Pub Date : 2019-11-01DOI: 10.1109/ICTKE47035.2019.8966822
W. Pannakkong, Lalitpat Aswanuwath, J. Buddhakulsomsiri, C. Jeenanunta, P. Parthanadee
Electricity demand forecasting is an important research area, most of the research focuses on forecasting the electricity consumption that is the critical process for planning the electric utilities to avoid a blackout in peak time. This paper focuses on forecasting the medium term (1-month ahead and 1-year ahead) of electricity peak demand in Thailand by using three machine learnings and ensemble method. The machine learnings include artificial neural network (ANN), support vector machine (SVM), and deep belief network (DBN). For the comparative performance between each model, mean absolute percentage error (MAPE) is used as the measurement. The result implies that the ensemble model of ANN and DBN is the best method for 1-month ahead with MAPE 1.44%, and ANN is the best method for 1-year ahead forecasting with MAPE 1.47%.
{"title":"Forecasting medium-term electricity demand in Thailand: comparison of ANN, SVM, DBN, and their ensembles","authors":"W. Pannakkong, Lalitpat Aswanuwath, J. Buddhakulsomsiri, C. Jeenanunta, P. Parthanadee","doi":"10.1109/ICTKE47035.2019.8966822","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966822","url":null,"abstract":"Electricity demand forecasting is an important research area, most of the research focuses on forecasting the electricity consumption that is the critical process for planning the electric utilities to avoid a blackout in peak time. This paper focuses on forecasting the medium term (1-month ahead and 1-year ahead) of electricity peak demand in Thailand by using three machine learnings and ensemble method. The machine learnings include artificial neural network (ANN), support vector machine (SVM), and deep belief network (DBN). For the comparative performance between each model, mean absolute percentage error (MAPE) is used as the measurement. The result implies that the ensemble model of ANN and DBN is the best method for 1-month ahead with MAPE 1.44%, and ANN is the best method for 1-year ahead forecasting with MAPE 1.47%.","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":"131031848","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/ICTKE47035.2019.8966789
W. Pannakkong, Nittaya Chemkomnerd, Tanatorn Tanantong
Many hospitals have a problem dealing with the queue system in every department. The front-end department, which is the medical record department, is the first place to contact patients. It provides service for all type of out-patients, so out-patients have to wait for a long time. This results in low satisfaction of the patients. However, this department is working 24 hours, thus, it isdifficult to improve the queue system in a real environment. The simulation is an effective tool to solve the problem. Based on the data collection from Thammasat University hospital, the discrete event simulation model of this department is developed. Model verification and validation are conducted and the resultconfirms that the model is worked as calculated and generates the same result as in the real system. The aim of this paper is to develop the simulation model that provides an accurate analysis to help support a decision making of the hospital in the medical record department.
{"title":"Simulation Analysis of University Hospital in the Medical Record Department","authors":"W. Pannakkong, Nittaya Chemkomnerd, Tanatorn Tanantong","doi":"10.1109/ICTKE47035.2019.8966789","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966789","url":null,"abstract":"Many hospitals have a problem dealing with the queue system in every department. The front-end department, which is the medical record department, is the first place to contact patients. It provides service for all type of out-patients, so out-patients have to wait for a long time. This results in low satisfaction of the patients. However, this department is working 24 hours, thus, it isdifficult to improve the queue system in a real environment. The simulation is an effective tool to solve the problem. Based on the data collection from Thammasat University hospital, the discrete event simulation model of this department is developed. Model verification and validation are conducted and the resultconfirms that the model is worked as calculated and generates the same result as in the real system. The aim of this paper is to develop the simulation model that provides an accurate analysis to help support a decision making of the hospital in the medical record department.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"14 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":"132784134","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/ICTKE47035.2019.8966893
P. Porouhan
This research is a synergy of Internet of Things (IoT), Process Mining (PM) and Behavior Analysis (BA) fields of study. In the first IoT-related part of the paper, a Wi-Fi P2P system (Peer to Peer) including a set of Smart Sofas, which were easily connected with a Xbox Kinect Camera without requiring a wireless access, was initially designed and developed. The Smart Sofas contained Weight Pressure Sensors, whereas, the Smart Cameras worked based on a Facial Recognition algorithm. The system was capable of identifying, recording and storing the exact location of a couple (i.e., two individuals) sitting on them in addition to their body language/gesture. Subsequently, 74 couples (or 148 persons) were voluntarily invited to join an experiment with the purpose of studying their behavior —within the duration of time they were used to getting back home from work— when sitting (or lying down) on a pair of smart sofas that was deliberately inserted in the living room of their home. The experiment lasted for one month and excluded the