Pub Date : 2019-11-01DOI: 10.1109/ICTKE47035.2019.8966872
A. Tungkasthan, Pitaya Poompuang, S. Intarasema
A government holds vast amounts of public sector data that it collects from everyday work delivering services to citizens. These data have significant potential to inform policy development, evaluate projects, contribute to economic growth, and support government plans, for the benefit of all people. Technology of “Big Data” analytics is relatively new for public administration, it can be analyzed for insights that lead to better decisions and strategic government services moves. This paper presents the framework for data sharing among government agencies that are secure and reliable. Additionally, the data-sharing model based on the DIKW hierarchy model is proposed to data classification belong to the level of data usage requirement of the user group to protect data with the right users for the right purpose.
{"title":"Government Data Sharing Framework based on DIKW Hierarchy Model","authors":"A. Tungkasthan, Pitaya Poompuang, S. Intarasema","doi":"10.1109/ICTKE47035.2019.8966872","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966872","url":null,"abstract":"A government holds vast amounts of public sector data that it collects from everyday work delivering services to citizens. These data have significant potential to inform policy development, evaluate projects, contribute to economic growth, and support government plans, for the benefit of all people. Technology of “Big Data” analytics is relatively new for public administration, it can be analyzed for insights that lead to better decisions and strategic government services moves. This paper presents the framework for data sharing among government agencies that are secure and reliable. Additionally, the data-sharing model based on the DIKW hierarchy model is proposed to data classification belong to the level of data usage requirement of the user group to protect data with the right users for the right purpose.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"22 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":"115314636","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/ICTKE47035.2019.8966841
Suntarin Sangsavate, Suparatana Tanthanongsakkun, S. Sinthupinyo
Sentiment classification is an instrument used for predicting stock price movement. This paper presents a comparison of sentiment classification performance using machine learning techniques consisting of the Naïve Bayes classifier and support vector machine (SVM) to provide a positive, neutral, or negative sentiment in Thai FinTech news and opinions on the tweet corpus. Accordingly, machine learning algorithms are employed to analyze how tweets correlate with stock market price behavior. Finally, the actual and prediction errors are examined by evaluating classifier performance. The results show that the Support Vector Machine has a better performance than the Naïve Bayes classifier.
{"title":"Stock Market Sentiment Classification from FinTech News","authors":"Suntarin Sangsavate, Suparatana Tanthanongsakkun, S. Sinthupinyo","doi":"10.1109/ICTKE47035.2019.8966841","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966841","url":null,"abstract":"Sentiment classification is an instrument used for predicting stock price movement. This paper presents a comparison of sentiment classification performance using machine learning techniques consisting of the Naïve Bayes classifier and support vector machine (SVM) to provide a positive, neutral, or negative sentiment in Thai FinTech news and opinions on the tweet corpus. Accordingly, machine learning algorithms are employed to analyze how tweets correlate with stock market price behavior. Finally, the actual and prediction errors are examined by evaluating classifier performance. The results show that the Support Vector Machine has a better performance than the Naïve Bayes classifier.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"13 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":"114861507","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/ICTKE47035.2019.8966844
Thanat Nonthaputha, M. Kumngern
This paper presents a new simply full-wave rectifier in voltage and current mode employing two second generation current conveyors (CCIIs) that can be programmed. The different of bias currents has been used for the CCIIs operating. They are biased currents that are used to controlled by on-off, the so call “current conveyor analog switches (CCASs)”. So, the different of bias currents is used to control the output by programme that the input alternative voltage and current signal is supplied. They will be rectified into two symmetrical voltage and current outputs. The proposed full-wave rectifier can be simultaneously realized to full-wave in voltage and current modes. The proposed full-wave rectifier circuit has been simulated using 0.18 μm CMOS from TSMC. The simulation results are used to confirm the workability of the proposed circuit.
