2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)最新文献_第9页

Character Extraction and Recognition For Myanmar Script Signboard Images using Block based Pixel Count and Chain Codes 基于块像素计数和链码的缅甸文字招牌图像字符提取与识别

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466512

Kyi Pyar Zaw, Zin Mar Kyu

{"title":"Character Extraction and Recognition For Myanmar Script Signboard Images using Block based Pixel Count and Chain Codes","authors":"Kyi Pyar Zaw, Zin Mar Kyu","doi":"10.1109/ICIS.2018.8466512","DOIUrl":"https://doi.org/10.1109/ICIS.2018.8466512","url":null,"abstract":"This paper presents a very simple and efficient method for the text extraction and recognition of the Myanmar text from color natural signboard images taken by a mobile phone camera. Text extraction, line segmentation, character segmentation and recognition are the important steps in text understanding from natural signboard images. In this system, the color enhancement is firstly processed to overcome various illumination conditions. Background noises on the binary images are removed by four filtering features such as color threshold based filtering, aspect ratio based filtering, boundary based filtering and region area based filtering. After removing the noise, line segmentation and character segmentation are done. Horizontal projection profile is used for line segmentation and vertical projection profile and bounding box methods are used to segment the characters. These connected component characters are recognized by using 4×4 blocks based pixel density and total chain codes, 4-rows based pixel density, 4-columns based pixel density and count of eight directions chain code on the whole character image and on each block of character image. This system is investigated by feature based approach of template matching, and 83.15% character recognition accuracy is achieved on 2854 correctly extracted characters from 150 camera-captured Myanmar warning signboards.","PeriodicalId":447019,"journal":{"name":"2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115193820","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

Comparative Study of Prediction Models for Final GPA Score: A Case Study of Rajabhat Rajanagarindra University 最终GPA成绩预测模型的比较研究——以Rajabhat Rajanagarindra大学为例

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466475

Narongsak Putpuek, Natcha Rojanaprasert, K. Atchariyachanvanich, Thananya Thamrongthanyawong

{"title":"Comparative Study of Prediction Models for Final GPA Score: A Case Study of Rajabhat Rajanagarindra University","authors":"Narongsak Putpuek, Natcha Rojanaprasert, K. Atchariyachanvanich, Thananya Thamrongthanyawong","doi":"10.1109/ICIS.2018.8466475","DOIUrl":"https://doi.org/10.1109/ICIS.2018.8466475","url":null,"abstract":"Recently, the analysis of educational data has become important to all universities. Rajabhat Rajanagarindra University, Thailand, wanted to study and analyze the students’ performance based on their personal background. Thus, this research aimed to compare prediction models for the level of the final grade point average (GPA) score of graduated students using the data from the Faculty of Education during the 2010 to 2012 academic years. Two decision tree (C4.5 and ID3) algorithms, plus Naïve Bayes and K-nearest neighbor data mining techniques were adopted to analyze the data according to the CRISP-DM process. Factors that were proposed to influence the graduation GPA include the student’s gender, scholarship awarded, previous educational background, admission type, talent and province of high school. The analysis revealed that the Naïve Bayes algorithm gave the best overall accuracy of 43.18%. This could help predict the graduation GPA score of students in the future and support teachers to make educational advice for their students and to develop the student quality in the future.","PeriodicalId":447019,"journal":{"name":"2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115286690","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: 20

The Robotic Fish Strategy Based on The Extreme Learning Machine Optimized by Particle Swarm Optimization Algorithm 基于粒子群算法优化的极限学习机的机器鱼策略

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466496

Xuexi Zhang, Shuibiao Chen, Zhiguang Cao, Shuting Cai, Zerong Peng, Xiaoming Xiong