weekends so as to give the couples enough privacy they needed to carry on their normal life without any annoyance or disturbance. In the second part of the study, the Fuzzy Miner algorithm, which is a process discovery technique, was applied on the collected/synchronized (sofa and camera) data. To do this, the Disco Fluxicon, which is a Process Mining platform/tool, was used. The couples' data (i.e., the Sofa and Camera data) included 888 Events with a Mean Case Duration of 3.1 minutes. In the third part of the study, the data was divided into two separate event logs as the following: (1) The event log of the couples who showed/reported “Having Problems” in their relationship while taking part in the experiment, versus, (2) The event log of the couples who showed/reported “Not Having Any Problems”. Furthermore, the “Not Having Any Problems” data also was divided into another two sub-sets as follows: (i) Those who felt “Extremely Happy and Satisfied” versus (ii) Those who felt happy but in a very “Normal and Ordinary (Neutral)” way in their relationship. Subsequently, a statistical analysis of binary classification in terms of a Confusion Matrix and based on the F1-Score coefficient (or F-measure) was conducted so as to consider both the Precision and the Recall coefficients of the results. According to the findings of the study, with rather a high degree of accuracy (i.e., F-Score = 0.7563), it was realized that the couples who were “Not Having Any Problems” in their relationship showed tendency to represent/manifest one (or a mixture) of the following sitting positions while sitting on a sofa, respectively: “Legs on Lap”, “Cuddling in the Corner”, “Corner Cuddle with Tucked Legs”, “Side-by-Side (Touching but Not Cuddling)” and “Cuddling in the Middle”. And finally, the current work provides groundwork for further research and studies. In the future, more couples in a longer period of time (including the weekends) also will
{"title":"Using Process Mining for Predicting Relationships of Couples Sitting on a Sofa","authors":"P. Porouhan","doi":"10.1109/ICTKE47035.2019.8966893","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966893","url":null,"abstract":"This research is a synergy of Internet of Things (IoT), Process Mining (PM) and Behavior Analysis (BA) fields of study. In the first IoT-related part of the paper, a Wi-Fi P2P system (Peer to Peer) including a set of Smart Sofas, which were easily connected with a Xbox Kinect Camera without requiring a wireless access, was initially designed and developed. The Smart Sofas contained Weight Pressure Sensors, whereas, the Smart Cameras worked based on a Facial Recognition algorithm. The system was capable of identifying, recording and storing the exact location of a couple (i.e., two individuals) sitting on them in addition to their body language/gesture. Subsequently, 74 couples (or 148 persons) were voluntarily invited to join an experiment with the purpose of studying their behavior —within the duration of time they were used to getting back home from work— when sitting (or lying down) on a pair of smart sofas that was deliberately inserted in the living room of their home. The experiment lasted for one month and excluded the weekends so as to give the couples enough privacy they needed to carry on their normal life without any annoyance or disturbance. In the second part of the study, the Fuzzy Miner algorithm, which is a process discovery technique, was applied on the collected/synchronized (sofa and camera) data. To do this, the Disco Fluxicon, which is a Process Mining platform/tool, was used. The couples' data (i.e., the Sofa and Camera data) included 888 Events with a Mean Case Duration of 3.1 minutes. In the third part of the study, the data was divided into two separate event logs as the following: (1) The event log of the couples who showed/reported “Having Problems” in their relationship while taking part in the experiment, versus, (2) The event log of the couples who showed/reported “Not Having Any Problems”. Furthermore, the “Not Having Any Problems” data also was divided into another two sub-sets as follows: (i) Those who felt “Extremely Happy and Satisfied” versus (ii) Those who felt happy but in a very “Normal and Ordinary (Neutral)” way in their relationship. Subsequently, a statistical analysis of binary classification in terms of a Confusion Matrix and based on the F1-Score coefficient (or F-measure) was conducted so as to consider both the Precision and the Recall coefficients of the results. According to the findings of the study, with rather a high degree of accuracy (i.e., F-Score = 0.7563), it was realized that the couples who were “Not Having Any Problems” in their relationship showed tendency to represent/manifest one (or a mixture) of the following sitting positions while sitting on a sofa, respectively: “Legs on Lap”, “Cuddling in the Corner”, “Corner Cuddle with Tucked Legs”, “Side-by-Side (Touching but Not Cuddling)” and “Cuddling in the Middle”. And finally, the current work provides groundwork for further research and studies. In the future, more couples in a longer period of time (including the weekends) also will ","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"25 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":"130124616","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/ICTKE47035.2019.8966890
Wilawan Inchamnan, Punyawee Anunpattana
This survey design aims to examine knowledge-based systems design for Mutual Fund, which is a matter of investment 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 through a knowledge-based system. This positive feedback will persuade players to change their investment concept. This is a working research to apply the gamification workflow which encourages people to live their lives with advanced technology.
