Pub Date : 2019-11-01DOI: 10.1109/ICTKE47035.2019.8966933
Tubagus Mohammad Akhriza, Indah Dwi Mumpuni
Gamification is an activity that models non-game systems by integrating game components into the system. Applying gamification to the higher education career system aims to bring an atmosphere of fair competition among educators in achieving higher career positions in their career journey. In the game environment, the atmosphere of the competition can be present through the leaderboards. However, traditional leaderboards usually rank the players' achievements linearly on a pile of pages, limiting the overall view of the map of competition between educators. This article introduces a new leaderboard using a dartboard-like model. Educator career transition paths were first defined as Mealy machines. The pathways are then visualized circularly using the proposed dartboard model so that the career paths of all educators can be seen effectively, and therefore, a map of competition between educators is also obtained. This helps career development management to make decisions about educator career promotion.
{"title":"Dartboard-like Leaderboard for Mapping Educator Career Competition in a Gamification System","authors":"Tubagus Mohammad Akhriza, Indah Dwi Mumpuni","doi":"10.1109/ICTKE47035.2019.8966933","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966933","url":null,"abstract":"Gamification is an activity that models non-game systems by integrating game components into the system. Applying gamification to the higher education career system aims to bring an atmosphere of fair competition among educators in achieving higher career positions in their career journey. In the game environment, the atmosphere of the competition can be present through the leaderboards. However, traditional leaderboards usually rank the players' achievements linearly on a pile of pages, limiting the overall view of the map of competition between educators. This article introduces a new leaderboard using a dartboard-like model. Educator career transition paths were first defined as Mealy machines. The pathways are then visualized circularly using the proposed dartboard model so that the career paths of all educators can be seen effectively, and therefore, a map of competition between educators is also obtained. This helps career development management to make decisions about educator career promotion.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116651650","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 : 2019-11-01DOI: 10.1109/ICTKE47035.2019.8966853
Kyeong-Jin Park, Keun-Chang Kwak
With the recent development of medical imaging equipment, image segmentation techniques for medical diagnosis have become important role as digital image acquisition with good clarity has become possible. In addition, a lot of dental imaging studies have been conducted due to the active segmentation, classification and recognition research using artificial intelligence such as deep learning and CNN (Convolutional Neural Network). In the paper, trends reviews are conducted on dental image processing. For methods using deep learning, AlexNet, GoogLeNet, and other various methods were conducted. For general methods, Otsu's method, O. Nomir's method, Level-Set, Watershed, and other various methods were used. As a result, these methods mostly showed 80% ~ 90% accuracy in the case of dental image segmentation.
{"title":"A Trends Analysis of Dental Image Processing","authors":"Kyeong-Jin Park, Keun-Chang Kwak","doi":"10.1109/ICTKE47035.2019.8966853","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966853","url":null,"abstract":"With the recent development of medical imaging equipment, image segmentation techniques for medical diagnosis have become important role as digital image acquisition with good clarity has become possible. In addition, a lot of dental imaging studies have been conducted due to the active segmentation, classification and recognition research using artificial intelligence such as deep learning and CNN (Convolutional Neural Network). In the paper, trends reviews are conducted on dental image processing. For methods using deep learning, AlexNet, GoogLeNet, and other various methods were conducted. For general methods, Otsu's method, O. Nomir's method, Level-Set, Watershed, and other various methods were used. As a result, these methods mostly showed 80% ~ 90% accuracy in the case of dental image segmentation.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122309337","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 : 2019-11-01DOI: 10.1109/ICTKE47035.2019.8966796
S. Shakhari, A. K. Verma, I. Banerjee
Over the years, analysis of water quality parameters is becoming paramount because of the increasing water pollution which results in the loss of aquatic life which becomes detrimental for the ecosystem. To predict the values of the water quality parameters of places for which the data is not available, a predictive model comes to the fore. Regression Analysis aids us in predictive analysis of the physio-chemical parameters of water quality and perform error analysis by comparing the predicted values with the actual values of the parameters.
