Pub Date : 2018-10-01DOI: 10.1109/CYBERC.2018.00056
A. Aburas, Muhammed Mehtab, Yusuf Mehtab
The ICC 2019 Cricket World Cup is scheduled to be hosted by England and Wales. This research work aims to predict the winner of the 12th version of ICC world cup using Business Intelligent (BI) and K Nearest Neighbors KNN bigdata approach. The research works will start off with business intelligent (BI) case and the Big Data lifecycle. Machine Learning, KNN and R Language will be defined in depth. Then, it will give a detailed to extract all patterns that suitable for machine learning tool to predict the winner. Thereafter, data reduction algorithm will be presented. Additionally, explain in details all steps are taken to achieve the KNN classifications. The source and selected datasets required are given. Finally, the root of this paper, the prediction of the winner of the 2019 ICC Cricket World Cup is declared.
{"title":"ICC World Cup Prediction Based Data Analytics and Business Intelligent (BI) Techniques","authors":"A. Aburas, Muhammed Mehtab, Yusuf Mehtab","doi":"10.1109/CYBERC.2018.00056","DOIUrl":"https://doi.org/10.1109/CYBERC.2018.00056","url":null,"abstract":"The ICC 2019 Cricket World Cup is scheduled to be hosted by England and Wales. This research work aims to predict the winner of the 12th version of ICC world cup using Business Intelligent (BI) and K Nearest Neighbors KNN bigdata approach. The research works will start off with business intelligent (BI) case and the Big Data lifecycle. Machine Learning, KNN and R Language will be defined in depth. Then, it will give a detailed to extract all patterns that suitable for machine learning tool to predict the winner. Thereafter, data reduction algorithm will be presented. Additionally, explain in details all steps are taken to achieve the KNN classifications. The source and selected datasets required are given. Finally, the root of this paper, the prediction of the winner of the 2019 ICC Cricket World Cup is declared.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"427 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123437992","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}
Pub Date : 2018-10-01DOI: 10.1109/CYBERC.2018.00047
Thandar Aung, Hla Yin Min, A. Maw
Nowadays, Real time messaging system is the essential thing in enabling time-critical decision making in many applications where it is important to deal with real-time requirements and reliability requirements simultaneously. For dependability reasons, we intend to maximize the reliability requirement of real time messaging system. To develop real time messaging system, we create real time big data pipeline by using Apache Kafka and Apache Storm. This paper focuses on analyzing the performance of producer and consumer in Apache Kafka processing. The performance of Kafka processing modify to be more reliable on the pipeline architecture. Then, the experiment will be conducted the processing time in the performance of the producer and consumer on various partitions. The performance evaluation of Kafka can impact on messaging system in real time big data pipeline architecture.
{"title":"Performance Evaluation for Real-Time Messaging System in Big Data Pipeline Architecture","authors":"Thandar Aung, Hla Yin Min, A. Maw","doi":"10.1109/CYBERC.2018.00047","DOIUrl":"https://doi.org/10.1109/CYBERC.2018.00047","url":null,"abstract":"Nowadays, Real time messaging system is the essential thing in enabling time-critical decision making in many applications where it is important to deal with real-time requirements and reliability requirements simultaneously. For dependability reasons, we intend to maximize the reliability requirement of real time messaging system. To develop real time messaging system, we create real time big data pipeline by using Apache Kafka and Apache Storm. This paper focuses on analyzing the performance of producer and consumer in Apache Kafka processing. The performance of Kafka processing modify to be more reliable on the pipeline architecture. Then, the experiment will be conducted the processing time in the performance of the producer and consumer on various partitions. The performance evaluation of Kafka can impact on messaging system in real time big data pipeline architecture.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126522040","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}
Pub Date : 2018-10-01DOI: 10.1109/CYBERC.2018.00049
Shuo Zhang, F. Meng, Zhongyi Zhang
In an Infrastructure as a Service (IaaS) environment, a key requirement is the rational placement of virtual machines that consumers apply for. In order to reduce the energy consumption of servers and switches in the data center, reduce the operating cost of data center, this paper proposes a virtual machine placement scheme based on energy optimization. Aiming at the shortcomings of genetic algorithm in intelligent algorithm which is easy to fall into local optimum, the immune algorithm is combined to generate an optimized immune genetic algorithm and a placement scheme is generated. In this paper, the data center structure is constructed, the energy consumption model of data center is constructed, and the simulation experiment is carried out. Through the experiment comparison, the proposed virtual machine placement scheme can effectively reduce data center energy consumption, and achieve effective results.