{"title":"CMOS Programmable Full-Wave Rectifier Using Current Conveyor Analogue Switches","authors":"Thanat Nonthaputha, M. Kumngern","doi":"10.1109/ICTKE47035.2019.8966844","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966844","url":null,"abstract":"This paper presents a new simply full-wave rectifier in voltage and current mode employing two second generation current conveyors (CCIIs) that can be programmed. The different of bias currents has been used for the CCIIs operating. They are biased currents that are used to controlled by on-off, the so call “current conveyor analog switches (CCASs)”. So, the different of bias currents is used to control the output by programme that the input alternative voltage and current signal is supplied. They will be rectified into two symmetrical voltage and current outputs. The proposed full-wave rectifier can be simultaneously realized to full-wave in voltage and current modes. The proposed full-wave rectifier circuit has been simulated using 0.18 μm CMOS from TSMC. The simulation results are used to confirm the workability of the proposed circuit.","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":"129218598","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/ICTKE47035.2019.8966825
Alfan Kurnia Yudha, S. Starita
Vehicle routing problems play a critical role in logistics distribution, allowing companies to minimize operational parameters such as cost, fuel consumption, emissions etc. This paper studies a customized vehicle routing problem incorporating nighttime delivery options for a heavily congested urban area. The aim is to identify the optimal combination of day and night routes by trading off between fuel and staff costs. A Linear Programming (LP) formulation for the Night Time Vehicle Routing Problem (NTVRP) is introduced. The model is then applied to a case study using real data from a department store in Bangkok, Thailand. A fuel consumption model is used alongside an emission model to estimate the beneficial impact of NTVRP on both costs and emissions. Results show that when demand is high and 55 tonne heavy goods vehicles are used, the cost savings are about 16.7 percent. More significantly, CO2 emissions are reduced by more than 30 percent. With low demand, cost savings are more than 30.8 percent, together with a 28.2 percent reduction in CO2 emissions. Overall, the case study shows that nighttime delivery is a viable option to increase efficiency and sustainability of a logistics company.
{"title":"Nighttime Vehicle Routing for Sustainable Urban Logistics","authors":"Alfan Kurnia Yudha, S. Starita","doi":"10.1109/ICTKE47035.2019.8966825","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966825","url":null,"abstract":"Vehicle routing problems play a critical role in logistics distribution, allowing companies to minimize operational parameters such as cost, fuel consumption, emissions etc. This paper studies a customized vehicle routing problem incorporating nighttime delivery options for a heavily congested urban area. The aim is to identify the optimal combination of day and night routes by trading off between fuel and staff costs. A Linear Programming (LP) formulation for the Night Time Vehicle Routing Problem (NTVRP) is introduced. The model is then applied to a case study using real data from a department store in Bangkok, Thailand. A fuel consumption model is used alongside an emission model to estimate the beneficial impact of NTVRP on both costs and emissions. Results show that when demand is high and 55 tonne heavy goods vehicles are used, the cost savings are about 16.7 percent. More significantly, CO2 emissions are reduced by more than 30 percent. With low demand, cost savings are more than 30.8 percent, together with a 28.2 percent reduction in CO2 emissions. Overall, the case study shows that nighttime delivery is a viable option to increase efficiency and sustainability of a logistics company.","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":"130515846","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/ICTKE47035.2019.8966805
Yoshiro Yamamoto, T. Funayama, Kazuki Konda, H. Takenaka, K. Murata, T. Nakajima
A data interface system was constructed to enable the effective use of quasi-real-time solar radiation and solar power generation estimates based on data from the Himawari satellite in the energy management system. As one of the efforts introduced to apply meteorological data to the management of energy systems with renewable energy, we provided weather information to our solar car race team. We constructed a data interface system that provided data via web forms on the Azure cloud and on-premises servers. The implementation of a JSON format WebAPI enabled seamless data provision.