{"title":"Remote Location Water Quality Prediction of the Indian River Ganga: Regression and Error Analysis","authors":"S. Shakhari, A. K. Verma, I. Banerjee","doi":"10.1109/ICTKE47035.2019.8966796","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966796","url":null,"abstract":"Over the years, analysis of water quality parameters is becoming paramount because of the increasing water pollution which results in the loss of aquatic life which becomes detrimental for the ecosystem. To predict the values of the water quality parameters of places for which the data is not available, a predictive model comes to the fore. Regression Analysis aids us in predictive analysis of the physio-chemical parameters of water quality and perform error analysis by comparing the predicted values with the actual values of the parameters.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128471041","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 : 2019-11-01DOI: 10.1109/ICTKE47035.2019.8966913
S. Shakhari, A. K. Verma, Debasmita Ghosh, K. Bhar, I. Banerjee
Over the years, the growing concern for the most primary resource of life sustenance is reaching an acme. This work is aimed at providing a data pattern analysis using cluster and correlation methods. This research analyses the water quality of the river Ganga, for the various purposes of social work, based on the data of the molecular and nonmolecular water quality parameters. Correlations are useful because they can indicate a predictive relationship and based on the data of the physio-chemical parameters of the River Ganga, we can find the year-wise correlation matrix. We found five clusters for DO, pH and BOD and another five clusters for Conductivity, Fecal Coliform and Total Coliform.
{"title":"Diverse Water Quality Data Pattern Study of the Indian River Ganga: Correlation and Cluster Analysis","authors":"S. Shakhari, A. K. Verma, Debasmita Ghosh, K. Bhar, I. Banerjee","doi":"10.1109/ICTKE47035.2019.8966913","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966913","url":null,"abstract":"Over the years, the growing concern for the most primary resource of life sustenance is reaching an acme. This work is aimed at providing a data pattern analysis using cluster and correlation methods. This research analyses the water quality of the river Ganga, for the various purposes of social work, based on the data of the molecular and nonmolecular water quality parameters. Correlations are useful because they can indicate a predictive relationship and based on the data of the physio-chemical parameters of the River Ganga, we can find the year-wise correlation matrix. We found five clusters for DO, pH and BOD and another five clusters for Conductivity, Fecal Coliform and Total Coliform.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124156517","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 : 2019-11-01DOI: 10.1109/ICTKE47035.2019.8966902
Prasitchai Veerayuttwilai
Smart City Services with embedded mobile device and real time information technology system are in focus to be adopted with context-aware roadside services availability model to provide the traveler in the city specially in the congested zone. How the city will support the traveler to live better in the looping traffic jam. The Smart Roadside Services system will be a key to consolidate available service in the area and integrate with real time traffic report as well as GPS Navigation system to support traveler to decide next step action to live better in their environment.
{"title":"Roadside Services Model for Congested Traffic in a Smart City","authors":"Prasitchai Veerayuttwilai","doi":"10.1109/ICTKE47035.2019.8966902","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966902","url":null,"abstract":"Smart City Services with embedded mobile device and real time information technology system are in focus to be adopted with context-aware roadside services availability model to provide the traveler in the city specially in the congested zone. How the city will support the traveler to live better in the looping traffic jam. The Smart Roadside Services system will be a key to consolidate available service in the area and integrate with real time traffic report as well as GPS Navigation system to support traveler to decide next step action to live better in their environment.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121942506","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 : 2019-11-01DOI: 10.1109/ICTKE47035.2019.8966908
P. Moungnoul, Wathana Srakupan, P. Anunvrapong
This paper was studied about an application behavior of coexistence LTE-TDD and LTE-FDD networks, which used by network provider for optimal the capacity of networks. The results shown bandwidth, packets size and type of protocols are affected the system throughput. The power allocation and guard band techniques are improve the behavior of coexistence network by 30%.
