Pub Date : 2018-07-01DOI: 10.1109/JCSSE.2018.8457391
Phusanisa Charoen-Ung, Pradit Mittrapiyanuruk
This paper presents a machine learning based model for predicting the sugarcane yield grade of an individual plot. The dataset used in this work is obtained from a set of sugarcane plots around a sugar mill in Thailand. The features used in the prediction consist of the plot characteristics (soil type, plot area, groove width, plot yield/ yield grade of the last year), sugarcane characteristics (cane class and type), plot cultivation scheme (water resource type, irrigation method, epidemic control method, fertilizer type/formula) and rain volume. We use two predictive algorithms: (i) random forest classification, and (ii) gradient boosting tree classification. The accuracies of our machine learning based predictive methods are 71.83% and 71.64%, respectively. Meanwhile, the accuracies of two non-machine-learning baselines are 51.52% (using the actual yield of the last year as the prediction) and 65.50% (the target yield of each plot is manually predicted by human expert), respectively. This shows that our work is accurate enough to be applied for decision making of sugar mill operation planning.
{"title":"Sugarcane Yield Grade Prediction using Random Forest and Gradient Boosting Tree Techniques","authors":"Phusanisa Charoen-Ung, Pradit Mittrapiyanuruk","doi":"10.1109/JCSSE.2018.8457391","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457391","url":null,"abstract":"This paper presents a machine learning based model for predicting the sugarcane yield grade of an individual plot. The dataset used in this work is obtained from a set of sugarcane plots around a sugar mill in Thailand. The features used in the prediction consist of the plot characteristics (soil type, plot area, groove width, plot yield/ yield grade of the last year), sugarcane characteristics (cane class and type), plot cultivation scheme (water resource type, irrigation method, epidemic control method, fertilizer type/formula) and rain volume. We use two predictive algorithms: (i) random forest classification, and (ii) gradient boosting tree classification. The accuracies of our machine learning based predictive methods are 71.83% and 71.64%, respectively. Meanwhile, the accuracies of two non-machine-learning baselines are 51.52% (using the actual yield of the last year as the prediction) and 65.50% (the target yield of each plot is manually predicted by human expert), respectively. This shows that our work is accurate enough to be applied for decision making of sugar mill operation planning.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115578232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-01DOI: 10.1109/JCSSE.2018.8457352
Pongjarun Kosolyudhthasarn, V. Visoottiviseth, Doudou Fall, S. Kashihara
Unmanned Aerial Vehicle (UAV) as known as Drone has been becoming very popular around the world. However, a consumer UAV can be controlled from a long distance to record a video of occupants without permission, which causes privacy issues. Existing drone detection systems are required specific hardware and specialists to operate and deploy which are expensive for personal use. In this paper, we propose a drone detection and identification system which utilizes inexpensive commercial off-the-shelf (COTS) hardware and does not requires specialist knowledge to deploy. Our technical approach is to passively listen to the wireless signal between drone and its controller to observe for packet transmission characteristics of each drone. We evaluate our prototype system with three types of drones, which are Spark, AR, and Dobby. Our experiment results illustrate the feasibility of using the data frame length to identify the type of flying drone within 20 seconds.
{"title":"Drone Detection and Identification by Using Packet Length Signature","authors":"Pongjarun Kosolyudhthasarn, V. Visoottiviseth, Doudou Fall, S. Kashihara","doi":"10.1109/JCSSE.2018.8457352","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457352","url":null,"abstract":"Unmanned Aerial Vehicle (UAV) as known as Drone has been becoming very popular around the world. However, a consumer UAV can be controlled from a long distance to record a video of occupants without permission, which causes privacy issues. Existing drone detection systems are required specific hardware and specialists to operate and deploy which are expensive for personal use. In this paper, we propose a drone detection and identification system which utilizes inexpensive commercial off-the-shelf (COTS) hardware and does not requires specialist knowledge to deploy. Our technical approach is to passively listen to the wireless signal between drone and its controller to observe for packet transmission characteristics of each drone. We evaluate our prototype system with three types of drones, which are Spark, AR, and Dobby. Our experiment results illustrate the feasibility of using the data frame length to identify the type of flying drone within 20 seconds.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114667039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-01DOI: 10.1109/JCSSE.2018.8457330
Kanuengnij Kubola, P. Wayalun
One of the source used to diagnose the genetic disorders and abnormalities is the light microscopic images of the chromosomes. The first step to check for the abnormalities is to count the chromosome. Many researches have been done on chromosome counting from the images, but the results still need an improvement on complicated case, the cluster of mixing patterns of chromosomes including touching, overlapping, and other patterns. The main objective of this research is to focus and increase the performance of chromosome number determination especially the cluster with the complicated pattern of chromosome. The paper presents a new technique, to determine the number of complicated chromosome image (DNCC) using geometric features including endpoints, and intersection points of the skeletonized chromosome image after pre-processing. The results yield 100% for the clusters with single chromosome, 100% for the clusters with overlapping of two chromosomes, and 79.12% for the cluster of complicated patterns of chromosomes.