{"title":"The Robotic Fish Strategy Based on The Extreme Learning Machine Optimized by Particle Swarm Optimization Algorithm","authors":"Xuexi Zhang, Shuibiao Chen, Zhiguang Cao, Shuting Cai, Zerong Peng, Xiaoming Xiong","doi":"10.1109/ICIS.2018.8466496","DOIUrl":"https://doi.org/10.1109/ICIS.2018.8466496","url":null,"abstract":"Aiming at URWPGSim2D platform, in order to realize rapid and accurate adjustment of the robotic fish, action decision strategy based on the extreme learning machine optimized by particle swarm algorithm is put forward. According to the current environmental information of robotic fish, use the extreme learning machine to choose the optimal hitting point independently, and to determine the optimal combination of velocity and angular velocity of robotic fish. At the same time, the particle swarm optimization algorithm is introduced to optimize the extreme learning machine, which can improve the accuracy and robustness of the extreme learning machine. Verified by URWPGSim2D platform show that: the robotic fish path can be adjusted according to the strategy, realize combinatorial optimization of speed and direction, and find the target in the shortest time and distance. This shows that action decision-making strategy based on extreme learning machine can fully consider the real-time information of robotic fish and water polo, choose a different strategy in different cases, have a strong ability to adapt, meet the requirements of robotic fish for the action decisions.","PeriodicalId":447019,"journal":{"name":"2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)","volume":"30 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123274507","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

Advertising Email Management Using Multi-Level Reputation System 广告电子邮件管理使用多层次的信誉系统

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466418

Akkarach Kawbunjun, W. Lilakiatsakun, Ubon Thongsatapornwatana

{"title":"Advertising Email Management Using Multi-Level Reputation System","authors":"Akkarach Kawbunjun, W. Lilakiatsakun, Ubon Thongsatapornwatana","doi":"10.1109/ICIS.2018.8466418","DOIUrl":"https://doi.org/10.1109/ICIS.2018.8466418","url":null,"abstract":"Currently, a large number of emails from sources either wanted or unwanted sites are sent to our mail box every day. Most of emails from unwanted sites are left unread and finally deleted without opening. Emails even from some subscribed sites that are no longer interested are also left unread. The problem is that it could not have an easy way to unsubscribe or make unwanted senders stop sending emails to us. Thus, we propose Email Management Framework to help recipients gradually reducing emails from unwanted senders automatically. This framework has used centralized user feedback databases and multi-level reputation system. We divided false reporting feedback into positive and negative feedback to prevent sending mistaken emails to recipients. From our experiments, the results show that the proposed framework can increase accuracy rate of classification 6.20% compared to traditional process. Furthermore, the framework can provide the way to manage email sending automatically by using feedback from recipient’s behavior. Recipients are ensured that they would finally receive only wanted emails. Unwanted emails will gradually be reduced automatically. Additionally, for sender’s perspective, the framework also provide value-added information who are their real customers and are still interested in their information.","PeriodicalId":447019,"journal":{"name":"2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129057189","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

Visual Servoing in a Prioritized Constraint-based Torque Control Framework 基于优先约束的转矩控制框架中的视觉伺服

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466398

Caixia Cai, N. Somani

引用次数: 2

The Implementation of Winnowing Algorithm for Plagiarism Detection in Moodle-based E-learning 基于moodle的在线学习中抄袭检测的筛选算法的实现

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466429

Eric Ganiwijaya Hasan, Arya Wicaksana, S. Hansun

{"title":"The Implementation of Winnowing Algorithm for Plagiarism Detection in Moodle-based E-learning","authors":"Eric Ganiwijaya Hasan, Arya Wicaksana, S. Hansun","doi":"10.1109/ICIS.2018.8466429","DOIUrl":"https://doi.org/10.1109/ICIS.2018.8466429","url":null,"abstract":"The act of plagiarism is inappropriate and untrue for any reasons, especially in the academic world. Academicians are aware of this and try to avoid the act of plagiarism by any means necessary. In Universitas Multimedia Nusantara (UMN), an online learning (E-learning) system is used as an additional tool to support teaching process. The E-learning system is Moodle-based with one of its features is the assignment submission. The preliminary survey shows that 23.2% respondents had committed the act of plagiarism when doing assignments in E-learning in the past. Due to the lack of plagiarism detection feature in Moodle-based E-learning system, thus this research proposes a plagiarism detection feature as a plug-in for the E-learning system. The plagiarism detection plug-in handles assignments that are in the format of text documents. The plug-in makes use of Winnowing algorithm for fingerprinting the assignment documents and the hashing technique chosen for the Winnowing algorithm is Rolling Hash. The similarity value is calculated using Jaccard Coefficient. As a demonstration of feasibility, the implementation is done in Moodle-based E-learning UMN. The test results show the combinations of parameters (k-gram, window length, and the base prime number) for the plug-in and the f-measure. The plug-in successfully detects plagiarism on student assignment in E-learning UMN.","PeriodicalId":447019,"journal":{"name":"2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116937076","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: 9