{"title":"Applications Behavior of Coexistence LTE-FDD/TDD","authors":"P. Moungnoul, Wathana Srakupan, P. Anunvrapong","doi":"10.1109/ICTKE47035.2019.8966908","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966908","url":null,"abstract":"This paper was studied about an application behavior of coexistence LTE-TDD and LTE-FDD networks, which used by network provider for optimal the capacity of networks. The results shown bandwidth, packets size and type of protocols are affected the system throughput. The power allocation and guard band techniques are improve the behavior of coexistence network by 30%.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"491 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130045536","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 : 2019-11-01DOI: 10.1109/ICTKE47035.2019.8966835
Thirakan Veingkam, K. Kungcharoen, P. Porouhan, P. Palangsantikul, W. Premchaiswadi
This research presents the use of process mine to find food purchasing behavior outside of the specified time. By studying from information on selling products and food in the school The research process is as follows: 1. Gathering information about selling products in schools 2. Importing data 3. Data analysis. The analysis of the mining process by using programs Disco results were found that: 1. When students buy food outside the specified time 2. There are courses that stop studying before the scheduled time. 3. Restaurants that sell food outside the specified time. It can be seen that with individual students who have to buy food outside the hours during which masters courses. And which stores sell products outside of time which a violation of the school's regulations. So, the results of this research can be used to find guidelines for solving problems as appropriate for the school.
{"title":"Applying Process Mining to Analyze the Purchasing Behavior for Food outside School Mealtimes","authors":"Thirakan Veingkam, K. Kungcharoen, P. Porouhan, P. Palangsantikul, W. Premchaiswadi","doi":"10.1109/ICTKE47035.2019.8966835","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966835","url":null,"abstract":"This research presents the use of process mine to find food purchasing behavior outside of the specified time. By studying from information on selling products and food in the school The research process is as follows: 1. Gathering information about selling products in schools 2. Importing data 3. Data analysis. The analysis of the mining process by using programs Disco results were found that: 1. When students buy food outside the specified time 2. There are courses that stop studying before the scheduled time. 3. Restaurants that sell food outside the specified time. It can be seen that with individual students who have to buy food outside the hours during which masters courses. And which stores sell products outside of time which a violation of the school's regulations. So, the results of this research can be used to find guidelines for solving problems as appropriate for the school.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131795149","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 : 2019-11-01DOI: 10.1109/ICTKE47035.2019.8966887
Adomar L. Ilao, Arnel C. Fajardo
Communication is a vital component of everyday life. Through technology via social media, communication becomes more dynamic generating huge volume of data. Each data represents sentiments toward a public issue. Sentiment analysis algorithms able classify whether positive, negative or neutral. This paper introduces a hybrid algorithm combining two lexicon-based algorithms namely SentiWordNet and VADER algorithms. Three algorithms were tested using different data sources. It achieved an accuracy of 88.83% which 21.44% improvement from most commonly used algorithm SentiWordNet.
{"title":"Sentiment Analysis of Tweet Messages using Hybrid Approach Algorithm","authors":"Adomar L. Ilao, Arnel C. Fajardo","doi":"10.1109/ICTKE47035.2019.8966887","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966887","url":null,"abstract":"Communication is a vital component of everyday life. Through technology via social media, communication becomes more dynamic generating huge volume of data. Each data represents sentiments toward a public issue. Sentiment analysis algorithms able classify whether positive, negative or neutral. This paper introduces a hybrid algorithm combining two lexicon-based algorithms namely SentiWordNet and VADER algorithms. Three algorithms were tested using different data sources. It achieved an accuracy of 88.83% which 21.44% improvement from most commonly used algorithm SentiWordNet.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130188392","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 : 2019-11-01DOI: 10.1109/ICTKE47035.2019.8966806
Jaya R Ganasan
The amount of data collected and stored in various industries has grown exponentially in the last decade. Data is collected and stored from industries consisting of large consumers such as telecommunications, banking or financial sectors. Further, given the advent of cloud computing and software availability in the cloud being cheaper, smaller industries are utilizing data storage for competitive advantage. Companies increasingly rely on analysis of huge amounts of data to gain a strategic advantage, improving on product quality and providing better services to their end users be it the employee, consumer or customer. A combination of statistical techniques and file management tools once sufficed for analyzing mounds of data. The costs of analysis are often charged out at very high rates for companies that require data analysis and the output is dependent very much on analyzing the correct attributes within large databases to ensure the data analyzed provides the relevant result. The most known technique or tools are the subject of the growing field of knowledge discovery in databases (KDD) [1]. Using business process data mapping (BPDM) to define the targeted data along with the process of knowledge discovery mapping in the database may provide a more targeted approach with much lest costs expended.