{"title":"Automatic Determination of The G-band Chromosomes Number based on Geometric Features","authors":"Kanuengnij Kubola, P. Wayalun","doi":"10.1109/JCSSE.2018.8457330","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457330","url":null,"abstract":"One of the source used to diagnose the genetic disorders and abnormalities is the light microscopic images of the chromosomes. The first step to check for the abnormalities is to count the chromosome. Many researches have been done on chromosome counting from the images, but the results still need an improvement on complicated case, the cluster of mixing patterns of chromosomes including touching, overlapping, and other patterns. The main objective of this research is to focus and increase the performance of chromosome number determination especially the cluster with the complicated pattern of chromosome. The paper presents a new technique, to determine the number of complicated chromosome image (DNCC) using geometric features including endpoints, and intersection points of the skeletonized chromosome image after pre-processing. The results yield 100% for the clusters with single chromosome, 100% for the clusters with overlapping of two chromosomes, and 79.12% for the cluster of complicated patterns of chromosomes.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114887409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-01DOI: 10.1109/JCSSE.2018.8457389
J. Mitrpanont, Wudhichart Sawangphol, Jirayu Roungsuriyaviboon, Thada Sathapornwatanakul, Tinnapat Pillavas, Pattaraporn Sangaroonsilp, J. Haga
A number of medical research is produced by healthcare organizations in Thailand from their routine work, called Routine to Research (R2R). The information from R2R is becoming more important and can be analyzed to find hidden useful knowledge, which may lead to policy of the country. To analyze the data, Association Rule Finding technique and Knowledge Discovery are applied to find relevant patterns and relationship of the related variables in R2R data. The result can be used as evidence for policy makers in the process of policy making. In addition, MedThaiSAGE--a web-based application is also developed to allow users to see the relevant set of rules and visualize related information. The results show that there are some interesting rules that likely to be used by Public Health Policy Makers to develop a policy in order to improve and provide Thai people better health care services.
{"title":"MedThaiSAGE: Decision Support System to Suggest Healthcare Policies using Rule Findings Technique","authors":"J. Mitrpanont, Wudhichart Sawangphol, Jirayu Roungsuriyaviboon, Thada Sathapornwatanakul, Tinnapat Pillavas, Pattaraporn Sangaroonsilp, J. Haga","doi":"10.1109/JCSSE.2018.8457389","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457389","url":null,"abstract":"A number of medical research is produced by healthcare organizations in Thailand from their routine work, called Routine to Research (R2R). The information from R2R is becoming more important and can be analyzed to find hidden useful knowledge, which may lead to policy of the country. To analyze the data, Association Rule Finding technique and Knowledge Discovery are applied to find relevant patterns and relationship of the related variables in R2R data. The result can be used as evidence for policy makers in the process of policy making. In addition, MedThaiSAGE--a web-based application is also developed to allow users to see the relevant set of rules and visualize related information. The results show that there are some interesting rules that likely to be used by Public Health Policy Makers to develop a policy in order to improve and provide Thai people better health care services.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125871769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-01DOI: 10.1109/JCSSE.2018.8457348
Kanok Konglar, Yuthapong Somchit
Software-Defined Networking (SDN) enabled by OpenFlow is a new paradigm of the network that is managed by a single centralized controller. In a large-scale network, distributed controller mechanism using multiple controllers is introduced to solve a problem of single point of failure. This mechanism requires load balancing to balance the loads of the controllers. The current load controller methods still have some issues. This work proposes a load balance method called Load Distribution mechanism and an algorithm based On controller Performance (LDOP). The LDOP can reduce loads of the overloaded controller by migrating switches to other controllers. Moreover, LDOP can reduce the impact when many controllers are overloaded. In this work, we evaluate the performance of LDOP by simulation. The experimental results show that LDOP mechanism can reduce loads of overloaded controller and does not make other controllers become overloaded. In case of many controllers are overloaded, it also reduces the impact of this. In addition, textbfLDOP also reduces the number of messages that controllers use in load balancing compared to other protocols.