{"title":"Provision and Visualization of Solar Radiation Data for Energy Management System","authors":"Yoshiro Yamamoto, T. Funayama, Kazuki Konda, H. Takenaka, K. Murata, T. Nakajima","doi":"10.1109/ICTKE47035.2019.8966805","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966805","url":null,"abstract":"A data interface system was constructed to enable the effective use of quasi-real-time solar radiation and solar power generation estimates based on data from the Himawari satellite in the energy management system. As one of the efforts introduced to apply meteorological data to the management of energy systems with renewable energy, we provided weather information to our solar car race team. We constructed a data interface system that provided data via web forms on the Azure cloud and on-premises servers. The implementation of a JSON format WebAPI enabled seamless data provision.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"30 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":"130085196","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/ICTKE47035.2019.8966836
Pawee Werawithayaset, Suratose Tritilanunt
This research was prepared to predict the closing price of the stock in the Stock Exchange of Thailand (SET). We are using the Multi-Layer Perceptron model, Support Vector Machine model, and Partial Least Square Classifier to predict the closing price of the stock. In the present, people have more knowledge and understanding of investing in the stock market then the Thai stock market has grown significantly. From the statistical data, we can find the movement of stock prices in that stock market move in a cycle. Form this point; we have the idea that if we can predict the stock price nearby real price. We can be investing at the right time and help investors to reduce investment risks. The experimental result shows that Partial Least Square is the best algorithm of the three algorithms to predict the stock closing price.
{"title":"Stock Closing Price Prediction Using Machine Learning","authors":"Pawee Werawithayaset, Suratose Tritilanunt","doi":"10.1109/ICTKE47035.2019.8966836","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966836","url":null,"abstract":"This research was prepared to predict the closing price of the stock in the Stock Exchange of Thailand (SET). We are using the Multi-Layer Perceptron model, Support Vector Machine model, and Partial Least Square Classifier to predict the closing price of the stock. In the present, people have more knowledge and understanding of investing in the stock market then the Thai stock market has grown significantly. From the statistical data, we can find the movement of stock prices in that stock market move in a cycle. Form this point; we have the idea that if we can predict the stock price nearby real price. We can be investing at the right time and help investors to reduce investment risks. The experimental result shows that Partial Least Square is the best algorithm of the three algorithms to predict the stock closing price.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"87 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":"131852495","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}
This survey design aims to examine financial literacy, which is a matter of concern in Thailand. The conceptual gamification design in this study aims to illustrate the impact of positive feedback during game activities on players' behavior. Gamified activities are designed to provide positive feedback by using a saving and expense financial activity. This positive feedback will persuade players to change their financial behavior. The significance of the financial literacy findings are positive, implying that the higher the level of the satisfaction about their financial plan and financial knowledge, the more likely it is that it will affect their behavior. This is a working research to apply the gamification workflow which encourages people to live their lives with advanced technology.
{"title":"Gamification-Driven Process: Financial Literacy in Thailand","authors":"Wilawan Inchamnan, Winyu Niranatlamphong, Natlapat Engbunmeesakul","doi":"10.1109/ICTKE47035.2019.8966885","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966885","url":null,"abstract":"This survey design aims to examine financial literacy, which is a matter of concern in Thailand. The conceptual gamification design in this study aims to illustrate the impact of positive feedback during game activities on players' behavior. Gamified activities are designed to provide positive feedback by using a saving and expense financial activity. This positive feedback will persuade players to change their financial behavior. The significance of the financial literacy findings are positive, implying that the higher the level of the satisfaction about their financial plan and financial knowledge, the more likely it is that it will affect their behavior. This is a working research to apply the gamification workflow which encourages people to live their lives with advanced technology.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"62 3 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":"133039674","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/ICTKE47035.2019.8966854
Kankamon Thaweephol, Nuwee Wiwatwattana
Accurately forecasting fine particulate matter of less than a 2.5 micrometer diameter (PM2.5) concentration levels is important to better manage the air pollution situation and to give advance warnings to residents and officials. In this paper, a Long Short-Term Memory (LSTM) deep neural network model and a Seasonal AutoRegressive Integrated Moving Average with eXogenous regressor (SARIMAX) were trained on air quality and meteorological time series data at the Chokchai metropolitan police station area in Bangkok from 2017 to 2018. After figuring out the best configuration of both algorithms, the performance of the LSTM model to predict PM2.5 concentrations for 24 hours was evaluated and compared against the SARIMAX model. Our experiments indicated that LSTM had a better prediction accuracy as indicated by the RMSE and MAE values for each of the time steps. LSTM could forecast one hour ahead at a very low RMSE of 3.11 micrograms per cubic meter on average, and a MAE of 2.36 micrograms per cubic meter on average, while SARIMAX errors were more than doubled. When the time steps were farther apart, the number of errors were higher for both models.
