the accuracy of the prediction. Based on 16,496,675 related Sina Weibo feeds and Douban movie reviews in a restricted time zone, with the prediction algorithm proposed in the paper, the prediction result yields an R2=95.71%. Which means the success of Zhan Lang 2 is utterly predictable.
{"title":"Microblog Mood Predicts the Box Office Performance","authors":"Xiaoyang Qiu, T. Tang","doi":"10.1145/3299819.3299839","DOIUrl":"https://doi.org/10.1145/3299819.3299839","url":null,"abstract":"Zhan Lang 2 (Wolf Warrior 2), an action movie directed and acted by Wu Jing, won a huge success in the Chinese film market in 2017. Its earned 5.6 billion yuan in the box office, become the biggest film of all time in China. The discussion of the movie on social media plays an indispensable role in influencing the box office performance. This study aims to predict movie box office performance based on affective computation on the related feeds on social media. Since the research on Chinese sentiment analysis is limited, and the accuracy of the analysis highly depends on the context, this study proposes to combine topic's hotness degree with emotion score to improve the accuracy of the prediction. Based on 16,496,675 related Sina Weibo feeds and Douban movie reviews in a restricted time zone, with the prediction algorithm proposed in the paper, the prediction result yields an R2=95.71%. Which means the success of Zhan Lang 2 is utterly predictable.","PeriodicalId":119217,"journal":{"name":"Artificial Intelligence and Cloud Computing Conference","volume":"36 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":"127203043","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}
A. Mohamed, M. Wahab, S. S. Suhaily, Darshan Babu L. Arasu
The smart mirror projects consisting of observable mirror, microcontroller, camera and PC monitor. Existing smart projects are limited with features available and only displaying information based on command receiving directly from the user. To make this mirror to be smarter, artificial intelligence are added in this project. Facial expression detection will be implemented so that smart mirror is able to interact with user and recognize changes of the facial muscle. By accordingly to their expression, smart mirror will make decision to display related information. Only recognized user can utilize this smart mirror via face recognition. At end of the project, a working smart mirror is expected and have ability to become one of these connected devices in our households.
{"title":"Smart Mirror Design Powered by Raspberry PI","authors":"A. Mohamed, M. Wahab, S. S. Suhaily, Darshan Babu L. Arasu","doi":"10.1145/3299819.3299840","DOIUrl":"https://doi.org/10.1145/3299819.3299840","url":null,"abstract":"The smart mirror projects consisting of observable mirror, microcontroller, camera and PC monitor. Existing smart projects are limited with features available and only displaying information based on command receiving directly from the user. To make this mirror to be smarter, artificial intelligence are added in this project. Facial expression detection will be implemented so that smart mirror is able to interact with user and recognize changes of the facial muscle. By accordingly to their expression, smart mirror will make decision to display related information. Only recognized user can utilize this smart mirror via face recognition. At end of the project, a working smart mirror is expected and have ability to become one of these connected devices in our households.","PeriodicalId":119217,"journal":{"name":"Artificial Intelligence and Cloud Computing Conference","volume":"171 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":"132055828","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}
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.
{"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}
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.
{"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}
The alarming rate of big data usage in the cloud makes data exposed easily. Cloud which consists of many servers linked to each other is used for data storage. Having owned by third parties, the security of the cloud needs to be looked at. Risks of storing data in cloud need to be checked further on the severity level. There should be a way to access the risks. Thus, the objective of this paper is to use SLR so that we can have extensive background of literatures on risk assessment for big data in cloud computing environment from the perspective of security, privacy and trust.
{"title":"Risk Assessment for Big Data in Cloud: Security, Privacy and Trust","authors":"Hazirah Bee bt Yusof Ali, Lili Marziana Abdullah, M. Kartiwi, Azlin Nordin","doi":"10.1145/3299819.3299841","DOIUrl":"https://doi.org/10.1145/3299819.3299841","url":null,"abstract":"The alarming rate of big data usage in the cloud makes data exposed easily. Cloud which consists of many servers linked to each other is used for data storage. Having owned by third parties, the security of the cloud needs to be looked at. Risks of storing data in cloud need to be checked further on the severity level. There should be a way to access the risks. Thus, the objective of this paper is to use SLR so that we can have extensive background of literatures on risk assessment for big data in cloud computing environment from the perspective of security, privacy and trust.","PeriodicalId":119217,"journal":{"name":"Artificial Intelligence and Cloud Computing Conference","volume":"18 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":"114622522","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}
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.
{"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}
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