{"title":"A Cloud Data Center Virtual Machine Placement Scheme Based on Energy Optimization","authors":"Shuo Zhang, F. Meng, Zhongyi Zhang","doi":"10.1109/CYBERC.2018.00049","DOIUrl":"https://doi.org/10.1109/CYBERC.2018.00049","url":null,"abstract":"In an Infrastructure as a Service (IaaS) environment, a key requirement is the rational placement of virtual machines that consumers apply for. In order to reduce the energy consumption of servers and switches in the data center, reduce the operating cost of data center, this paper proposes a virtual machine placement scheme based on energy optimization. Aiming at the shortcomings of genetic algorithm in intelligent algorithm which is easy to fall into local optimum, the immune algorithm is combined to generate an optimized immune genetic algorithm and a placement scheme is generated. In this paper, the data center structure is constructed, the energy consumption model of data center is constructed, and the simulation experiment is carried out. Through the experiment comparison, the proposed virtual machine placement scheme can effectively reduce data center energy consumption, and achieve effective results.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129560747","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}
Pub Date : 2018-10-01DOI: 10.1109/cyberc.2018.00010
M. Abolhasan, S. Adibi, Deyun Gao
Mohd Riduan Ahmad Universiti Teknikal Malaysia Melaka Malaysia Naoyuki Aikawa Tokyo University of Science Japan Annamalai Annamalai Prairie View A&M University USA Kaoru Arakawa Meiji University Japan Kunihiko Asakura Yonago National College of Technology Japan Akira Asano Kansai University Japan Rafael Asorey-Cacheda Technical University of Cartagena Spain Edward Au Huawei Technologies Co., Ltd. Canada Surapong Auwatanamongkol NIDA Thailand Vo Nguyen Quoc Bao Posts and Telecommunications Institute of Technology Vietnam Thibault Bernard University of Reims Champagne Ardenne France Huynh Thi Thanh Binh HUST Vietnam Huynh Thi Thanh Binh HUST Vietnam Smich Butcharoen King Mongkut's University of Technology North Bangkok Thailand Rhandley Cajote University of the Philippines Philippines Thuong Canh 2-8, Yamadaoka, Suita Japan KyungHi Chang Inha University Korea Shi-Chung Chang National Taiwan University Taiwan Su Fong Chien MIMOS Berhad Malaysia Jae-Kark Choi Hanwha Systems Korea Qimei Cui Beijing University of Posts and Telecommunications P.R. China Nguyen Cuong 102 Hung Vuong Tam Ky Quang Nam Vietnam Haibo Dai Nanjing University of Posts and Telecommunications P.R. China Nhu-Ngoc Dao University of Bern Switzerland Son Hoang Dau RMIT University Australia Mérouane Debbah Huawei France Hoang Dinh University of Technology Sydney (UTS) Australia Quang-Thang Duong Nara Institute of Science and Technology Japan Alban Duverdier Centre National D'Etudes Spatiales (CNES) France Tobias Eggendorfer Hochschule Ravensburg-Weingarten Germany Ulrich Engelke Commonwealth Scientific and Industrial Research Organisation (CSIRO) Australia
Mohd Riduan Ahmad Universiti Teknikal Malaysia Melaka Malaysia 相川直之 东京理科大学 日本 Annamalai Annamalai Prairie View A&M University 美国 Kaoru Arakawa Meiji University 日本 Kunihiko Asakura Yonago National College of Technology 日本 Akira Asano Kansai University 日本 Rafael Asorey-Cacheda Technical University of Cartagena 西班牙 Edward Au 华为技术有限公司 加拿大 Surapong Auwatanamongkol NIDA 泰国 Vo Nguyen Quoc Bao Post and Telecommunications Institute of Technology 越南 Thibault Bernard University Reim Champagne Ardenne 越南加拿大 Surapong Auwatanamongkol NIDA 泰国 Vo Nguyen Quoc Bao 邮电技术学院 越南 Thibault Bernard 兰斯香槟阿登大学 法国 Huynh Thi Thanh Binh HUST 越南 Huynh Thi Thanh Binh HUST 越南 Smich Butcharoen King Mongkut's University of Technology North Bangkok 泰国 Rhandley Cajote University of the Philippines 菲律宾 Thuong Canh 2-8、Yamadaoka, Suita 日本 KyungHi Chang Inha University 韩国 Shi-Chung Chang 国立台湾大学 台湾 Su Fong Chien MIMOS Berhad 马来西亚 Jae-Kark Choi Hanwha Systems 韩国 Qimei Cui 北京邮电大学 P. R. China Nguyen Chengh, H. HUST 越南Nguyen Cuong 102 Hung Vuong Tam Ky Quang Nam Vietnam Haibo Dai Nanjing University of Posts and Telecommunications P.R. China Nhu-Ngoc Dao.中国 Nhu-Ngoc Dao 瑞士伯尔尼大学 Son Hoang Dau 澳大利亚皇家墨尔本理工大学 Mérouane Debbah Huawei 法国 Hoang Dinh 悉尼科技大学 (UTS) 澳大利亚 Quang-Thang Duong 奈良科学技术学院 日本 Alban Duverdier 法国国家空间研究中心 (CNES) Tobias Eggendorfer Hochschule Ravensburg-Weingarten 德国 Ulrich Engelke 澳大利亚联邦科学与工业研究组织 (CSIRO)