{"title":"Long Short-Term Memory Deep Neural Network Model for PM2.5 Forecasting in the Bangkok Urban Area","authors":"Kankamon Thaweephol, Nuwee Wiwatwattana","doi":"10.1109/ICTKE47035.2019.8966854","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966854","url":null,"abstract":"Accurately forecasting fine particulate matter of less than a 2.5 micrometer diameter (PM2.5) concentration levels is important to better manage the air pollution situation and to give advance warnings to residents and officials. In this paper, a Long Short-Term Memory (LSTM) deep neural network model and a Seasonal AutoRegressive Integrated Moving Average with eXogenous regressor (SARIMAX) were trained on air quality and meteorological time series data at the Chokchai metropolitan police station area in Bangkok from 2017 to 2018. After figuring out the best configuration of both algorithms, the performance of the LSTM model to predict PM2.5 concentrations for 24 hours was evaluated and compared against the SARIMAX model. Our experiments indicated that LSTM had a better prediction accuracy as indicated by the RMSE and MAE values for each of the time steps. LSTM could forecast one hour ahead at a very low RMSE of 3.11 micrograms per cubic meter on average, and a MAE of 2.36 micrograms per cubic meter on average, while SARIMAX errors were more than doubled. When the time steps were farther apart, the number of errors were higher for both models.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"466 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":"123877495","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/ICTKE47035.2019.8966888
P. Porouhan, W. Premchaiswadi
This study is an extension of our another research entitled “Using Process Mining for Predicting Relationships of Couples Sitting on a Sofa”, whereas the 5 most frequent/possible sitting positions for Happy Couples were identified/discovered as well as the 2 most frequent/possible sitting positions for Unhappy Couples. The main focus and emphasis of the current work is on Speech Behavior Analysis of the both Happy and Unhappy Couples, for each of the above-discussed sitting positions, in a semantic approach. To do this, 8 semantic keywords (i.e., in order to convey/represent the emotional status of the verbal words and phrases exchanged between the couples) were initially defined, and two Process Mining (process discovery) techniques/algorithms were later applied on the (previously collected) Sofa Data as the following: (1) Social Network Miner algorithm (based on the Subcontracting metric) supported by the ProM 6 Package Manager. (2) Fuzzy Miner algorithm (via frequency-based metric) supported by the Disco Fluxicon. Accordingly, the results showed that the occurrence of the keywords “Happy”, “Excited”, “Satisfied” and “In Love” was more frequent/possible in the following sitting positions: “Cuddling in the middle”, “Cuddling in the corner, “Side-by-Side (touching without cuddling)”, “Corner cuddle with tucked leggs” and “Legs on lap”. Alternatively, the occurrence of the keywords “Irritated”, “Sad”, “Angry” and “Worried” was more frequent/possible in the following sitting positions: “Opposite sides of the sofa” and “Sat on different sofas”. This study provides groundwork for further and future studies.