{"title":"Load Distribution of Software-Defined Networking Based on Controller Performance","authors":"Kanok Konglar, Yuthapong Somchit","doi":"10.1109/JCSSE.2018.8457348","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457348","url":null,"abstract":"Software-Defined Networking (SDN) enabled by OpenFlow is a new paradigm of the network that is managed by a single centralized controller. In a large-scale network, distributed controller mechanism using multiple controllers is introduced to solve a problem of single point of failure. This mechanism requires load balancing to balance the loads of the controllers. The current load controller methods still have some issues. This work proposes a load balance method called Load Distribution mechanism and an algorithm based On controller Performance (LDOP). The LDOP can reduce loads of the overloaded controller by migrating switches to other controllers. Moreover, LDOP can reduce the impact when many controllers are overloaded. In this work, we evaluate the performance of LDOP by simulation. The experimental results show that LDOP mechanism can reduce loads of overloaded controller and does not make other controllers become overloaded. In case of many controllers are overloaded, it also reduces the impact of this. In addition, textbfLDOP also reduces the number of messages that controllers use in load balancing compared to other protocols.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122625105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-01DOI: 10.1109/JCSSE.2018.8457396
Thitiphong Raksarikorn, Thanapat Kangkachit
Facial expression classification p lays c rucial role in human-computer interaction. A large number of automated methods have been proposed since the past decades. Recently, deep learning is broadly applied in computer vision field as well as facial expression classification. The reasons are to avoid complex feature extraction process and obtained satisfied classification p erformance. In this work, w e p ropose a deep convolutional neural networks (CNNs) model, inspired from XCEPTION, to classify seven groups of facial expressions. To efficiently use o f m odel parameters, the model a rchitecture has only 2.2 million parameters which is about 10 times less than XCEPTION. The experimental results on FER-2013 dataset show that our model offers comparable accuracy (0.7169) to the state-of-the-art methods and the upper-bound level of human accuracy $( 0.65 pm 5)$. In addition, our model uses less number of parameters than the state-of-the-art models and without using extra features and data augmentation.
{"title":"Facial Expression Classification using Deep Extreme Inception Networks","authors":"Thitiphong Raksarikorn, Thanapat Kangkachit","doi":"10.1109/JCSSE.2018.8457396","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457396","url":null,"abstract":"Facial expression classification p lays c rucial role in human-computer interaction. A large number of automated methods have been proposed since the past decades. Recently, deep learning is broadly applied in computer vision field as well as facial expression classification. The reasons are to avoid complex feature extraction process and obtained satisfied classification p erformance. In this work, w e p ropose a deep convolutional neural networks (CNNs) model, inspired from XCEPTION, to classify seven groups of facial expressions. To efficiently use o f m odel parameters, the model a rchitecture has only 2.2 million parameters which is about 10 times less than XCEPTION. The experimental results on FER-2013 dataset show that our model offers comparable accuracy (0.7169) to the state-of-the-art methods and the upper-bound level of human accuracy $( 0.65 pm 5)$. In addition, our model uses less number of parameters than the state-of-the-art models and without using extra features and data augmentation.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125248565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-01DOI: 10.1109/JCSSE.2018.8457382
Ananchai Konthong, Anavat Monprajuck, Rashrita Rattanavorragant, Y. Jewajinda
This paper presents an intelligent locomotion control architecture for a hexapod robot. The proposed architecture provides a hexapod robot abilities to perceive different terrains and adjust gait accordingly by utilizing CPG- network, torque sensing, and radial basis function neural network. The architecture is suitable for implementing in an embedded system for on-board small hexapod robots. The effectiveness of the proposed intelligent control architecture is demonstrated through real robot experiments.
{"title":"An Intelligent Locomotion Control Architecture for Hexapod Robot","authors":"Ananchai Konthong, Anavat Monprajuck, Rashrita Rattanavorragant, Y. Jewajinda","doi":"10.1109/JCSSE.2018.8457382","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457382","url":null,"abstract":"This paper presents an intelligent locomotion control architecture for a hexapod robot. The proposed architecture provides a hexapod robot abilities to perceive different terrains and adjust gait accordingly by utilizing CPG- network, torque sensing, and radial basis function neural network. The architecture is suitable for implementing in an embedded system for on-board small hexapod robots. The effectiveness of the proposed intelligent control architecture is demonstrated through real robot experiments.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"33 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120987999","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}
In this paper, new method to analyze the skills required from labor market through different jobs recruitments websites is proposed. Skill mismatch which is one type of education-job mismatch becomes one of the major issues and mainly impacts in economic faced by various countries around the world today. There were several proposed analysis methods investigating skill mismatch problem in labor market. However, most of the methods proposed are based on Survey dataset providing the skills from young populations. This paper explores the skills demand in labor market from job recruitment websites which contribute job’s information by different job companies in Thailand. The paper makes use of multiple techniques; including web scraping, keyword extraction and visualization on scraped information from job recruitment websites. The final output is reported as word cloud for jobs description and qualification of each job function.