{"title":"Technical Program Committee","authors":"M. Abolhasan, S. Adibi, Deyun Gao","doi":"10.1109/cyberc.2018.00010","DOIUrl":"https://doi.org/10.1109/cyberc.2018.00010","url":null,"abstract":"Mohd Riduan Ahmad Universiti Teknikal Malaysia Melaka Malaysia Naoyuki Aikawa Tokyo University of Science Japan Annamalai Annamalai Prairie View A&M University USA Kaoru Arakawa Meiji University Japan Kunihiko Asakura Yonago National College of Technology Japan Akira Asano Kansai University Japan Rafael Asorey-Cacheda Technical University of Cartagena Spain Edward Au Huawei Technologies Co., Ltd. Canada Surapong Auwatanamongkol NIDA Thailand Vo Nguyen Quoc Bao Posts and Telecommunications Institute of Technology Vietnam Thibault Bernard University of Reims Champagne Ardenne France Huynh Thi Thanh Binh HUST Vietnam Huynh Thi Thanh Binh HUST Vietnam Smich Butcharoen King Mongkut's University of Technology North Bangkok Thailand Rhandley Cajote University of the Philippines Philippines Thuong Canh 2-8, Yamadaoka, Suita Japan KyungHi Chang Inha University Korea Shi-Chung Chang National Taiwan University Taiwan Su Fong Chien MIMOS Berhad Malaysia Jae-Kark Choi Hanwha Systems Korea Qimei Cui Beijing University of Posts and Telecommunications P.R. China Nguyen Cuong 102 Hung Vuong Tam Ky Quang Nam Vietnam Haibo Dai Nanjing University of Posts and Telecommunications P.R. China Nhu-Ngoc Dao University of Bern Switzerland Son Hoang Dau RMIT University Australia Mérouane Debbah Huawei France Hoang Dinh University of Technology Sydney (UTS) Australia Quang-Thang Duong Nara Institute of Science and Technology Japan Alban Duverdier Centre National D'Etudes Spatiales (CNES) France Tobias Eggendorfer Hochschule Ravensburg-Weingarten Germany Ulrich Engelke Commonwealth Scientific and Industrial Research Organisation (CSIRO) Australia","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130678223","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}
Pub Date : 2018-10-01DOI: 10.1109/CYBERC.2018.00071
Lina Xu, Ning Cao
As small cells and network Access Points (APs) have been massively deployed in the cities, industrial, academic campuses, communication techniques utilising same or different spectrum bands are required to coordinate each other and coexist in harmony. Meanwhile, User Equipments (UEs) including Internet of Things (IoT) devices are more advanced in terms of communication ability. Simultaneously supporting data transmission through multiple network interfaces is prevalent nowadays. The current networks have many diversities and will continually involves more user scenarios and more advanced smart devices. The communication between those devices, is mostly through wireless media, such as, bluetooth, WiFi or LTE. Meanwhile, with the evolution of wireless technology, the communication in most systems will migrate to Heterogeneous Networks (HetNets)/5G networks. The devices in those systems will be able to support 5G communication, enabling more than one radio access interfaces through Multiple-In-Multiple-Out (MIMO) antennas. In order to realise 5G communication, massive regulations and standards are proposed and HetNets have become one popular means since it can utilise the existing infrastructure. The focus of 5G has changes since the last generation from operator centric to user centric services. For purpose of achieving Quality of user Experience (QoE) supported 5G, network selection in HetNets based approach is necessary and also should be intelligent. In this paper, we have proposed that the classic utility function can be applied in such a HetNets scenario. With modifications on the classic utility approach, more advanced features can be enhanced to support QoE aware communication in HetNets based 5G networks. Furthermore, we have demonstrated this idea through a LTE and WiFi integrated network. A solution for smart network selection is delivered in order to achieve 1) better user experience, 2) manageable resource allocation and 3) price control. Based on the analysis, we argue that such a solution can also be extended and implemented for more complex 5G networks to accomplish QoE awareness.