{"title":"Gamification in Mutual Fund Knowledge-Based Systems","authors":"Wilawan Inchamnan, Punyawee Anunpattana","doi":"10.1109/ICTKE47035.2019.8966890","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966890","url":null,"abstract":"This survey design aims to examine knowledge-based systems design for Mutual Fund, which is a matter of investment 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 through a knowledge-based system. This positive feedback will persuade players to change their investment concept. 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":"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":"129609037","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/ICTKE47035.2019.8966927
Khin Sandar Kyaw, S. Limsiroratana
Since today is the age of data which are presented using electronic documents, knowledge discovery process (KDP) for different types of data is become a popular topic in various application areas for developing automatic systems. Meanwhile, the capacity of computation intelligence (CI) for solving complex problem, for instance complex features, in KDP is also become a critical role in order to provide effective performance and efficient computation time. In this paper, we observed case study about new trend for KDP using CI for the area of text document classification (TDC). According to the experimental results from different cases, CI can enhance the performance of TDC by looking for optimal subset of feature according to the objective function of learning models.
{"title":"Case Study: Knowledge Discovery Process using Computation Intelligence with Feature Selection Approach","authors":"Khin Sandar Kyaw, S. Limsiroratana","doi":"10.1109/ICTKE47035.2019.8966927","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966927","url":null,"abstract":"Since today is the age of data which are presented using electronic documents, knowledge discovery process (KDP) for different types of data is become a popular topic in various application areas for developing automatic systems. Meanwhile, the capacity of computation intelligence (CI) for solving complex problem, for instance complex features, in KDP is also become a critical role in order to provide effective performance and efficient computation time. In this paper, we observed case study about new trend for KDP using CI for the area of text document classification (TDC). According to the experimental results from different cases, CI can enhance the performance of TDC by looking for optimal subset of feature according to the objective function of learning models.","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":"121561899","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/ICTKE47035.2019.8966900
Supanat Jintawatsakoon, Werayuth Charoenruengkit
A multi-class image classification application plays a vital role in our lives. Traditional approaches focus on a close-set classification problem. However, an open-set classification problem often occur in the real-world applications. This paper focuses on the convolution neural network based image classification for beverage bottle image classification under the open-set environment, in which the input image may not appear in any known classes during training time. The proposed models explore the approaches based on the N-Binary, N+unknown, and N+combination models. The results show that N+unknown approach perform better than that of the N+combination and N-Binary approach in terms of accuracy and time.
{"title":"Open-Set Bottle Classifying using a Convolution Neural Network","authors":"Supanat Jintawatsakoon, Werayuth Charoenruengkit","doi":"10.1109/ICTKE47035.2019.8966900","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966900","url":null,"abstract":"A multi-class image classification application plays a vital role in our lives. Traditional approaches focus on a close-set classification problem. However, an open-set classification problem often occur in the real-world applications. This paper focuses on the convolution neural network based image classification for beverage bottle image classification under the open-set environment, in which the input image may not appear in any known classes during training time. The proposed models explore the approaches based on the N-Binary, N+unknown, and N+combination models. The results show that N+unknown approach perform better than that of the N+combination and N-Binary approach in terms of accuracy and time.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"338 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":"122752078","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/ICTKE47035.2019.8966880
Karuna Yampray, Wilawan Inchamnan
Using game design elements in non-game contexts is an established concept around the world. Gamification is all about improving actual user engagement with the system, making users contribute more time and resources for data collection. This study reviews an overview of gamification and user motivations for playing games, and the researchers discuss basic game design elements. The game mechanics are included points, levels, leaderboards, badges and challenges. Gamification can be applied to behavioral data collection processes in terms of visualization. The findings show the reliability of a questionnaire that is designed to measure the investment risk. The visualization questionnaire can represent the behavioral data that will be applied to gamification design.