{"title":"Analysis Of Skill Demand In Thai Labor Market From Online Jobs Recruitments Websites","authors":"Aniwat Phaphuangwittayakul, Supalin Saranwong, Satta Panyakaew, Papangkorn Inkeaw, Jeerayut Chaijaruwanich","doi":"10.1109/JCSSE.2018.8457393","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457393","url":null,"abstract":"In this paper, new method to analyze the skills required from labor market through different jobs recruitments websites is proposed. Skill mismatch which is one type of education-job mismatch becomes one of the major issues and mainly impacts in economic faced by various countries around the world today. There were several proposed analysis methods investigating skill mismatch problem in labor market. However, most of the methods proposed are based on Survey dataset providing the skills from young populations. This paper explores the skills demand in labor market from job recruitment websites which contribute job’s information by different job companies in Thailand. The paper makes use of multiple techniques; including web scraping, keyword extraction and visualization on scraped information from job recruitment websites. The final output is reported as word cloud for jobs description and qualification of each job function.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131744501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-01DOI: 10.1109/JCSSE.2018.8457333
Ryan A. Subong, Arnel C. Fajardo, Yoon-Joong Kim
The less obvious that an image has been modified, the less it will be suspected of containing a secret message or image. This paper proposes an image steganographic approach wherein the bit information of the secret message replaces the LSBs (least significant bit) of the RGB (red green blue) bytes of the cover image just like many of the LSB image steganography methods, except that the bits of the secret message undergo a series of evaluated and scored bit rotation and inversion operations prior replacement. Using MSE and PSNR as a measure of image quality, the stego image generated by this proposed approach produced lesser distortion than the existing four bits per byte replacement approach of LSB Replacement and Adaptive LSB Embedding algorithms. The proposed approach however does not offer significant improvement of robustness in terms of security.
{"title":"LSB Rotation and Inversion Scoring Approach to Image Steganography","authors":"Ryan A. Subong, Arnel C. Fajardo, Yoon-Joong Kim","doi":"10.1109/JCSSE.2018.8457333","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457333","url":null,"abstract":"The less obvious that an image has been modified, the less it will be suspected of containing a secret message or image. This paper proposes an image steganographic approach wherein the bit information of the secret message replaces the LSBs (least significant bit) of the RGB (red green blue) bytes of the cover image just like many of the LSB image steganography methods, except that the bits of the secret message undergo a series of evaluated and scored bit rotation and inversion operations prior replacement. Using MSE and PSNR as a measure of image quality, the stego image generated by this proposed approach produced lesser distortion than the existing four bits per byte replacement approach of LSB Replacement and Adaptive LSB Embedding algorithms. The proposed approach however does not offer significant improvement of robustness in terms of security.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127376078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-01DOI: 10.1109/JCSSE.2018.8457335
J. Mitrpanont, Wudhichart Sawangphol, Chanoknan Chankong, Ananya Jitsuphap, Nannaphat Wongkhumsin
Integrated Well-being IoT System for Healthiness (I-WISH) has been developed to improve the quality of life, wellbeing, healthiness, and behavior by using Internet of Things technology. The concept of I-WISH system is to integrate between a 4WD Robot Car and the variety of sensors for monitoring and analyzing the improper environment inside the buildings. All sensors are used to sense improper environment. The collected data will be sent to real time Firebase database system via Internet and sharing to the mobile application which provides useful features such as notification and appropriate suggestion. As a result, I-WISH system will encourage the users to aware of the improper environment and realize the importance of healthiness. This system is considered as a new tool that can help improving the quality of human life.
I-WISH (Integrated well - IoT System for health)是一种利用物联网技术改善生活质量、福祉、健康和行为的健康物联网系统。I-WISH系统的概念是将一辆四轮驱动机器人汽车与各种传感器集成在一起，用于监测和分析建筑物内的不当环境。所有的传感器都用来感知不合适的环境。收集到的数据将通过互联网实时发送到Firebase数据库系统，并共享到提供通知和适当建议等有用功能的移动应用程序。因此，I-WISH系统将鼓励用户意识到不适当的环境，并意识到健康的重要性。该系统被认为是一种有助于提高人类生活质量的新工具。
{"title":"I-WISH: Integrated Well-Being IoT System for Healthiness","authors":"J. Mitrpanont, Wudhichart Sawangphol, Chanoknan Chankong, Ananya Jitsuphap, Nannaphat Wongkhumsin","doi":"10.1109/JCSSE.2018.8457335","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457335","url":null,"abstract":"Integrated Well-being IoT System for Healthiness (I-WISH) has been developed to improve the quality of life, wellbeing, healthiness, and behavior by using Internet of Things technology. The concept of I-WISH system is to integrate between a 4WD Robot Car and the variety of sensors for monitoring and analyzing the improper environment inside the buildings. All sensors are used to sense improper environment. The collected data will be sent to real time Firebase database system via Internet and sharing to the mobile application which provides useful features such as notification and appropriate suggestion. As a result, I-WISH system will encourage the users to aware of the improper environment and realize the importance of healthiness. This system is considered as a new tool that can help improving the quality of human life.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127380305","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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