{"title":"A Smart QoE Aware Network Selection Solution for IoT Systems in HetNets Based 5G Scenarios","authors":"Lina Xu, Ning Cao","doi":"10.1109/CYBERC.2018.00071","DOIUrl":"https://doi.org/10.1109/CYBERC.2018.00071","url":null,"abstract":"As small cells and network Access Points (APs) have been massively deployed in the cities, industrial, academic campuses, communication techniques utilising same or different spectrum bands are required to coordinate each other and coexist in harmony. Meanwhile, User Equipments (UEs) including Internet of Things (IoT) devices are more advanced in terms of communication ability. Simultaneously supporting data transmission through multiple network interfaces is prevalent nowadays. The current networks have many diversities and will continually involves more user scenarios and more advanced smart devices. The communication between those devices, is mostly through wireless media, such as, bluetooth, WiFi or LTE. Meanwhile, with the evolution of wireless technology, the communication in most systems will migrate to Heterogeneous Networks (HetNets)/5G networks. The devices in those systems will be able to support 5G communication, enabling more than one radio access interfaces through Multiple-In-Multiple-Out (MIMO) antennas. In order to realise 5G communication, massive regulations and standards are proposed and HetNets have become one popular means since it can utilise the existing infrastructure. The focus of 5G has changes since the last generation from operator centric to user centric services. For purpose of achieving Quality of user Experience (QoE) supported 5G, network selection in HetNets based approach is necessary and also should be intelligent. In this paper, we have proposed that the classic utility function can be applied in such a HetNets scenario. With modifications on the classic utility approach, more advanced features can be enhanced to support QoE aware communication in HetNets based 5G networks. Furthermore, we have demonstrated this idea through a LTE and WiFi integrated network. A solution for smart network selection is delivered in order to achieve 1) better user experience, 2) manageable resource allocation and 3) price control. Based on the analysis, we argue that such a solution can also be extended and implemented for more complex 5G networks to accomplish QoE awareness.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116245447","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}
Pub Date : 2018-10-01DOI: 10.1109/CYBERC.2018.00054
Lei Gao, L. Guan
As a recent proposed information fusion tool, Kernel Entropy Component Analysis (KECA) has attracted more attentions from the research communities of multimedia. It utilizes descriptor of entropy estimation and achieves improved performance for information fusion. However, KECA roughly reduces to sorting the importance of kernel eigenvectors by entropy instead of by variance as in Kernel Principal Components Analysis (KPCA), without extracting the optimal features retaining more entropy of the input data. In this paper, a novel approach Optimized Kernel Entropy Components Analysis (OKECA) is introduced to information fusion, which can be considered as an alternative method to KECA for information fusion. Since OKECA explicitly extracts the optimal features that retain most informative content, it leads to improving the final performance or classification accuracy. To demonstrate the effectiveness of the proposed solution, experiments are conducted on Ryerson Multimedia Lab (RML) and eNTERFACE emotion datasets. Experimental results show that the proposed solution outperforms the existing methods based on the similar principles, and the Deep Learning (DL) based method.