{"title":"Big Data Mining: Managing the Costs of Data Mining","authors":"Jaya R Ganasan","doi":"10.1109/ICTKE47035.2019.8966806","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966806","url":null,"abstract":"The amount of data collected and stored in various industries has grown exponentially in the last decade. Data is collected and stored from industries consisting of large consumers such as telecommunications, banking or financial sectors. Further, given the advent of cloud computing and software availability in the cloud being cheaper, smaller industries are utilizing data storage for competitive advantage. Companies increasingly rely on analysis of huge amounts of data to gain a strategic advantage, improving on product quality and providing better services to their end users be it the employee, consumer or customer. A combination of statistical techniques and file management tools once sufficed for analyzing mounds of data. The costs of analysis are often charged out at very high rates for companies that require data analysis and the output is dependent very much on analyzing the correct attributes within large databases to ensure the data analyzed provides the relevant result. The most known technique or tools are the subject of the growing field of knowledge discovery in databases (KDD) [1]. Using business process data mapping (BPDM) to define the targeted data along with the process of knowledge discovery mapping in the database may provide a more targeted approach with much lest costs expended.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116517564","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 : 2019-11-01DOI: 10.1109/ICTKE47035.2019.8966878
Kazuki Konda, Yoshiro Yamamoto
Nippon Professional Baseball is played annually by 12 teams from March to October at various baseball fields from Hokkaido to Okinawa at temperatures ranging from under 0 °C to over 30 °C. In this study, we aim to analyze the relationship between temperature and batting evaluation metrics, including Batting Average, Slugging percentage, Isolated power, Batting Average on Balls In Play, weighted On-Base Average, Homerun percentage, On-Base Percentage, Strikeout percentage, Walk percentage, and Error play percentage. We used baseball data from 2,574 matches spanning from 2016 to 2018 official seasons. Temperature data was observed by the Himawari 8 geostationary meteorological satellite and was classified as “Warm,” “Average-High,” “Average-Low,” and “Cold.” We tested the metrics in each classification, and the result shows that some metrics were significantly different. Finally, this study discussed how to use the analysis results.
{"title":"Analysis of the Impact of Temperature on Nippon Professional Baseball","authors":"Kazuki Konda, Yoshiro Yamamoto","doi":"10.1109/ICTKE47035.2019.8966878","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966878","url":null,"abstract":"Nippon Professional Baseball is played annually by 12 teams from March to October at various baseball fields from Hokkaido to Okinawa at temperatures ranging from under 0 °C to over 30 °C. In this study, we aim to analyze the relationship between temperature and batting evaluation metrics, including Batting Average, Slugging percentage, Isolated power, Batting Average on Balls In Play, weighted On-Base Average, Homerun percentage, On-Base Percentage, Strikeout percentage, Walk percentage, and Error play percentage. We used baseball data from 2,574 matches spanning from 2016 to 2018 official seasons. Temperature data was observed by the Himawari 8 geostationary meteorological satellite and was classified as “Warm,” “Average-High,” “Average-Low,” and “Cold.” We tested the metrics in each classification, and the result shows that some metrics were significantly different. Finally, this study discussed how to use the analysis results.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131337157","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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