{"title":"A Method to Visualization Data Collection by Using Gamification","authors":"Karuna Yampray, Wilawan Inchamnan","doi":"10.1109/ICTKE47035.2019.8966880","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966880","url":null,"abstract":"Using game design elements in non-game contexts is an established concept around the world. Gamification is all about improving actual user engagement with the system, making users contribute more time and resources for data collection. This study reviews an overview of gamification and user motivations for playing games, and the researchers discuss basic game design elements. The game mechanics are included points, levels, leaderboards, badges and challenges. Gamification can be applied to behavioral data collection processes in terms of visualization. The findings show the reliability of a questionnaire that is designed to measure the investment risk. The visualization questionnaire can represent the behavioral data that will be applied to gamification design.","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":"130776277","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/ICTKE47035.2019.8966799
A. Myat, M. Tun
The palm oil market in Myanmar greatly depends on the world palm oil price changes, especially on the price changes in the export countries of palm oil to Myanmar. As palm oil market in Myanmar represents the back bone of Myanmar Edible Oil Dealers Association (MEODA), we propose the predictive model to aid in decision making process of palm oil importers whether they should conduct import transaction today or not in this paper. This prediction of palm oil price condition in Myanmar has been taken on the previous dataset supported by MEODA to eliminate ever-increasing risks and uncertainties in the future. This model will forecast whether the price of palm oil in Myanmar will rise or not in 14 days from today, the length of period is necessary to be ready to trade imported palm oil in local market for Myanmar importers. Our model is trained using C4.5 Random Forest Classification Algorithm on the palm oil market dataset from MOEDA. Hyperparameter tuning techniques are conducted to analyze whether the predictive performance can be enhanced. From the obtainable dataset in Myanmar palm oil market, the predictive model with chosen hyperparameters set achieves the prediction accuracy of 91.11% on the test dataset.
{"title":"Predicting Palm Oil Price Direction using Random Forest","authors":"A. Myat, M. Tun","doi":"10.1109/ICTKE47035.2019.8966799","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966799","url":null,"abstract":"The palm oil market in Myanmar greatly depends on the world palm oil price changes, especially on the price changes in the export countries of palm oil to Myanmar. As palm oil market in Myanmar represents the back bone of Myanmar Edible Oil Dealers Association (MEODA), we propose the predictive model to aid in decision making process of palm oil importers whether they should conduct import transaction today or not in this paper. This prediction of palm oil price condition in Myanmar has been taken on the previous dataset supported by MEODA to eliminate ever-increasing risks and uncertainties in the future. This model will forecast whether the price of palm oil in Myanmar will rise or not in 14 days from today, the length of period is necessary to be ready to trade imported palm oil in local market for Myanmar importers. Our model is trained using C4.5 Random Forest Classification Algorithm on the palm oil market dataset from MOEDA. Hyperparameter tuning techniques are conducted to analyze whether the predictive performance can be enhanced. From the obtainable dataset in Myanmar palm oil market, the predictive model with chosen hyperparameters set achieves the prediction accuracy of 91.11% on the test dataset.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"26 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":"124542085","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/ICTKE47035.2019.8966860
Chamas Matthanawongsakorn, Norranut Saguansakdiyotin, P. Porouhan, Poohridate Arpasat, Wichian Premochaiswadi
This research proposes the application of process mining to analyze consumption behavior that affects the overweight status of school-aged children to encourage students to get good nutritional health and prevent malnutrition, in case of overnourished, during school-age years. The research includes the following two steps: 1) Collect data of students' food purchases made through food digital cards, 2) Analyze data using the Fuzzy Miner Algorithm in Disco program. The results of the study found the food consumption behaviors of individual students along with meal frequency with high calories food that affects overweight status standardized by the Ministry of Public Health. The results of this study enable schools to provide individual dietary recommendations to overweight students and parents and adjust the diet menu so that students receive the right amount of calories.
{"title":"Applying process mining to investigate the relation between food purchase behavior and children's weight based on the food digital cards","authors":"Chamas Matthanawongsakorn, Norranut Saguansakdiyotin, P. Porouhan, Poohridate Arpasat, Wichian Premochaiswadi","doi":"10.1109/ICTKE47035.2019.8966860","DOIUrl":"https://doi.org/10.1109/ICTKE47035.2019.8966860","url":null,"abstract":"This research proposes the application of process mining to analyze consumption behavior that affects the overweight status of school-aged children to encourage students to get good nutritional health and prevent malnutrition, in case of overnourished, during school-age years. The research includes the following two steps: 1) Collect data of students' food purchases made through food digital cards, 2) Analyze data using the Fuzzy Miner Algorithm in Disco program. The results of the study found the food consumption behaviors of individual students along with meal frequency with high calories food that affects overweight status standardized by the Ministry of Public Health. The results of this study enable schools to provide individual dietary recommendations to overweight students and parents and adjust the diet menu so that students receive the right amount of calories.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"116 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":"126017709","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/ictke47035.2019.8966923
{"title":"[Front matter]","authors":"","doi":"10.1109/ictke47035.2019.8966923","DOIUrl":"https://doi.org/10.1109/ictke47035.2019.8966923","url":null,"abstract":"","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"44 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":"125798756","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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