{"title":"Information Fusion VIA Optimized KECA with Application to Audio Emotion Recognition","authors":"Lei Gao, L. Guan","doi":"10.1109/CYBERC.2018.00054","DOIUrl":"https://doi.org/10.1109/CYBERC.2018.00054","url":null,"abstract":"As a recent proposed information fusion tool, Kernel Entropy Component Analysis (KECA) has attracted more attentions from the research communities of multimedia. It utilizes descriptor of entropy estimation and achieves improved performance for information fusion. However, KECA roughly reduces to sorting the importance of kernel eigenvectors by entropy instead of by variance as in Kernel Principal Components Analysis (KPCA), without extracting the optimal features retaining more entropy of the input data. In this paper, a novel approach Optimized Kernel Entropy Components Analysis (OKECA) is introduced to information fusion, which can be considered as an alternative method to KECA for information fusion. Since OKECA explicitly extracts the optimal features that retain most informative content, it leads to improving the final performance or classification accuracy. To demonstrate the effectiveness of the proposed solution, experiments are conducted on Ryerson Multimedia Lab (RML) and eNTERFACE emotion datasets. Experimental results show that the proposed solution outperforms the existing methods based on the similar principles, and the Deep Learning (DL) based method.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114724242","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}
Pub Date : 2018-10-01DOI: 10.1109/CYBERC.2018.00066
Zonghao Ma, Yanhui Lu, Lingfeng Shen, Yili Liu, Ning Wang
In this work, physical layer secure transmission of a cooperative wireless communication system over a three-phase amplify-and-forward (AF) two-way relaying channels is studied. A cooperative jamming and multi-antenna relay beamforming scheme that improves physical layer security of the system is proposed. Specifically, it is suggested that in the first and second timeslots when the legitimate users transmit alternatively, a jamming signal can be transmitted by the legitimate party that is not transmitting information-bearing signals This scheme, compared with the approach using a dedicated jammer, achieves a similar jamming effect with reduced complexity. In the third time slot of the protocol, the relay uses two beamforming matrices to process the bidirectional information signals. The jamming signal components at the relay are projected onto the null space of the legitimate relay-destination channels such that they do not affect legitimate communications. Through joint optimization, the sum throughput of the legitimate communication is maximized. In the numerical examples, the proposed scheme is shown to improve the system's secrecy transmission performance.
{"title":"Cooperative Jamming and Relay Beamforming Design for Physical Layer Secure Two-Way Relaying","authors":"Zonghao Ma, Yanhui Lu, Lingfeng Shen, Yili Liu, Ning Wang","doi":"10.1109/CYBERC.2018.00066","DOIUrl":"https://doi.org/10.1109/CYBERC.2018.00066","url":null,"abstract":"In this work, physical layer secure transmission of a cooperative wireless communication system over a three-phase amplify-and-forward (AF) two-way relaying channels is studied. A cooperative jamming and multi-antenna relay beamforming scheme that improves physical layer security of the system is proposed. Specifically, it is suggested that in the first and second timeslots when the legitimate users transmit alternatively, a jamming signal can be transmitted by the legitimate party that is not transmitting information-bearing signals This scheme, compared with the approach using a dedicated jammer, achieves a similar jamming effect with reduced complexity. In the third time slot of the protocol, the relay uses two beamforming matrices to process the bidirectional information signals. The jamming signal components at the relay are projected onto the null space of the legitimate relay-destination channels such that they do not affect legitimate communications. Through joint optimization, the sum throughput of the legitimate communication is maximized. In the numerical examples, the proposed scheme is shown to improve the system's secrecy transmission performance.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125459482","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}
Pub Date : 2018-10-01DOI: 10.1109/CYBERC.2018.00031
Jieli Sun, Yao Zhai, Yanxia Zhao, Jianke Li, Naishi Yan
In the paper, we discuss the theories of the information acquisition and analysis and the information quality of the case-based reasoning (CBR) personalized recommendation system. We also take a deep study of the key techniques of acquiring and analyzing information quality. With research results of this paper, combined with the content-based recommendation technology and recommendation results of collaborative filtering, a CBR-based personalized combinatorial recommendation algorithm is designed.