这项研究是我们另一项名为“使用过程挖掘来预测坐在沙发上的夫妇的关系”的研究的延伸，而快乐夫妇的5种最常见/可能的坐姿被确定/发现，而不快乐夫妇的2种最常见/可能的坐姿被确定/发现。当前工作的主要焦点和重点是对上述每种坐姿的快乐和不快乐夫妇的言语行为分析，采用语义方法。为此，最初定义了8个语义关键字(即，为了传达/表示夫妻之间交换的口头单词和短语的情感状态)，随后在(先前收集的)沙发数据上应用了两种过程挖掘(过程发现)技术/算法，如下所示:(1)由ProM 6 Package Manager支持的社交网络挖掘算法(基于分包度量)。(2) Disco Fluxicon支持的Fuzzy Miner算法(基于频率的度量)。结果显示，“快乐”、“兴奋”、“满意”、“恋爱”等关键词在以下坐姿中出现的频率更高:“中间拥抱”、“角落拥抱”、“并排拥抱(不拥抱)”、“蜷腿拥抱”和“腿放在膝盖上”。另外，“烦躁”、“悲伤”、“愤怒”和“担心”等关键词在“沙发的两侧”和“坐在不同的沙发上”的坐姿中出现的频率更高。本研究为进一步的研究奠定了基础。
{"title":"Using Social Network Mining for Speech Behavior Analysis of Couples Sitting on a Sofa: (A Semantic Comparison between Happy and Unhappy Relationships)","authors":"P. Porouhan, W. Premchaiswadi","doi":"10.1109/ICTKE47035.2019.8966888","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966888","url":null,"abstract":"This study is an extension of our another research entitled “Using Process Mining for Predicting Relationships of Couples Sitting on a Sofa”, whereas the 5 most frequent/possible sitting positions for Happy Couples were identified/discovered as well as the 2 most frequent/possible sitting positions for Unhappy Couples. The main focus and emphasis of the current work is on Speech Behavior Analysis of the both Happy and Unhappy Couples, for each of the above-discussed sitting positions, in a semantic approach. To do this, 8 semantic keywords (i.e., in order to convey/represent the emotional status of the verbal words and phrases exchanged between the couples) were initially defined, and two Process Mining (process discovery) techniques/algorithms were later applied on the (previously collected) Sofa Data as the following: (1) Social Network Miner algorithm (based on the Subcontracting metric) supported by the ProM 6 Package Manager. (2) Fuzzy Miner algorithm (via frequency-based metric) supported by the Disco Fluxicon. Accordingly, the results showed that the occurrence of the keywords “Happy”, “Excited”, “Satisfied” and “In Love” was more frequent/possible in the following sitting positions: “Cuddling in the middle”, “Cuddling in the corner, “Side-by-Side (touching without cuddling)”, “Corner cuddle with tucked leggs” and “Legs on lap”. Alternatively, the occurrence of the keywords “Irritated”, “Sad”, “Angry” and “Worried” was more frequent/possible in the following sitting positions: “Opposite sides of the sofa” and “Sat on different sofas”. This study provides groundwork for further and future studies.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"6 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":"131967355","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/ICTKE47035.2019.8966809
Sheena I. Sapuay, B. Gerardo, A. Hernandez
The use of biometric data for authentication in a networked environment brings both security guarantee and fear to many. It poses strong authentication because of its uniqueness; however, unlike passwords and smartcards, biometric information is non-repudiable. Therefore, it should be treated with utmost confidentiality and protection. Authentications hould be improved further because as people and machines advances the designs of security approaches and mechanisms, the threat increases in volume and variability also. In this paper, a dynamic third-factor authenticator for Biometric Authentication Systems is proposed. Not only that it protects biometric information, it also possesses the quality of dynamism that ensures security by addressing the threat before it takes place. This proposed enhancement follows the standards and guidelines prescribed for digital identity and data security.
{"title":"Enhancing Security in Biometric Authentication Systems using Dynamic Third-factor","authors":"Sheena I. Sapuay, B. Gerardo, A. Hernandez","doi":"10.1109/ICTKE47035.2019.8966809","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966809","url":null,"abstract":"The use of biometric data for authentication in a networked environment brings both security guarantee and fear to many. It poses strong authentication because of its uniqueness; however, unlike passwords and smartcards, biometric information is non-repudiable. Therefore, it should be treated with utmost confidentiality and protection. Authentications hould be improved further because as people and machines advances the designs of security approaches and mechanisms, the threat increases in volume and variability also. In this paper, a dynamic third-factor authenticator for Biometric Authentication Systems is proposed. Not only that it protects biometric information, it also possesses the quality of dynamism that ensures security by addressing the threat before it takes place. This proposed enhancement follows the standards and guidelines prescribed for digital identity and data security.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"20 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":"123312725","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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