Floor Heating Customer Prediction Model Based on Random Forest 基于随机森林的地暖客户预测模型

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466420

Zhihuan Yao, Xian Xu, Huiqun Yu

引用次数: 3

A Novel Approach to Define and Manage Security Indicators for the Fulfillment of Agreed Assurance Levels in Clouds 一种定义和管理安全指标的新方法，用于在云中实现商定的保证级别

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466452

Faraz Fatemi Moghaddam, P. Wieder, R. Yahyapour, Tayyebe Emadinia

{"title":"A Novel Approach to Define and Manage Security Indicators for the Fulfillment of Agreed Assurance Levels in Clouds","authors":"Faraz Fatemi Moghaddam, P. Wieder, R. Yahyapour, Tayyebe Emadinia","doi":"10.1109/ICIS.2018.8466452","DOIUrl":"https://doi.org/10.1109/ICIS.2018.8466452","url":null,"abstract":"One of the most challenging obstacles for the advancement of clouds is the lack of assurance and transparency, along with the current paucity of techniques to quantify security. A fundamental requirement for solving this problem is to provide proper levels of security based on the requirements of cloud customers and sensitivity of data. However, generating these security levels only partially serves the requirements of cloud customers especially if it’s not linked to the management of SLC commitments. Accordingly, a novel approach has been proposed in this paper to define and manage security indicators in the generated security levels. These indicators are used to react to eventualities that may threaten the established mechanisms of the generated security rings, to ensure the fulfillment of agreed assurance levels, and to minimize the damages in case of attacks, unpredictable events or unavoidable changes. The proposed schema uses simultaneous monitoring with two different stand-alone agents per security level. These agents are initialized based on all security policies in the SLA to enhance the process of monitoring and rectification and to increase the rate of satisfaction and reliability in clouds.","PeriodicalId":447019,"journal":{"name":"2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122902060","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

Research and Optimization of Massive Music Data Access Based on HDFS 基于HDFS的海量音乐数据访问研究与优化

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466493

Tong Ouyang, Yizhen Cao

{"title":"Research and Optimization of Massive Music Data Access Based on HDFS","authors":"Tong Ouyang, Yizhen Cao","doi":"10.1109/ICIS.2018.8466493","DOIUrl":"https://doi.org/10.1109/ICIS.2018.8466493","url":null,"abstract":"To build a storage management platform for music big data, we need to collect massive heterogeneous music resources from the Internet and store them on big data platforms. Therefore, it is a key problem to build a storage management system that is high performance, extensible, scalable and capable of supporting big data. Building big data platform based on HDFS is a feasible scheme. However, HDFS has good performance for accessing large files, but it is very inefficient for small files such as music source files and music metadata. In view of this, this paper proposes a geared to the needs of mass music based on HDFS small file access optimization scheme, using the format of the music data classification, build multistage merger queue, merging small files into large files in order to reduce the number of files, and create an indexing mechanism to access small files. The index file is then stored in the Phoenix + HBase storage repository and associated with the music metadata to improve the reading efficiency of music small files and music metadata. The experimental test verifies the effectiveness of the optimized scheme and meets the demand of music big data storage management platform.","PeriodicalId":447019,"journal":{"name":"2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126701750","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

Character Relationship Management of Play Script System Based On SaaS 基于SaaS的剧本系统角色关系管理

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) Pub Date : 2018-06-01 DOI: 10.1109/ICIS.2018.8466530

Yangchen Wang, Chunfang Li, Minyong Shi, Mengyu Liu

引用次数: 2