{"title":"Information Acquisition and Analysis Technology of Personalized Recommendation System Based on Case-Based Reasoning for Internet of Things","authors":"Jieli Sun, Yao Zhai, Yanxia Zhao, Jianke Li, Naishi Yan","doi":"10.1109/CYBERC.2018.00031","DOIUrl":"https://doi.org/10.1109/CYBERC.2018.00031","url":null,"abstract":"In the paper, we discuss the theories of the information acquisition and analysis and the information quality of the case-based reasoning (CBR) personalized recommendation system. We also take a deep study of the key techniques of acquiring and analyzing information quality. With research results of this paper, combined with the content-based recommendation technology and recommendation results of collaborative filtering, a CBR-based personalized combinatorial recommendation algorithm is designed.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122882189","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}
Pub Date : 2018-10-01DOI: 10.1109/CYBERC.2018.00060
Weibing Long, Kunli Zhang, Hongchao Ma, Donghui Yue, Zhuang Lei
The data-driven medical health information processing has become a new development direction, especially the auxiliary diagnosis based on the electronic medical records (EMRs), which is of great significance to improve population health. In this paper, to obtain excellent obstetric auxiliary diagnostic results, the Chinese obstetric EMRs is analyzed and processed, and finally the auxiliary diagnosis task is transformed into a multi-label classification problem. Moreover, two effective global error functions are proposed by enhancing pairwise labels discrimination to improve the Backpropagation for Multi-label Learning (BP-MLL) that depends on the neural network model. The experiment results of some public multi-label datasets and the Chinese obstetric dataset show that the two error functions have better overall performance compared with BP-MLL original error function and some well-established multi-label learning algorithms.
{"title":"Neural Network Multi-label Learning Based on Enhancing Pairwise Labels Discrimination for Obstetric Auxiliary Diagnosis","authors":"Weibing Long, Kunli Zhang, Hongchao Ma, Donghui Yue, Zhuang Lei","doi":"10.1109/CYBERC.2018.00060","DOIUrl":"https://doi.org/10.1109/CYBERC.2018.00060","url":null,"abstract":"The data-driven medical health information processing has become a new development direction, especially the auxiliary diagnosis based on the electronic medical records (EMRs), which is of great significance to improve population health. In this paper, to obtain excellent obstetric auxiliary diagnostic results, the Chinese obstetric EMRs is analyzed and processed, and finally the auxiliary diagnosis task is transformed into a multi-label classification problem. Moreover, two effective global error functions are proposed by enhancing pairwise labels discrimination to improve the Backpropagation for Multi-label Learning (BP-MLL) that depends on the neural network model. The experiment results of some public multi-label datasets and the Chinese obstetric dataset show that the two error functions have better overall performance compared with BP-MLL original error function and some well-established multi-label learning algorithms.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129256739","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}
Pub Date : 2018-10-01DOI: 10.1109/CYBERC.2018.00058
Juan Liang, Jiapeng Xiu
With the rapid development of smart phones, mobile advertising has taken up more than half of the advertising market and has great potential in the future. At present, there are many mature researches on the conversion rate of search advertising, but few studies have been done on the conversion rate of mobile APP advertising. This paper studies the preliminary data of Tencent's first social advertising algorithm competition and provides a method for prediction of mobile APP advertising conversion rate based on machine learning.
{"title":"Prediction of Mobile APP Advertising Conversion Rate Based on Machine Learning","authors":"Juan Liang, Jiapeng Xiu","doi":"10.1109/CYBERC.2018.00058","DOIUrl":"https://doi.org/10.1109/CYBERC.2018.00058","url":null,"abstract":"With the rapid development of smart phones, mobile advertising has taken up more than half of the advertising market and has great potential in the future. At present, there are many mature researches on the conversion rate of search advertising, but few studies have been done on the conversion rate of mobile APP advertising. This paper studies the preliminary data of Tencent's first social advertising algorithm competition and provides a method for prediction of mobile APP advertising conversion rate based on machine learning.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130614726","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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